PsyCourse Codebook - Version 6.0

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General remarks: Please read!

This codebook (data dictionary) and the accompanying phenotype dataset were created using R version 4.1.2 on a machine running Ubuntu 22.04.2 LTS The metadata contained in this document describe most of the variables that were assessed in the longitudinal PsyCourse Study. For a more in-depth description of the study, please refer to and cite this article.

The official period of data collection was from the 1st of January, 2012 until the 31st of December, 2019. A few participants were recruited in a pilot study, and had their baseline assessment during the last months of 2011. These individuals are also included in the dataset.

The PsyCourse 6.0 dataset contains data of 1320 clinical and 466 control participants, who were assessed at the first (baseline) study visit. Of the clinical participants, 788, 661, and 589 were also assessed at the second, third, and fourth study visits (follow-up), respectively. Of the control participants, 288, 280, and 251 were also assessed at the second, third, and fourth study visits (follow-up), respectively.

The variable “v1_stat” gives information on clinical/control status.

Three very important points to consider:

1. PsyCourse is purely observational, i.e. there is no intervention at baseline, at or between the follow-ups.

2. PsyCourse is very heterogeneous in terms of participants.

3. Longitudinal measurements were approximately six months apart, which is a rather short time, so most longitudinal measurements are highly correlated.

Interviews were conducted in German, using German translations of the tests and scales presented herein. Some scales were measured only cross-sectionally, others were measured longitudinally, some were only measured in clinical participants, others only in control participants (see below). Wherever possible, we alternately used parallel versions of tests assessed multiple times to avoid recall effects.

Importantly, the assessment period of many variables varies greatly, this should be taken into account when making inferences. For example, the SCID questions generally assess if a symptom has ever occurred (lifetime assessment), whereas other rating scales use the past week, or shorter, as assessment period.

We created this dataset and the accompanying codebook for the following reasons:

1. Some items (especially demographic information) in the original case-report forms (CRFs, “Phänotypisierungsinventar”) are rather complicated (e.g. items on the German educational system) and virtually every item needs explanation.

2. Missing values in our phenotype database do not distinguish between structurally missing data (missing because not applicable, e.g. skipped because a screening question was answered negatively) and missing because the information was not collected (for whatever reason, e.g. drop-out of study participants). This is especially important for machine learning analyses. In this dataset, structurally missing data are coded as -999; this includes items that were not assessed in the clinical or control subsample. The remaining missing values are coded as NA.

3. The original CRFs are in German language, so they are of little use for most foreign colleagues.

4. The data of clinical and control participants are saved in different databases, and are combined into a comprehensive dataset using the code provided herein.

Apart from providing a transparent and reproducible codebook for the PsyCourse Study, this file also contains some descriptive statistics (e.g. how many NAs, distribution of data).

For easy navigation, the table of contents contanins hyperlinks to the respective sections of the document, also the overview of the measured variables contains some hyperlinks.

Variable names

Names of variables in the wide dataset (see below) are contained in the heading describing each variable. For example, “Age at first interview (continuous [years], v1_age)”, means that age at first interview is measured on a continuous scale, unit years, and that it has the name “v1_age” in the dataset.

If you request data from us in the context of an analysis proposal:

• Please use the corresponding variable name, e.g. “v1_age”, and not some description of the variable (e.g., “Age of participant”)
• Please indicate whether you would like the data in long or wide format. If you request data in long format, please use the long format variable names, if possible. See also below (“Format of dataset”).

Scale levels

Variables in this dataset have one of the following scale levels:

• continuous
  
• ordinal
  
• categorical
  
• dichotomous
  
• checkbox
  
• character

For continuous variables, the units (e.g., cm) are usually given in square brackets following the variable description([]). Ordinal or categorical variables often have a list of their levels/categories in square brackets.

Please note that for ordinal (factor) variables, “-999” (if applicable) constitutes a factor level itself. Care should thus be taken not to analyze the -999 level together with the other ordinal levels.

Dichotomous variables are usually coded “Y” (yes) and “N” (no), if not mentioned otherwise.

Checkboxes are either checked (coded “Y”) or “-999”.

Important: When analyzing GAF or other continuous or ordinal variables containing “-999”, please keep in mind that the “-999” will falsify your analysis, if you do not recode it.

Format of dataset

We now provide the dataset both in wide format (one row corresponds to one individual), and in long format (in which each row represents a study visit of one individual).

The variable names given in this codebook refer to the variables in the wide format dataset. These have a “vX_” prefix, where “X” indicates the particular study visit. In long format, variables that were measured multiple times (e.g., PANSS) do not have this prefix in the variable names (but the prefix is still present in cross-sectionally measured variables).

The wide format dataset is contained in the file “230614_v6.0_psycourse_wd.RData”, the long format dataset is contained in the file “230614_v6.0_psycourse_ln.RData”.

Also, .csv files are provided (“230614_v6.0_psycourse_wd.csv” and “230614_v6.0_psycourse_ln.csv). The field separator used in the .csv files is tab. Please note that information on scale levels is lost when using the .csv files. We recommend to analyze these data with the R software, using the”.RData” files.

Additionally, we now also provide the raw (i.e. unmodified) data on medication and illicit drugs as separate (sub-)datasets. These data are provided separately for each visit and separately for clinical and control participants.

The datasets containing the raw data on medication have the following names:

Clinical participants
- 230614_v6.0_psycourse_clin_raw_med_visit1.csv (see also here)
- 230614_v6.0_psycourse_clin_raw_med_visit2.csv (see also here)
- 230614_v6.0_psycourse_clin_raw_med_visit3.csv (see also here)
- 230614_v6.0_psycourse_clin_raw_med_visit4.csv (see also here)

Control participants
- 230614_v6.0_psycourse_con_raw_med_visit1.csv (see also here)
- 230614_v6.0_psycourse_con_raw_med_visit2.csv (see also here)
- 230614_v6.0_psycourse_con_raw_med_visit3.csv (see also here)
- 230614_v6.0_psycourse_con_raw_med_visit4.csv (see also here)

The datasets containing the raw data on illicit drugs have the following names:

Clinical participants
- 230614_v6.0_psycourse_clin_raw_ill_drg_visit1.csv (see also here)
- 230614_v6.0_psycourse_clin_raw_ill_drg_visit2.csv (see also here)
- 230614_v6.0_psycourse_clin_raw_ill_drg_visit3.csv (see also here)
- 230614_v6.0_psycourse_clin_raw_ill_drg_visit4.csv (see also here)

Control participants
- 230614_v6.0_psycourse_con_raw_ill_drg_visit1.csv (see also here)
- 230614_v6.0_psycourse_con_raw_ill_drg_visit2.csv (see also here)
- 230614_v6.0_psycourse_con_raw_ill_drg_visit3.csv (see also here)
- 230614_v6.0_psycourse_con_raw_ill_drg_visit4.csv (see also here)

Available biological analysis data

A number of biological analyses have been carried out. Information on these can be found in this section of the codebook.

Disclaimer

This dataset was created from exports of our phenotype databases. Recruitment and data entry into these databases is complete. The following tests/variables in these source data have been checked for data entry errors: Diagnosis according to DSM-IV, Childhood Trauma Screener, Opcrit Item 90, ALDA Scale, IDS-C₃₀, YMRS, PANSS, and the result sheet summarizing all cognitive tests.

Even though we do our best to be as accurate as possible, both this codebook and the dataset may contain errors.

For example, the re-coding of many variables introduces errors. Therefore, it is mandatory to check the data you are analyzing carefully, and to contact us if suspicion arises, or if errors are found.

Again, please be sure to inspect the data you are analyzing carefully.

Investigators are encouraged to regularly check the PsyCourse website, to make sure they use the latest version of the codebook/dataset. Errors in the codebook/dataset will be uploaded to the repository in which the codebook is deposited, until a new version of the codebook/dataset has been published.

Longitudinally and cross-sectionally assessed information

In the following table, “cl” means a test was only measured in clinical participants, “cn” that a test was only measured in control participants, and “b” that it was measured in both clinical and control participants.

Control individuals were recruited at four clinical centers:

• UMG Göttingen,
  
• LMU Munich,
  
• Medical University of Graz, and
  
• from the MImicSS study

All psychiatric rating scales (PANSS, IDS-C₃₀, YMRS, and GAF) were also assessed in control individuals. However, at the beginning of control recruitment, these rating scales were not included in the assessment protocol. Therefore these data are missing from a subset of control individuals (recruited in the clinical center UMG Göttingen), as is the CAPE-42 (also introduced later). Furthermore, a subset of controls (recruited at the Medical University of Graz) were only assessed at two points in time (at the first and the third visit).

Section	Instrument	Visit 1	Visit 2	Visit 3	Visit 4
Demographic information		b	b	b	b
Psychiatric history		cl
Medication		b	b	b	b
	ALDA scale				cl1
Family history of psychiat. ill.		b
Physical measures and somatic ill.		b2
Substance abuse		b	b	b	b
Event triggering first ill. ep.			cl
Illness episodes between study visits			cl	cl	cl
Life events precipitating ill. ep.			cl	cl	cl
Psych. problems between study visits			cn	cn	cn
Screening for psychiatric disorders	MINI-DIPS	cn3
DSM-IV Diagnosis	SCID I Depression	cl
	SCID I Hypo-/Mania	cl
	SCID I Psychosis	cl
	SCID I Suicidality	cl	cl	cl	cl
Neuropsychology (cognitive tests)	Trail-Making-Test	b	b	b	b
	Verbal digit span	b	b	b	b
	Digit-symbol-test	b	b	b	b
	MWT-B	b
	VLMT		b	b	b
Rating scales	PANSS	b	b	b	b
	IDS-C30	b	b	b	b
	YMRS	b	b	b	b
	CGI	cl	cl	cl	cl
	GAF	b	b	b	b
	OPCRIT item 90				cl
Questionnaires	SF-12	cn	cn	cn	cn
	CAPE-42	cn
	Religious beliefs	b			b4
	Med. adherence	cl	cl	cl	cl
	CTS			b
	BDI-II	b	b	b	b
	ASRM	b	b	b	b
	MSS	b	b	b	b
	LEQ	b	b	b	b
	WHOQOL-BREF	b	b	b	b
	Personality	b

In- and exclusion criteria of the study

Briefly, adult (minimum age 18 years) control and clinical participants were recruited. Clinical participants with the following ICD-10 diagnoses were recruited: schizophrenia (F20.X), acute and transient psychotic disorder (F23.X), schizoaffective disorder (F25.X), bipolar disorder (F31.X), manic episode (F30.X) and recurrent depressive disorder (F33.X). After conducting the SCID Interview (part of the first study visit), we ascertained that participants met one of the following corresponding DSM-IV diagnoses: schizophrenia (295.1/.2/.3/.6/.9), schizophreniform disorder (295.4), brief psychotic disorder (298.8), schizoaffective disorder (295.7), bipolar disorder (296.X [bipolar disorders incl. manic episode]) or recurrent major depression (296.3). If the DSM-IV diagnosis ascertained through SCID interview differed from the aforementioned DSM-IV diagnostic categories, the participant was excluded. Control participants were excluded from the study if they had ever been treated as inpatient for one of the investigated ICD-10 diagnoses.

Please note that in a subset of PsyCourse participants (“MImicSS”), diagnoses were not reassessed within the DSM-IV framework. This is described in the respective section.

Functions that are needed to recode variables from the original dataset to the described variables are defined here. This section is not relevant for people that want only to analyze data.

Load libraries

library("sjmisc") #neccessary for row_sum function

## Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!

Define functions for descriptive analyses

desc <- function(x) {
  noquote(cbind(c("No. cases", "Percent"),rbind(summary(x), 
  round(summary(x)/length(x)*100,1)),
  c(length(x),sum(summary(x)/length(x)*100))))
  }

descT <- function(x) {
  noquote(cbind(c("No. cases", "Percent"),rbind(table(x, useNA="ifany"),
  round(table(x,useNA="ifany")/length(x)*100,1)),
  c(length(x),sum(table(x, useNA="ifany")/length(x)*100))))
  }

as.numeric.factor <- function(x) {as.numeric(levels(x))[x]}

Define functions to autmatically recode items from specific tests

CAPE-42 A and B items

#First argument w/o quotes, second with quotes
v1_cape_recode <- function(cape_old_name,cape_new_name) {
    
    itm_cape<-ifelse((is.na(v1_con$v1_cape_cape_korrekt) | v1_con$v1_cape_cape_korrekt!=2),
                  cape_old_name,NA) 
    
    all_itm_cape<-c(rep(-999,dim(v1_clin)[1]),itm_cape) #add -999 for clinical subjects
    
    assign(cape_new_name,all_itm_cape,envir=.GlobalEnv)
    
    descT(all_itm_cape)}

SF-12 items, Visit 1

#First argument w/o quotes, second with quotes
v1_sf12_recode <- function(v1_sf12_old_name,v1_sf12_new_name) {
    
    itm_sf12<-ifelse((is.na(v1_con$v1_sf12_sf12_korrekt) | v1_con$v1_sf12_sf12_korrekt!=2),
                  v1_sf12_old_name,NA) 
    
    v1_all_itm_sf12<-c(rep(-999,dim(v1_clin)[1]),itm_sf12) #add -999 for clinical subjects
    
    assign(v1_sf12_new_name,v1_all_itm_sf12,envir=.GlobalEnv)
    
    descT(v1_all_itm_sf12)}

SF-12 items, Visit 2

#First argument w/o quotes, second with quotes
v2_sf12_recode <- function(v2_sf12_old_name,v2_sf12_new_name) {
    
    itm_sf12<-ifelse((is.na(v2_con$v2_sf12_sf12_korrekt) | v2_con$v2_sf12_sf12_korrekt!=2),
                  v2_sf12_old_name,NA) 
    
    v2_all_itm_sf12<-c(rep(-999,dim(v2_clin)[1]),itm_sf12) #add -999 for clinical subjects
    
    assign(v2_sf12_new_name,v2_all_itm_sf12,envir=.GlobalEnv)
    
    descT(v2_all_itm_sf12)}

SF-12 items, Visit 3

#First argument w/o quotes, second with quotes
v3_sf12_recode <- function(v3_sf12_old_name,v3_sf12_new_name) {
    
    itm_sf12<-ifelse((is.na(v3_con$v3_sf12_sf12_korrekt) | v3_con$v3_sf12_sf12_korrekt!=2),
                  v3_sf12_old_name,NA) 
    
    v3_all_itm_sf12<-c(rep(-999,dim(v3_clin)[1]),itm_sf12) #add -999 for clinical subjects
    
    assign(v3_sf12_new_name,v3_all_itm_sf12,envir=.GlobalEnv)
    
    descT(v3_all_itm_sf12)}

SF-12 items, Visit 4

#First argument w/o quotes, second with quotes
v4_sf12_recode <- function(v4_sf12_old_name,v4_sf12_new_name) {
   
    itm_sf12<-ifelse((is.na(v4_con$v4_sf12_sf12_korrekt) | v4_con$v4_sf12_sf12_korrekt!=2),
                  v4_sf12_old_name,NA) 
    
    v4_all_itm_sf12<-c(rep(-999,dim(v4_clin)[1]),itm_sf12) #add -999 for clinical subjects
    
    assign(v4_sf12_new_name,v4_all_itm_sf12,envir=.GlobalEnv)
    
    descT(v4_all_itm_sf12)}

BDI-2 items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_bdi2_recode <- function(bdi2_clin_old_name,bdi2_con_old_name,bdi2_new_name) {
    
    v1_itm_bdi2_chk_clin<-v1_clin$v1_bdi2_s1_verwer_fragebogen
    v1_itm_bdi2_chk_con<-v1_con$v1_bdi2_s1_bdi_korrekt
    
    v1_itm_bdi2_clin<-ifelse((is.na(v1_itm_bdi2_chk_clin) | v1_itm_bdi2_chk_clin!=2),
                  bdi2_clin_old_name,NA) 
    
    v1_itm_bdi2_con<-ifelse((is.na(v1_itm_bdi2_chk_con) | v1_itm_bdi2_chk_con!=2),
                  bdi2_con_old_name,NA)               
    
    v1_all_itm_bdi2<-factor(c(v1_itm_bdi2_clin,v1_itm_bdi2_con),ordered=T)
    
    assign(bdi2_new_name,v1_all_itm_bdi2,envir=.GlobalEnv)
    
    descT(v1_all_itm_bdi2)}

BDI-2 items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_bdi2_recode <- function(bdi2_clin_old_name,bdi2_con_old_name,bdi2_new_name) {
    
    v2_itm_bdi2_chk_clin<-v2_clin$v2_bdi2_s1_verwer_fragebogen
    v2_itm_bdi2_chk_con<-v2_con$v2_bdi2_s1_bdi_korrekt
    
    v2_itm_bdi2_clin<-ifelse((is.na(v2_itm_bdi2_chk_clin) | v2_itm_bdi2_chk_clin!=2),
                  bdi2_clin_old_name,NA) 
    
    v2_itm_bdi2_con<-ifelse((is.na(v2_itm_bdi2_chk_con) | v2_itm_bdi2_chk_con!=2),
                  bdi2_con_old_name,NA)               
    
    v2_all_itm_bdi2<-factor(c(v2_itm_bdi2_clin,v2_itm_bdi2_con),ordered=T) 
    
    assign(bdi2_new_name,v2_all_itm_bdi2,envir=.GlobalEnv)
    
    descT(v2_all_itm_bdi2)}

BDI-2 items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_bdi2_recode <- function(bdi2_clin_old_name,bdi2_con_old_name,bdi2_new_name) {
    
    v3_itm_bdi2_chk_clin<-v3_clin$v3_bdi2_s1_verwer_fragebogen
    v3_itm_bdi2_chk_con<-v3_con$v3_bdi2_s1_bdi_korrekt
    
    v3_itm_bdi2_clin<-ifelse((is.na(v3_itm_bdi2_chk_clin) | v3_itm_bdi2_chk_clin!=2),
                  bdi2_clin_old_name,NA) 
    
    v3_itm_bdi2_con<-ifelse((is.na(v3_itm_bdi2_chk_con) | v3_itm_bdi2_chk_con!=2),
                  bdi2_con_old_name,NA)               
    
    v3_all_itm_bdi2<-factor(c(v3_itm_bdi2_clin,v3_itm_bdi2_con),ordered=T) 
    
    assign(bdi2_new_name,v3_all_itm_bdi2,envir=.GlobalEnv)
    
    descT(v3_all_itm_bdi2)}

BDI-2 items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_bdi2_recode <- function(bdi2_clin_old_name,bdi2_con_old_name,bdi2_new_name) {
    
    v4_itm_bdi2_chk_clin<-v4_clin$v4_bdi2_s1_verwer_fragebogen
    v4_itm_bdi2_chk_con<-v4_con$v4_bdi2_s1_bdi_korrekt
    
    v4_itm_bdi2_clin<-ifelse((is.na(v4_itm_bdi2_chk_clin) | v4_itm_bdi2_chk_clin!=2),
                  bdi2_clin_old_name,NA) 
    
    v4_itm_bdi2_con<-ifelse((is.na(v4_itm_bdi2_chk_con) | v4_itm_bdi2_chk_con!=2),
                  bdi2_con_old_name,NA)               
    
    v4_all_itm_bdi2<-factor(c(v4_itm_bdi2_clin,v4_itm_bdi2_con),ordered=T) 
    
    assign(bdi2_new_name,v4_all_itm_bdi2,envir=.GlobalEnv)
    
    descT(v4_all_itm_bdi2)}

ASRM items, Visit 1

#First and second arguments w/o quotes, second with quotes
v1_asrm_recode <- function(asrm_clin_old_name,asrm_con_old_name,asrm_new_name) {
    
    v1_itm_asrm_chk_clin<-v1_clin$v1_asrm_verwer_fragebogen
    v1_itm_asrm_chk_con<-v1_con$v1_asrm_asrm_korrekt
    
    v1_itm_asrm_clin<-ifelse((is.na(v1_itm_asrm_chk_clin) | v1_itm_asrm_chk_clin!=2),
                  asrm_clin_old_name,NA) 
    
    v1_itm_asrm_con<-ifelse((is.na(v1_itm_asrm_chk_con) | v1_itm_asrm_chk_con!=2),
                  asrm_con_old_name,NA)               
    
    v1_all_itm_asrm<-factor(c(v1_itm_asrm_clin,v1_itm_asrm_con),ordered=T) 
    
    assign(asrm_new_name,v1_all_itm_asrm,envir=.GlobalEnv)
    
    descT(v1_all_itm_asrm)}

ASRM items, Visit 2

#First and second arguments w/o quotes, second with quotes
v2_asrm_recode <- function(asrm_clin_old_name,asrm_con_old_name,asrm_new_name) {
    
    v2_itm_asrm_chk_clin<-v2_clin$v2_asrm_verwer_fragebogen
    v2_itm_asrm_chk_con<-v2_con$v2_asrm_asrm_korrekt
    
    v2_itm_asrm_clin<-ifelse((is.na(v2_itm_asrm_chk_clin) | v2_itm_asrm_chk_clin!=2),
                  asrm_clin_old_name,NA) 
    
    v2_itm_asrm_con<-ifelse((is.na(v2_itm_asrm_chk_con) | v2_itm_asrm_chk_con!=2),
                  asrm_con_old_name,NA)               
    
    v2_all_itm_asrm<-factor(c(v2_itm_asrm_clin,v2_itm_asrm_con),ordered=T) 
    
    assign(asrm_new_name,v2_all_itm_asrm,envir=.GlobalEnv)
    
    descT(v2_all_itm_asrm)}

ASRM items, Visit 3

#First and second arguments w/o quotes, second with quotes
v3_asrm_recode <- function(asrm_clin_old_name,asrm_con_old_name,asrm_new_name) {
    
    v3_itm_asrm_chk_clin<-v3_clin$v3_asrm_verwer_fragebogen
    v3_itm_asrm_chk_con<-v3_con$v3_asrm_asrm_korrekt
    
    v3_itm_asrm_clin<-ifelse((is.na(v3_itm_asrm_chk_clin) | v3_itm_asrm_chk_clin!=2),
                  asrm_clin_old_name,NA) 
    
    v3_itm_asrm_con<-ifelse((is.na(v3_itm_asrm_chk_con) | v3_itm_asrm_chk_con!=2),
                  asrm_con_old_name,NA)               
    
    v3_all_itm_asrm<-factor(c(v3_itm_asrm_clin,v3_itm_asrm_con),ordered=T) 
    
    assign(asrm_new_name,v3_all_itm_asrm,envir=.GlobalEnv)
    
    descT(v3_all_itm_asrm)}

ASRM items, Visit 4

#First and second arguments w/o quotes, second with quotes
v4_asrm_recode <- function(asrm_clin_old_name,asrm_con_old_name,asrm_new_name) {
    
    v4_itm_asrm_chk_clin<-v4_clin$v4_asrm_verwer_fragebogen
    v4_itm_asrm_chk_con<-v4_con$v4_asrm_asrm_korrekt
    
    v4_itm_asrm_clin<-ifelse((is.na(v4_itm_asrm_chk_clin) | v4_itm_asrm_chk_clin!=2),
                  asrm_clin_old_name,NA) 
    
    v4_itm_asrm_con<-ifelse((is.na(v4_itm_asrm_chk_con) | v4_itm_asrm_chk_con!=2),
                  asrm_con_old_name,NA)               
    
    v4_all_itm_asrm<-factor(c(v4_itm_asrm_clin,v4_itm_asrm_con),ordered=T) 
    
    assign(asrm_new_name,v4_all_itm_asrm,envir=.GlobalEnv)
    
    descT(v4_all_itm_asrm)}

MSS items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_mss_recode <- function(mss_clin_old_name,mss_con_old_name,mss_new_name) {
    
    v1_itm_mss_chk_clin<-v1_clin$v1_mss_s1_verwer_fragebogen
    v1_itm_mss_chk_con<-v1_con$v1_mss_s1_mss_korrekt
    
    v1_itm_mss_clin<-ifelse((is.na(v1_itm_mss_chk_clin) | v1_itm_mss_chk_clin!=2),
                  mss_clin_old_name,NA) 
    
    v1_itm_mss_con<-ifelse((is.na(v1_itm_mss_chk_con) | v1_itm_mss_chk_con!=2),
                  mss_con_old_name,NA)               
    
    v1_all_itm_mss<-c(v1_itm_mss_clin,v1_itm_mss_con)
    v1_all_itm_mss<-factor(ifelse(v1_all_itm_mss==1,"Y","N"))
    
    assign(mss_new_name,v1_all_itm_mss,envir=.GlobalEnv)
    
    descT(v1_all_itm_mss)}

MSS items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_mss_recode <- function(mss_clin_old_name,mss_con_old_name,mss_new_name) {
    
    v2_itm_mss_chk_clin<-v2_clin$v2_mss_s1_verwer_fragebogen
    v2_itm_mss_chk_con<-v2_con$v2_mss_s1_mss_korrekt
    
    v2_itm_mss_clin<-ifelse((is.na(v2_itm_mss_chk_clin) | v2_itm_mss_chk_clin!=2),
                  mss_clin_old_name,NA) 
    
    v2_itm_mss_con<-ifelse((is.na(v2_itm_mss_chk_con) | v2_itm_mss_chk_con!=2),
                  mss_con_old_name,NA)               
    
    v2_all_itm_mss<-c(v2_itm_mss_clin,v2_itm_mss_con)
    v2_all_itm_mss<-factor(ifelse(v2_all_itm_mss==1,"Y","N"))
    
    assign(mss_new_name,v2_all_itm_mss,envir=.GlobalEnv)
    
    descT(v2_all_itm_mss)}

MSS items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_mss_recode <- function(mss_clin_old_name,mss_con_old_name,mss_new_name) {
    
    v3_itm_mss_chk_clin<-v3_clin$v3_mss_s1_verwer_fragebogen
    v3_itm_mss_chk_con<-v3_con$v3_mss_s1_mss_korrekt
    
    v3_itm_mss_clin<-ifelse((is.na(v3_itm_mss_chk_clin) | v3_itm_mss_chk_clin!=2),
                  mss_clin_old_name,NA) 
    
    v3_itm_mss_con<-ifelse((is.na(v3_itm_mss_chk_con) | v3_itm_mss_chk_con!=2),
                  mss_con_old_name,NA)               
    
    v3_all_itm_mss<-c(v3_itm_mss_clin,v3_itm_mss_con)
    v3_all_itm_mss<-factor(ifelse(v3_all_itm_mss==1,"Y","N"))
    
    assign(mss_new_name,v3_all_itm_mss,envir=.GlobalEnv)
    
    descT(v3_all_itm_mss)}

MSS items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_mss_recode <- function(mss_clin_old_name,mss_con_old_name,mss_new_name) {
    
    v4_itm_mss_chk_clin<-v4_clin$v4_mss_s1_verwer_fragebogen
    v4_itm_mss_chk_con<-v4_con$v4_mss_s1_mss_korrekt
    
    v4_itm_mss_clin<-ifelse((is.na(v4_itm_mss_chk_clin) | v4_itm_mss_chk_clin!=2),
                  mss_clin_old_name,NA) 
    
    v4_itm_mss_con<-ifelse((is.na(v4_itm_mss_chk_con) | v4_itm_mss_chk_con!=2),
                  mss_con_old_name,NA)               
    
    v4_all_itm_mss<-c(v4_itm_mss_clin,v4_itm_mss_con)
    v4_all_itm_mss<-factor(ifelse(v4_all_itm_mss==1,"Y","N"))
    
    assign(mss_new_name,v4_all_itm_mss,envir=.GlobalEnv)
    
    descT(v4_all_itm_mss)}

LEQ A Items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_leq_a_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
  
    v1_itm_leq_chk_clin<-v1_clin$v1_leq_a_verwer_fragebogen
    v1_itm_leq_chk_con<-v1_con$v1_leq_a_leq_korrekt
    
    v1_itm_leq_clin<-rep(NA,dim(v1_clin)[1])
    v1_itm_leq_con<-rep(NA,dim(v1_con)[1])
    
    v1_itm_leq_clin<-ifelse(((is.na(v1_itm_leq_chk_clin) | v1_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)==F),
                  leq_clin_old_name, 
                      ifelse(((is.na(v1_itm_leq_chk_clin) | v1_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)), -999, v1_itm_leq_clin))
    
    v1_itm_leq_con<-ifelse(((is.na(v1_itm_leq_chk_con) | v1_itm_leq_chk_con!=2) & is.na(leq_con_old_name)==F),
                  leq_con_old_name,
                      ifelse(((is.na(v1_itm_leq_chk_con) | v1_itm_leq_chk_con!=2) & is.na(leq_con_old_name)), -999, v1_itm_leq_con))    
    
    v1_all_itm_leq<-c(v1_itm_leq_clin,v1_itm_leq_con)
    
    v1_all_itm_leq[v1_all_itm_leq==1]<-"good"
    v1_all_itm_leq[v1_all_itm_leq==0]<-"bad"
    v1_all_itm_leq<-factor(v1_all_itm_leq)
    
    assign(leq_new_name,v1_all_itm_leq,envir=.GlobalEnv)
    
    descT(v1_all_itm_leq)}

LEQ A Items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_leq_a_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v2_itm_leq_chk_clin<-v2_clin$v2_leq_a_verwer_fragebogen
    v2_itm_leq_chk_con<-v2_con$v2_leq_a_leq_korrekt
    
    v2_itm_leq_clin<-rep(NA,dim(v2_clin)[1])
    v2_itm_leq_con<-rep(NA,dim(v2_con)[1])
    
    v2_itm_leq_clin<-ifelse(is.na(v2_clin$v2_ausschluss1_rekr_datum), NA,
                      ifelse(((is.na(v2_itm_leq_chk_clin) | v2_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)==F),leq_clin_old_name,
                       ifelse(((is.na(v2_itm_leq_chk_clin) | v2_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)), -999, v2_itm_leq_clin)))
    
    v2_itm_leq_con<-ifelse(is.na(v2_con$v2_rekru_visit_rekr_datum), NA,
                     ifelse(((is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con!=2) & is.na(leq_con_old_name)==F),leq_con_old_name,
                      ifelse(((is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con!=2) & is.na(leq_con_old_name)), -999, v2_itm_leq_con)))
    
    v2_all_itm_leq<-c(v2_itm_leq_clin,v2_itm_leq_con)
    
    v2_all_itm_leq[v2_all_itm_leq==1]<-"good"
    v2_all_itm_leq[v2_all_itm_leq==0]<-"bad"
    v2_all_itm_leq<-factor(v2_all_itm_leq)
    
    assign(leq_new_name,v2_all_itm_leq,envir=.GlobalEnv)
    
    descT(v2_all_itm_leq)}

LEQ A Items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_leq_a_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v3_itm_leq_chk_clin<-v3_clin$v3_leq_a_verwer_fragebogen
    v3_itm_leq_chk_con<-v3_con$v3_leq_a_leq_korrekt
    
    v3_itm_leq_clin<-rep(NA,dim(v3_clin)[1])
    v3_itm_leq_con<-rep(NA,dim(v3_con)[1])
    
    v3_itm_leq_clin<-ifelse(is.na(v3_clin$v3_ausschluss1_rekr_datum), NA,
                      ifelse(((is.na(v3_itm_leq_chk_clin) | v3_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)==F),leq_clin_old_name,
                       ifelse(((is.na(v3_itm_leq_chk_clin) | v3_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)), -999, v3_itm_leq_clin)))
    
    v3_itm_leq_con<-ifelse(is.na(v3_con$v3_rekru_visit_rekr_datum), NA,
                     ifelse(((is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con!=2) & is.na(leq_con_old_name)==F),leq_con_old_name,
                      ifelse(((is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con!=2) & is.na(leq_con_old_name)), -999, v3_itm_leq_con)))
    
    v3_all_itm_leq<-c(v3_itm_leq_clin,v3_itm_leq_con)
    
    v3_all_itm_leq[v3_all_itm_leq==1]<-"good"
    v3_all_itm_leq[v3_all_itm_leq==0]<-"bad"
    v3_all_itm_leq<-factor(v3_all_itm_leq)
    
    assign(leq_new_name,v3_all_itm_leq,envir=.GlobalEnv)
    
    descT(v3_all_itm_leq)}

LEQ A Items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_leq_a_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v4_itm_leq_chk_clin<-v4_clin$v4_leq_a_verwer_fragebogen
    v4_itm_leq_chk_con<-v4_con$v4_leq_a_leq_korrekt
    
    v4_itm_leq_clin<-rep(NA,dim(v4_clin)[1])
    v4_itm_leq_con<-rep(NA,dim(v4_con)[1])
    
    v4_itm_leq_clin<-ifelse(is.na(v4_clin$v4_ausschluss1_rekr_datum), NA,
                      ifelse(((is.na(v4_itm_leq_chk_clin) | v4_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)==F),leq_clin_old_name,
                       ifelse(((is.na(v4_itm_leq_chk_clin) | v4_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)), -999, v4_itm_leq_clin)))
    
    v4_itm_leq_con<-ifelse(is.na(v4_con$v4_rekru_visit_rekr_datum), NA,
                     ifelse(((is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con!=2) & is.na(leq_con_old_name)==F),leq_con_old_name,
                      ifelse(((is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con!=2) & is.na(leq_con_old_name)), -999, v4_itm_leq_con)))
    
    v4_all_itm_leq<-c(v4_itm_leq_clin,v4_itm_leq_con)
    
    v4_all_itm_leq[v4_all_itm_leq==1]<-"good"
    v4_all_itm_leq[v4_all_itm_leq==0]<-"bad"
    v4_all_itm_leq<-factor(v4_all_itm_leq)
    
    assign(leq_new_name,v4_all_itm_leq,envir=.GlobalEnv)
    
    descT(v4_all_itm_leq)}

LEQ B Items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_leq_b_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v1_itm_leq_chk_clin<-v1_clin$v1_leq_a_verwer_fragebogen
    v1_itm_leq_chk_con<-v1_con$v1_leq_a_leq_korrekt
    
    v1_itm_leq_b_clin<-rep(NA,dim(v1_clin)[1])
    v1_itm_leq_b_con<-rep(NA,dim(v1_con)[1])
    
    v1_itm_leq_b_clin<-ifelse((is.na(v1_itm_leq_chk_clin) | v1_itm_leq_chk_clin!=2) & 
                 is.na(leq_clin_old_name), -999,   
                #data present but this LEQ item empty -> -999
                ifelse((is.na(v1_itm_leq_chk_clin) | v1_itm_leq_chk_clin!=2) &
                        !is.na(leq_clin_old_name),leq_clin_old_name,NA))
    
    v1_itm_leq_b_con<-ifelse((is.na(v1_itm_leq_chk_con) | v1_itm_leq_chk_con!=2) & 
                 is.na(leq_con_old_name), -999,   
                #data present but this LEQ item empty -> -999
                ifelse((is.na(v1_itm_leq_chk_con) | v1_itm_leq_chk_con!=2) &
                        !is.na(leq_con_old_name),leq_con_old_name,NA))
    
    v1_all_itm_leq_b<-factor(c(v1_itm_leq_b_clin,v1_itm_leq_b_con),ordered=T)

    assign(leq_new_name,v1_all_itm_leq_b,envir=.GlobalEnv)
    
    descT(v1_all_itm_leq_b)}

LEQ B Items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_leq_b_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
  
    v2_itm_leq_chk_clin<-v2_clin$v2_leq_a_verwer_fragebogen
    v2_itm_leq_chk_con<-v2_con$v2_leq_a_leq_korrekt
    
    v2_itm_leq_b_clin<-rep(NA,dim(v2_clin)[1])
    v2_itm_leq_b_con<-rep(NA,dim(v2_con)[1])
    
    v2_itm_leq_b_clin<-ifelse(is.na(v2_clin$v2_ausschluss1_rekr_datum), NA,
                        ifelse((is.na(v2_itm_leq_chk_clin) | v2_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name), -999, #data but LEQ item empty -> -999
                          ifelse((is.na(v2_itm_leq_chk_clin) | v2_itm_leq_chk_clin!=2) & !is.na(leq_clin_old_name),leq_clin_old_name,NA)))
    
    v2_itm_leq_b_con<-ifelse(is.na(v2_con$v2_rekru_visit_rekr_datum), NA,
                       ifelse((is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con!=2) & is.na(leq_con_old_name), -999, #data but LEQ item empty -> -999
                        ifelse((is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con!=2) & !is.na(leq_con_old_name),leq_con_old_name,NA)))
    
    v2_all_itm_leq_b<-factor(c(v2_itm_leq_b_clin,v2_itm_leq_b_con),ordered=T)

    assign(leq_new_name,v2_all_itm_leq_b,envir=.GlobalEnv)
    
    descT(v2_all_itm_leq_b)}

LEQ B Items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_leq_b_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v3_itm_leq_chk_clin<-v3_clin$v3_leq_a_verwer_fragebogen
    v3_itm_leq_chk_con<-v3_con$v3_leq_a_leq_korrekt
    
    v3_itm_leq_b_clin<-rep(NA,dim(v3_clin)[1])
    v3_itm_leq_b_con<-rep(NA,dim(v3_con)[1])
    
    v3_itm_leq_b_clin<-ifelse(is.na(v3_clin$v3_ausschluss1_rekr_datum), NA,
                        ifelse((is.na(v3_itm_leq_chk_clin) | v3_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name), -999, #data but LEQ item empty -> -999
                          ifelse((is.na(v3_itm_leq_chk_clin) | v3_itm_leq_chk_clin!=2) & !is.na(leq_clin_old_name),leq_clin_old_name,NA)))
    
    v3_itm_leq_b_con<-ifelse(is.na(v3_con$v3_rekru_visit_rekr_datum), NA,
                       ifelse((is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con!=2) & is.na(leq_con_old_name), -999, #data but LEQ item empty -> -999
                        ifelse((is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con!=2) & !is.na(leq_con_old_name),leq_con_old_name,NA)))
    
    v3_all_itm_leq_b<-factor(c(v3_itm_leq_b_clin,v3_itm_leq_b_con),ordered=T)

    assign(leq_new_name,v3_all_itm_leq_b,envir=.GlobalEnv)
    
    descT(v3_all_itm_leq_b)}

LEQ B Items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_leq_b_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v4_itm_leq_chk_clin<-v4_clin$v4_leq_a_verwer_fragebogen
    v4_itm_leq_chk_con<-v4_con$v4_leq_a_leq_korrekt
    
    v4_itm_leq_b_clin<-rep(NA,dim(v4_clin)[1])
    v4_itm_leq_b_con<-rep(NA,dim(v4_con)[1])
    
    v4_itm_leq_b_clin<-ifelse(is.na(v4_clin$v4_ausschluss1_rekr_datum), NA,
                        ifelse((is.na(v4_itm_leq_chk_clin) | v4_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name), -999, #data but LEQ item empty -> -999
                          ifelse((is.na(v4_itm_leq_chk_clin) | v4_itm_leq_chk_clin!=2) & !is.na(leq_clin_old_name),leq_clin_old_name,NA)))
    
    v4_itm_leq_b_con<-ifelse(is.na(v4_con$v4_rekru_visit_rekr_datum), NA,
                       ifelse((is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con!=2) & is.na(leq_con_old_name), -999, #data but LEQ item empty -> -999
                        ifelse((is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con!=2) & !is.na(leq_con_old_name),leq_con_old_name,NA)))
    
    v4_all_itm_leq_b<-factor(c(v4_itm_leq_b_clin,v4_itm_leq_b_con),ordered=T)

    assign(leq_new_name,v4_all_itm_leq_b,envir=.GlobalEnv)
    
    descT(v4_all_itm_leq_b)}

WHOQOL-BREF Items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_quol_recode <- function(quol_clin_old_name,quol_con_old_name,quol_new_name,recode) {
    
    v1_itm_quol_chk_clin<-v1_clin$v1_whoqol_bref_verwer_fragebogen
    v1_itm_quol_chk_con<-v1_con$v1_whoqol_bref_whoqol_korrekt
    
    v1_itm_quol_clin<-ifelse((is.na(v1_itm_quol_chk_clin) | v1_itm_quol_chk_clin!=2),
                  quol_clin_old_name,NA) 
    
    v1_itm_quol_con<-ifelse((is.na(v1_itm_quol_chk_con) | v1_itm_quol_chk_con!=2),
                  quol_con_old_name,NA)               
    
    if(recode==0) {v1_all_itm_quol<-factor(c(v1_itm_quol_clin,v1_itm_quol_con),ordered=T)}
    else          {v1_all_itm_quol<-factor(6-c(v1_itm_quol_clin,v1_itm_quol_con),ordered=T)}
                      
    assign(quol_new_name,v1_all_itm_quol,envir=.GlobalEnv)
    
    desc(v1_all_itm_quol)}

WHOQOL-BREF Items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_quol_recode <- function(quol_clin_old_name,quol_con_old_name,quol_new_name,recode) {
    
    v2_itm_quol_chk_clin<-v2_clin$v2_whoqol_bref_verwer_fragebogen
    v2_itm_quol_chk_con<-v2_con$v2_whoqol_bref_whoqol_korrekt
    
    v2_itm_quol_clin<-ifelse((is.na(v2_itm_quol_chk_clin) | v2_itm_quol_chk_clin!=2),
                  quol_clin_old_name,NA) 
    
    v2_itm_quol_con<-ifelse((is.na(v2_itm_quol_chk_con) | v2_itm_quol_chk_con!=2),
                  quol_con_old_name,NA)               
    
    if(recode==0) {v2_all_itm_quol<-factor(c(v2_itm_quol_clin,v2_itm_quol_con),ordered=T)}
    else          {v2_all_itm_quol<-factor(6-c(v2_itm_quol_clin,v2_itm_quol_con),ordered=T)}
                      
    assign(quol_new_name,v2_all_itm_quol,envir=.GlobalEnv)
    
    desc(v2_all_itm_quol)}

WHOQOL-BREF Items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_quol_recode <- function(quol_clin_old_name,quol_con_old_name,quol_new_name,recode) {
    
    v3_itm_quol_chk_clin<-v3_clin$v3_whoqol_bref_verwer_fragebogen
    v3_itm_quol_chk_con<-v3_con$v3_whoqol_bref_whoqol_korrekt
    
    v3_itm_quol_clin<-ifelse((is.na(v3_itm_quol_chk_clin) | v3_itm_quol_chk_clin!=2),
                  quol_clin_old_name,NA) 
    
    v3_itm_quol_con<-ifelse((is.na(v3_itm_quol_chk_con) | v3_itm_quol_chk_con!=2),
                  quol_con_old_name,NA)               
    
    if(recode==0) {v3_all_itm_quol<-factor(c(v3_itm_quol_clin,v3_itm_quol_con),ordered=T)}
    else          {v3_all_itm_quol<-factor(6-c(v3_itm_quol_clin,v3_itm_quol_con),ordered=T)}
                      
    assign(quol_new_name,v3_all_itm_quol,envir=.GlobalEnv)
    
    desc(v3_all_itm_quol)}

WHOQOL-BREF Items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_quol_recode <- function(quol_clin_old_name,quol_con_old_name,quol_new_name,recode) {
    
    v4_itm_quol_chk_clin<-v4_clin$v4_whoqol_bref_verwer_fragebogen
    v4_itm_quol_chk_con<-v4_con$v4_whoqol_bref_whoqol_korrekt
    
    v4_itm_quol_clin<-ifelse((is.na(v4_itm_quol_chk_clin) | v4_itm_quol_chk_clin!=2),
                  quol_clin_old_name,NA) 
    
    v4_itm_quol_con<-ifelse((is.na(v4_itm_quol_chk_con) | v4_itm_quol_chk_con!=2),
                  quol_con_old_name,NA)               
    
    if(recode==0) {v4_all_itm_quol<-factor(c(v4_itm_quol_clin,v4_itm_quol_con),ordered=T)}
    else          {v4_all_itm_quol<-factor(6-c(v4_itm_quol_clin,v4_itm_quol_con),ordered=T)}
                      
    assign(quol_new_name,v4_all_itm_quol,envir=.GlobalEnv)
    
    desc(v4_all_itm_quol)}

Big Five Personality Items

#First and second arguments w/o quotes, third with quotes
big_five_recode <- function(big_five_clin_old_name,big_five_con_old_name,big_five_new_name,recode) {
    
    itm_big_five_chk_clin<-v1_clin$v1_bfi_10_verwer_fragebogen
    itm_big_five_chk_con<-v1_con$v1_bfi_10_bfi_korrekt
    
    itm_big_five_clin<-ifelse((is.na(itm_big_five_chk_clin) | itm_big_five_chk_clin!=2),
                  big_five_clin_old_name,NA) 
    
    itm_big_five_con<-ifelse((is.na(itm_big_five_chk_con) | itm_big_five_chk_con!=2),
                  big_five_con_old_name,NA)               
    
    if(recode==0) {all_itm_big_five<-factor(c(itm_big_five_clin,itm_big_five_con),ordered=T)}
    else          {all_itm_big_five<-factor(6-c(itm_big_five_clin,itm_big_five_con),ordered=T)}
                      
    assign(big_five_new_name,all_itm_big_five,envir=.GlobalEnv)
    
    desc(all_itm_big_five)}

Life event between study visits, LEQ item number, Visit 2

leq_event_recode_v2<- function(leq_ev_old_name,leq_ev_new_name) {
  
  leq_itm_no<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
  
  leq_itm_no<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v2_con)[1])))==F,
                            c(leq_ev_old_name,rep(-999,dim(v2_con)[1])),
                            
                        ifelse((v2_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v2_con)[1])))) |              
                                 v2_clin_ill_ep_snc_lst=="N" | v2_clin_ill_ep_snc_lst=="C",-999,leq_itm_no))

  leq_itm_no<-factor(leq_itm_no)
  assign(leq_ev_new_name,leq_itm_no,envir=.GlobalEnv)
  descT(leq_itm_no)}

Life event between study visits, LEQ item number, Visit 3

leq_event_recode_v3<- function(leq_ev_old_name,leq_ev_new_name) {
  
  leq_itm_no<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
  
  leq_itm_no<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v3_con)[1])))==F,
                            c(leq_ev_old_name,rep(-999,dim(v3_con)[1])),
                            
                        ifelse((v3_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v3_con)[1])))) |              
                                 v3_clin_ill_ep_snc_lst=="N" | v3_clin_ill_ep_snc_lst=="C",-999,leq_itm_no))

  leq_itm_no<-factor(leq_itm_no)
  assign(leq_ev_new_name,leq_itm_no,envir=.GlobalEnv)
  descT(leq_itm_no)}

Life event between study visits, LEQ item number, Visit 4

leq_event_recode_v4<- function(leq_ev_old_name,leq_ev_new_name) {
  
  leq_itm_no<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
  
  leq_itm_no<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v4_con)[1])))==F,
                            c(leq_ev_old_name,rep(-999,dim(v4_con)[1])),
                            
                        ifelse((v4_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v4_con)[1])))) |              
                                 v4_clin_ill_ep_snc_lst=="N" | v4_clin_ill_ep_snc_lst=="C",-999,leq_itm_no))

  leq_itm_no<-factor(leq_itm_no)
  assign(leq_ev_new_name,leq_itm_no,envir=.GlobalEnv)
  descT(leq_itm_no)}

Life events between study visits, occurred before illness episode, Visit 2

#First argument w/o quotes, second with quotes
b4_event_recode_v2<- function(between_clin_old_name,between_new_name) {
  
  itm_btw<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
  
  itm_btw<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v2_con)[1]))==1,"Y",
                         ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v2_con)[1]))==2,"N",-999))
                              
  itm_btw<-factor(itm_btw)
  assign(between_new_name,itm_btw,envir=.GlobalEnv)
  descT(itm_btw)}

Life events between study visits, occurred before illness episode, Visit 3

#First argument w/o quotes, second with quotes
b4_event_recode_v3<- function(between_clin_old_name,between_new_name) {
  
  itm_btw<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
  
  itm_btw<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v3_con)[1]))==1,"Y",
                         ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v3_con)[1]))==2,"N",-999))
                              
  itm_btw<-factor(itm_btw)
  assign(between_new_name,itm_btw,envir=.GlobalEnv)
  descT(itm_btw)}

Life events between study visits, occurred before illness episode, Visit 4

#First argument w/o quotes, second with quotes
b4_event_recode_v4<- function(between_clin_old_name,between_new_name) {
  
  itm_btw<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
  
  itm_btw<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v4_con)[1]))==1,"Y",
                         ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v4_con)[1]))==2,"N",-999))
                              
  itm_btw<-factor(itm_btw)
  assign(between_new_name,itm_btw,envir=.GlobalEnv)
  descT(itm_btw)}

Life events between study visits, was a preciptitating factor, Visit 2

#First argument w/o quotes, second with quotes
prcp_event_recode_v2<- function(prcp_clin_old_name,prcp_new_name) {
  
  itm_prcp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
  
  itm_prcp<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v2_con)[1]))==1,"Y",
                         ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v2_con)[1]))==2,"N",
                              ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v2_con)[1]))==3,"U",-999))) 
                                
  itm_prcp<-factor(itm_prcp)
  assign(prcp_new_name,itm_prcp,envir=.GlobalEnv)
  descT(itm_prcp)}

Life events between study visits, was a preciptitating factor, Visit 3

#First argument w/o quotes, second with quotes
prcp_event_recode_v3<- function(prcp_clin_old_name,prcp_new_name) {
  
  itm_prcp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
  
  itm_prcp<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v3_con)[1]))==1,"Y",
                         ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v3_con)[1]))==2,"N",
                              ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v3_con)[1]))==3,"U",-999))) 
                                
  itm_prcp<-factor(itm_prcp)
  assign(prcp_new_name,itm_prcp,envir=.GlobalEnv)
  descT(itm_prcp)}

Life events between study visits, was a preciptitating factor, Visit 4

#First argument w/o quotes, second with quotes
prcp_event_recode_v4<- function(prcp_clin_old_name,prcp_new_name) {
  
  itm_prcp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
  
  itm_prcp<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v4_con)[1]))==1,"Y",
                         ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v4_con)[1]))==2,"N",
                              ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v4_con)[1]))==3,"U",-999))) 
                                
  itm_prcp<-factor(itm_prcp)
  assign(prcp_new_name,itm_prcp,envir=.GlobalEnv)
  descT(itm_prcp)}

Childhood Trauma Screener

#First and second arguments w/o quotes, third with quotes
cts_recode <- function(cts_clin_old_name,cts_con_old_name,cts_new_name,recode) {
    
    itm_cts_chk_clin<-v3_clin$v3_chidlhood_verwer_fragebogen
    itm_cts_chk_con<-v3_con$v3_chidlhood_childhood_korrekt
    
    itm_cts_clin<-ifelse((is.na(itm_cts_chk_clin) | itm_cts_chk_clin!=2),
                  cts_clin_old_name,NA) 
    
    itm_cts_clin[itm_cts_clin==0]<-NA #0 means missing
    
    itm_cts_con<-ifelse((is.na(itm_cts_chk_con) | itm_cts_chk_con!=2),
                  cts_con_old_name,NA)     
    
    itm_cts_con[itm_cts_con==0]<-NA #0 means missing
    
    if(recode==0) {all_itm_cts<-factor(c(itm_cts_clin,itm_cts_con),ordered=T)}
    else          {all_itm_cts<-factor(6-c(itm_cts_clin,itm_cts_con),ordered=T)}
                      
    assign(cts_new_name,all_itm_cts,envir=.GlobalEnv)
    
    desc(all_itm_cts)}

Visit 1: Data preparation

Read in data of clinical participants and show number

## [1] 1320

Read in data of control participants and show number

## [1] 466

Visit 1: Recruitment data (code partly not shown)

Create participant identity variable (categorical [id], v1_id)

Create clinical/control status variable (categorical [stat], v1_stat)

Correct date of interview in some participants

In some participants, an incorrect date of interview was entered into the original phenotype database, which I correct here.

## [1] 20170902

## [1] "20160902"

Create date of interview variable (categorical [year-month-day], v1_interv_date)

Create recruitment center variable (categorical [see below], v1_center)

Clinical participants

Control participants

Combine clincal and control participants Code as factor

v1_center<-factor(c(v1_clin_center,v1_con_center),ordered=F)

Number of participants (clinical and control combined) by recruitment center

descT(v1_center)

##                1   2   3   4   5   6   7   10  11  12 13  14   16   18  19 
## [1,] No. cases 19  39  13  5   50  62  32  8   13  36 13  264  378  104 100
## [2,] Percent   1.1 2.2 0.7 0.3 2.8 3.5 1.8 0.4 0.7 2  0.7 14.8 21.2 5.8 5.6
##      20   21  22  23  25      
## [1,] 227  147 102 47  127 1786
## [2,] 12.7 8.2 5.7 2.6 7.1 100

par(mar=c(5.1,4.1,2.1,0))
ctr<-barplot(table(v1_center),las=2,ylim=c(0,400),lwd=2,horiz=F,axisnames=F, ylab="Number of baseline interviews", xlab="Study Center")
nmctr<-names(table(v1_center))
text(ctr, par("usr")[3], labels=nmctr, srt = 45, adj = c(1.1,1.1), xpd = TRUE, cex=.8)

Interviewers (categorical, v1_tstlt)

Interviewers were de-identified due to data protection requirements.

Clinical participants

Control participants

Combine clincal and control participants

v1_tstlt<-as.factor(c(as.character(v1_clin_int),as.character(v1_con_int)))

Determine number of interviewers (teams counted as separate raters)

length(unique(v1_tstlt)) 

## [1] 98

Create dataset

v1_rec<-data.frame(v1_stat,v1_center,v1_tstlt,v1_interv_date) 

Visit 1: Demographic information

Sex (dichotomous [M,F], v1_sex)

v1_clin_sex<-ifelse(v1_clin$v1_demogr_s1_dem1_ses01_geschl==1,"M","F")
v1_con_sex<-ifelse(v1_con$v1_demo1_sex==1,"M","F")
v1_sex<-c(v1_clin_sex,v1_con_sex)
v1_sex<-as.factor(v1_sex)

descT(v1_sex) 

##                F    M        
## [1,] No. cases 869  917  1786
## [2,] Percent   48.7 51.3 100

Age at first interview (continuous [years], v1_age)

v1_age_years_clin<-as.numeric(substr(v1_clin$v1_ausschluss_rekr_datum,1,4))-as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))
v1_age_years_clin[v1_age_years_clin==-54]<-46 #correct age of one participant, typo in phenotype database

v1_age_years_con<-as.numeric(substr(v1_con$v1_rek_rekrdat,1,4))-as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))

v1_age_years<-c(v1_age_years_clin,v1_age_years_con)

v1_age<-ifelse(c(as.numeric(substr(v1_clin$v1_ausschluss_rekr_datum,5,6)),as.numeric(substr(v1_con$v1_rek_rekrdat,5,6)))<
                 c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
                   v1_age_years-1,v1_age_years)
summary(v1_age) 

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   18.00   28.00   40.00   40.87   52.00   86.00

Year of birth (continuous [year], v1_yob)

v1_yob_clin<-as.integer(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))
v1_yob_con<-as.integer(substr(v1_con$v1_demo1_gebdat,1,4))
v1_yob<-c(v1_yob_clin,v1_yob_con)
summary(v1_yob) 

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1931    1963    1974    1974    1986    2000

Season of birth (categorical [spring, summer, fall, winter], v1_seas_birth)

v1_seas_birth_clin<-rep(NA,dim(v1_clin)[1])

v1_seas_birth_clin<-ifelse(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6) %in% c("03","04","05"), "Spring", 
                   ifelse(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6) %in% c("06","07","08"), "Summer",  
                      ifelse(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6) %in% c("09","10","11"), "Fall", 
                         ifelse(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6) %in% c("12","01","02"),"Winter", v1_seas_birth_clin))))

table(v1_seas_birth_clin)

## v1_seas_birth_clin
##   Fall Spring Summer Winter 
##    340    335    318    327

v1_seas_birth_con<-rep(NA,dim(v1_con)[1])

v1_seas_birth_con<-ifelse(substr(v1_con$v1_demo1_gebdat,5,6) %in% c("03","04","05"), "Spring", 
                   ifelse(substr(v1_con$v1_demo1_gebdat,5,6) %in% c("06","07","08"), "Summer",  
                      ifelse(substr(v1_con$v1_demo1_gebdat,5,6) %in% c("09","10","11"), "Fall", 
                         ifelse(substr(v1_con$v1_demo1_gebdat,5,6) %in% c("12","01","02"),"Winter", v1_seas_birth_con))))

table(v1_seas_birth_con)

## v1_seas_birth_con
##   Fall Spring Summer Winter 
##    108    144    107    107

v1_seas_birth<-c(v1_seas_birth_clin,v1_seas_birth_con)

v1_seas_birth<-as.factor(v1_seas_birth)
table(v1_seas_birth)

## v1_seas_birth
##   Fall Spring Summer Winter 
##    448    479    425    434

Age of parents at birth

Age of mother at birth (continuous [years], v1_age_m_birth)

v1_age_m_birth_clin<-as.integer(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))-as.integer(
  substr(v1_clin$v1_demogr_s1_dem4_geb_m,1,4))

v1_age_m_birth_clin[v1_age_m_birth_clin==0]<-NA #set one case of 0 to NA

v1_age_m_birth_con<-as.integer(substr(v1_con$v1_demo1_gebdat,1,4))-as.integer(substr(v1_con$v1_demo1_mgebdat,1,4))

v1_age_m_birth<-c(v1_age_m_birth_clin,v1_age_m_birth_con)
summary(v1_age_m_birth)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   12.00   24.00   28.00   28.22   32.00  174.00     257

Age of father at birth (continuous [years], v1_age_f_birth)

v1_age_f_birth_clin<-as.integer(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))-as.integer(substr(v1_clin$v1_demogr_s1_dem5_geb_v,1,4))
v1_age_f_birth_con<-as.integer(substr(v1_con$v1_demo1_gebdat,1,4))-as.integer(substr(v1_con$v1_demo1_vgebdat,1,4))

v1_age_f_birth<-c(v1_age_f_birth_clin,v1_age_f_birth_con)
summary(v1_age_f_birth)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   13.00   27.00   31.00   31.86   36.00   62.00     339

Marital status (categorical [married, married but living separately, single, divorced, widowed], v1_marital_stat)

This variable reflects the official/legal German marriage status. It does not neccessarily reflect whether or not an individual has close personal relationships (see next variable).

v1_mar_clin<-v1_clin$v1_demogr_s1_dem6_ses12_famstand
v1_mar_con<-v1_con$v1_demo1_famstand

cat_mar<-c("Married","Married_living_sep","Single","Divorced","Widowed")

v1_marital_stat_clin<-cat_mar[v1_mar_clin]
v1_marital_stat_con<-cat_mar[v1_mar_con]

v1_marital_stat<-as.factor(c(v1_marital_stat_clin,v1_marital_stat_con))
descT(v1_marital_stat)

##                Divorced Married Married_living_sep Single Widowed <NA>     
## [1,] No. cases 225      380     74                 1032   23      52   1786
## [2,] Percent   12.6     21.3    4.1                57.8   1.3     2.9  100

Relationship status

“Do you currently have a partner?” (dichotomous, v1_partner)

v1_clin_partner<-ifelse(v1_clin$v1_demogr_s1_dem9_ses13_partner==1,"Y","N")
v1_con_partner<-ifelse(v1_con$v1_demo1_partner==1,"Y","N")

v1_partner<-as.factor(c(v1_clin_partner,v1_con_partner))
desc(v1_partner)

##                N    Y    NA's     
## [1,] No. cases 795  870  121  1786
## [2,] Percent   44.5 48.7 6.8  100

Children

Biological (continuous [number], v1_no_bio_chld)

v1_no_bio_chld<-c(v1_clin$v1_demogr_s1_dem10_ses15a_lkind,v1_con$v1_demo1_lkind)
descT(v1_no_bio_chld)

##                0    1    2    3   4   5   <NA>     
## [1,] No. cases 1013 279  210  96  28  4   156  1786
## [2,] Percent   56.7 15.6 11.8 5.4 1.6 0.2 8.7  100

Non-biological

Adoptive children (continuous [number], v1_no_adpt_chld)

v1_no_adpt_chld<-c(v1_clin$v1_demogr_s1_dem11_ses15b_akind,v1_con$v1_demo1_adkind)
descT(v1_no_adpt_chld)  

##                0    1   2   <NA>     
## [1,] No. cases 1607 2   2   175  1786
## [2,] Percent   90   0.1 0.1 9.8  100

Step children (continuous [number], v1_stp_chld)

v1_stp_chld<-c(v1_clin$v1_demogr_s1_dem12_skind,v1_con$v1_demo1_stkind)
descT(v1_stp_chld)      

##                0    1   2   3   4   <NA>     
## [1,] No. cases 1547 26  27  7   2   177  1786
## [2,] Percent   86.6 1.5 1.5 0.4 0.1 9.9  100

Siblings

Full siblings

Brothers (continuous [number], v1_brothers)

v1_brothers<-c(v1_clin$v1_demogr_s1_dem13_brueder,v1_con$v1_demo1_bruder)
descT(v1_brothers)      

##                0    1    2    3   4   5   6   7   <NA>     
## [1,] No. cases 627  631  216  65  20  7   2   1   217  1786
## [2,] Percent   35.1 35.3 12.1 3.6 1.1 0.4 0.1 0.1 12.2 100

Sisters (continuous [number], v1_sisters)

v1_sisters<-c(v1_clin$v1_demogr_s1_dem13_schwestern,v1_con$v1_demo1_schwester)
descT(v1_sisters)       

##                0    1    2    3   4   5   6   7   8   <NA>     
## [1,] No. cases 704  579  189  51  9   11  2   2   1   238  1786
## [2,] Percent   39.4 32.4 10.6 2.9 0.5 0.6 0.1 0.1 0.1 13.3 100

Half-siblings

Half-brothers (continuous [number], v1_hlf_brthrs)

v1_hlf_brthrs<-c(v1_clin$v1_demogr_s1_dem13_halbbrueder,v1_con$v1_demo1_hbruder)
descT(v1_hlf_brthrs)    

##                0    1   2  3  4   5   6   <NA>     
## [1,] No. cases 1285 110 36 17 2   1   1   334  1786
## [2,] Percent   71.9 6.2 2  1  0.1 0.1 0.1 18.7 100

Half-sisters (continuous [number], v1_hlf_sstrs)

v1_hlf_sstrs<-c(v1_clin$v1_demogr_s1_dem13_halbschwestern,v1_con$v1_demo1_hschwester)
descT(v1_hlf_sstrs) 

##                0    1   2   3   4   5   6   9   <NA>     
## [1,] No. cases 1276 115 41  9   6   3   1   1   334  1786
## [2,] Percent   71.4 6.4 2.3 0.5 0.3 0.2 0.1 0.1 18.7 100

Non-biological siblings

Step-brothers (continuous [number], v1_stp_brthrs)

v1_stp_brthrs<-c(v1_clin$v1_demogr_s1_dem13_as_brueder,v1_con$v1_demo1_adbrueder)
descT(v1_stp_brthrs) 

##                0    1   2   3   <NA>     
## [1,] No. cases 1396 32  9   1   348  1786
## [2,] Percent   78.2 1.8 0.5 0.1 19.5 100

Step-sisters (continuous [number], v1_stp_sstrs)

v1_stp_sstrs<-c(v1_clin$v1_demogr_s1_dem13_as_schwestern,v1_con$v1_demo1_adschwester)
descT(v1_stp_sstrs)

##                0    1   2   3   <NA>     
## [1,] No. cases 1392 30  10  3   351  1786
## [2,] Percent   77.9 1.7 0.6 0.2 19.7 100

Twins

“Do you have a first degree relative who is a twin?” (dichotomous, v1_twin_fam)

v1_clin_twin_fam<-ifelse(v1_clin$v1_demogr_s1_dem14_zwillinge==1,"Y","N")   
v1_con_twin_fam<-ifelse(v1_con$v1_demo1_zwillinge==1,"Y","N")   
v1_twin_fam<-as.factor(c(v1_clin_twin_fam,v1_con_twin_fam))

descT(v1_twin_fam)

##                N    Y   <NA>     
## [1,] No. cases 1451 160 175  1786
## [2,] Percent   81.2 9   9.8  100

“Are you a twin yourself?” (dichotomous, v1_twin_slf)

v1_clin_twin_slf<-ifelse(v1_clin$v1_demogr_s1_dem15_selbst_zwill==1,"Y","N")
v1_con_twin_slf<-ifelse(v1_con$v1_demo1_szwilling==1,"Y","N")
v1_twin_slf<-as.factor(c(v1_clin_twin_slf,v1_con_twin_slf))

descT(v1_twin_slf)

##                N    Y   <NA>     
## [1,] No. cases 1570 47  169  1786
## [2,] Percent   87.9 2.6 9.5  100

Living alone (dichotomous, v1_liv_aln)

v1_clin_liv_aln<-ifelse(v1_clin$v1_demogr_s1_dem17_allein==1,"Y","N")   
v1_con_liv_aln<-ifelse(v1_con$v1_demo1_allein==1,"Y","N")   
v1_liv_aln<-as.factor(c(v1_clin_liv_aln,v1_con_liv_aln))

descT(v1_liv_aln)

##                N    Y        
## [1,] No. cases 1138 648  1786
## [2,] Percent   63.7 36.3 100

Education

Status in the German educational system is assessed in detail. However, many specialized types of German schools are unknown to English-speaking investigators and detailed information does not seem to play a role. Furthermore, high-school and professional education are assessed seperately in the interview. In order to combine the aforementioned types of education, we have transformed both scales to values that can be added together to form an “Educational status” variable. High-school level education was transformed into an ordinal scale from 0 to 3 (people still in high school at the time of the interview are give NA). Professional eduction is also transformed into an ordinal scale from 0 to 3. These two scales are added together to give an ordinal educational status scale ranging from 0 to 6.

High-school level education (ordinal [0,1,2,3], v1_school)

The following transformation was used: “no information”/“missing”-NA, “no graduation”-0, “Hauptschule”-1, “Realschule”-2, “Polytechnische Oberschule”-2, “Fachhochschule”-3, “Allgemeine Hochschulreife”-3; still in school”/“other degree”- -999.

NB: Transformation to ordered factor below, after creation of v1_ed_status variable.

v1_clin_school<-rep(NA,dim(v1_clin)[1])
v1_con_school<-rep(NA,dim(v1_con)[1])

v1_clin_school<-ifelse(v1_clin$v1_demogr_s2_dem18_ses18_sabschl %in% c(1:2),v1_clin$v1_demogr_s2_dem18_ses18_sabschl-1,v1_clin_school)
v1_clin_school<-ifelse(v1_clin$v1_demogr_s2_dem18_ses18_sabschl %in% c(3:4),2,v1_clin_school)
v1_clin_school<-ifelse(v1_clin$v1_demogr_s2_dem18_ses18_sabschl %in% c(5:6),3,v1_clin_school)
v1_clin_school<-ifelse(v1_clin$v1_demogr_s2_dem18_ses18_sabschl %in% c(7:8),-999,v1_clin_school)

v1_con_school<-ifelse(v1_con$v1_demo2_abschl %in% c(1:2),v1_con$v1_demo2_abschl-1,v1_con_school)
v1_con_school<-ifelse(v1_con$v1_demo2_abschl %in% c(3:4),2,v1_con_school)
v1_con_school<-ifelse(v1_con$v1_demo2_abschl %in% c(5:6),3,v1_con_school)
v1_con_school<-ifelse(v1_con$v1_demo2_abschl %in% c(7:8),-999,v1_con_school)

v1_school<-c(v1_clin_school,v1_con_school)
descT(v1_school)

##                -999 0   1   2    3    <NA>     
## [1,] No. cases 24   28  303 402  1012 17   1786
## [2,] Percent   1.3  1.6 17  22.5 56.7 1    100

Professional education (ordinal [0,1,2,3], v1_prof_dgr)

The following transformation was used: - missing or no information-NA,
- “no professional education”/“beruflich-betriebliche Anlernzeit, aber keine Lehre; Teilfacharbeiterabschluss”/“in professional education”-0,
- “beruflich-betriebliche Ausbildung (Lehre)”-1,
- “beruflich-schulische Ausbildung”-2,
- “Fachhochschul-/Universitätsabschluss”-3,
- “other professional degree” - -999,

NB: Transformation to ordered factor below, after creation of v1_ed_status variable.

v1_clin_prof_dgr<-rep(NA,dim(v1_clin)[1])
v1_con_prof_dgr<-rep(NA,dim(v1_con)[1])

v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_aba==1,-999,v1_clin_prof_dgr) 
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_kba==1,0,v1_clin_prof_dgr)    
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_anlern==1,0,v1_clin_prof_dgr) 
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_in_ausb==1,0,v1_clin_prof_dgr)
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_lehre==1,1,v1_clin_prof_dgr)  
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_ausb==1,2,v1_clin_prof_dgr)   
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_fachs==1,2,v1_clin_prof_dgr)  
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_fh_uni==1,3,v1_clin_prof_dgr)

v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_a_babschl==1,-999,v1_con_prof_dgr)  
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_kba==1,0,v1_con_prof_dgr)   
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_anlern==1,0,v1_con_prof_dgr)    
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_ausbild==1,0,v1_con_prof_dgr)
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_lehre==1,1,v1_con_prof_dgr) 
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_ausb==1,2,v1_con_prof_dgr)  
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_fachs==1,2,v1_con_prof_dgr) 
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_fhuni==1,3,v1_con_prof_dgr)

v1_prof_dgr<-c(v1_clin_prof_dgr,v1_con_prof_dgr)
descT(v1_prof_dgr)

##                -999 0    1    2    3    <NA>     
## [1,] No. cases 17   439  426  365  454  85   1786
## [2,] Percent   1    24.6 23.9 20.4 25.4 4.8  100

Important: more than one answer is possible, as people may have several professional degrees. The order of the commands above makes sure higher professional degrees overwrite lower ones.

Educational status scale (ordinal [0,1,2,3,4,5,6], v1_ed_status)

As describe above, this scale was newly created.

v1_ed_status<-v1_school+v1_prof_dgr
v1_ed_status[v1_ed_status<0]<-NA
v1_ed_status<-factor(v1_ed_status, ordered=T)

descT(v1_ed_status)

##                0   1   2    3    4    5    6    <NA>     
## [1,] No. cases 22  80  251  433  249  182  441  128  1786
## [2,] Percent   1.2 4.5 14.1 24.2 13.9 10.2 24.7 7.2  100

Transform school and professional degree variables to factors.

v1_school<-factor(v1_school, ordered=T)
v1_prof_dgr<-factor(v1_prof_dgr, ordered=T)

Employment

Currently paid employment (dichotomous, v1_curr_paid_empl)

Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.

v1_clin_curr_paid_empl<-rep(NA,dim(v1_clin)[1])
v1_con_curr_paid_empl<-rep(NA,dim(v1_con)[1])

v1_clin_curr_paid_empl<-ifelse(v1_clin$v1_demogr_s2_dem20_ses20_erwbtaet %in% c(1:5,7:10),"Y",v1_clin_curr_paid_empl)
v1_clin_curr_paid_empl<-ifelse(v1_clin$v1_demogr_s2_dem20_ses20_erwbtaet %in% c(6,11),"N",v1_clin_curr_paid_empl)   

v1_con_curr_paid_empl<-ifelse(v1_con$v1_demo2_erwerb %in% c(1:5,7:10),"Y",v1_con_curr_paid_empl)
v1_con_curr_paid_empl<-ifelse(v1_con$v1_demo2_erwerb %in% c(6,11),"N",v1_con_curr_paid_empl)    

v1_curr_paid_empl<-c(v1_clin_curr_paid_empl,v1_con_curr_paid_empl)
v1_curr_paid_empl<-as.factor(v1_curr_paid_empl)
descT(v1_curr_paid_empl)

##                N    Y    <NA>     
## [1,] No. cases 945  767  74   1786
## [2,] Percent   52.9 42.9 4.1  100

Disability pension due to psychological/psychiatric illness (dichotomous, v1_disabl_pens)

v1_clin_disabl_pens<-ifelse(v1_clin$v1_demogr_s2_dem20_ses20_rente==1,"Y","N")      
v1_con_disabl_pens<-ifelse(v1_con$v1_demo2_rente==1,"Y","N")        

v1_disabl_pens<-as.factor(c(v1_clin_disabl_pens,v1_con_disabl_pens))
descT(v1_disabl_pens)

##                N    Y    <NA>     
## [1,] No. cases 853  398  535  1786
## [2,] Percent   47.8 22.3 30   100

Employed in workshop for handicapped persons (dichotomous, v1_spec_emp)

v1_clin_spec_emp<-ifelse(v1_clin$v1_demogr_s2_dem20_ses20_werk==1,"Y","N")          
v1_con_spec_emp<-ifelse(v1_con$v1_demo2_wfbm==1,"Y","N")            

v1_spec_emp<-as.factor(c(v1_clin_spec_emp,v1_con_spec_emp))
descT(v1_spec_emp)

##                N    Y   <NA>     
## [1,] No. cases 638  75  1073 1786
## [2,] Percent   35.7 4.2 60.1 100

Months of work absence due to psychological distress in past five years (continuous [months], v1_wrk_abs_pst_5_yrs)

Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >60 months are set to -999.

v1_clin_wrk_abs_pst_5_yrs<-ifelse((v1_clin$v1_demogr_s2_dem23_unbekannt==1 | v1_clin$v1_demogr_s2_dem23_rente==1 | v1_clin$v1_demogr_s2_dem23_arbeitsausf>60),-999, v1_clin$v1_demogr_s2_dem23_arbeitsausf)

v1_con_wrk_abs_pst_5_yrs<-ifelse((v1_con$v1_demo2_ausfallu==1 | v1_con$v1_demo2_rente==1 | v1_con$v1_demo2_ausfallm>60),-999, v1_con$v1_demo2_ausfallm) 

v1_wrk_abs_pst_5_yrs<-c(v1_clin_wrk_abs_pst_5_yrs,v1_con_wrk_abs_pst_5_yrs)
descT(v1_wrk_abs_pst_5_yrs)

##                -999 0    1   2   3   4   5   6   7   8   9   10  11  12  13 
## [1,] No. cases 549  363  55  66  42  32  32  51  11  22  14  20  3   41  7  
## [2,] Percent   30.7 20.3 3.1 3.7 2.4 1.8 1.8 2.9 0.6 1.2 0.8 1.1 0.2 2.3 0.4
##      14  15  16  17  18  20  22  23  24  25  26  27  28  30  33  35  36  38 
## [1,] 3   9   6   3   23  6   3   1   33  1   2   2   1   12  1   2   14  1  
## [2,] 0.2 0.5 0.3 0.2 1.3 0.3 0.2 0.1 1.8 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.8 0.1
##      42  45  48  50  52  54  60  <NA>     
## [1,] 3   1   13  2   1   2   41  292  1786
## [2,] 0.2 0.1 0.7 0.1 0.1 0.1 2.3 16.3 100

Currently impaired by psychological/psychiatric symptoms in exercising profession (dichotomous, v1_cur_work_restr)

Important: if receiving pension, this question refers to impairments in the household

v1_clin_cur_work_restr<-ifelse(v1_clin$v1_demogr_s2_dem24_arbeitseinschr==1,"Y","N")    
v1_con_cur_work_restr<-ifelse(v1_con$v1_demo2_psyeinsch==1,"Y","N") 
v1_cur_work_restr<-as.factor(c(v1_clin_cur_work_restr,v1_con_cur_work_restr))

descT(v1_cur_work_restr)

##                N   Y    <NA>     
## [1,] No. cases 732 816  238  1786
## [2,] Percent   41  45.7 13.3 100

Create dataset

v1_dem<-data.frame(v1_sex,v1_age,v1_yob,v1_seas_birth,v1_age_m_birth,v1_age_f_birth,
                   v1_marital_stat,v1_partner,v1_no_bio_chld,v1_no_adpt_chld,
                   v1_stp_chld,v1_brothers,v1_sisters,v1_hlf_brthrs,v1_hlf_sstrs,
                   v1_stp_brthrs,v1_stp_sstrs,v1_twin_fam,v1_twin_slf,v1_liv_aln,
                   v1_school,v1_prof_dgr,v1_ed_status,v1_curr_paid_empl,
                   v1_disabl_pens,v1_spec_emp,v1_wrk_abs_pst_5_yrs,v1_cur_work_restr)

Visit 1: Ethnicity

Country of birth (categorical [country], v1_cntr_brth)

v1_clin_cntr_brth<-v1_clin$v1_demogr_s2_dem25_ses03a_lst_lnd
v1_clin_cntr_brth<-ifelse(is.na(v1_clin_cntr_brth) & v1_clin$v1_demogr_s2_dem25_ses03a_land_st==1, 
                     "Deutschland",as.character(v1_clin_cntr_brth))

v1_con_cntr_brth<-v1_con$v1_demo2_land1 
v1_con_cntr_brth<-ifelse(is.na(v1_con_cntr_brth) & v1_con$v1_demo2_gebort==1, 
                     "Deutschland",as.character(v1_con$v1_con_cntr_brth))
v1_cntr_brth<-as.factor(c(v1_clin_cntr_brth,v1_con_cntr_brth))
descT(v1_cntr_brth)

##                Afghanistan Ägypten anderes Land Argentinien Äthiopien
## [1,] No. cases 1           1       1            1           2        
## [2,] Percent   0.1         0.1     0.1          0.1         0.1      
##      Australien Belarus (Weißrussland) Bosnien und Herzegowina Brasilien
## [1,] 1          1                      3                       1        
## [2,] 0.1        0.1                    0.2                     0.1      
##      Deutschland Eritrea Estland Finnland Frankreich Griechenland Irak
## [1,] 1370        2       1       1        2          1            2   
## [2,] 76.7        0.1     0.1     0.1      0.1        0.1          0.1 
##      Iran, Islamische Republik Italien Kasachstan Kirgisistan Kroatien Marokko
## [1,] 3                         1       8          2           4        1      
## [2,] 0.2                       0.1     0.4        0.1         0.2      0.1    
##      Mazedonien, ehem. jugoslawische Republik Moldawien (Republik Moldau)
## [1,] 2                                        1                          
## [2,] 0.1                                      0.1                        
##      Mongolei Niederlande Nigeria Österreich Pakistan Polen Rumänien
## [1,] 1        1           1       153        1        27    14      
## [2,] 0.1      0.1         0.1     8.6        0.1      1.5   0.8     
##      Russische Föderation Senegal Serbien Slowakei Slowenien Sri Lanka
## [1,] 11                   1       9       1        1         1        
## [2,] 0.6                  0.1     0.5     0.1      0.1       0.1      
##      Südafrika Tadschikistan Thailand Tschechische Republik Türkei Ukraine
## [1,] 1         1             1        4                     9      1      
## [2,] 0.1       0.1           0.1      0.2                   0.5    0.1    
##      Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 2      1          2                             
## [2,] 0.1    0.1        0.1                           
##      Vereinigtes Königreich Großbritannien und Nordirland Vietnam <NA>     
## [1,] 2                                                    1       126  1786
## [2,] 0.1                                                  0.1     7.1  100

Country of birth mother (categorical [country], v1_cntr_brth_m)

v1_clin_cntr_brth_m<-v1_clin$v1_demogr_s2_dem26_ses06_lm_lnd
v1_clin_cntr_brth_m<-ifelse(is.na(v1_clin_cntr_brth_m) & v1_clin$v1_demogr_s2_dem26_ses06_land_m==1, 
                       "Deutschland",as.character(v1_clin_cntr_brth_m))

v1_con_cntr_brth_m<-v1_con$v1_demo2_land2
v1_con_cntr_brth_m<-ifelse(is.na(v1_con_cntr_brth_m) & v1_con$v1_demo2_gebortm==1, 
                       "Deutschland",as.character(v1_con_cntr_brth_m))
v1_cntr_brth_m<-as.factor(c(v1_clin_cntr_brth_m,v1_con_cntr_brth_m))
descT(v1_cntr_brth_m)

##                Afghanistan Algerien anderes Land Argentinien Äthiopien
## [1,] No. cases 1           1        2            1           2        
## [2,] Percent   0.1         0.1      0.1          0.1         0.1      
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Brasilien Bulgarien
## [1,] 2                      1       6                       1         2        
## [2,] 0.1                    0.1     0.3                     0.1       0.1      
##      Chile Dänemark Deutschland Eritrea Estland Finnland Frankreich
## [1,] 2     1        1075        2       2       1        2         
## [2,] 0.1   0.1      60.2        0.1     0.1     0.1      0.1       
##      Griechenland Indien Indonesien Irak Iran, Islamische Republik Irland
## [1,] 1            1      2          2    6                         1     
## [2,] 0.1          0.1    0.1        0.1  0.3                       0.1   
##      Israel Italien Japan Kasachstan Kenia Kirgisistan
## [1,] 1      9       1     9          1     1          
## [2,] 0.1    0.5     0.1   0.5        0.1   0.1        
##      Korea, Republik (Südkorea) Kroatien
## [1,] 1                          10      
## [2,] 0.1                        0.6     
##      Libysch-Arabische Dschamahirija (Libyen) Litauen Luxemburg Marokko
## [1,] 1                                        1       3         1      
## [2,] 0.1                                      0.1     0.2       0.1    
##      Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3                                        1        2       1          
## [2,] 0.2                                      0.1      0.1     0.1        
##      Nigeria Norwegen Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 1       1        199        1        97    16       25                  
## [2,] 0.1     0.1      11.1       0.1      5.4   0.9      1.4                 
##      Schweiz (Confoederatio Helvetica) Senegal Serbien Singapur Slowakei
## [1,] 2                                 1       13      1        3       
## [2,] 0.1                               0.1     0.7     0.1      0.2     
##      Slowenien Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine
## [1,] 5         3       3         1        24                    20     10     
## [2,] 0.3       0.2     0.2       0.1      1.3                   1.1    0.6    
##      Ungarn Usbekistan Vereinigtes Königreich Großbritannien und Nordirland
## [1,] 12     1          3                                                   
## [2,] 0.7    0.1        0.2                                                 
##      <NA>     
## [1,] 181  1786
## [2,] 10.1 100

Country of birth father (categorical [country], v1_cntr_brth_f)

v1_clin_cntr_brth_f<-v1_clin$v1_demogr_s2_dem27_ses07_lv_lnd
v1_clin_cntr_brth_f<-ifelse(is.na(v1_clin_cntr_brth_f)==T & v1_clin$v1_demogr_s2_dem27_ses07_land_v==1, 
                       "Deutschland",as.character(v1_clin_cntr_brth_f))

v1_con_cntr_brth_f<-v1_con$v1_demo2_land3
v1_con_cntr_brth_f<-ifelse(is.na(v1_con_cntr_brth_f)==T & v1_con$v1_demo2_gebortv==1, 
                       "Deutschland",as.character(v1_con_cntr_brth_f))

v1_cntr_brth_f<-as.factor(c(v1_clin_cntr_brth_f,v1_con_cntr_brth_f))
descT(v1_cntr_brth_f)

##                Afghanistan Ägypten anderes Land Argentinien Armenien Äthiopien
## [1,] No. cases 1           1       4            3           1        1        
## [2,] Percent   0.1         0.1     0.2          0.2         0.1      0.1      
##      Australien Belarus (Weißrussland) Belgien Bosnien und Herzegowina
## [1,] 2          1                      2       4                      
## [2,] 0.1        0.1                    0.1     0.2                    
##      Bulgarien Chile Deutschland Dominikanische Republik Eritrea Estland
## [1,] 3         3     1034        1                       3       3      
## [2,] 0.2       0.2   57.9        0.1                     0.2     0.2    
##      Frankreich Griechenland Indien Indonesien Irak Iran, Islamische Republik
## [1,] 5          1            1      1          2    6                        
## [2,] 0.3        0.1          0.1    0.1        0.1  0.3                      
##      Israel Italien Japan Jemen Kasachstan Kenia Korea, Republik (Südkorea)
## [1,] 1      10      2     1     8          1     1                         
## [2,] 0.1    0.6     0.1   0.1   0.4        0.1   0.1                       
##      Kroatien Libanon Luxemburg Marokko
## [1,] 5        1       1         2      
## [2,] 0.3      0.1     0.1       0.1    
##      Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Nigeria Norwegen
## [1,] 3                                        1        2       2       2       
## [2,] 0.2                                      0.1      0.1     0.1     0.1     
##      Österreich Pakistan Palästinensische Autonomiegebiete Polen Rumänien
## [1,] 209        2        1                                 114   14      
## [2,] 11.7       0.1      0.1                               6.4   0.8     
##      Russische Föderation Senegal Serbien Slowakei Slowenien Spanien Sri Lanka
## [1,] 32                   1       9       2        3         1       2        
## [2,] 1.8                  0.1     0.5     0.1      0.2       0.1     0.1      
##      Tadschikistan Thailand Tschechische Republik Türkei Ukraine Ungarn
## [1,] 1             1        23                    24     8       7     
## [2,] 0.1           0.1      1.3                   1.3    0.4     0.4   
##      Usbekistan Vereinigte Staaten von Amerika
## [1,] 1          5                             
## [2,] 0.1        0.3                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 4                                                    196  1786
## [2,] 0.2                                                  11   100

Country of birth grandmother, maternal side (categorical [country], v1_cntr_brth_gmm)

v1_clin_cntr_brth_gmm<-v1_clin$v1_demogr_s2_dem28_land_gm_ms_lnd
v1_clin_cntr_brth_gmm<-ifelse(is.na(v1_clin_cntr_brth_gmm)==T & v1_clin$v1_demogr_s2_dem28_land_gm_ms==1, 
                         "Deutschland",as.character(v1_clin_cntr_brth_gmm))

v1_con_cntr_brth_gmm<-v1_con$v1_demo2_land4
v1_con_cntr_brth_gmm<-ifelse(is.na(v1_con_cntr_brth_gmm)==T & v1_con$v1_demo2_gebortgmm==1, 
                         "Deutschland",as.character(v1_con_cntr_brth_gmm))

v1_cntr_brth_gmm<-as.factor(c(v1_clin_cntr_brth_gmm,v1_con_cntr_brth_gmm))
descT(v1_cntr_brth_gmm)

##                Afghanistan anderes Land Argentinien Äthiopien
## [1,] No. cases 1           2            1           1        
## [2,] Percent   0.1         0.1          0.1         0.1      
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien Chile
## [1,] 2                      1       7                       1         2    
## [2,] 0.1                    0.1     0.4                     0.1       0.1  
##      China, Volksrepublik Dänemark Deutschland Eritrea Estland Finnland
## [1,] 1                    2        893         2       2       2       
## [2,] 0.1                  0.1      50          0.1     0.1     0.1     
##      Frankreich Georgien Griechenland Indien Indonesien Irak
## [1,] 1          1        3            2      1          2   
## [2,] 0.1        0.1      0.2          0.1    0.1        0.1 
##      Iran, Islamische Republik Israel Italien Japan Kasachstan Kenia Kolumbien
## [1,] 6                         2      9       1     5          1     1        
## [2,] 0.3                       0.1    0.5     0.1   0.3        0.1   0.1      
##      Korea, Republik (Südkorea) Kroatien Litauen Luxemburg Marokko
## [1,] 1                          10       2       3         1      
## [2,] 0.1                        0.6      0.1     0.2       0.1    
##      Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3                                        1        1       3          
## [2,] 0.2                                      0.1      0.1     0.2        
##      Nigeria Norwegen Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 1       1        183        1        127   12       21                  
## [2,] 0.1     0.1      10.2       0.1      7.1   0.7      1.2                 
##      Schweden Schweiz (Confoederatio Helvetica) Senegal Serbien Slowakei
## [1,] 1        3                                 1       13      2       
## [2,] 0.1      0.2                               0.1     0.7     0.1     
##      Slowenien Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine
## [1,] 6         4       3         1        34                    18     16     
## [2,] 0.3       0.2     0.2       0.1      1.9                   1      0.9    
##      Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 15     1          1                             
## [2,] 0.8    0.1        0.1                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 3                                                    338  1786
## [2,] 0.2                                                  18.9 100

Country of birth grandfather, maternal side (catergorical [country], v1_cntr_brth_gfm)

v1_clin_cntr_brth_gfm<-v1_clin$v1_demogr_s2_dem29_land_gv_ms_lnd
v1_clin_cntr_brth_gfm<-ifelse(is.na(v1_clin_cntr_brth_gfm)==T & v1_clin$v1_demogr_s2_dem29_land_gv_ms==1, 
                         "Deutschland",as.character(v1_clin_cntr_brth_gfm))

v1_con_cntr_brth_gfm<-v1_con$v1_demo2_land5
v1_con_cntr_brth_gfm<-ifelse(is.na(v1_con_cntr_brth_gfm)==T & v1_con$v1_demo2_gebortgmv==1, 
                         "Deutschland",as.character(v1_con_cntr_brth_gfm))

v1_cntr_brth_gfm<-as.factor(c(v1_clin_cntr_brth_gfm,v1_con_cntr_brth_gfm))
descT(v1_cntr_brth_gfm)

##                Afghanistan anderes Land Argentinien Aserbaidschan Äthiopien
## [1,] No. cases 1           2            1           1             1        
## [2,] Percent   0.1         0.1          0.1         0.1           0.1      
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien Chile
## [1,] 3                      1       7                       1         2    
## [2,] 0.2                    0.1     0.4                     0.1       0.1  
##      China, Volksrepublik Dänemark Deutschland Eritrea Estland Finnland
## [1,] 2                    1        821         2       2       1       
## [2,] 0.1                  0.1      46          0.1     0.1     0.1     
##      Frankreich Georgien Griechenland Indien Indonesien Irak
## [1,] 3          1        4            2      1          2   
## [2,] 0.2        0.1      0.2          0.1    0.1        0.1 
##      Iran, Islamische Republik Israel Italien Japan Kasachstan Kenia
## [1,] 6                         2      7       1     5          1    
## [2,] 0.3                       0.1    0.4     0.1   0.3        0.1  
##      Korea, Demokratische Volksrepublik (Nordkorea) Korea, Republik (Südkorea)
## [1,] 1                                              1                         
## [2,] 0.1                                            0.1                       
##      Kroatien Luxemburg Marokko Mazedonien, ehem. jugoslawische Republik
## [1,] 10       3         1       3                                       
## [2,] 0.6      0.2       0.1     0.2                                     
##      Mongolei Namibia Niederlande Nigeria Norwegen Österreich Pakistan Polen
## [1,] 1        2       5           1       1        178        1        114  
## [2,] 0.1      0.1     0.3         0.1     0.1      10         0.1      6.4  
##      Rumänien Russische Föderation Schweden Schweiz (Confoederatio Helvetica)
## [1,] 11       26                   1        2                                
## [2,] 0.6      1.5                  0.1      0.1                              
##      Senegal Serbien Slowakei Slowenien Spanien Sri Lanka Thailand
## [1,] 1       12      3        5         4       3         1       
## [2,] 0.1     0.7     0.2      0.3       0.2     0.2       0.1     
##      Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 24                    19     8       16     1         
## [2,] 1.3                   1.1    0.4     0.9    0.1       
##      Vereinigte Staaten von Amerika
## [1,] 1                             
## [2,] 0.1                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 2                                                    440  1786
## [2,] 0.1                                                  24.6 100

Country of birth grandmother, paternal side (catergorical [country], v1_cntr_brth_gmf)

v1_clin_cntr_brth_gmf<-v1_clin$v1_demogr_s2_dem30_land_gm_vs_lnd
v1_clin_cntr_brth_gmf<-ifelse(is.na(v1_clin_cntr_brth_gmf)==T & v1_clin$v1_demogr_s2_dem30_land_gm_vs==1, 
                         "Deutschland",as.character(v1_clin_cntr_brth_gmf))

v1_con_cntr_brth_gmf<-v1_con$v1_demo2_land6
v1_con_cntr_brth_gmf<-ifelse(is.na(v1_con_cntr_brth_gmf)==T & v1_con$v1_demo2_gebortgvm==1, 
                         "Deutschland",as.character(v1_con_cntr_brth_gmf))

v1_cntr_brth_gmf<-as.factor(c(v1_clin_cntr_brth_gmf,v1_con_cntr_brth_gmf))
descT(v1_cntr_brth_gmf)

##                Afghanistan Ägypten anderes Land Argentinien Australien
## [1,] No. cases 1           1       5            1           1         
## [2,] Percent   0.1         0.1     0.3          0.1         0.1       
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien Chile
## [1,] 1                      1       4                       2         3    
## [2,] 0.1                    0.1     0.2                     0.1       0.2  
##      Dänemark Deutschland Dominikanische Republik Eritrea Estland Frankreich
## [1,] 1        814         1                       3       2       6         
## [2,] 0.1      45.6        0.1                     0.2     0.1     0.3       
##      Ghana Griechenland Indien Irak Iran, Islamische Republik Israel Italien
## [1,] 1     3            2      2    6                         1      10     
## [2,] 0.1   0.2          0.1    0.1  0.3                       0.1    0.6    
##      Japan Jemen Kasachstan Kenia Korea, Republik (Südkorea) Kroatien Libanon
## [1,] 2     1     3          1     1                          4        2      
## [2,] 0.1   0.1   0.2        0.1   0.1                        0.2      0.1    
##      Luxemburg Marokko Mazedonien, ehem. jugoslawische Republik
## [1,] 1         1       3                                       
## [2,] 0.1       0.1     0.2                                     
##      Moldawien (Republik Moldau) Mongolei Namibia Niederlande Nigeria Norwegen
## [1,] 2                           2        1       2           2       2       
## [2,] 0.1                         0.1      0.1     0.1         0.1     0.1     
##      Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 191        1        109   12       31                  
## [2,] 10.7       0.1      6.1   0.7      1.7                 
##      Schweiz (Confoederatio Helvetica) Senegal Serbien Slowakei Slowenien
## [1,] 1                                 1       10      2        4        
## [2,] 0.1                               0.1     0.6     0.1      0.2      
##      Spanien Sri Lanka Südafrika Thailand Tschechische Republik Türkei Ukraine
## [1,] 3       2         1         1        29                    21     8      
## [2,] 0.2     0.1       0.1       0.1      1.6                   1.2    0.4    
##      Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 10     1          4                             
## [2,] 0.6    0.1        0.2                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 4                                                    438  1786
## [2,] 0.2                                                  24.5 100

Country of birth grandfather, paternal side (catergorical [country], v1_cntr_brth_gff)

v1_clin_cntr_brth_gff<-v1_clin$v1_demogr_s2_dem31_land_gv_vs_lnd
v1_clin_cntr_brth_gff<-ifelse(is.na(v1_clin_cntr_brth_gff)==T & v1_clin$v1_demogr_s2_dem31_land_gv_vs==1, 
                         "Deutschland",as.character(v1_clin_cntr_brth_gff))

v1_con_cntr_brth_gff<-v1_con$v1_demo2_land7
v1_con_cntr_brth_gff<-ifelse(is.na(v1_con_cntr_brth_gff)==T & v1_con$v1_demo2_gebortgvv==1, 
                         "Deutschland",as.character(v1_con_cntr_brth_gff))

v1_cntr_brth_gff<-as.factor(c(v1_clin_cntr_brth_gff,v1_con_cntr_brth_gff))
descT(v1_cntr_brth_gff)

##                Afghanistan Ägypten Algerien anderes Land Australien
## [1,] No. cases 1           1       1        5            1         
## [2,] Percent   0.1         0.1     0.1      0.3          0.1       
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien Chile
## [1,] 1                      2       4                       2         4    
## [2,] 0.1                    0.1     0.2                     0.1       0.2  
##      Deutschland Eritrea Estland Frankreich Ghana Griechenland Indien Irak
## [1,] 847         3       1       5          1     1            2      2   
## [2,] 47.4        0.2     0.1     0.3        0.1   0.1          0.1    0.1 
##      Iran, Islamische Republik Israel Italien Japan Jemen Kasachstan Kenia
## [1,] 7                         1      9       2     1     3          1    
## [2,] 0.4                       0.1    0.5     0.1   0.1   0.2        0.1  
##      Korea, Republik (Südkorea) Kroatien Litauen Luxemburg Marokko
## [1,] 1                          5        2       1         1      
## [2,] 0.1                        0.3      0.1     0.1       0.1    
##      Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3                                        1        2       1          
## [2,] 0.2                                      0.1      0.1     0.1        
##      Nigeria Norwegen Österreich Pakistan Palästinensische Autonomiegebiete
## [1,] 2       2        191        1        2                                
## [2,] 0.1     0.1      10.7       0.1      0.1                              
##      Polen Rumänien Russische Föderation Senegal Serbien Slowakei Slowenien
## [1,] 109   12       31                   1       9       3        5        
## [2,] 6.1   0.7      1.7                  0.1     0.5     0.2      0.3      
##      Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine Ungarn
## [1,] 3       2         1        26                    22     7       11    
## [2,] 0.2     0.1       0.1      1.5                   1.2    0.4     0.6   
##      Usbekistan Vereinigte Staaten von Amerika
## [1,] 1          6                             
## [2,] 0.1        0.3                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 3                                                    411  1786
## [2,] 0.2                                                  23   100

Create dataset

v1_eth<-data.frame(v1_cntr_brth,v1_cntr_brth_m,v1_cntr_brth_f,v1_cntr_brth_gmm,
                  v1_cntr_brth_gfm,v1_cntr_brth_gmf,v1_cntr_brth_gff)

Visit 1: Psychiatric history

Current psychiatric treatment (ordinal [1,2,3,4], v1_cur_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.

v1_clin_cur_psy_trm<-rep(NA,dim(v1_clin)[1])
v1_con_cur_psy_trm<-rep(NA,dim(v1_con)[1])

v1_clin_cur_psy_trm<-ifelse(v1_clin$v1_psy_vorg_akt_pva1_akt_behand==0,"1",
                        ifelse(v1_clin$v1_psy_vorg_akt_pva1_akt_behand==3,"2", 
                          ifelse(v1_clin$v1_psy_vorg_akt_pva1_akt_behand==2,"3",
                            ifelse(v1_clin$v1_psy_vorg_akt_pva1_akt_behand==1,"4",v1_clin_cur_psy_trm)))) 

v1_con_cur_psy_trm<-ifelse(v1_con$v1_psyaktuel_aktbehand==0,"1",
                      ifelse(v1_con$v1_psyaktuel_aktbehand==3,"2",
                        ifelse(v1_con$v1_psyaktuel_aktbehand==2,"3",
                          ifelse(v1_con$v1_psyaktuel_aktbehand==1,"4",v1_con_cur_psy_trm))))

v1_cur_psy_trm<-ordered(as.factor(c(v1_clin_cur_psy_trm,v1_con_cur_psy_trm)))
descT(v1_cur_psy_trm)

##                1    2    3   4    <NA>     
## [1,] No. cases 476  640  78  555  37   1786
## [2,] Percent   26.7 35.8 4.4 31.1 2.1  100

Ever treated as outpatient due to any mental health reason (ordinal [1,2,3,4], v1_outpat_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, consultation or short treatment”-2, “yes, continuous treatment for six months or numerous short episodes”-3, “yes, continuous treatment for several years or many short episodes”-4. The option “No information” was coded as “-999”.

v1_clin_outpat_psy_trm<-ifelse(v1_clin$v1_psy_vorg_akt_pva2_jemals_behand==0,-999,v1_clin$v1_psy_vorg_akt_pva2_jemals_behand)
v1_con_outpat_psy_trm<-ifelse(v1_con$v1_psyaktuel_jembehand==0,-999,v1_con$v1_psyaktuel_jembehand)

v1_outpat_psy_trm<-factor(c(v1_clin_outpat_psy_trm,v1_con_outpat_psy_trm),ordered=T)
summary(v1_outpat_psy_trm)

## -999    1    2    3    4 NA's 
##   27  428  181  151  967   32

Age at first ambulatory treatment due to any mental health reason (continuous [years], v1_age_1st_out_trm)

If controls were never treated for any mental health reason, they are coded as -999.

v1_age_1st_out_trm<-c(v1_clin$v1_psy_vorg_akt_pva3_alter_jahre,
                      ifelse(is.na(v1_con$v1_psyaktuel_alterj),-999,v1_con$v1_psyaktuel_alterj))
summary(v1_age_1st_out_trm)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## -999.00    8.25   23.00 -221.19   32.00   73.00     120

Ever treated as inpatient or daypatient due to any mental health reason (dichotomous, v1_daypat_inpat_trm)

v1_clin_daypat_inpat_trm<-ifelse(v1_clin$v1_psy_vorg_akt_pva4_teilst_behand==1, "Y","N") 
v1_con_daypat_inpat_trm<-ifelse(v1_con$v1_psyaktuel_tstbehand==1, "Y","N") 
                           
v1_daypat_inpat_trm<-factor(c(v1_clin_daypat_inpat_trm,v1_con_daypat_inpat_trm))
descT(v1_daypat_inpat_trm)

##                N    Y    <NA>     
## [1,] No. cases 470  1281 35   1786
## [2,] Percent   26.3 71.7 2    100

Age at first inpatient treatment due to any mental health reason (continuous [years], v1_age_1st_inpat_trm)

v1_age_1st_inpat_trm<-c(v1_clin$v1_psy_vorg_akt_pva5_alter_jahre,
                      ifelse(is.na(v1_con$v1_psyaktuel_stalterj),-999,v1_con$v1_psyaktuel_stalterj))

summary(v1_age_1st_inpat_trm[v1_age_1st_inpat_trm>0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.00   22.00   27.00   30.25   37.00   73.00      74

Duration of illness (continuous [years], v1_dur_illness)

This is a newly created variable by subtracting age at first inpatient treatment from age at first visit (unit is years). Please note that this assumes that the patient was hospitalized for the diagnosis given in here and may therefore not be entirely accurate. In control individuals this was set to -999.

v1_dur_illness_clin<-v1_age[v1_stat=="CLINICAL"]-v1_age_1st_inpat_trm[v1_stat=="CLINICAL"]
table(v1_dur_illness_clin)

## v1_dur_illness_clin
##  -1   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18 
##   9 116  77  59  55  45  57  43  46  46  49  44  57  39  51  32  31  43  27  17 
##  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38 
##  20  21  20  27  15  15  17  12  22  15  18   8  16   7   5   9   5   6   5   6 
##  39  40  41  42  43  44  45  46  47  50  51  52  53 
##   5   9   1   4   4   2   2   1   2   1   1   1   1

#Set negative values to NA
v1_dur_illness_clin[v1_dur_illness_clin<0]<-NA 
table(v1_dur_illness_clin)

## v1_dur_illness_clin
##   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19 
## 116  77  59  55  45  57  43  46  46  49  44  57  39  51  32  31  43  27  17  20 
##  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39 
##  21  20  27  15  15  17  12  22  15  18   8  16   7   5   9   5   6   5   6   5 
##  40  41  42  43  44  45  46  47  50  51  52  53 
##   9   1   4   4   2   2   1   2   1   1   1   1

#Set controls to -999
v1_dur_illness_con<-rep(-999,dim(v1_con)[1])

v1_dur_illness<-c(v1_dur_illness_clin,v1_dur_illness_con)
table(v1_dur_illness)

## v1_dur_illness
## -999    0    1    2    3    4    5    6    7    8    9   10   11   12   13   14 
##  466  116   77   59   55   45   57   43   46   46   49   44   57   39   51   32 
##   15   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30 
##   31   43   27   17   20   21   20   27   15   15   17   12   22   15   18    8 
##   31   32   33   34   35   36   37   38   39   40   41   42   43   44   45   46 
##   16    7    5    9    5    6    5    6    5    9    1    4    4    2    2    1 
##   47   50   51   52   53 
##    2    1    1    1    1

First-episode patient (only in clinical participants) (dichotomous, v1_1st_ep)

This is a newly created variable. Clinical participants with duration of illness of zero years are labeled as first-episode. Control participants have “-999”.

v1_1st_ep_con<-rep(-999,dim(v1_con)[1])
v1_1st_ep_clin<-ifelse(v1_dur_illness_clin==0,"Y","N")

v1_1st_ep<-factor(c(v1_1st_ep_clin,v1_1st_ep_con))
descT(v1_1st_ep)

##                -999 N    Y   <NA>     
## [1,] No. cases 466  1121 116 83   1786
## [2,] Percent   26.1 62.8 6.5 4.6  100

Times treated as day- or inpatient (continuous [times], v1_tms_daypat_outpat_trm)

Interviewers were instructed to use the lowest number if information is imprecise and to use “99” if too many treatments occurred. If the participant is currently treated as in- or daypatient, the number given contains this treatment.

v1_clin_tms_daypat_outpat_trm<-v1_clin$v1_psy_vorg_akt_pva6_anz_psy_behand
v1_con_tms_daypat_outpat_trm<-v1_con$v1_psyaktuel_anzahl

v1_tms_daypat_outpat_trm<-c(v1_clin_tms_daypat_outpat_trm,v1_con_tms_daypat_outpat_trm)
summary(v1_tms_daypat_outpat_trm)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   2.000   4.000   6.204   6.000  99.000     542

Times treated as day- or inpatient (ordinal [1,2,3,4], v1_cat_daypat_outpat_trm)

We have decided to transform this variable into an ordinal variable using the following gradings: “smaller or equal five times”-1, “six to ten times”-2, “eleven to fourteen times”-3, “fifteen or more times”-4.

v1_clin_cat_daypat_outpat_trm<-rep(NA,dim(v1_clin)[1])
v1_con_cat_daypat_outpat_trm<-rep(NA,dim(v1_con)[1])

v1_clin_cat_daypat_outpat_trm<-ifelse(v1_clin_tms_daypat_outpat_trm<=5,"1",v1_clin_cat_daypat_outpat_trm)
v1_clin_cat_daypat_outpat_trm<-ifelse(v1_clin_tms_daypat_outpat_trm %in% c(6:10),"2",v1_clin_cat_daypat_outpat_trm)
v1_clin_cat_daypat_outpat_trm<-ifelse(v1_clin_tms_daypat_outpat_trm %in% c(11:14),"3",v1_clin_cat_daypat_outpat_trm)
v1_clin_cat_daypat_outpat_trm<-ifelse(v1_clin_tms_daypat_outpat_trm>=15,"4",v1_clin_cat_daypat_outpat_trm)

v1_con_cat_daypat_outpat_trm<-ifelse(v1_con_tms_daypat_outpat_trm<=5,"1",v1_con_cat_daypat_outpat_trm)
v1_con_cat_daypat_outpat_trm<-ifelse(v1_con_tms_daypat_outpat_trm %in% c(6:10),"2",v1_con_cat_daypat_outpat_trm)
v1_con_cat_daypat_outpat_trm<-ifelse(v1_con_tms_daypat_outpat_trm %in% c(11:14),"3",v1_con_cat_daypat_outpat_trm)
v1_con_cat_daypat_outpat_trm<-ifelse(v1_con_tms_daypat_outpat_trm>=15,"4",v1_con_cat_daypat_outpat_trm)

v1_cat_daypat_outpat_trm<-factor(c(v1_clin_cat_daypat_outpat_trm,v1_con_cat_daypat_outpat_trm),ordered=T)
descT(v1_cat_daypat_outpat_trm)

##                1    2    3   4   <NA>     
## [1,] No. cases 841  270  49  84  542  1786
## [2,] Percent   47.1 15.1 2.7 4.7 30.3 100

Create dataset

v1_psy_trtmt<-data.frame(v1_cur_psy_trm,v1_outpat_psy_trm,v1_age_1st_out_trm,v1_daypat_inpat_trm,v1_age_1st_inpat_trm,
                        v1_dur_illness,v1_1st_ep,v1_tms_daypat_outpat_trm,v1_cat_daypat_outpat_trm)

Visit 1: Medication

The raw medication datasets and their description below show that the assessed medication variables are rather complex. To provide simple measures of medication load, we create variables below that quantify the number of different medications belonging to one category a study participant currently takes.

Variables for the following categories were created:

Number of antidepressants prescribed (continuous [number], v1_Antidepressants)
Number of antipsychotics prescribed (continuous [number], v1_Antipsychotics)
Number of mood stabilizers prescribed (continuous [number], v1_Mood_stabilizers)
Number of tranquilizers prescribed (continuous [number], v1_Tranquilizers)
Number of other psychiatric medications (continuous [number], v1_Other_psychiatric)

Clinical participants

#get the following variables from v1_clin
#1. Medication name     ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_1"]
#6. Bedarf category     ["_bedarf_kategorie_1"]

v1_clin_medication_variables_1<-as.data.frame(v1_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v1_clin))])
dim(v1_clin_medication_variables_1) 

## [1] 1320   61

#recode the variables that are coded as characters/logicals in the "v1_clin_medication_variables_1" as factors
v1_clin_medication_variables_1$v1_medikabehand3_med_medi_199998_17<-as.factor(v1_clin_medication_variables_1$v1_medikabehand3_med_medi_199998_17)

v1_clin_medication_variables_1$v1_medikabehand3_med_kategorie_199998_17<-as.factor(v1_clin_medication_variables_1$v1_medikabehand3_med_kategorie_199998_17)

v1_clin_medication_variables_1$v1_medikabehand3_bedarf_medi_199584_10<-as.factor(v1_clin_medication_variables_1$v1_medikabehand3_bedarf_medi_199584_10)

v1_clin_medication_variables_1$v1_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v1_clin_medication_variables_1$v1_medikabehand3_bedarf_kategorie_199584_10)

#make the duplicated data frame
v1_clin_medications_duplicated_1<-as.data.frame(t(apply(v1_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v1_clin_medications_duplicated_1) 

## [1] 1320   30

#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v1_clin, not as character 
v1_clin_medication_variables_1[,!c(TRUE, FALSE)][v1_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v1_clin_medication_variables_1) 

## [1] 1320   61

#bind columns id and medication names, but not categories together 
v1_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v1_clin_medication_variables_1[,1], v1_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v1_clin_medication_name_1) 

## [1] 1320   31

#get the medication categories from the "_medication_variables_1" dataframe
v1_clin_medication_categories_1<-as.data.frame(v1_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v1_clin_medication_categories_1) 

## [1] 1320   31

#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v1_clin, not as character 
#Important: v1_clin_medication_name_1=="NA" replaced with is.na(v1_clin_medication_name_1)
v1_clin_medication_categories_1[is.na(v1_clin_medication_name_1)] <- NA
#write.csv(v1_clin_medication_categories_1, file="v1_clin_medication_group_1.csv") 

#Make a count table of medications
v1_clin_med_table<-data.frame("mnppsd"=v1_clin$mnppsd)
v1_clin_med_table$v1_Antidepressants<-rowSums(v1_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v1_clin_med_table$v1_Antipsychotics<-rowSums(v1_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v1_clin_med_table$v1_Mood_stabilizers<-rowSums(v1_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v1_clin_med_table$v1_Tranquilizers<-rowSums(v1_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v1_clin_med_table$v1_Other_psychiatric<-rowSums(v1_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Control participants

#get the following variables from v1_con
#1. Medication name     ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_2"]
#6. Bedarf category     ["_bedarf_kategorie_2"]

v1_con_medication_variables_1<-as.data.frame(v1_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v1_con))])
dim(v1_con_medication_variables_1) 

## [1] 466  29

#recode the variables that are coded as characters/logicals in the "v1_con_medication_variables_1" as factors
v1_con_medication_variables_1$v1_medikabehand3_depot_medi_201224_2<-as.factor(v1_con_medication_variables_1$v1_medikabehand3_depot_medi_201224_2)

v1_con_medication_variables_1$v1_medikabehand3_depot_kategorie_201224_2<-as.factor(v1_con_medication_variables_1$v1_medikabehand3_depot_kategorie_201224_2)

v1_con_medication_variables_1$v1_medikabehand3_bedarf_medi_201187_4<-as.factor(v1_con_medication_variables_1$v1_medikabehand3_bedarf_medi_201187_4)

v1_con_medication_variables_1$v1_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v1_con_medication_variables_1$v1_medikabehand3_bedarf_kategorie_201187_4)

#make the duplicated data frame
v1_con_medications_duplicated_1<-as.data.frame(t(apply(v1_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v1_con_medications_duplicated_1) 

## [1] 466  14

#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v1_con, not as character 
v1_con_medication_variables_1[,!c(TRUE, FALSE)][v1_con_medications_duplicated_1=="TRUE"] <- NA
dim(v1_con_medication_variables_1) 

## [1] 466  29

#bind columns id and medication names, but not categories together 
v1_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v1_con_medication_variables_1[,1], v1_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v1_con_medication_name_1) 

## [1] 466  15

#get the medication categories from the "_medication_variables_1" dataframe
v1_con_medication_categories_1<-as.data.frame(v1_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v1_con_medication_categories_1) 

## [1] 466  15

#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v1_con, not as character
#Important: v1_con_medication_name_1=="NA" replaced with is.na(v1_con_medication_name_1)
v1_con_medication_categories_1[is.na(v1_con_medication_name_1)] <- NA
#write.csv(v1_con_medication_categories_1, file="v1_con_medication_group_1.csv")

#Make a count table of medications
v1_con_med_table<-data.frame("mnppsd"=v1_con$mnppsd)
v1_con_med_table$v1_Antidepressants<-rowSums(v1_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v1_con_med_table$v1_Antipsychotics<-rowSums(v1_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v1_con_med_table$v1_Mood_stabilizers<-rowSums(v1_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v1_con_med_table$v1_Tranquilizers<-rowSums(v1_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v1_con_med_table$v1_Other_psychiatric<-rowSums(v1_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Bind v1_clin and v1_con together by rows

v1_drugs<-rbind(v1_clin_med_table,v1_con_med_table)
dim(v1_drugs) 

## [1] 1786    6

#check if the id column of v1_drugs and v1_id match
table(v1_drugs[,1]==v1_id)

## 
## TRUE 
## 1786

Adverse events under current medication (only in clinical participants) (dichotomous, v1_adv)

In control participants, this item is coded “-999”, as it was not assessed.

v1_clin_adv<-ifelse(v1_clin$v1_medikabehand_medi2_nebenwirk==1,"Y","N")
v1_con_adv<-rep("-999",dim(v1_con)[1])
v1_adv<-factor(c(v1_clin_adv,v1_con_adv))
descT(v1_adv)

##                -999 N   Y    <NA>     
## [1,] No. cases 466  304 558  458  1786
## [2,] Percent   26.1 17  31.2 25.6 100

Psychiatric medication change during the past six months (dichotomous, v1_medchange)

In control participants, this item is coded “-999”, as it was not assessed.

v1_clin_medchange<-rep(NA,dim(v1_clin)[1])
v1_clin_medchange<-ifelse(v1_clin$v1_medikabehand_medi3_mediaenderung==1,"Y","N")
v1_con_medchange<-rep("-999",dim(v1_con)[1])

v1_medchange<-as.factor(c(v1_clin_medchange,v1_con_medchange))
descT(v1_medchange)

##                -999 N    Y    <NA>     
## [1,] No. cases 466  259  595  466  1786
## [2,] Percent   26.1 14.5 33.3 26.1 100

Lithium

The following two items on medication with lithium are asked in all study vists, in order not to miss participants eligible for the ALDA scale, which is assessed at the last study visit.

“Did you ever take lithium?” (dichotomous, v1_lith)

v1_clin_lith<-rep(NA,dim(v1_clin)[1])
v1_clin_lith<-ifelse(v1_clin$v1_medikabehand_med_zusatz_lithium==1,"Y","N")
v1_con_lith<-rep("-999",dim(v1_con)[1])

v1_lith<-as.factor(c(v1_clin_lith,v1_con_lith))
v1_lith<-as.factor(v1_lith)

descT(v1_lith)

##                -999 N   Y    <NA>     
## [1,] No. cases 466  535 248  537  1786
## [2,] Percent   26.1 30  13.9 30.1 100

“If yes, for how long?” (ordinal [1,2,3], v1_lith_prd)

Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3. Clinical paricipants not treated with lithium or control participants have “-999” here.

v1_clin_lith_prd<-rep(NA,dim(v1_clin)[1])
v1_con_lith_prd<-rep(-999,dim(v1_con)[1])

v1_clin_lith_prd<-ifelse(v1_clin_lith=="N", -999, ifelse(v1_clin$v1_medikabehand_med_zusatz_dauer==2,1,
                  ifelse(v1_clin$v1_medikabehand_med_zusatz_dauer==1,2,    
                  ifelse(v1_clin$v1_medikabehand_med_zusatz_dauer==0,3,NA))))
                                                     
v1_lith_prd<-factor(c(v1_clin_lith_prd,v1_con_lith_prd))
descT(v1_lith_prd)

##                -999 1   2   3   <NA>     
## [1,] No. cases 1001 106 26  116 537  1786
## [2,] Percent   56   5.9 1.5 6.5 30.1 100

Create dataset

v1_med<-data.frame(v1_drugs[,2:6],v1_adv,v1_medchange,v1_lith,v1_lith_prd)

Create datasets with raw medication information

Here, separate datasets for clinical and control participants are created that contain the raw medication information at visit 1, as specified in the phenotype database.

For each medication that the individual took at visit 1 (including non-psychiatric drugs), the information given below is assessed.

The last character of each variable name always refers to the medication in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.

The medications were not assessed in any specific order, i.e. the order was determined by the individual participant (whatever she or he mentioned first). To classify medications, we used a catalogue, from which the categories and subcategories a medication belongs to were selected (see below).

Below, the variable names of clinical/control participants, respectively, are given in quotes, and the coding is explained in the parentheses.

1.Was the individual treated with any medication? (1-yes, 2-no, 99-not assessed)
“v1_medikabehand3_keine_med”/“v1_medikabehand3_keine_med”

2. Regular medication: Name of the medication (character)
   “v1_medikabehand3_med_medi_199998”/“v1_medikabehand3_med_medi_200705”

3. Regular medication: Category to which the medication belongs (character)
   “v1_medikabehand3_med_kategorie_199998”/“v1_medikabehand3_med_kategorie_200705”

4. Regular medication: Subcategory to which the medication belongs (character)
   “v1_medikabehand3_med_kategorie_sub_199998”/“v1_medikabehand3_med_kategorie_sub_200705”

5. Regular medication: Psychiatric medication? (0-no, 1-yes) “v1_medikabehand3_med_zusatz_199998”/“v1_medikabehand3_med_zusatz_200705”

6. Regular medication: Dose in the morning (unitless)
   “v1_medikabehand3_s_medi1_morgens_199998”/“v1_medikabehand3_s_medi1_morgens_200705”

7. Regular medication: Dose at midday (unitless)
   “v1_medikabehand3_smedi1_mittags_199998”/“v1_medikabehand3_smedi1_mittags_200705”

8. Regular medication: Dose in the evening (unitless)
   “v1_medikabehand3_smedi1_abends_199998”/“v1_medikabehand3_smedi1_abends_200705”

9. Regular medication: Dose at night (unitless)
   “v1_medikabehand3_smedi1_nachts_199998”/“v1_medikabehand3_smedi1_nachts_200705”

10. Regular medication: Unit of the medication asked in the last four questions (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
    “v1_medikabehand3_smedi1_einheit_199998”/“v1_medikabehand3_smedi1_einheit_200705”

11. Regular medication: Total dose of the medication per day (unitless)
    “v1_medikabehand3_smedi1_gesamtdosis_199998”/“v1_medikabehand3_smedi1_gesamtdosis_200705”

12. Regular medication: Unit of the medication asked in the last question (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
    “v1_medikabehand3_smedi1_einheit1_199998”/“v1_medikabehand3_smedi1_einheit1_200705”

13. Regular medication: Medication name, if not contained in our catalog (character)
    “v1_medikabehand3_medikament_text_199998”/“v1_medikabehand3_medikament_text_200705”

14. Depot medication: Name of the medication (character) “v1_medikabehand3_depot_medi_200170”/“v1_medikabehand3_depot_medi_201224

15. Depot medication: Category to which the medication belongs (character) “v1_medikabehand3_depot_kategorie_200170”/“v1_medikabehand3_depot_kategorie_201224

16. Depot medication: Subcategory to which the medication belongs (character)
    “v1_medikabehand3_depot_kategorie_sub_200170”/“v1_medikabehand3_depot_kategorie_sub_201224

17. Depot medication: Psychiatric medication? (0-no, 1-yes) “v1_medikabehand3_depot_zusatz_200170”/“v1_medikabehand3_depot_zusatz_201224”

18. Depot medication: Total Dose (unitless) “v1_medikabehand3_s_depot_gesamtdosis_200170”/“v1_medikabehand3_s_depot_gesamtdosis_201224”

19. Depot medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v1_medikabehand3_s_depot_einheit_200170”/ “v1_medikabehand3_s_depot_einheit_201224”

20. Interval, at which the depot medication is given (days) “v1_medikabehand3_s_depot_tage_200170”/“v1_medikabehand3_s_depot_tage_201224”

21. Medication name, if not contained in our catalog (character) “v1_medikabehand3_medikament_text_200170”/“v1_medikabehand3_medikament_text_201224”

22. Pro re nata (PRN) medication: Name of the medication (character) “v1_medikabehand3_bedarf_medi_199584”/“v1_medikabehand3_bedarf_medi_201187”

23. Pro re nata (PRN) medication: Category to which the medication belongs (character)
    “v1_medikabehand3_bedarf_kategorie_199584”/“v1_medikabehand3_bedarf_kategorie_201187”

24. Pro re nata (PRN) medication: Subcategory to which the medication belongs (character) “v1_medikabehand3_bedarf_kategorie_sub_199584”/“v1_medikabehand3_bedarf_kategorie_sub_201187”

25. Pro re nata (PRN) medication: Psychiatric medication? (0-no, 1-yes) “v1_medikabehand3_bedarf_zusatz_199584”/“v1_medikabehand3_bedarf_zusatz_201187”

26. Pro re nata (PRN) medication: Total dose up to (unitless) “v1_medikabehand3_s_bedarf_gesamtdosis_199584”/“v1_medikabehand3_s_bedarf_kommentar_201187

27. Pro re nata (PRN) medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v1_medikabehand3_s_bedarf_einheit1_199584”/“v1_medikabehand3_s_bedarf_einheit1_201187”

28. Pro re nata (PRN) medication: Comment (character) “v1_medikabehand3_s_bedarf_kommentar_199584”/“v1_medikabehand3_s_bedarf_kommentar_201187”

29. Pro re nata (PRN) medication: Medication name, if not contained in our catalog (character) “v1_medikabehand3_medikament_text_199584”/“v1_medikabehand3_medikament_text_201187”

Make datasets containing only information on medication

v1_med_clin_orig<-data.frame(v1_clin$mnppsd,v1_clin[,285:593])
names(v1_med_clin_orig)[1]<-"v1_id"

v1_med_con_orig<-data.frame(v1_con$mnppsd,v1_con[,247:391])
names(v1_med_con_orig)[1]<-"v1_id"

Save raw medication datasets of visit 1

save(v1_med_clin_orig, file="230614_v6.0_psycourse_clin_raw_med_visit1.RData")
save(v1_med_con_orig, file="230614_v6.0_psycourse_con_raw_med_visit1.RData")

Write .csv file

write.table(v1_med_clin_orig,file="230614_v6.0_psycourse_clin_raw_med_visit1.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v1_med_con_orig,file="230614_v6.0_psycourse_con_raw_med_visit1.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Visit 1: Family history of psychiatric illness

Family member ever affected by psychiatric disorder (dichotomous, v1_fam_hist)

The option “Participant does not know” is coded as “NA”. The option “Participant does not want to disclose information” is coded as “-999”.

v1_clin_fam_hist<-rep(NA,dim(v1_clin)[1])
v1_con_fam_hist<-rep(NA,dim(v1_con)[1])

v1_clin_fam_hist<-ifelse(v1_clin$v1_psy_famanam_pfa_angeh_pe==0,"N",
                    ifelse(v1_clin$v1_psy_famanam_pfa_angeh_pe==1,"Y",
                    ifelse(v1_clin$v1_psy_famanam_pfa_angeh_pe==2,"-999",
                    ifelse(v1_clin$v1_psy_famanam_pfa_angeh_pe==3,NA,v1_clin_fam_hist)))) 

v1_con_fam_hist<-ifelse(v1_con$v1_famanam_psyangeh==0,"N",
                    ifelse(v1_con$v1_famanam_psyangeh==1,"Y",
                    ifelse(v1_con$v1_famanam_psyangeh==2,"-999",
                    ifelse(v1_con$v1_famanam_psyangeh==3,NA,v1_con_fam_hist))))                     


v1_fam_hist<-as.factor(c(v1_clin_fam_hist,v1_con_fam_hist))
descT(v1_fam_hist)

##                -999 N    Y    <NA>     
## [1,] No. cases 10   528  1120 128  1786
## [2,] Percent   0.6  29.6 62.7 7.2  100

Also, in the original assessment of psychiatric history, each affected family member is listed, together with an indication whther the diagnosis is certain or not. This detailed information is available on request.

Visit 1: Physical measures and somatic diseases

The method of assessment of physical measures and somatic diseases changed during the course of the study. Initially, it was assessed whether a participant was ever affected by a group of diseases, and the specific disease was added as free text, with certain examples diseases specifically mentioned. The later method assessed selected diseases specifically as dichotomous yes/no items. To make use of all information, both systems were harmonized under the supervision of a medical doctor.

There are a few important points that have to be kept in mind when using this dataset:

1. With respect to the harmonization:

• Autoimmune diseases, traumatic brain injury, and disability status (present mode of assessment) were not covered by the original method of assessment, but were coded as “yes” when mentioned in the “other diseases category”, which was an option in the original method of assessment. As a “no” to these questions cannot be assumed in individuals with missing data, these items should be treated with caution.

• Most items do not consider the pathogenesis of the disease in question. The category of autoimmune diseases does, however, and it should be noted that this item does only contain autoimmune diseases not captured by the other items (e.g. neurodermatitis).

2. With respect to the data collection:

• Please note that all of the questions below are self-reported measurements and that interviews were mainly conducted by research/clinical psychologists. For diseases, participants were asked whether a medical doctor had ever diagnosed them with the disease in question.

Height (continuous [centimeters], v1_height)

v1_clin_height<-v1_clin$v1_medwkeii_groesse
v1_con_height<-v1_con$v1_medwkeii_groesse

v1_height<-c(v1_clin_height,v1_con_height)
summary(v1_height)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   114.0   167.0   174.0   173.8   180.0   203.0      37

Weight (continuous [kilograms], v1_weight)

v1_clin_weight<-v1_clin$v1_medwkeii_gewicht
v1_con_weight<-v1_con$v1_medwkeii_gewicht

v1_weight<-c(v1_clin_weight,v1_con_weight)
summary(v1_weight)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      39      67      79      82      92     190      45

Waist circumference (continouos [centimeters], v1_waist) This item was only recorded in a subset of individuals, because the question was introduced during the course of the study.

v1_clin_waist<-v1_clin$v1_medwkeii_mw1_taillenumfang
v1_con_waist<-v1_con$v1_medwkeii_mw1_taillenumfang

v1_waist<-c(v1_clin_waist,v1_con_waist)
summary(v1_waist)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   61.00   76.00   86.00   88.65   99.00  149.00    1275

Body mass index (BMI, continuous [BMI], v1_bmi) We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.

v1_bmi<-round(v1_weight/(v1_height/100)^2,2)
summary(v1_bmi)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.18   22.79   25.89   27.08   30.04   75.41      45

1. Elevated cholesterol or triglyceride levels (dichotomous,v1_chol_trig)

v1_clin_chol_trig<-ifelse(v1_clin$v1_medwkeii_medwkeii_ecot==1,"Y","N")
v1_con_chol_trig<-ifelse(v1_con$v1_medwkeii_medwkeii_ecot==1,"Y","N")

v1_chol_trig<-factor(c(v1_clin_chol_trig,v1_con_chol_trig))
descT(v1_chol_trig)

##                N    Y    <NA>     
## [1,] No. cases 1403 213  170  1786
## [2,] Percent   78.6 11.9 9.5  100

2. Hypertension (dichotomous, v1_hyperten)

v1_clin_hyperten<-ifelse(v1_clin$v1_medwkeii_medwkeii_hyperton==1,"Y","N")
v1_con_hyperten<-ifelse(v1_con$v1_medwkeii_medwkeii_hyperton==1,"Y","N")

v1_hyperten<-factor(c(v1_clin_hyperten,v1_con_hyperten))
descT(v1_hyperten)

##                N    Y    <NA>     
## [1,] No. cases 1328 293  165  1786
## [2,] Percent   74.4 16.4 9.2  100

3. Angina pectoris (dichotomous, v1_ang_pec)

v1_clin_ang_pec<-ifelse(v1_clin$v1_medwkeii_medwkeii_angpec==1,"Y","N")
v1_con_ang_pec<-ifelse(v1_con$v1_medwkeii_medwkeii_angpec==1,"Y","N")

v1_ang_pec<-factor(c(v1_clin_ang_pec,v1_con_ang_pec))
descT(v1_ang_pec)

##                N    Y   <NA>     
## [1,] No. cases 1596 23  167  1786
## [2,] Percent   89.4 1.3 9.4  100

4. Heart attack (dichotomous, v1_heart_att)

v1_clin_heart_att<-ifelse(v1_clin$v1_medwkeii_medwkeii_herzinf==1,"Y","N")
v1_con_heart_att<-ifelse(v1_con$v1_medwkeii_medwkeii_herzinf==1,"Y","N")

v1_heart_att<-factor(c(v1_clin_heart_att,v1_con_heart_att))
descT(v1_heart_att)

##                N    Y   <NA>     
## [1,] No. cases 1596 22  168  1786
## [2,] Percent   89.4 1.2 9.4  100

5. Stroke (dichotomous, v1_stroke)

v1_clin_stroke<-ifelse(v1_clin$v1_medwkeii_medwkeii_schlag==1,"Y","N")
v1_con_stroke<-ifelse(v1_con$v1_medwkeii_medwkeii_schlag==1,"Y","N")

v1_stroke<-factor(c(v1_clin_stroke,v1_con_stroke))
descT(v1_stroke)

##                N    Y   <NA>     
## [1,] No. cases 1604 15  167  1786
## [2,] Percent   89.8 0.8 9.4  100

6. Diabetes (dichotomous, v1_diabetes)

v1_clin_diabetes<-ifelse(v1_clin$v1_medwkeii_medwkeii_zuck==1,"Y","N")
v1_con_diabetes<-ifelse(v1_con$v1_medwkeii_medwkeii_zuck==1,"Y","N")

v1_diabetes<-factor(c(v1_clin_diabetes,v1_con_diabetes))
descT(v1_diabetes)

##                N    Y   <NA>     
## [1,] No. cases 1491 126 169  1786
## [2,] Percent   83.5 7.1 9.5  100

7. Hyperthyroidism (dichotomous, v1_hyperthy)

v1_clin_hyperthy<-ifelse(v1_clin$v1_medwkeii_medwkeii_hypothy==1,"Y","N")
v1_con_hyperthy<-ifelse(v1_con$v1_medwkeii_medwkeii_hypothy==1,"Y","N")

v1_hyperthy<-factor(c(v1_clin_hyperthy,v1_con_hyperthy))
descT(v1_hyperthy)

##                N    Y    <NA>     
## [1,] No. cases 1319 258  209  1786
## [2,] Percent   73.9 14.4 11.7 100

8. Hypothyroidism (dichotomous, v1_hypothy)

v1_clin_hypothy<-ifelse(v1_clin$v1_medwkeii_medwkeii_hypothy==1,"Y","N")
v1_con_hypothy<-ifelse(v1_con$v1_medwkeii_medwkeii_hypothy==1,"Y","N")

v1_hypothy<-factor(c(v1_clin_hypothy,v1_con_hypothy))
descT(v1_hypothy)

##                N    Y    <NA>     
## [1,] No. cases 1319 258  209  1786
## [2,] Percent   73.9 14.4 11.7 100

9. Osteoporosis (dichotomous, v1_osteopor)

v1_clin_osteopor<-ifelse(v1_clin$v1_medwkeii_medwkeii_osterop==1,"Y","N")
v1_con_osteopor<-ifelse(v1_con$v1_medwkeii_medwkeii_osterop==1,"Y","N")

v1_osteopor<-factor(c(v1_clin_osteopor,v1_con_osteopor))
descT(v1_osteopor)

##                N    Y   <NA>     
## [1,] No. cases 1571 33  182  1786
## [2,] Percent   88   1.8 10.2 100

10. Asthma (dichotomous, v1_asthma)

v1_clin_asthma<-ifelse(v1_clin$v1_medwkeii_medwkeii_asthma==1,"Y","N")
v1_con_asthma<-ifelse(v1_con$v1_medwkeii_medwkeii_asthma==1,"Y","N")

v1_asthma<-factor(c(v1_clin_asthma,v1_con_asthma))
descT(v1_asthma)

##                N    Y   <NA>     
## [1,] No. cases 1486 134 166  1786
## [2,] Percent   83.2 7.5 9.3  100

11. COPD/chronic Bronchitis (dichotomous, v1_copd)

v1_clin_copd<-ifelse(v1_clin$v1_medwkeii_medwkeii_copd==1,"Y","N")
v1_con_copd<-ifelse(v1_con$v1_medwkeii_medwkeii_copd==1,"Y","N")

v1_copd<-factor(c(v1_clin_copd,v1_con_copd))
descT(v1_copd)

##                N    Y   <NA>     
## [1,] No. cases 1542 78  166  1786
## [2,] Percent   86.3 4.4 9.3  100

12. Allergies (dichotomus, v1_allerg)

v1_clin_allerg<-ifelse(v1_clin$v1_medwkeii_medwkeii_allerg==1,"Y","N")
v1_con_allerg<-ifelse(v1_con$v1_medwkeii_medwkeii_allerg==1,"Y","N")

v1_allerg<-factor(c(v1_clin_allerg,v1_con_allerg))
descT(v1_allerg)

##                N    Y    <NA>     
## [1,] No. cases 942  673  171  1786
## [2,] Percent   52.7 37.7 9.6  100

13. Neurodermatitis (dichotomous, v1_neuroder)

v1_clin_neuroder<-ifelse(v1_clin$v1_medwkeii_medwkeii_neurod==1,"Y","N")
v1_con_neuroder<-ifelse(v1_con$v1_medwkeii_medwkeii_neurod==1,"Y","N")

v1_neuroder<-factor(c(v1_clin_neuroder,v1_con_neuroder))
descT(v1_neuroder)

##                N    Y   <NA>     
## [1,] No. cases 1509 102 175  1786
## [2,] Percent   84.5 5.7 9.8  100

14. Psoriasis (dichotomous, v1_psoriasis)

v1_clin_psoriasis<-ifelse(v1_clin$v1_medwkeii_medwkeii_neurod==1,"Y","N")
v1_con_psoriasis<-ifelse(v1_con$v1_medwkeii_medwkeii_neurod==1,"Y","N")

v1_psoriasis<-factor(c(v1_clin_psoriasis,v1_con_psoriasis))
descT(v1_psoriasis)

##                N    Y   <NA>     
## [1,] No. cases 1509 102 175  1786
## [2,] Percent   84.5 5.7 9.8  100

15. Autoimmune diseases (dichotomous, v1_autoimm) This item was not contained in the initial method of assessment, and therefore contains many missing values.

v1_clin_autoimm_yn<-ifelse(v1_clin$v1_medwkeii_medwkeii_autoim==1,"Y","N")
v1_con_autoimm_yn<-ifelse(v1_con$v1_medwkeii_medwkeii_autoim==1,"Y","N")
 
v1_autoimm<-factor(c(v1_clin_autoimm_yn,v1_con_autoimm_yn))
descT(v1_autoimm)

##                N    Y   <NA>     
## [1,] No. cases 533  55  1198 1786
## [2,] Percent   29.8 3.1 67.1 100

16. Cancer (dichotomous, v1_cancer)

v1_clin_cancer<-ifelse(v1_clin$v1_medwkeii_medwkeii_kreberk==1,"Y","N")
v1_con_cancer<-ifelse(v1_con$v1_medwkeii_medwkeii_kreberk==1,"Y","N")

v1_cancer<-factor(c(v1_clin_cancer,v1_con_cancer))
descT(v1_cancer)

##                N    Y   <NA>     
## [1,] No. cases 1537 82  167  1786
## [2,] Percent   86.1 4.6 9.4  100

17. Stomach ulcer (dichotomous, v1_stom_ulc)

v1_clin_stom_ulc<-ifelse(v1_clin$v1_medwkeii_medwkeii_maggesch==1,"Y","N")
v1_con_stom_ulc<-ifelse(v1_con$v1_medwkeii_medwkeii_maggesch==1,"Y","N")

v1_stom_ulc<-factor(c(v1_clin_stom_ulc,v1_con_stom_ulc))
descT(v1_stom_ulc)

##                N    Y   <NA>     
## [1,] No. cases 1579 39  168  1786
## [2,] Percent   88.4 2.2 9.4  100

18. Kidney failure (dichotomous, v1_kid_fail)

v1_clin_kid_fail<-ifelse(v1_clin$v1_medwkeii_medwkeii_nierver==1,"Y","N")
v1_con_kid_fail<-ifelse(v1_con$v1_medwkeii_medwkeii_nierver==1,"Y","N")

v1_kid_fail<-factor(c(v1_clin_kid_fail,v1_con_kid_fail))
descT(v1_kid_fail)

##                N    Y  <NA>     
## [1,] No. cases 1598 17 171  1786
## [2,] Percent   89.5 1  9.6  100

19. Kidney-/Gallstone (dichotomous, v1_stone)

v1_clin_stone<-ifelse(v1_clin$v1_medwkeii_medwkeii_nierga==1,"Y","N")
v1_con_stone<-ifelse(v1_con$v1_medwkeii_medwkeii_nierga==1,"Y","N")
               
v1_stone<-factor(c(v1_clin_stone,v1_con_stone))
descT(v1_stone)

##                N    Y   <NA>     
## [1,] No. cases 1521 91  174  1786
## [2,] Percent   85.2 5.1 9.7  100

20. Epilepsy (dichotomous, v1_epilepsy)

v1_clin_epilepsy<-ifelse(v1_clin$v1_medwkeii_medwkeii_epile==1,"Y","N")
v1_con_epilepsy<-ifelse(v1_con$v1_medwkeii_medwkeii_epile==1,"Y","N")

v1_epilepsy<-factor(c(v1_clin_epilepsy,v1_con_epilepsy))
descT(v1_epilepsy)

##                N    Y   <NA>     
## [1,] No. cases 1576 34  176  1786
## [2,] Percent   88.2 1.9 9.9  100

21. Migraine (dichotomous, v1_migraine)

v1_clin_migraine<-ifelse(v1_clin$v1_medwkeii_medwkeii_mig==1,"Y","N")
v1_con_migraine<-ifelse(v1_con$v1_medwkeii_medwkeii_mig==1,"Y","N")

v1_migraine<-factor(c(v1_clin_migraine,v1_con_migraine))
descT(v1_migraine)

##                N    Y   <NA>     
## [1,] No. cases 1467 143 176  1786
## [2,] Percent   82.1 8   9.9  100

22. Parkinson syndrome (dichotomous, v1_parkinson)

v1_clin_parkinson<-ifelse(v1_clin$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_con_parkinson<-ifelse(v1_con$v1_medwkeii_medwkeii_parks==1,"Y","N")

v1_parkinson<-factor(c(v1_clin_parkinson,v1_con_parkinson))
descT(v1_parkinson)

##                N    Y   <NA>     
## [1,] No. cases 1600 9   177  1786
## [2,] Percent   89.6 0.5 9.9  100

23. Liver cirrhosis or inflammation (dichotomous, v1_liv_cir_inf)

v1_clin_liv_cir_inf<-ifelse(v1_clin$v1_medwkeii_medwkeii_leberz==1,"Y","N")
v1_con_liv_cir_inf<-ifelse(v1_con$v1_medwkeii_medwkeii_leberz==1,"Y","N")
 
v1_liv_cir_inf<-factor(c(v1_clin_liv_cir_inf,v1_con_liv_cir_inf))
descT(v1_liv_cir_inf)

##                N    Y   <NA>     
## [1,] No. cases 1606 7   173  1786
## [2,] Percent   89.9 0.4 9.7  100

24. Traumatic brain injury (dichotomous, v1_tbi) This item was not contained in the initial method of assessment, and therefore contains many missing values.

v1_clin_tbi<-ifelse(v1_clin$v1_medwkeii_medwkeii_sht==1,"Y","N")
v1_con_tbi<-ifelse(v1_con$v1_medwkeii_medwkeii_sht==1,"Y","N")
  
v1_tbi<-factor(c(v1_clin_tbi,v1_con_tbi))
descT(v1_tbi)

##                N    Y   <NA>     
## [1,] No. cases 540  29  1217 1786
## [2,] Percent   30.2 1.6 68.1 100

25. Severely disabled status (dichotomous, v1_beh) NOTE: This is a term of the Social Welfare Code (“Sozialgesetzbuch”). There is some more information, available on request: percentage, to which a person is severely disabled, and the specific type of disability. This item was not contained in the initial method of assessment, and therefore contains many missing values.

v1_clin_beh<-ifelse(v1_clin$v1_medwkeii_medwkeii_schwe==1,"Y","N")
v1_con_beh<-ifelse(v1_con$v1_medwkeii_medwkeii_schwe==1,"Y","N")

v1_beh<-factor(c(v1_clin_beh,v1_con_beh))
descT(v1_beh)

##                N    Y   <NA>     
## [1,] No. cases 497  75  1214 1786
## [2,] Percent   27.8 4.2 68   100

26. Disorders of the eyes or ears (dichotomous, v1_eyear) NOTE: Near- or farsightedness, according to our guidelines, is not a disease and should not be coded as such.

v1_clin_eyear<-ifelse(v1_clin$v1_medwkeii_medwkeii_auo==1,"Y","N")
v1_con_eyear<-ifelse(v1_con$v1_medwkeii_medwkeii_auo==1,"Y","N")
                        
v1_eyear<-factor(c(v1_clin_eyear,v1_con_eyear))
descT(v1_eyear)

##                N    Y    <NA>     
## [1,] No. cases 1400 216  170  1786
## [2,] Percent   78.4 12.1 9.5  100

27. Infectious diseases (dichotomous, v1_inf) Importantly, assessment of this item was modified during the study. At some point, interviewers were instructed not to code childhood diseases (e.g. chickenpox) as infectious diseases anymore. During the harmonization process, in individuals answering “yes” to this question (initial method of assessment), the free text description of the disease in question was screened for a number of childhood diseases, and, if appropriate, set to “no” in the current method of assessment.

v1_clin_inf<-ifelse(v1_clin$v1_medwkeii_medwkeii_infek==1,"Y","N")
v1_con_inf<-ifelse(v1_con$v1_medwkeii_medwkeii_infek==1,"Y","N")
                        
v1_inf<-factor(c(v1_clin_inf,v1_con_inf))
descT(v1_inf)

##                N    Y   <NA>     
## [1,] No. cases 1450 132 204  1786
## [2,] Percent   81.2 7.4 11.4 100

Create dataset

v1_som_dsrdr<-data.frame(v1_height,
                         v1_weight,
                         v1_waist,
                         v1_bmi,
                         v1_chol_trig,
                         v1_hyperten,
                         v1_ang_pec,
                         v1_heart_att,
                         v1_stroke,
                         v1_diabetes,
                         v1_hyperthy,
                         v1_hypothy,
                         v1_osteopor,
                         v1_asthma,
                         v1_copd,
                         v1_allerg,
                         v1_neuroder,
                         v1_psoriasis,
                         v1_autoimm,
                         v1_cancer,
                         v1_stom_ulc,
                         v1_kid_fail,
                         v1_stone,
                         v1_epilepsy,
                         v1_migraine,
                         v1_parkinson,
                         v1_liv_cir_inf,
                         v1_tbi,
                         v1_beh,
                         v1_eyear,
                         v1_inf)

Visit 1: Substance abuse

Tobacco

There is some more information available (whether smokers have stopped smoking more than one year, if yes how many years, how many pipes, cigars and cigarillos), not part of the present dataset.

Did you ever smoke tobacco products? (categorical [Y,N,F], v1_ever_smkd)

This is a categorical item with three optional answers: “never”-N, “yes”-Y, and “former” (having smoked in the past but not now)-F. It is required to have stopped smoking for at least three months to qualify as former smoker.

v1_clin_ever_smkd<-ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==1,"N",
                     ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==2,"Y",
                            ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==3,"F",NA))) 

v1_con_ever_smkd<-ifelse(v1_con$v1_tabalk_tabak1==1,"N",
                     ifelse(v1_con$v1_tabalk_tabak1==2,"Y",
                            ifelse(v1_con$v1_tabalk_tabak1==3,"F",NA))) 

v1_ever_smkd<-factor(c(v1_clin_ever_smkd,v1_con_ever_smkd))
descT(v1_ever_smkd)

##                F    N    Y    <NA>     
## [1,] No. cases 298  593  805  90   1786
## [2,] Percent   16.7 33.2 45.1 5    100

At what age did you start smoking? (continuous [age], v1_age_smk)

The original item has three optional answers (age unknown, age or alternatively year in which smoking started). Here, we give age at which smoking started. In cases in which the year in which smoking started was given, we calculated this as follows: year in which smoking stated minus (year of interview minus age at interview). For people who never smoked, this question is coded as “-999”.

v1_clin_age_smk<-ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==1, -999, ifelse(is.na(v1_clin$v1_tabalk_ta2_alter_jahre)==T,
          (v1_clin$v1_tabalk_ta2_anf_jahr-(as.numeric(format(v1_interv_date,'%Y'))-v1_clin$v1_ageBL)),v1_clin$v1_tabalk_ta2_alter_jahre))

v1_con_age_smk<-ifelse(v1_con$v1_tabalk_tabak1, -999, ifelse(is.na(v1_con$v1_tabalk_tabak2_alter)==T,
            (v1_con$v1_tabalk_tabak2_jahr-(as.numeric(format(interv_date,'%Y'))-v1_ageBL)),v1_con$v1_tabalk_ta2_alter_jahre))

v1_age_smk<-factor(c(v1_clin_age_smk,v1_con_age_smk))
summary(v1_age_smk)

## -999    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19 
##  767    2    2    2    1    9    4   11   36   53  109  120  174   58   89   30 
##   20   21   22   23   24   25   26   27   28   29   30   31   32   33   35   36 
##   43   23   10   11    7   11    2    3    5    5   13    2    2    2    4    1 
##   38   40   44   45   48   53   57   61 NA's 
##    1    1    2    1    1    1    1    1  166

“How many cigarettes do you presently smoke on average?” (continuous [number cigarettes], v1_no_cig)

In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.

Please note that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur. For people presently do not smoke this question is coded as “-999”.

v1_clin_no_cig<-ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==1 | v1_clin_ever_smkd=="F", -999, 
                  ifelse(v1_clin$v1_tabalk_ta3_zig_pro_zeit==1, v1_clin$v1_tabalk_ta3_anz_zig*365,
                  ifelse(v1_clin$v1_tabalk_ta3_zig_pro_zeit==2, v1_clin$v1_tabalk_ta3_anz_zig*52,
                  ifelse(v1_clin$v1_tabalk_ta3_zig_pro_zeit==3, v1_clin$v1_tabalk_ta3_anz_zig*12,NA))))       

v1_con_no_cig<-ifelse((v1_con$v1_tabalk_tabak1==1 | v1_con_ever_smkd=="F"), -999, 
                  ifelse(v1_con$v1_tabalk_tabak3_zeit==1, v1_con$v1_tabalk_ta3_anz_zig*365,
                  ifelse(v1_con$v1_tabalk_tabak3_zeit==2, v1_con$v1_tabalk_ta3_anz_zig*52,
                  ifelse(v1_con$v1_tabalk_tabak3_zeit==3, v1_con$v1_tabalk_ta3_anz_zig*12,NA))))                         

v1_no_cig<-c(v1_clin_no_cig,v1_con_no_cig)
summary(v1_no_cig)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    -999    -999    -999    2478    6570   23725     205

Alcohol

“How often did you consume alcoholic beverages during the past twelve months?”” (ordinal [1,2,3,4,5,6,7], v1_alc_pst12_mths)

This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.

v1_alc_pst12_mths<-factor(c(v1_clin$v1_tabalk_ta9_alkkonsum,v1_con$v1_tabalk_alkohol9), ordered=T)
descT(v1_alc_pst12_mths)

##                1    2    3    4    5    6   7  <NA>     
## [1,] No. cases 299  356  191  390  206  70  71 203  1786
## [2,] Percent   16.7 19.9 10.7 21.8 11.5 3.9 4  11.4 100

“On how many occasions during the past twelve months did you drink FIVE (men)/FOUR (women) or more alcoholic beverages?” (ordinal [1,2,3,4,5,6,7,8,9], v1_alc_5orm)

This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.

v1_clin_alc_5orm<-ifelse((v1_clin$v1_tabalk_ta9_alkkonsum==1 | v1_clin$v1_tabalk_ta9_alkkonsum==2 | v1_clin$v1_tabalk_ta9_alkkonsum==3), 
                    -999,ifelse(is.na(v1_clin$v1_tabalk_ta10_alk_haeufigk_m)==T, 
                    v1_clin$v1_tabalk_ta11_alk_haeufigk_f,v1_clin$v1_tabalk_ta10_alk_haeufigk_m))

v1_con_alc_5orm<-ifelse((v1_con$v1_tabalk_alkohol9==1 | v1_con$v1_tabalk_alkohol9==2 | v1_con$v1_tabalk_alkohol9==3), 
                    -999,ifelse(is.na(v1_con$v1_tabalk_alkohol10)==T, 
                    v1_con$v1_tabalk_alkohol11,v1_con$v1_tabalk_alkohol10))

v1_alc_5orm<-factor(c(v1_clin_alc_5orm,v1_con_alc_5orm), ordered=T)
descT(v1_alc_5orm)

##                -999 1    2   3   4   5   6   7   8  9   <NA>     
## [1,] No. cases 846  228  106 107 69  70  80  42  17 13  208  1786
## [2,] Percent   47.4 12.8 5.9 6   3.9 3.9 4.5 2.4 1  0.7 11.6 100

Lifetime alcohol dependence? (dichotomous, v1_lftm_alc_dep)

The criteria for alcohol dependence are checked, resulting in a dichotomous assessment whether lifetime alcohol dependence is present.

v1_clin_lftm_alc_dep<-ifelse(v1_clin$v1_tabalk_ta12_alk_vorhanden==1,"N",ifelse(v1_clin$v1_tabalk_ta12_alk_vorhanden==3,"Y",NA))
v1_con_lftm_alc_dep<-ifelse(v1_con$v1_tabalk_alkohol_abhaengig==1,"N",ifelse(v1_con$v1_tabalk_alkohol_abhaengig==3,"Y",NA))

v1_lftm_alc_dep<-factor(c(v1_clin_lftm_alc_dep,v1_con_lftm_alc_dep))
descT(v1_lftm_alc_dep)

##                N    Y   <NA>     
## [1,] No. cases 1359 131 296  1786
## [2,] Percent   76.1 7.3 16.6 100

Illicit drugs

In the PsyCourse Study, much information on illigit drugs was collected. Specifically, at the first visit, it was assessed whether the participant ever consumed illicit drugs, and, if yes:

• The name of each illicit drug ever consumed
• The category of each illicit drug ever consumed
• The frequency of consumption of each illicit drug during the period in which the drug was most heavily used
• The time period over which each illicit drug was consumed
• Whether each illicit drug was consumed in the past six months
• How frequently each drug was consumed per month
• Whether the individual developed tolerance to the drug

In the present dataset, only a few variables (see below and follow-up visits) are included. At the end of this section, a specific file is exported containing the raw illicit drug data of the first visit (see below), which can be referred to if raw data is needed.

Preparations for preparing data on illicit drugs

Check whether for each illicit drug, only one category is ticked (code and results not shown).

Clinical participants

After review of the original data, modify the data of one individual, in which two categories were checked

Control participants

Recode several original items because they are wrongly coded in secuTrial exports (the graphical user interface and the exports do not match).

Assessment of frequency consumed when most frequently consumed

#clinical
#define function that recodes
v1_clin_mst_oft_recode <- function(drg_no) {

attach(v1_clin) 
v1_clin_ill_mst_frq_oftn_mod<-ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==5,1,
                               ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==4,5,
                                ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==3,4,
                                 ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==2,3,
                                  ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==1,2,NA)))))
detach(v1_clin)   

assign(noquote(paste("v1_clin_drg_",drg_no,"_mst_frq_oftn",sep="")),v1_clin_ill_mst_frq_oftn_mod, envir=globalenv())
}

#apply function to all drugs (maximal number: 9)
for(no in c(1:9)){v1_clin_mst_oft_recode(no)}
  
#control
#define function that recodes
v1_con_mst_oft_recode <- function(drg_no) {

attach(v1_con) 
v1_con_ill_mst_frq_oftn_mod<-ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==5,1,
                               ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==4,5,
                                ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==3,4,
                                 ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==2,3,
                                  ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==1,2,NA)))))
detach(v1_con)  
assign(noquote(paste("v1_con_drg_",drg_no,"_mst_frq_oftn",sep="")),v1_con_ill_mst_frq_oftn_mod, envir=globalenv())
}

#apply function to all drugs (maximal number: 8)
for(no in c(1:8)){v1_con_mst_oft_recode(no)}

Assessment of frequency consumed in the past six months

v1_clin_pst_six_frq_recode <- function(drg_no) {

attach(v1_clin)
v1_clin_ill_pst_six_frq_mod<-ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==5,1,
                               ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==4,5,
                                ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==3,4,
                                 ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==2,3,
                                  ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==1,2,NA)))))
detach(v1_clin)

assign(noquote(paste("v1_clin_drg_",drg_no,"_pst_six_oftn",sep="")),v1_clin_ill_pst_six_frq_mod, envir=globalenv())
}

#apply function to all drugs (maximal number: 9)
for(no in c(1:9)){v1_clin_pst_six_frq_recode(no)}


v1_con_pst_six_frq_recode <- function(drg_no) {

attach(v1_con)
v1_con_ill_pst_six_frq_mod<-ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==5,1,
                               ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==4,5,
                                ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==3,4,
                                 ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==2,3,
                                  ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==1,2,NA)))))
detach(v1_con) 
assign(noquote(paste("v1_con_drg_",drg_no,"_pst_six_oftn",sep="")),v1_con_ill_pst_six_frq_mod, envir=globalenv())
}

#apply function to all drugs (maximal number: 8)
for(no in c(1:8)){v1_con_pst_six_frq_recode(no)}

Prepare the illicit drug items contained in this dataset

“Have you ever taken illicit drugs?” (dichotomous, v1_evr_ill_drg)

v1_clin_evr_ill_drg<-ifelse(v1_clin$v1_drogen_dg1_konsum==2, "Y", "N")
v1_con_evr_ill_drg<-ifelse(v1_con$v1_drogen_drogenkonsum==2, "Y", "N")

v1_evr_ill_drg<-factor(c(v1_clin_evr_ill_drg,v1_con_evr_ill_drg))
descT(v1_evr_ill_drg)

##                N    Y    <NA>     
## [1,] No. cases 894  695  197  1786
## [2,] Percent   50.1 38.9 11   100

Make datasets containing only information on illicit drugs

v1_drg_clin<-v1_clin[,880:1023]
v1_drg_con<-v1_con[,504:631]

Create vectors containg the different names of the different drug categories (and their dataframes; see below)

v1_drg_cat<-c("sti","can","opi","kok","hal","inh","tra","var")
v1_drg_cat_df_clin<-c("v1_sti_cli","v1_can_cli","v1_opi_cli","v1_kok_cli","v1_hal_cli","v1_inh_cli","v1_tra_cli","v1_var_cli")
v1_drg_cat_df_con<-c("v1_sti_con","v1_can_con","v1_opi_con","v1_kok_con","v1_hal_con","v1_inh_con","v1_tra_con","v1_var_con")

names(v1_drg_cat_df_clin)<-c(1:8)
names(v1_drg_cat_df_con)<-c(1:8)

The following variables are created below:

“Number of stimulants drugs ever consumed” (continuous [number], v1_sti_cat_evr)
“Number of cannabis drugs ever consumed” (continuous [number], v1_can_cat_evr)
“Number of opioid drugs ever consumed” (continuous [number], v1_opi_cat_evr)
“Number of cocaine drugs ever consumed” (continuous [number], v1_kok_cat_evr)
“Number of hallucinogenic drugs ever consumed” (continuous [number], v1_hal_cat_evr)
“Number of inhalant drugs ever consumed” (continuous [number], v1_inh_cat_evr)
“Number of tranquillizer drugs ever consumed” (continuous [number], v1_tra_cat_evr)
“Number of other drugs ever consumed” (continuous [number], v1_var_cat_evr)

The category of each drug is classified by checking a checkbox, corresponding to the category (e.g. stimulants). Here, for each individual drug ever taken, I select each checkbox corresponding to the same category, and sum across these, resulting in the number of drugs from the category (e.g. “stimulants”) that was ever taken. The same is done for all other categories.

Create dataframes, each for a different drug category, separately for clinical and control individuals. The last column of each of these dataframes contains the count across the rows of each dataframe.

for(i in c(1:8)){
   assign(v1_drg_cat_df_clin[i],row_sums(v1_drg_clin[,grep(paste("v1_drogen_s_dg_drogekt",i,"_30043_",sep=""),names(v1_drg_clin))],var="v1_clin_evr",n=1))
   assign(v1_drg_cat_df_con[i],row_sums(v1_drg_con[,grep(paste("v1_drogen_droge",i,"_117983_",sep=""),names(v1_drg_con))],var="v1_con_evr",n=1))
}

Bind each last column of the dataframes created above together. This results in a dataframe, in which the number of drugs from each category a participant has EVER TAKEN are listed.

Clinical participants: Combine into one dataframe

v1_drg_evr_cats_clin<-data.frame(v1_sti_cli[,dim(v1_sti_cli)[2]],
                         v1_can_cli[,dim(v1_can_cli)[2]],
                         v1_opi_cli[,dim(v1_opi_cli)[2]],
                         v1_kok_cli[,dim(v1_kok_cli)[2]],
                         v1_hal_cli[,dim(v1_hal_cli)[2]],
                         v1_inh_cli[,dim(v1_inh_cli)[2]],
                         v1_tra_cli[,dim(v1_tra_cli)[2]],
                         v1_var_cli[,dim(v1_var_cli)[2]])

names(v1_drg_evr_cats_clin)<-paste("v1_",v1_drg_cat,"_cat_evr",sep="")

Control participants: Combine into one dataframe

v1_drg_evr_cats_con<-data.frame(v1_sti_con[,dim(v1_sti_con)[2]],
                         v1_can_con[,dim(v1_can_con)[2]],
                         v1_opi_con[,dim(v1_opi_con)[2]],
                         v1_kok_con[,dim(v1_kok_con)[2]],
                         v1_hal_con[,dim(v1_hal_con)[2]],
                         v1_inh_con[,dim(v1_inh_con)[2]],
                         v1_tra_con[,dim(v1_tra_con)[2]],
                         v1_var_con[,dim(v1_var_con)[2]])

names(v1_drg_evr_cats_con)<-names(v1_drg_evr_cats_clin)

**Combine clinical and control drug datasets into dataset drg_evr_cats

drg_evr_cats<-rbind(v1_drg_evr_cats_clin,v1_drg_evr_cats_con)

“Was the participant ever a HEAVY USER of ANY DRUG? (dichotomous, v1_evr_hvy_usr)

clin_evr_hvy_usr<-ifelse(apply(data.frame(v1_clin_drg_1_mst_frq_oftn,
                                          v1_clin_drg_2_mst_frq_oftn,
                                          v1_clin_drg_3_mst_frq_oftn,
                                          v1_clin_drg_4_mst_frq_oftn,
                                          v1_clin_drg_5_mst_frq_oftn,
                                          v1_clin_drg_6_mst_frq_oftn,
                                          v1_clin_drg_7_mst_frq_oftn,
                                          v1_clin_drg_8_mst_frq_oftn,
                                          v1_clin_drg_9_mst_frq_oftn), 1, function(m) any(m %in% c(4,5))),"Y","N")

con_evr_hvy_usr<-ifelse(apply(data.frame(v1_con_drg_1_mst_frq_oftn,
                                          v1_con_drg_2_mst_frq_oftn,
                                          v1_con_drg_3_mst_frq_oftn,
                                          v1_con_drg_4_mst_frq_oftn,
                                          v1_con_drg_5_mst_frq_oftn,
                                          v1_con_drg_6_mst_frq_oftn,
                                          v1_con_drg_7_mst_frq_oftn,
                                          v1_con_drg_8_mst_frq_oftn), 1, function(m) any(m %in% c(4,5))),"Y","N")

drg_evr_cats$v1_evr_hvy_usr<-c(clin_evr_hvy_usr,con_evr_hvy_usr)

“During the past six months, did you take ANY illicit drugs?” (dichotomous, v1_pst6_ill_drg)

v1_clin_lst6_any<-ifelse(apply(v1_drg_clin[,grep("v1_drogen_s_dgd_letzte6m_30043_",names(v1_drg_clin))], 1, function(r) any(r %in% 1)),"Y","N")
v1_con_lst6_any<-ifelse(apply(v1_drg_con[,grep("v1_drogen_droge_letzte6m_117983_",names(v1_drg_con))], 1, function(r) any(r %in% 1)),"Y","N")

v1_pst6_ill_drg<-c(v1_clin_lst6_any,v1_con_lst6_any)
v1_pst6_ill_drg[is.na(v1_evr_ill_drg)]<-NA #make NA if this item was not assessed

IMPORTANT: Make all row in df drg_evr_cats NA in individuals in which the drug consumption section was NOT assessed.

drg_evr_cats[is.na(v1_evr_ill_drg),]<-NA

Create dataset

v1_subst<-data.frame(v1_ever_smkd,
                     v1_age_smk,
                     v1_no_cig,
                     v1_alc_pst12_mths,
                     v1_alc_5orm,
                     v1_lftm_alc_dep,
                     v1_evr_ill_drg,
                     drg_evr_cats,
                     v1_pst6_ill_drg)

Create dataset with raw illicit drug information

Here, separate datasets for clinical and control participants are created that contain the raw information on illicit drugs at visit 1, exactly as specified in the phenotype database.

For each illicit drug ever taken, the information given below is assessed.

The last character of each variable name always refers to the drug in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.

The drugs are not assessed in any specific order, i.e. the order is determined by the individual participant (whatever she or he mentions first).

Below, the variable names of clinical/control participants are given in quotes, and the coding is explained in the parentheses.

1. The name of the drug: “v1_drogen_s_dg_droge_30043”/“v1_drogen_droge_117983” (character)

The category to which the drug belongs (each item below is a checkbox: 0-not checked, 1-checked):

2. Stimulants: “v1_drogen_s_dg_drogekt1_30043”/“v1_drogen_droge1_117983”
3. Cannabis: “v1_drogen_s_dg_drogekt2_30043”/“v1_drogen_droge2_117983”
4. Opiates and pain reliefers: “v1_drogen_s_dg_drogekt3_30043”/“v1_drogen_droge3_117983”
5. Cocaine: “v1_drogen_s_dg_drogekt4_30043”/“v1_drogen_droge4_117983”
6. Hallucinogens: “v1_drogen_s_dg_drogekt5_30043”/“v1_drogen_droge5_117983”
7. Inhalants: “v1_drogen_s_dg_drogekt6_30043”/“v1_drogen_droge6_117983”
8. Tranquilizers: “v1_drogen_s_dg_drogekt7_30043”/“v1_drogen_droge7_117983”
9. Other: “v1_drogen_s_dg_drogekt8_30043”/“v1_drogen_droge8_117983”

10. “Referring to the time you consumed the drug most often, how often did you consume it?” “v1_drogen_s_dga_haeufigk_30043”/“v1_drogen_droge_haeufig_117983”

The coding is given below:
1 - tried 1 time
2 - less than once a month
3 - about once a month
4 - at least 2 times but less than 10 times a month
5 - at least 10 times a month

“How long was the period of time during which you consumed the drug?”
11. Checkbox, if the period during which the drug was most often consumed cannot be assessed: “v1_drogen_s_dgb_zr_unbekannt_30043”/“v1_drogen_droge_zeit_u_117983”

12. Time (months): “v1_drogen_s_dgb_zeitraum_30043”/“v1_drogen_droge_zeit_117983”

13. “Referring to the period of time during which you consumed the drug most often, did you have to take more of the drug to achieve the same effect?” (Coding: 1-no, 2-yes). “v1_drogen_s_dgc_dosis_30043”/“v1_drogen_droge_dosis_117983”

14. “Did you ever consume this drug during the last six months?” (Coding: 1-no, 2-yes). “v1_drogen_s_dgd_letzte6m_30043/”v1_drogen_droge_letzte6m_117983”

If yes to question 14: *15.”Referring to the past six months, how often did you take consume the substance?“*”v1_drogen_s_dge_l6m_haeufig_30043”/“v1_drogen_droge_haeufig6m_117983_1”

The coding is given below: 2 - less than once a month
3 - about once a month
4 - at least two times but less than ten times a month
5 - at least ten times a month

16. “Referring to the past six months, did you have to take more of the drug to achieve the same effect?” (Coding: 1-no, 2-yes). “v1_drogen_s_dgf_l6m_dosis_30043”/“v1_drogen_droge_dosis6m_117983

Important: There is an error in the original phenotype database, that affects the coding of item 10 and 15 (above). In all drugs the exports of the phenotype database do not reflect the input into the graphical user interface. Below, the incorrect variables are replaced with the corrected ones

Clinical participants

v1_clin_ill_drugs_orig<-data.frame(v1_clin$mnppsd,v1_drg_clin)
names(v1_clin_ill_drugs_orig)[1]<-"v1_id"

#recode wrongly coded item 10
for(i in c(0:8)){

v1_clin_ill_drugs_orig[,11+i*16]<-ifelse(v1_clin_ill_drugs_orig[,11+i*16]==5,1,
                               ifelse(v1_clin_ill_drugs_orig[,11+i*16]==4,5,
                                ifelse(v1_clin_ill_drugs_orig[,11+i*16]==3,4,
                                 ifelse(v1_clin_ill_drugs_orig[,11+i*16]==2,3,
                                  ifelse(v1_clin_ill_drugs_orig[,11+i*16]==1,2,NA)))))}

#recode wrongly coded item 15
for(i in c(0:8)){

v1_clin_ill_drugs_orig[,16+i*16]<-ifelse(v1_clin_ill_drugs_orig[,16+i*16]==5,1,
                               ifelse(v1_clin_ill_drugs_orig[,16+i*16]==4,5,
                                ifelse(v1_clin_ill_drugs_orig[,16+i*16]==3,4,
                                 ifelse(v1_clin_ill_drugs_orig[,16+i*16]==2,3,
                                  ifelse(v1_clin_ill_drugs_orig[,16+i*16]==1,2,NA)))))}

Control participants

v1_con_ill_drugs_orig<-data.frame(v1_con$mnppsd,v1_drg_con)
names(v1_con_ill_drugs_orig)[1]<-"v1_id"

#recode wrongly coded item 10
for(i in c(0:7)){

v1_con_ill_drugs_orig[,11+i*16]<-ifelse(v1_con_ill_drugs_orig[,11+i*16]==5,1,
                               ifelse(v1_con_ill_drugs_orig[,11+i*16]==4,5,
                                ifelse(v1_con_ill_drugs_orig[,11+i*16]==3,4,
                                 ifelse(v1_con_ill_drugs_orig[,11+i*16]==2,3,
                                  ifelse(v1_con_ill_drugs_orig[,11+i*16]==1,2,NA)))))}

#recode wrongly coded item 15
for(i in c(0:7)){

v1_con_ill_drugs_orig[,16+i*16]<-ifelse(v1_con_ill_drugs_orig[,16+i*16]==5,1,
                               ifelse(v1_con_ill_drugs_orig[,16+i*16]==4,5,
                                ifelse(v1_con_ill_drugs_orig[,16+i*16]==3,4,
                                 ifelse(v1_con_ill_drugs_orig[,16+i*16]==2,3,
                                  ifelse(v1_con_ill_drugs_orig[,16+i*16]==1,2,NA)))))}

Save raw illicit drug dataset from visit 1

save(v1_clin_ill_drugs_orig, file="230614_v6.0_psycourse_clin_raw_ill_drg_visit1.RData")
save(v1_con_ill_drugs_orig, file="230614_v6.0_psycourse_con_raw_ill_drg_visit1.RData")

Write .csv file

write.table(v1_clin_ill_drugs_orig,file="230614_v6.0_psycourse_clin_raw_ill_drg_visit1.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v1_con_ill_drugs_orig,file="230614_v6.0_psycourse_con_raw_ill_drg_visit1.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Visit 1: DSM-IV Diagnosis (only in clinical participants)

Parts of the Structured Clinical Interview for DSM Disorders (SCID) were carried out (sections A Affective Syndromes, B Psychotic and Associated Symptoms, X Suicide attempts and suicidal ideation, and either section C (Differential diagnosis of Psychotic disorders) or section D (Differential diagnosis of Affective disorders). The most important variables of sections A, B, ans X are included in this dataset, usually coded “Y”-yes, “N”-no and “U”-unknown.

For control participants, every SCID variable is coded “-999”.

Diagnosis according to DSM-IV

DSM-IV number diagnosis (categorical [295.10, 295.20, 295.30, 295.40, 295.60, 295.70, 295.90, 296.X, 296.3, 296.89, 298.80, MImicSS], v1_scid_dsm_dx)

We have included the DSM-IV diagnosis resulting from the combined efforts of a SCID interview and a screening of medical records (if available). Difficult cases were resolved by a discussion of experts.

There are study participants in which this item reads “MImicSS”. These are PsyCourse participants, but they have been recruited using a modified protocol, in which their ICD-10 diagnoses were not reassessed within the DSM-IV framework.

All of these clinical participants have ICD-10 Schizophrenia (F20.0).

v1_clin_scid_dsm_dx<-v1_clin$v1_skid_deckblatt_dsmiv_konsensusdiag_34407_1
v1_clin_scid_dsm_dx<-as.character(v1_clin_scid_dsm_dx)

v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="296.x"]<-"296.X"
v1_con_scid_dsm_dx<-rep("-999",dim(v1_con)[1])

v1_scid_dsm_dx<-c(v1_clin_scid_dsm_dx,v1_con_scid_dsm_dx)
descT(v1_scid_dsm_dx)

##                -999 295.10 295.20 295.30 295.40 295.60 295.70 295.90 296.3
## [1,] No. cases 466  15     11     437    12     4      100    12     101  
## [2,] Percent   26.1 0.8    0.6    24.5   0.7    0.2    5.6    0.7    5.7  
##      296.89 296.X 298.80 MImicSS     
## [1,] 120    446   6      56      1786
## [2,] 6.7    25    0.3    3.1     100

DSM-IV diagnosis categories (categorical [Recurrent Depression, Schizophrenia, Schizoaffective Disorder, Brief Psychotic Disorder, Bipolar-I Disorder, Bipolar-II Disorder], v1_scid_dsm_dx_cat)

DSM-IV diagnoses in human readable form for non-clinical researchers. Please note the following:

• Schizophrenia and schizoaffective disorder are often grouped together under the label schizophrenia, although these are two different disorders.
  
• Schizophreniform disorder is diagnosed when symptoms of schizophrenia are not (yet) present for the full six months required for a proper schizophrenia diagnosis.
  
• Brief psychotic disorder is distinct from schizophrenia.
  
• Bipolar-I disorder is diagnosed when both manic and depressive phases are present in an individual; a distinct diagnosis is bipolar-II in which “only” hypomanic and depressive phases are present. Bipolar-I and bipolar-II are both proper bipolar disorders.
  
• The MImicSS subjects (see description of last item) are labeled as “ICD-10 Schizophrenia”.
  
• Control subjects are labeled as “Control”.

v1_clin_scid_dsm_dx_cat<-rep("NA",dim(v1_clin)[1])
v1_con_scid_dsm_dx_cat<-rep("NA",dim(v1_con)[1])

v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("295.10", "295.20", "295.30", "295.60", 
                               "295.90")]<-"Schizophrenia"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("295.70")]<-"Schizoaffective Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("295.40")]<-"Schizophreniform Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("298.80")]<-"Brief Psychotic Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("296.X")]<-"Bipolar-I Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("296.89")]<-"Bipolar-II Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("296.3")]<-"Recurrent Depression"

v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("MImicSS")]<-"ICD-10 Schizophrenia"

v1_con_scid_dsm_dx_cat<-rep("Control",dim(v1_con)[1])

v1_scid_dsm_dx_cat<-c(v1_clin_scid_dsm_dx_cat,v1_con_scid_dsm_dx_cat)
descT(v1_scid_dsm_dx_cat)

##                Bipolar-I Disorder Bipolar-II Disorder Brief Psychotic Disorder
## [1,] No. cases 446                120                 6                       
## [2,] Percent   25                 6.7                 0.3                     
##      Control ICD-10 Schizophrenia Recurrent Depression Schizoaffective Disorder
## [1,] 466     56                   101                  100                     
## [2,] 26.1    3.1                  5.7                  5.6                     
##      Schizophrenia Schizophreniform Disorder     
## [1,] 479           12                        1786
## [2,] 26.8          0.7                       100

The following items were assessed in clincal participants only!

A Affective Syndromes

Major depressive episode (MDE)**

Age at first MDD episode (continuous [years], v1_scid_age_MDE) This item includes all individuals that ever fulfilled MDD criteria. Also a person with e.g. schizophrenia can have a value on this item. If the individual ever fulfilled MDD criteria, the age at the first MDD episode is given. NA on this item means that age at first MDD episode is missing. Individuals that never experienced an MDD episode are coded as “-999”.

Control individiuals are coded as “-999”.

v1_clin_scid_age_MDE<-ifelse(is.na(v1_clin$v1_sna_21_mde1_a79_beurteilung),-999,
  ifelse(v1_clin$v1_sna_21_mde1_a79_beurteilung==3, v1_clin$v1_sna_21_mde1_s_snx_alter_jahre_30038_1, 
       ifelse((v1_clin$v1_sna_21_mde1_a79_beurteilung==1 | v1_clin$v1_sna_21_mde1_a79_beurteilung==0), -999, NA)))

descT(v1_clin_scid_age_MDE)

##                -999 2   4   6   7   8   9   10  11  12  13  14 15  16 17 18 
## [1,] No. cases 409  1   1   3   4   1   4   8   10  12  14  27 22  40 40 37 
## [2,] Percent   31   0.1 0.1 0.2 0.3 0.1 0.3 0.6 0.8 0.9 1.1 2  1.7 3  3  2.8
##      19  20  21  22  23  24  25  26  27 28  29  30  31  32  33 34  35  36  37 
## [1,] 45  41  41  24  31  34  38  23  26 21  12  37  17  25  13 12  11  10  14 
## [2,] 3.4 3.1 3.1 1.8 2.3 2.6 2.9 1.7 2  1.6 0.9 2.8 1.3 1.9 1  0.9 0.8 0.8 1.1
##      38  39  40  41  42  43 44  45  46  47  48  49  50  51  52  53  54  55  56 
## [1,] 6   7   15  12  10  13 5   15  5   8   8   5   10  3   9   7   3   3   2  
## [2,] 0.5 0.5 1.1 0.9 0.8 1  0.4 1.1 0.4 0.6 0.6 0.4 0.8 0.2 0.7 0.5 0.2 0.2 0.2
##      57  58  59  60  61  69  <NA>     
## [1,] 1   1   1   3   1   1   58   1320
## [2,] 0.1 0.1 0.1 0.2 0.1 0.1 4.4  100

v1_scid_age_MDE<-c(v1_clin_scid_age_MDE,rep(-999,dim(v1_con)[1]))
descT(v1_scid_age_MDE)

##                -999 2   4   6   7   8   9   10  11  12  13  14  15  16  17  18 
## [1,] No. cases 875  1   1   3   4   1   4   8   10  12  14  27  22  40  40  37 
## [2,] Percent   49   0.1 0.1 0.2 0.2 0.1 0.2 0.4 0.6 0.7 0.8 1.5 1.2 2.2 2.2 2.1
##      19  20  21  22  23  24  25  26  27  28  29  30  31 32  33  34  35  36  37 
## [1,] 45  41  41  24  31  34  38  23  26  21  12  37  17 25  13  12  11  10  14 
## [2,] 2.5 2.3 2.3 1.3 1.7 1.9 2.1 1.3 1.5 1.2 0.7 2.1 1  1.4 0.7 0.7 0.6 0.6 0.8
##      38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55 
## [1,] 6   7   15  12  10  13  5   15  5   8   8   5   10  3   9   7   3   3  
## [2,] 0.3 0.4 0.8 0.7 0.6 0.7 0.3 0.8 0.3 0.4 0.4 0.3 0.6 0.2 0.5 0.4 0.2 0.2
##      56  57  58  59  60  61  69  <NA>     
## [1,] 2   1   1   1   3   1   1   58   1786
## [2,] 0.1 0.1 0.1 0.1 0.2 0.1 0.1 3.2  100

Number of MDD episodes (continuous [number], v1_scid_no_MDE) In individuals that ever fulfilled MDD criteria, the number of MDD episodes is given. Please note the following:

• NA on this item means that the number of MDD episodes is missing.
• Individuals that never experienced an MDD episode are coded as “-999”.
• If the episodes could only be poorly delimited from each other, “99” was coded.

v1_clin_scid_no_MDE<-ifelse(is.na(v1_clin$v1_sna_21_mde1_a79_beurteilung),-999,
  ifelse(v1_clin$v1_sna_21_mde1_a79_beurteilung==3, v1_clin$v1_sna_21_mde1_a81_anzahl, 
       ifelse((v1_clin$v1_sna_21_mde1_a79_beurteilung==1 | v1_clin$v1_sna_21_mde1_a79_beurteilung==0), -999, NA)))

descT(v1_clin_scid_no_MDE)

##                -999 1   2   3   4   5   6   7   8   9   10 11  12  13  14  15 
## [1,] No. cases 409  75  96  95  86  64  35  19  33  9   40 8   14  3   4   18 
## [2,] Percent   31   5.7 7.3 7.2 6.5 4.8 2.7 1.4 2.5 0.7 3  0.6 1.1 0.2 0.3 1.4
##      16  17  18  20  21  22  23  25  26  29  30  32  35  36  39  40  50  60 
## [1,] 2   3   1   21  1   1   1   3   2   2   12  1   1   1   1   2   2   2  
## [2,] 0.2 0.2 0.1 1.6 0.1 0.1 0.1 0.2 0.2 0.2 0.9 0.1 0.1 0.1 0.1 0.2 0.2 0.2
##      70  75  99   <NA>     
## [1,] 1   1   199  52   1320
## [2,] 0.1 0.1 15.1 3.9  100

v1_scid_no_MDE<-c(v1_clin_scid_no_MDE,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_MDE)

##                -999 1   2   3   4   5   6  7   8   9   10  11  12  13  14  15
## [1,] No. cases 875  75  96  95  86  64  35 19  33  9   40  8   14  3   4   18
## [2,] Percent   49   4.2 5.4 5.3 4.8 3.6 2  1.1 1.8 0.5 2.2 0.4 0.8 0.2 0.2 1 
##      16  17  18  20  21  22  23  25  26  29  30  32  35  36  39  40  50  60 
## [1,] 2   3   1   21  1   1   1   3   2   2   12  1   1   1   1   2   2   2  
## [2,] 0.1 0.2 0.1 1.2 0.1 0.1 0.1 0.2 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.1 0.1 0.1
##      70  75  99   <NA>     
## [1,] 1   1   199  52   1786
## [2,] 0.1 0.1 11.1 2.9  100

###Mania and hypomania Age at first manic episode (continuous, v1_scid_age_mania) This item includes all individuals that ever fulfilled mania criteria. Also a person with e.g. schizophrenia can have a value on this item. If the individual ever fulfilled mania criteria, the age at the first manic episode is given. NA on this item means that age at first mania episode is missing.

Individuals that never experienced a manic episode are coded as “-999”.

Control individiuals are coded as “-999”.

v1_clin_scid_age_mania<-ifelse(is.na(v1_clin$v1_sna_23_manie1_a142_beurteilung),-999,
  ifelse(v1_clin$v1_sna_23_manie1_a142_beurteilung==3, v1_clin$v1_sna_24_hypomane1_a143_alter_jahre, 
       ifelse((v1_clin$v1_sna_23_manie1_a142_beurteilung==1 | v1_clin$v1_sna_23_manie1_a142_beurteilung==0), -999, NA)))

descT(v1_clin_scid_age_mania)

##                -999 5   10  11  12  13  14  15  16  17  18  19 20  21  22  23 
## [1,] No. cases 775  1   1   2   2   3   6   9   18  12  18  13 31  15  19  21 
## [2,] Percent   58.7 0.1 0.1 0.2 0.2 0.2 0.5 0.7 1.4 0.9 1.4 1  2.3 1.1 1.4 1.6
##      24  25  26  27  28  29  30 31  32  33  34  35  36  37  38  39  40  41  42 
## [1,] 20  32  16  19  17  9   13 7   6   9   17  7   9   8   8   5   15  14  8  
## [2,] 1.5 2.4 1.2 1.4 1.3 0.7 1  0.5 0.5 0.7 1.3 0.5 0.7 0.6 0.6 0.4 1.1 1.1 0.6
##      43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  59  60  61 
## [1,] 6   6   9   5   3   2   4   4   4   4   4   3   2   3   3   1   2   1  
## [2,] 0.5 0.5 0.7 0.4 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.1 0.2 0.1
##      65  <NA>     
## [1,] 2   67   1320
## [2,] 0.2 5.1  100

v1_scid_age_mania<-c(v1_clin_scid_age_mania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_age_mania)

##                -999 5   10  11  12  13  14  15  16 17  18 19  20  21  22  23 
## [1,] No. cases 1241 1   1   2   2   3   6   9   18 12  18 13  31  15  19  21 
## [2,] Percent   69.5 0.1 0.1 0.1 0.1 0.2 0.3 0.5 1  0.7 1  0.7 1.7 0.8 1.1 1.2
##      24  25  26  27  28 29  30  31  32  33  34 35  36  37  38  39  40  41  42 
## [1,] 20  32  16  19  17 9   13  7   6   9   17 7   9   8   8   5   15  14  8  
## [2,] 1.1 1.8 0.9 1.1 1  0.5 0.7 0.4 0.3 0.5 1  0.4 0.5 0.4 0.4 0.3 0.8 0.8 0.4
##      43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  59  60  61 
## [1,] 6   6   9   5   3   2   4   4   4   4   4   3   2   3   3   1   2   1  
## [2,] 0.3 0.3 0.5 0.3 0.2 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.1 0.1 0.1
##      65  <NA>     
## [1,] 2   67   1786
## [2,] 0.1 3.8  100

Number of manic episodes (continuous [number], v1_scid_no_mania) In individuals that ever fulfilled criteria for mania, the number of mania episodes is given. Please note the following:

• NA on this item means that the number of mania episodes is missing.
• Individuals that never experienced an episode of mania are coded as “-999”.
• If the episodes could only be poorly delimited from each other, “99” was coded.

v1_clin_scid_no_mania<-ifelse(is.na(v1_clin$v1_sna_23_manie1_a142_beurteilung),-999,
  ifelse(v1_clin$v1_sna_23_manie1_a142_beurteilung==3, v1_clin$v1_sna_24_hypomane1_a145_anzahl2, 
       ifelse((v1_clin$v1_sna_23_manie1_a142_beurteilung==1 | v1_clin$v1_sna_23_manie1_a142_beurteilung==0), -999, NA)))

descT(v1_clin_scid_no_mania)

##                -999 1   2   3   4   5   6   7   8   10  11  12  13  14  15  17 
## [1,] No. cases 775  90  68  54  37  33  20  12  14  25  2   5   1   2   3   1  
## [2,] Percent   58.7 6.8 5.2 4.1 2.8 2.5 1.5 0.9 1.1 1.9 0.2 0.4 0.1 0.2 0.2 0.1
##      20  22  24  25  30  50  96  99  <NA>     
## [1,] 12  1   1   1   4   1   1   47  110  1320
## [2,] 0.9 0.1 0.1 0.1 0.3 0.1 0.1 3.6 8.3  100

v1_scid_no_mania<-c(v1_clin_scid_no_mania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_mania)

##                -999 1  2   3  4   5   6   7   8   10  11  12  13  14  15  17 
## [1,] No. cases 1241 90 68  54 37  33  20  12  14  25  2   5   1   2   3   1  
## [2,] Percent   69.5 5  3.8 3  2.1 1.8 1.1 0.7 0.8 1.4 0.1 0.3 0.1 0.1 0.2 0.1
##      20  22  24  25  30  50  96  99  <NA>     
## [1,] 12  1   1   1   4   1   1   47  110  1786
## [2,] 0.7 0.1 0.1 0.1 0.2 0.1 0.1 2.6 6.2  100

Age at first hypomanic episode (continuous, v1_scid_age_hypomania) This item includes all individuals that ever fulfilled criteria for hypomania, but never fullfilled criteria for mania. Also a person with e.g. schizophrenia can have a value on this item. If the individual ever fulfilled hypomania criteria (without ever fulfilling criteria of mania), the age at the first hypomanic episode is given. NA on this item means that age at first hypomanic episode is missing.

Individuals that never experienced a hypomanic episode, or had one or more mania episodes, are coded as “-999”.

Control individiuals are coded as “-999”.

v1_clin_scid_age_hypomania<-ifelse(is.na(v1_clin$v1_sna_24_hypomane1_a_160_beurteilung),-999,
  ifelse(v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==3, v1_clin$v1_sna_24_hypomane1_a160a_alter_jahre, 
       ifelse((v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==1 | v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==0), -999, NA)))

descT(v1_clin_scid_age_hypomania)

##                -999 13  15  16  17  18  19  20  21  22  23  24  25  26  27  28 
## [1,] No. cases 1185 2   5   4   1   5   6   4   7   5   7   1   4   3   4   1  
## [2,] Percent   89.8 0.2 0.4 0.3 0.1 0.4 0.5 0.3 0.5 0.4 0.5 0.1 0.3 0.2 0.3 0.1
##      29  30  31  32  33  34  35  36  37  38  40  42  43  45  46  47  48  50 
## [1,] 2   3   3   5   2   3   3   2   3   2   2   1   1   2   2   1   2   2  
## [2,] 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.2 0.1 0.2 0.2
##      51  53  54  55  65  <NA>     
## [1,] 1   1   5   5   1   22   1320
## [2,] 0.1 0.1 0.4 0.4 0.1 1.7  100

v1_scid_age_hypomania<-c(v1_clin_scid_age_hypomania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_age_hypomania)

##                -999 13  15  16  17  18  19  20  21  22  23  24  25  26  27  28 
## [1,] No. cases 1651 2   5   4   1   5   6   4   7   5   7   1   4   3   4   1  
## [2,] Percent   92.4 0.1 0.3 0.2 0.1 0.3 0.3 0.2 0.4 0.3 0.4 0.1 0.2 0.2 0.2 0.1
##      29  30  31  32  33  34  35  36  37  38  40  42  43  45  46  47  48  50 
## [1,] 2   3   3   5   2   3   3   2   3   2   2   1   1   2   2   1   2   2  
## [2,] 0.1 0.2 0.2 0.3 0.1 0.2 0.2 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
##      51  53  54  55  65  <NA>     
## [1,] 1   1   5   5   1   22   1786
## [2,] 0.1 0.1 0.3 0.3 0.1 1.2  100

Number of hypomanic episodes (continuous [number (but see below)], v1_scid_no_hypomania) In individuals that ever fulfilled criteria for hypomania, but never fullfilled criteria for mania, the number of hypomanic episodes is given. Please note the following:

• NA on this item means that the number of hypomanic episode is missing.
• Individuals that never experienced an episode of hypomania, or had one or more episodes of mania, are coded as “-999”.
• If the episodes could only be poorly delimited from each other, “99” was coded.

v1_clin_scid_no_hypomania<-ifelse(is.na(v1_clin$v1_sna_24_hypomane1_a_160_beurteilung),-999,
  ifelse(v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==3, v1_clin$v1_sna_24_hypomane1_a161_anzahl, 
       ifelse((v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==1 | v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==0), -999, NA)))

descT(v1_clin_scid_no_hypomania)

##                -999 1   2   3  4   5   6   7   8   10  12  14  15  18  20  30 
## [1,] No. cases 1185 19  14  13 7   4   2   6   2   5   1   1   2   1   2   1  
## [2,] Percent   89.8 1.4 1.1 1  0.5 0.3 0.2 0.5 0.2 0.4 0.1 0.1 0.2 0.1 0.2 0.1
##      70  75  99  <NA>     
## [1,] 1   1   15  38   1320
## [2,] 0.1 0.1 1.1 2.9  100

v1_scid_no_hypomania<-c(v1_clin_scid_no_hypomania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_hypomania)

##                -999 1   2   3   4   5   6   7   8   10  12  14  15  18  20  30 
## [1,] No. cases 1651 19  14  13  7   4   2   6   2   5   1   1   2   1   2   1  
## [2,] Percent   92.4 1.1 0.8 0.7 0.4 0.2 0.1 0.3 0.1 0.3 0.1 0.1 0.1 0.1 0.1 0.1
##      70  75  99  <NA>     
## [1,] 1   1   15  38   1786
## [2,] 0.1 0.1 0.8 2.1  100

B Psychotic and associated symptoms

This section was assessed in all study participants. If at least one item covering delusions (delusion item) or hallucinations (hallucination item) was answered in the affirmative this was coded “Y”, otherwise “N”. Please note that an individual was also coded as “N” if there was insufficient information (“0” on the original SCID item; occuring only in few individuals) or if the symptom was too mild to fulfill criteria (“2” on the original SCID item).

NA means that at least one question was not completed and all other completed questions were coded “N”.

Ever experienced delusions? (dichotomous, v1_scid_ever_delus)

v1_clin_scid_ever_delus<-ifelse((is.na(v1_clin$v1_snb_31_prodsymp1_b12_beeinflusswahn) &
                                  is.na(v1_clin$v1_snb_31_prodsymp1_b13_gedankenentzug) &
                                  is.na(v1_clin$v1_snb_31_prodsymp1_b14_gedankenueber) &        
                                  v1_clin$v1_snb_31_prodsymp1_b1_beziehungswahn!=3 & 
                                  v1_clin$v1_snb_31_prodsymp1_b2_verfolgungswahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b3_groessenwahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b4_koerper_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b6_relig_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b7_schuld_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b8_eifer_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b9_liebe_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b10_nihilist_wahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b11_veram_wahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b12_sonstiger_wahn!=3), "N",
                            ifelse((v1_clin$v1_snb_31_prodsymp1_b12_beeinflusswahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b13_gedankenentzug!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b14_gedankenueber!=3 &        
                                  v1_clin$v1_snb_31_prodsymp1_b1_beziehungswahn!=3 & 
                                  v1_clin$v1_snb_31_prodsymp1_b2_verfolgungswahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b3_groessenwahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b4_koerper_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b6_relig_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b7_schuld_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b8_eifer_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b9_liebe_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b10_nihilist_wahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b11_veram_wahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b12_sonstiger_wahn!=3), "N",      
                          ifelse((v1_clin$v1_snb_31_prodsymp1_b1_beziehungswahn==3 | 
                                  v1_clin$v1_snb_31_prodsymp1_b2_verfolgungswahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b3_groessenwahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b4_koerper_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b6_relig_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b7_schuld_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b8_eifer_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b9_liebe_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b10_nihilist_wahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b11_veram_wahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b12_sonstiger_wahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b12_beeinflusswahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b13_gedankenentzug==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b14_gedankenueber==3),"Y",NA)))

v1_scid_ever_delus<-factor(c(v1_clin_scid_ever_delus,rep(-999,dim(v1_con)[1])))
summary(v1_scid_ever_delus)

## -999    N    Y NA's 
##  466  333  873  114

Ever experienced hallucinations? (dichotomous, v1_scid_ever_halls)

v1_clin_scid_ever_halls<-ifelse(v1_clin$v1_snb_31_prodsymp1_b21_olfaktor_halluz==3 | 
                       v1_clin$v1_snb_31_prodsymp1_b_21_gustator_halluz==3 |
                       v1_clin$v1_snb_31_prodsymp1_b20_taktil_halluz==3  |
                       v1_clin$v1_snb_31_prodsymp1_b19_opt_halluz==3 |
                       v1_clin$v1_snb_31_prodsymp1_b16_akust_halluz==3, "Y", "N")

v1_scid_ever_halls<-factor(c(v1_clin_scid_ever_halls,rep(-999,dim(v1_con)[1])))
summary(v1_scid_ever_halls)

## -999    N    Y NA's 
##  466  617  612   91

Ever experienced psychotic symptoms? (dichotomous, v1_scid_ever_psyc)

This item combines the two previous items. If ever delusional or ever hallucinations then yes. This is a crude operational definition of psychosis, as it takes into account only symptoms and not e.g. level of functioning or other aspects.

v1_clin_scid_ever_psyc<-ifelse(v1_clin_scid_ever_delus=="Y" | v1_clin_scid_ever_halls=="Y","Y","N")
v1_scid_ever_psyc<-factor(c(v1_clin_scid_ever_psyc,rep("-999",dim(v1_con)[1])))
summary(v1_scid_ever_psyc)

## -999    N    Y NA's 
##  466  304  897  119

Age at first occurence of psychotic symptoms (continuous [years], v1_scid_age_fst_psyc)

v1_clin_scid_age_fst_psy<-c(v1_clin$v1_snc_41_schizo4_c17_alter_jahre,rep(-999,dim(v1_con)[1]))
v1_scid_age_fst_psyc<-ifelse(v1_scid_ever_psyc=="Y",v1_clin_scid_age_fst_psy,-999)
summary(v1_scid_age_fst_psyc[v1_scid_age_fst_psyc>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    6.00   21.00   27.00   29.32   35.75   73.00     606

Year in which first psychotic symptoms occured (continuous [year], v1_scid_yr_fst_psyc)

v1_clin_scid_yr_fst_psyc<-c(v1_clin$v1_snc_41_schizo4_c17_psycho_jahr,rep(-999,dim(v1_con)[1]))
v1_scid_yr_fst_psyc<-ifelse(v1_scid_ever_psyc=="Y",v1_clin_scid_yr_fst_psyc,-999)
descT(v1_scid_yr_fst_psyc)  

##                -999 1950 1962 1965 1967 1969 1970 1971 1973 1974 1975 1976 1977
## [1,] No. cases 770  1    1    1    1    1    1    2    3    2    1    1    6   
## [2,] Percent   43.1 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.2  0.1  0.1  0.1  0.3 
##      1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
## [1,] 3    1    6    1    3    5    4    3    4    10   5    5    9    6    9   
## [2,] 0.2  0.1  0.3  0.1  0.2  0.3  0.2  0.2  0.2  0.6  0.3  0.3  0.5  0.3  0.5 
##      1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
## [1,] 5    9    14   6    9    16   11   13   14   15   11   15   17   21   13  
## [2,] 0.3  0.5  0.8  0.3  0.5  0.9  0.6  0.7  0.8  0.8  0.6  0.8  1    1.2  0.7 
##      2009 2010 2011 2012 2013 2014 2015 <NA>     
## [1,] 10   16   20   12   13   8    4    649  1786
## [2,] 0.6  0.9  1.1  0.7  0.7  0.4  0.2  36.3 100

X Suicide attempts and suicidal ideation

Ever suicidal ideation (dichotomous, v1_scid_evr_suic_ide)

Please not that the following items on suicidal ideation were skipped (and coded -999) if this question was not answered positively. The answer “insufficient information” was coded as -999.

v1_scid_evr_suic_ide<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
                        
v1_scid_evr_suic_ide<-ifelse(c(v1_clin$v1_snx_112_suizged2_x7_suizid_gedanken,rep(-999,dim(v1_con)[1]))==0, -999,  
                      ifelse(c(v1_clin$v1_snx_112_suizged2_x7_suizid_gedanken,rep(-999,dim(v1_con)[1]))==1, "N", 
                      ifelse(c(v1_clin$v1_snx_112_suizged2_x7_suizid_gedanken,rep(-999,dim(v1_con)[1]))==3, "Y", 
                             v1_scid_evr_suic_ide))) 

descT(v1_scid_evr_suic_ide)

##                -999 N    Y    <NA>     
## [1,] No. cases 473  298  930  85   1786
## [2,] Percent   26.5 16.7 52.1 4.8  100

Suicidal ideation detailed (ordinal [1,2,3,4], v1_scid_suic_ide)

This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).

v1_scid_suic_ide<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))

v1_scid_suic_ide<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
                         ifelse(c(v1_clin$v1_snx_112_suizged2_x8_suizged_inhalt,rep(-999,dim(v1_con)[1]))==1, 1, 
                          ifelse(c(v1_clin$v1_snx_112_suizged2_x8_suizged_inhalt,rep(-999,dim(v1_con)[1]))==2, 2, 
                            ifelse(c(v1_clin$v1_snx_112_suizged2_x8_suizged_inhalt,rep(-999,dim(v1_con)[1]))==3, 3, 
                              ifelse(c(v1_clin$v1_snx_112_suizged2_x8_suizged_inhalt==4,rep(-999,dim(v1_con)[1])), 4,                                                v1_scid_suic_ide)))))

v1_scid_suic_ide<-factor(v1_scid_suic_ide,ordered=T)
descT(v1_scid_suic_ide)

##                -999 1    2   3   4    <NA>     
## [1,] No. cases 771  297  124 134 345  115  1786
## [2,] Percent   43.2 16.6 6.9 7.5 19.3 6.4  100

Thoughts about methods (ordinal [1,2,3], v1_scid_suic_thght_mth)

This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3. The item is coded “-999” if skipped (see above).

v1_scid_suic_thght_mth<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_scid_suic_thght_mth<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
                        ifelse(c(v1_clin$v1_snx_112_suizged2_x10_suiz_methoden,rep(-999,dim(v1_con)[1]))==1, 1, 
                        ifelse(c(v1_clin$v1_snx_112_suizged2_x10_suiz_methoden,rep(-999,dim(v1_con)[1]))==2, 2, 
                        ifelse(c(v1_clin$v1_snx_112_suizged2_x10_suiz_methoden,rep(-999,dim(v1_con)[1]))==3, 3,
                        v1_scid_suic_thght_mth))))

v1_scid_suic_thght_mth<-factor(v1_scid_suic_thght_mth,ordered=T)
descT(v1_scid_suic_thght_mth)

##                -999 1    2    3    <NA>     
## [1,] No. cases 771  271  284  328  132  1786
## [2,] Percent   43.2 15.2 15.9 18.4 7.4  100

Suicidal ideation: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v1_scid_suic_note_thgts)

This is on ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).

v1_scid_suic_note_thgts<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_scid_suic_note_thgts<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
                          ifelse(c(v1_clin$v1_snx_112_suizged2_x11_best_erledigt,rep(-999,dim(v1_con)[1]))==1, 1,  
                            ifelse(c(v1_clin$v1_snx_112_suizged2_x11_best_erledigt,rep(-999,dim(v1_con)[1]))==2, 2,  
                              ifelse(c(v1_clin$v1_snx_112_suizged2_x11_best_erledigt,rep(-999,dim(v1_con)[1]))==3, 3, 
                                ifelse(c(v1_clin$v1_snx_112_suizged2_x11_best_erledigt,rep(-999,dim(v1_con)[1]))==4, 4,             v1_scid_suic_note_thgts)))))  
                
v1_scid_suic_note_thgts<-factor(v1_scid_suic_note_thgts,ordered=T)
descT(v1_scid_suic_note_thgts)

##                -999 1    2   3   4   <NA>     
## [1,] No. cases 771  681  46  28  117 143  1786
## [2,] Percent   43.2 38.1 2.6 1.6 6.6 8    100

Suicide attempt (categorical [1,2,3], v1_suic_attmpt)

This is on ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”.The answer “insufficient information was coded as -999.

v1_suic_attmpt<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_suic_attmpt<-ifelse(c(v1_clin$v1_snx_111_suizvrs2_x1_suiz_vers,rep(-999,dim(v1_con)[1]))==0, -999, 
                  ifelse(c(v1_clin$v1_snx_111_suizvrs2_x1_suiz_vers,rep(-999,dim(v1_con)[1]))==1, 1, 
                    ifelse(c(v1_clin$v1_snx_111_suizvrs2_x1_suiz_vers,rep(-999,dim(v1_con)[1]))==2, 2, 
                      ifelse(c(v1_clin$v1_snx_111_suizvrs2_x1_suiz_vers,rep(-999,dim(v1_con)[1]))==3, 3, 
                             v1_suic_attmpt)))) 

v1_suic_attmpt<-factor(v1_suic_attmpt,ordered=T)
descT(v1_suic_attmpt)

##                -999 1    2  3    <NA>     
## [1,] No. cases 475  806  53 364  88   1786
## [2,] Percent   26.6 45.1 3  20.4 4.9  100

Number of suicide attempts (ordinal [1,2,3,4,5,6], v1_scid_no_suic_attmpt)

This is on ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6. The item is coded “-999” if skipped (see above).

Please note: the maximum number of suicide attemts in the dataset is three, although an even higher number of attempts can theoretically be coded in the eCRF.

v1_scid_no_suic_attmpt<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))

v1_scid_no_suic_attmpt<-ifelse(v1_suic_attmpt==1, -999, 
                ifelse(v1_suic_attmpt>1, c(v1_clin$v1_snx_111_suizvrs2_x2_suiz_anz,rep(-999,dim(v1_con)[1])), v1_scid_no_suic_attmpt))
           
v1_scid_no_suic_attmpt<-factor(v1_scid_no_suic_attmpt,ordered=T)
descT(v1_scid_no_suic_attmpt)

##                -999 1    2   3   <NA>     
## [1,] No. cases 1272 226  97  81  110  1786
## [2,] Percent   71.2 12.7 5.4 4.5 6.2  100

Preparation of suicide attempt (ordinal [1,2,3,4], v1_prep_suic_attp_ord)

This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4. The item is coded “-999” if skipped (see above).

v1_prep_suic_attp_ord<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))

v1_prep_suic_attp_ord<-ifelse(v1_suic_attmpt==1, -999, 
                          ifelse(v1_suic_attmpt>1 & 
                                   c(v1_clin$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==1, 1,
                          ifelse(v1_suic_attmpt>1 & 
                                   c(v1_clin$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==2, 2,     
                          ifelse(v1_suic_attmpt>1 & 
                                   c(v1_clin$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==3, 3,
                          ifelse(v1_suic_attmpt>1 & 
                                   c(v1_clin$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==4, 4,
                              v1_prep_suic_attp_ord))))) 

v1_prep_suic_attp_ord<-factor(v1_prep_suic_attp_ord,ordered=T)
descT(v1_prep_suic_attp_ord)

##                -999 1   2   3  4   <NA>     
## [1,] No. cases 1272 123 40  71 136 144  1786
## [2,] Percent   71.2 6.9 2.2 4  7.6 8.1  100

Suicide attempt: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v1_suic_note_attmpt)

This is on ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).

v1_suic_note_attmpt<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))

v1_suic_note_attmpt<-ifelse(v1_suic_attmpt==1, -999, 
                     ifelse(v1_suic_attmpt>1 & c(v1_clin$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==1, "1",
                     ifelse(v1_suic_attmpt>1 & c(v1_clin$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==2, "2",
                     ifelse(v1_suic_attmpt>1 & c(v1_clin$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==3, "3",
                     ifelse(v1_suic_attmpt>1 & c(v1_clin$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==4, "4",
                            v1_suic_note_attmpt))))) 

v1_suic_note_attmpt<-factor(v1_suic_note_attmpt,ordered=T)
descT(v1_suic_note_attmpt)

##                -999 1    2  3  4   <NA>     
## [1,] No. cases 1272 260  17 17 92  128  1786
## [2,] Percent   71.2 14.6 1  1  5.2 7.2  100

Age at first suicide attempt (continuous [years], v1_age_fst_suic_att)

v1_age_fst_suic_att<-ifelse(v1_suic_attmpt>2,c(v1_clin$v1_snx_111_suizvrs2_s_snx_alter_jahre_31171_1,rep(-999,dim(v1_con)[1])),-999)
descT(v1_age_fst_suic_att)         

##                -999 2   3   7   8   9   10  11  12  13  14  15  16  17  18  19 
## [1,] No. cases 1334 1   1   1   1   2   2   3   3   6   11  8   5   9   15  7  
## [2,] Percent   74.7 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.3 0.6 0.4 0.3 0.5 0.8 0.4
##      20  21  22  23  24 25 26  27  28  29  30  31  32  33  34  35  36  37  38 
## [1,] 14  14  11  10  17 17 6   19  8   13  15  7   6   6   6   7   8   3   4  
## [2,] 0.8 0.8 0.6 0.6 1  1  0.3 1.1 0.4 0.7 0.8 0.4 0.3 0.3 0.3 0.4 0.4 0.2 0.2
##      39  40  41  42  43  44  45  46  47  48  49  51  52  53  54  55  56  57 
## [1,] 8   3   4   5   2   6   9   3   6   4   2   2   3   1   2   4   2   1  
## [2,] 0.4 0.2 0.2 0.3 0.1 0.3 0.5 0.2 0.3 0.2 0.1 0.1 0.2 0.1 0.1 0.2 0.1 0.1
##      58  59  61  62  63  65  <NA>     
## [1,] 1   2   2   1   1   1   111  1786
## [2,] 0.1 0.1 0.1 0.1 0.1 0.1 6.2  100

Age at second suicide attempt (continuous [years], v1_age_sec_suic_att)

All individuals that did not attempt suicide, or attemted suicide only once are coded as -999

v1_age_sec_suic_att<-ifelse(v1_suic_attmpt>2 & v1_scid_no_suic_attmpt>1,c(v1_clin$v1_snx_111_suizvrs2_s_snx_alter_jahre_31171_2,rep(-999,dim(v1_con)[1])),-999)
descT(v1_age_sec_suic_att)

##                -999 8   12  13  14  15  16  17  18  19  20  21  22  23  24  25 
## [1,] No. cases 1527 1   2   1   3   1   4   1   5   2   6   1   5   7   4   7  
## [2,] Percent   85.5 0.1 0.1 0.1 0.2 0.1 0.2 0.1 0.3 0.1 0.3 0.1 0.3 0.4 0.2 0.4
##      26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43 
## [1,] 6   11  9   2   7   2   5   3   3   4   3   2   2   1   6   1   3   1  
## [2,] 0.3 0.6 0.5 0.1 0.4 0.1 0.3 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.3 0.1 0.2 0.1
##      45  46  47  48  49  50  52  54  57  58  63  64  <NA>     
## [1,] 3   3   2   1   2   1   3   1   2   1   1   1   117  1786
## [2,] 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 6.6  100

Age at third suicide attempt (continuous [years], v1_age_thr_suic_att)

All individuals that did not attempt suicide, or attemted suicide only once or twice are coded as -999

v1_age_thr_suic_att<-ifelse(v1_suic_attmpt>2 & v1_scid_no_suic_attmpt>2,c(v1_clin$v1_snx_111_suizvrs2_s_snx_alter_jahre_31171_3,rep(-999,dim(v1_con)[1])),-999)
descT(v1_age_thr_suic_att)

##                -999 13  16  17  19  21  22  23  24  26  27  28  29  30  31  32 
## [1,] No. cases 1612 3   4   2   2   2   1   2   4   3   2   2   2   3   2   2  
## [2,] Percent   90.3 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.1 0.1 0.2 0.1 0.1
##      34  35  36  38  40  41  42  44  46  47  49  50  52  54  56  59  62  <NA>
## [1,] 1   3   1   1   2   2   2   1   2   3   1   2   2   1   1   1   1   111 
## [2,] 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 6.2 
##          
## [1,] 1786
## [2,] 100

Create dataset

v1_scid<-data.frame(v1_scid_dsm_dx,
                    v1_scid_dsm_dx_cat,
                    v1_scid_age_MDE,
                    v1_scid_no_MDE,
                    v1_scid_age_mania,
                    v1_scid_no_mania,
                    v1_scid_age_hypomania,
                    v1_scid_no_hypomania,
                    v1_scid_ever_halls,
                    v1_scid_ever_delus,
                    v1_scid_ever_psyc,
                    v1_scid_age_fst_psyc,
                    v1_scid_yr_fst_psyc,
                    v1_scid_evr_suic_ide,
                    v1_scid_suic_ide,
                    v1_scid_suic_thght_mth,
                    v1_scid_suic_note_thgts,
                    v1_suic_attmpt,
                    v1_scid_no_suic_attmpt,
                    v1_prep_suic_attp_ord,
                    v1_suic_note_attmpt,
                    v1_age_fst_suic_att,
                    v1_age_sec_suic_att,
                    v1_age_thr_suic_att)

Visit 1: Symptom rating scales (interviewer rates individual)

Positive and Negative Syndrome Scale (PANSS)

The PANSS (Kay, Fiszbein, & Opler, 1987) is a rating scale measuring positive and negative symptoms in schizophrenia. It has three subscales: positive, negative and gereral psychopathology symptoms. Each item is rated on an ordinal scale from one to seven with the following gradation: “absent”-1, “minimal”-2, “mild”-3, “moderate”-4, “moderate severe”-5, “severe”-6, “extreme”-7. On all items, higher scores mean more severe symptoms. The ratings refer to the past seven days. Please find the items below.

Positive subscale

P1 Delusions (ordinal [1,2,3,4,5,6,7], v1_panss_p1)

v1_panss_p1<-c(v1_clin$v1_panss_p_p1_wahnideen,v1_con$v1_panss_p_p1_wahnideen)
v1_panss_p1<-factor(v1_panss_p1, ordered=T)
descT(v1_panss_p1)

##                1    2   3   4   5   6  <NA>     
## [1,] No. cases 1112 108 150 118 63  54 181  1786
## [2,] Percent   62.3 6   8.4 6.6 3.5 3  10.1 100

P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v1_panss_p2)

v1_panss_p2<-c(v1_clin$v1_panss_p_p2_form_denkst,v1_con$v1_panss_p_p2_form_denkst)
v1_panss_p2<-factor(v1_panss_p2, ordered=T)

descT(v1_panss_p2)

##                1    2    3    4   5   6   7   <NA>     
## [1,] No. cases 1019 186  225  117 51  8   1   179  1786
## [2,] Percent   57.1 10.4 12.6 6.6 2.9 0.4 0.1 10   100

P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v1_panss_p3)

v1_panss_p3<-c(v1_clin$v1_panss_p_p3_halluz,v1_con$v1_panss_p_p3_halluz)
v1_panss_p3<-factor(v1_panss_p3, ordered=T)

descT(v1_panss_p3)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 1336 81  69  59  46  15  1   179  1786
## [2,] Percent   74.8 4.5 3.9 3.3 2.6 0.8 0.1 10   100

P4 Excitement (ordinal [1,2,3,4,5,6,7], v1_panss_p4)

v1_panss_p4<-c(v1_clin$v1_panss_p_p4_erregung,v1_con$v1_panss_p_p4_erregung)
v1_panss_p4<-factor(v1_panss_p4, ordered=T)

descT(v1_panss_p4)

##                1    2   3    4   5  6   <NA>     
## [1,] No. cases 1095 160 269  60  17 3   182  1786
## [2,] Percent   61.3 9   15.1 3.4 1  0.2 10.2 100

P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v1_panss_p5)

v1_panss_p5<-c(v1_clin$v1_panss_p_p5_groessenideen,v1_con$v1_panss_p_p5_groessenideen)
v1_panss_p5<-factor(v1_panss_p5, ordered=T)

descT(v1_panss_p5)

##                1    2   3   4  5   6   <NA>     
## [1,] No. cases 1348 104 91  36 19  6   182  1786
## [2,] Percent   75.5 5.8 5.1 2  1.1 0.3 10.2 100

P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v1_panss_p6)

v1_panss_p6<-c(v1_clin$v1_panss_p_p6_misstr_verfolg,v1_con$v1_panss_p_p6_misstr_verfolg)
v1_panss_p6<-factor(v1_panss_p6, ordered=T)

descT(v1_panss_p6)

##                1    2   3    4   5   6   7   <NA>     
## [1,] No. cases 1147 157 192  61  34  13  1   181  1786
## [2,] Percent   64.2 8.8 10.8 3.4 1.9 0.7 0.1 10.1 100

P7 Hostility (ordinal [1,2,3,4,5,6,7], v1_panss_p7)

v1_panss_p7<-c(v1_clin$v1_panss_p_p7_feindseligkeit,v1_con$v1_panss_p_p7_feindseligkeit)
v1_panss_p7<-factor(v1_panss_p7, ordered=T)

descT(v1_panss_p7)

##                1    2   3   4  5   <NA>     
## [1,] No. cases 1400 104 78  18 4   182  1786
## [2,] Percent   78.4 5.8 4.4 1  0.2 10.2 100

PANSS Positive sum score (continuous [7-49], v1_panss_sum_pos)

v1_panss_sum_pos<-as.numeric.factor(v1_panss_p1)+
                  as.numeric.factor(v1_panss_p2)+
                  as.numeric.factor(v1_panss_p3)+
                  as.numeric.factor(v1_panss_p4)+
                  as.numeric.factor(v1_panss_p5)+
                  as.numeric.factor(v1_panss_p6)+
                  as.numeric.factor(v1_panss_p7)

summary(v1_panss_sum_pos)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    8.00   10.67   13.00   35.00     188

Negative subscale

N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v1_panss_n1)

v1_panss_n1<-c(v1_clin$v1_panss_n_n1_affektverflachung,v1_con$v1_panss_n_n1_affektverflachung)
v1_panss_n1<-factor(v1_panss_n1, ordered=T)

descT(v1_panss_n1)

##                1    2    3    4   5   6  7   <NA>     
## [1,] No. cases 896  188  229  156 115 18 1   183  1786
## [2,] Percent   50.2 10.5 12.8 8.7 6.4 1  0.1 10.2 100

N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v1_panss_n2)

v1_panss_n2<-c(v1_clin$v1_panss_n_n2_emot_rueckzug,v1_con$v1_panss_n_n2_emot_rueckzug)
v1_panss_n2<-factor(v1_panss_n2, ordered=T)

descT(v1_panss_n2)

##                1    2   3    4    5   6   7   <NA>     
## [1,] No. cases 973  179 209  192  37  10  1   185  1786
## [2,] Percent   54.5 10  11.7 10.8 2.1 0.6 0.1 10.4 100

N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v1_panss_n3)

v1_panss_n3<-c(v1_clin$v1_panss_n_n3_mang_aff_rapp,v1_con$v1_panss_n_n3_mang_aff_rapp)
v1_panss_n3<-factor(v1_panss_n3, ordered=T)

descT(v1_panss_n3)

##                1    2    3    4  5   6   <NA>     
## [1,] No. cases 1089 183  210  89 28  4   183  1786
## [2,] Percent   61   10.2 11.8 5  1.6 0.2 10.2 100

N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v1_panss_n4)

v1_panss_n4<-c(v1_clin$v1_panss_n_n4_soz_pass_apath,v1_con$v1_panss_n_n4_soz_pass_apath)
v1_panss_n4<-factor(v1_panss_n4, ordered=T)

descT(v1_panss_n4)

##                1    2   3    4   5   6   <NA>     
## [1,] No. cases 995  149 274  122 51  9   186  1786
## [2,] Percent   55.7 8.3 15.3 6.8 2.9 0.5 10.4 100

N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v1_panss_n5)

v1_panss_n5<-c(v1_clin$v1_panss_n_n5_abstr_denken,v1_con$v1_panss_n_n5_abstr_denken)
v1_panss_n5<-factor(v1_panss_n5, ordered=T)

descT(v1_panss_n5)

##                1    2    3    4   5   6   7   <NA>     
## [1,] No. cases 990  181  261  102 39  16  1   196  1786
## [2,] Percent   55.4 10.1 14.6 5.7 2.2 0.9 0.1 11   100

N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v1_panss_n6)

v1_panss_n6<-c(v1_clin$v1_panss_n_n6_spon_fl_sprache,v1_con$v1_panss_n_n6_spon_fl_sprache)
v1_panss_n6<-factor(v1_panss_n6, ordered=T)

descT(v1_panss_n6)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1194 144 149 78  34  4   183  1786
## [2,] Percent   66.9 8.1 8.3 4.4 1.9 0.2 10.2 100

N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v1_panss_n7)

v1_panss_n7<-c(v1_clin$v1_panss_n_n7_stereotyp_ged,v1_con$v1_panss_n_n7_stereotyp_ged)
v1_panss_n7<-factor(v1_panss_n7, ordered=T)

descT(v1_panss_n7)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1228 152 164 43  11  5   183  1786
## [2,] Percent   68.8 8.5 9.2 2.4 0.6 0.3 10.2 100

PANSS Negative sum score (continuous [7-49], v1_panss_sum_neg)

v1_panss_sum_neg<-as.numeric.factor(v1_panss_n1)+
                  as.numeric.factor(v1_panss_n2)+
                  as.numeric.factor(v1_panss_n3)+
                  as.numeric.factor(v1_panss_n4)+
                  as.numeric.factor(v1_panss_n5)+
                  as.numeric.factor(v1_panss_n6)+
                  as.numeric.factor(v1_panss_n7)

summary(v1_panss_sum_neg)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00   10.00   12.09   16.00   38.00     210

General psychopathology subscale

G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v1_panss_g1)

v1_panss_g1<-c(v1_clin$v1_panss_g_g1_sorge_gesundh,v1_con$v1_panss_g_g1_sorge_gesundh)
v1_panss_g1<-factor(v1_panss_g1, ordered=T)

descT(v1_panss_g1)

##                1    2   3    4   5  6   7   <NA>     
## [1,] No. cases 1083 197 192  87  35 9   3   180  1786
## [2,] Percent   60.6 11  10.8 4.9 2  0.5 0.2 10.1 100

G2 Anxiety (ordinal [1,2,3,4,5,6,7], v1_panss_g2)

v1_panss_g2<-c(v1_clin$v1_panss_g_g2_angst,v1_con$v1_panss_g_g2_angst)
v1_panss_g2<-factor(v1_panss_g2, ordered=T)

descT(v1_panss_g2)

##                1   2   3    4   5   6   7   <NA>     
## [1,] No. cases 946 160 310  135 48  6   1   180  1786
## [2,] Percent   53  9   17.4 7.6 2.7 0.3 0.1 10.1 100

G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v1_panss_g3)

v1_panss_g3<-c(v1_clin$v1_panss_g_g3_schuldgefuehle,v1_con$v1_panss_g_g3_schuldgefuehle)
v1_panss_g3<-factor(v1_panss_g3, ordered=T)

descT(v1_panss_g3)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 1076 176 196 123 30  5   1   179  1786
## [2,] Percent   60.2 9.9 11  6.9 1.7 0.3 0.1 10   100

G4 Tension (ordinal [1,2,3,4,5,6,7], v1_panss_g4)

v1_panss_g4<-c(v1_clin$v1_panss_g_g4_anspannung,v1_con$v1_panss_g_g4_anspannung)
v1_panss_g4<-factor(v1_panss_g4, ordered=T)

descT(v1_panss_g4)

##                1   2    3    4   5   6   <NA>     
## [1,] No. cases 929 241  288  117 23  6   182  1786
## [2,] Percent   52  13.5 16.1 6.6 1.3 0.3 10.2 100

G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v1_panss_g5)

v1_panss_g5<-c(v1_clin$v1_panss_g_g5_manier_koerperh,v1_con$v1_panss_g_g5_manier_koerperh)
v1_panss_g5<-factor(v1_panss_g5, ordered=T)

descT(v1_panss_g5)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 1375 115 80  20  6   8   1   181  1786
## [2,] Percent   77   6.4 4.5 1.1 0.3 0.4 0.1 10.1 100

G6 Depression (ordinal [1,2,3,4,5,6,7], v1_panss_g6)

v1_panss_g6<-c(v1_clin$v1_panss_g_g6_depression,v1_con$v1_panss_g_g6_depression)
v1_panss_g6<-factor(v1_panss_g6, ordered=T)

descT(v1_panss_g6)

##                1   2   3    4    5   6   7   <NA>     
## [1,] No. cases 821 169 282  199  116 16  4   179  1786
## [2,] Percent   46  9.5 15.8 11.1 6.5 0.9 0.2 10   100

G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v1_panss_g7)

v1_panss_g7<-c(v1_clin$v1_panss_g_g7_mot_verlangs,v1_con$v1_panss_g_g7_mot_verlangs)
v1_panss_g7<-factor(v1_panss_g7, ordered=T)

descT(v1_panss_g7)

##                1    2    3   4   5   6   <NA>     
## [1,] No. cases 1016 187  268 107 23  5   180  1786
## [2,] Percent   56.9 10.5 15  6   1.3 0.3 10.1 100

G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v1_panss_g8)

v1_panss_g8<-c(v1_clin$v1_panss_g_g8_unkoop_verh,v1_con$v1_panss_g_g8_unkoop_verh)
v1_panss_g8<-factor(v1_panss_g8, ordered=T)

descT(v1_panss_g8)

##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 1456 77  54 13  3   2   181  1786
## [2,] Percent   81.5 4.3 3  0.7 0.2 0.1 10.1 100

G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v1_panss_g9)

v1_panss_g9<-c(v1_clin$v1_panss_g_g9_ungew_denkinh,v1_con$v1_panss_g_g9_ungew_denkinh)
v1_panss_g9<-factor(v1_panss_g9, ordered=T)

descT(v1_panss_g9)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 1189 101 175 81  47  12  1   180  1786
## [2,] Percent   66.6 5.7 9.8 4.5 2.6 0.7 0.1 10.1 100

G10 Disorientation (ordinal [1,2,3,4,5,6,7], v1_panss_g10)

v1_panss_g10<-c(v1_clin$v1_panss_g_g10_desorient,v1_con$v1_panss_g_g10_desorient)
v1_panss_g10<-factor(v1_panss_g10, ordered=T)

descT(v1_panss_g10)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1421 108 67  4   4   2   180  1786
## [2,] Percent   79.6 6   3.8 0.2 0.2 0.1 10.1 100

G11 Poor attention (ordinal [1,2,3,4,5,6,7], v1_panss_g11)

v1_panss_g11<-c(v1_clin$v1_panss_g_g11_mang_aufmerks,v1_con$v1_panss_g_g11_mang_aufmerks)
v1_panss_g11<-factor(v1_panss_g11, ordered=T)

descT(v1_panss_g11)

##                1    2    3    4   5   6   <NA>     
## [1,] No. cases 889  185  360  134 28  3   187  1786
## [2,] Percent   49.8 10.4 20.2 7.5 1.6 0.2 10.5 100

G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v1_panss_g12)

v1_panss_g12<-c(v1_clin$v1_panss_g_g12_mang_urt_einsi,v1_con$v1_panss_g_g12_mang_urt_einsi)
v1_panss_g12<-factor(v1_panss_g12, ordered=T)

descT(v1_panss_g12)

##                1    2   3   4   5  6   7   <NA>     
## [1,] No. cases 1259 137 110 74  17 8   1   180  1786
## [2,] Percent   70.5 7.7 6.2 4.1 1  0.4 0.1 10.1 100

G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v1_panss_g13)

v1_panss_g13<-c(v1_clin$v1_panss_g_g13_willensschwae,v1_con$v1_panss_g_g13_willensschwae)
v1_panss_g13<-factor(v1_panss_g13, ordered=T)

descT(v1_panss_g13)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 1317 118 124 42  2   183  1786
## [2,] Percent   73.7 6.6 6.9 2.4 0.1 10.2 100

G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v1_panss_g14)

v1_panss_g14<-c(v1_clin$v1_panss_g_g14_mang_impulsk,v1_con$v1_panss_g_g14_mang_impulsk)
v1_panss_g14<-factor(v1_panss_g14, ordered=T)

descT(v1_panss_g14)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1327 109 143 20  1   2   184  1786
## [2,] Percent   74.3 6.1 8   1.1 0.1 0.1 10.3 100

G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v1_panss_g15)

v1_panss_g15<-c(v1_clin$v1_panss_g_g15_selbstbezog,v1_con$v1_panss_g_g15_selbstbezog)
v1_panss_g15<-factor(v1_panss_g15, ordered=T)

descT(v1_panss_g15)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1318 127 110 42  8   1   180  1786
## [2,] Percent   73.8 7.1 6.2 2.4 0.4 0.1 10.1 100

G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v1_panss_g16)

v1_panss_g16<-c(v1_clin$v1_panss_g_g16_aktsoz_vermeid,v1_con$v1_panss_g_g16_aktsoz_vermeid)
v1_panss_g16<-factor(v1_panss_g16, ordered=T)

descT(v1_panss_g16)

##                1    2   3    4   5   6   7   <NA>     
## [1,] No. cases 1108 156 224  75  34  6   1   182  1786
## [2,] Percent   62   8.7 12.5 4.2 1.9 0.3 0.1 10.2 100

PANSS General Psychopathology sum score (continuous [16-112], v1_panss_sum_gen)

v1_panss_sum_gen<-as.numeric.factor(v1_panss_g1)+
                  as.numeric.factor(v1_panss_g2)+
                  as.numeric.factor(v1_panss_g3)+
                  as.numeric.factor(v1_panss_g4)+
                  as.numeric.factor(v1_panss_g5)+
                  as.numeric.factor(v1_panss_g6)+
                  as.numeric.factor(v1_panss_g7)+
                  as.numeric.factor(v1_panss_g8)+
                  as.numeric.factor(v1_panss_g9)+
                  as.numeric.factor(v1_panss_g10)+
                  as.numeric.factor(v1_panss_g11)+
                  as.numeric.factor(v1_panss_g12)+
                  as.numeric.factor(v1_panss_g13)+
                  as.numeric.factor(v1_panss_g14)+
                  as.numeric.factor(v1_panss_g15)+
                  as.numeric.factor(v1_panss_g16)

summary(v1_panss_sum_gen)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.00   17.00   22.00   24.99   31.00   74.00     210

Create PANSS Total score (continuous [30-210], v1_panss_sum_tot)

v1_panss_sum_tot<-v1_panss_sum_pos+v1_panss_sum_neg+v1_panss_sum_gen
summary(v1_panss_sum_tot)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   30.00   32.00   42.00   47.72   58.00  141.00     245

Create dataset

v1_symp_panss<-data.frame(v1_panss_p1,v1_panss_p2,v1_panss_p3,v1_panss_p4,v1_panss_p5,v1_panss_p6,v1_panss_p7,
                          v1_panss_n1,v1_panss_n2,v1_panss_n3,v1_panss_n4,v1_panss_n5,v1_panss_n6,v1_panss_n7,
                          v1_panss_g1,v1_panss_g2,v1_panss_g3,v1_panss_g4,v1_panss_g5,v1_panss_g6,v1_panss_g7,
                          v1_panss_g8,v1_panss_g9,v1_panss_g10,v1_panss_g11,v1_panss_g12,v1_panss_g13,v1_panss_g14,
                          v1_panss_g15,v1_panss_g16,v1_panss_sum_pos,v1_panss_sum_neg,v1_panss_sum_gen,
                          v1_panss_sum_tot)

Inventory of depressive symptomatology (IDS-C₃₀)

The IDS-C₃₀ is is a 30-item rating scale used to assess the severity of depressive symptoms. Each item is rated on an ordinal scale from zero to three with zero indicating absence of the respective symptom. One item, #9, has additional information. The ratings refer to the past seven days. On all items, higher scores indicate more severe symptoms. Please find the items below.

Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v1_idsc_itm1)

v1_idsc_itm1<-c(v1_clin$v1_ids_c_s1_ids1_einschlafschw,v1_con$v1_ids_c_s1_ids1_einschlafschw)
v1_idsc_itm1<-factor(v1_idsc_itm1, ordered=T)

descT(v1_idsc_itm1)

##                0    1    2   3   <NA>     
## [1,] No. cases 1110 248  124 113 191  1786
## [2,] Percent   62.2 13.9 6.9 6.3 10.7 100

Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v1_idsc_itm2)

v1_idsc_itm2<-c(v1_clin$v1_ids_c_s1_ids2_naechtl_aufw,v1_con$v1_ids_c_s1_ids2_naechtl_aufw)
v1_idsc_itm2<-factor(v1_idsc_itm2, ordered=T)

descT(v1_idsc_itm2)

##                0   1    2    3   <NA>     
## [1,] No. cases 982 253  200  159 192  1786
## [2,] Percent   55  14.2 11.2 8.9 10.8 100

Item 3 Early morning insomnia (ordinal [0,1,2,3], v1_idsc_itm3)

v1_idsc_itm3<-c(v1_clin$v1_ids_c_s1_ids3_frueh_aufw,v1_con$v1_ids_c_s1_ids3_frueh_aufw)
v1_idsc_itm3<-factor(v1_idsc_itm3, ordered=T)

descT(v1_idsc_itm3)

##                0    1   2   3   <NA>     
## [1,] No. cases 1262 123 108 97  196  1786
## [2,] Percent   70.7 6.9 6   5.4 11   100

Item 4 Hypersomnia (ordinal [0,1,2,3], v1_idsc_itm4)

v1_idsc_itm4<-c(v1_clin$v1_ids_c_s1_ids4_hypersomnie,v1_con$v1_ids_c_s1_ids4_hypersomnie)
v1_idsc_itm4<-factor(v1_idsc_itm4, ordered=T)

descT(v1_idsc_itm4)

##                0    1    2   3   <NA>     
## [1,] No. cases 1077 329  155 31  194  1786
## [2,] Percent   60.3 18.4 8.7 1.7 10.9 100

Item 5 Mood (sad) (ordinal [0,1,2,3], v1_idsc_itm5)

v1_idsc_itm5<-c(v1_clin$v1_ids_c_s1_ids5_stimmung_trgk,v1_con$v1_ids_c_s1_ids5_stimmung_trgk)
v1_idsc_itm5<-factor(v1_idsc_itm5, ordered=T)

descT(v1_idsc_itm5)

##                0    1    2   3   <NA>     
## [1,] No. cases 916  436  149 92  193  1786
## [2,] Percent   51.3 24.4 8.3 5.2 10.8 100

Item 6 Mood (irritable) (ordinal [0,1,2,3], v1_idsc_itm6)

v1_idsc_itm6<-c(v1_clin$v1_ids_c_s1_ids6_stimmung_grzt,v1_con$v1_ids_c_s1_ids6_stimmung_grzt)
v1_idsc_itm6<-factor(v1_idsc_itm6, ordered=T)

descT(v1_idsc_itm6)

##                0    1    2   3  <NA>     
## [1,] No. cases 1172 326  60  36 192  1786
## [2,] Percent   65.6 18.3 3.4 2  10.8 100

Item 7 Mood (anxious) (ordinal [0,1,2,3], v1_idsc_itm7)

v1_idsc_itm7<-c(v1_clin$v1_ids_c_s1_ids7_stimmung_agst,v1_con$v1_ids_c_s1_ids7_stimmung_agst)
v1_idsc_itm7<-factor(v1_idsc_itm7, ordered=T)

descT(v1_idsc_itm7)

##                0    1    2   3   <NA>     
## [1,] No. cases 1021 349  143 78  195  1786
## [2,] Percent   57.2 19.5 8   4.4 10.9 100

Item 8 Reactivity of mood (ordinal [0,1,2,3], v1_idsc_itm8)

v1_idsc_itm8<-c(v1_clin$v1_ids_c_s1_ids8_reakt_stimmung,v1_con$v1_ids_c_s1_ids8_reakt_stimmung)
v1_idsc_itm8<-factor(v1_idsc_itm8, ordered=T)

descT(v1_idsc_itm8)

##                0    1    2   3   <NA>     
## [1,] No. cases 1259 207  80  47  193  1786
## [2,] Percent   70.5 11.6 4.5 2.6 10.8 100

Item 9 Mood Variation (ordinal [0,1,2,3], v1_idsc_itm9)

v1_idsc_itm9<-c(v1_clin$v1_ids_c_s1_ids9_stimmungsschw,v1_con$v1_ids_c_s1_ids9_stimmungsschw)
v1_idsc_itm9<-factor(v1_idsc_itm9, ordered=T)

descT(v1_idsc_itm9)

##                0    1   2   3   <NA>     
## [1,] No. cases 1190 150 88  160 198  1786
## [2,] Percent   66.6 8.4 4.9 9   11.1 100

Item 9A (categorical [M, A, N], v1_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).

v1_idsc_itm9a_pre<-c(v1_clin$v1_ids_c_s1_ids9a_stimmungsschw,v1_con$v1_ids_c_s1_ids9a_stimmungsschw)

v1_idsc_itm9a<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==1, "M", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==2, "A", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==3, "N", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-factor(v1_idsc_itm9a, ordered=F)

descT(v1_idsc_itm9a)

##                -999 A   M   N   <NA>     
## [1,] No. cases 1190 19  169 75  333  1786
## [2,] Percent   66.6 1.1 9.5 4.2 18.6 100

Item 9B (dichotomous, v1_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.

v1_idsc_itm9b_pre<-c(v1_clin$v1_ids_c_s1_ids9b_stimmungsschw,v1_con$v1_ids_c_s1_ids9b_stimmungsschw)

v1_idsc_itm9b<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm9b<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9b_pre==0, "N", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9b))
v1_idsc_itm9b<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9b_pre==1, "Y", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9b))
v1_idsc_itm9b<-factor(v1_idsc_itm9b, ordered=F)

descT(v1_idsc_itm9b)

##                -999 N   Y   <NA>     
## [1,] No. cases 1190 117 98  381  1786
## [2,] Percent   66.6 6.6 5.5 21.3 100

Item 10 Quality of mood (ordinal [0,1,2,3], v1_idsc_itm10)

v1_idsc_itm10<-c(v1_clin$v1_ids_c_s1_ids10_quali_stimmung,v1_con$v1_ids_c_s1_ids10_quali_stimmung)
v1_idsc_itm10<-factor(v1_idsc_itm10, ordered=T)

descT(v1_idsc_itm10)

##                0    1   2   3   <NA>     
## [1,] No. cases 1246 119 64  145 212  1786
## [2,] Percent   69.8 6.7 3.6 8.1 11.9 100

Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.

The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.

Item 11 (ordinal [0,1,2,3], v1_idsc_itm11)

v1_idsc_app_verm<-c(v1_clin$v1_ids_c_s2_ids11_appetit_verm,v1_con$v1_ids_c_s2_ids11_appetit_verm)
v1_idsc_app_gest<-c(v1_clin$v1_ids_c_s2_ids12_appetit_steig,v1_con$v1_ids_c_s2_ids12_appetit_steig)

v1_idsc_itm11<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm11<-ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==F, -999,                
                  ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==T,          
                         v1_idsc_app_verm, 
                     ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_verm>v1_idsc_app_gest), v1_idsc_app_verm,
                     ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_gest>=v1_idsc_app_verm),-999,v1_idsc_itm11)))))                                                   

#Important: do not code as factor, see after calculation of sum score!
descT(v1_idsc_itm11)

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 457  949  146 27  12  195  1786
## [2,] Percent   25.6 53.1 8.2 1.5 0.7 10.9 100

Item 12 (ordinal [0,1,2,3], v1_idsc_itm12)

v1_idsc_itm12<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm12<-ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==F,    
                         v1_idsc_app_gest,                
                  ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==T,          
                         -999, 
                     ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_verm>v1_idsc_app_gest), -999,   
                     ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_gest>=v1_idsc_app_verm), v1_idsc_app_gest,v1_idsc_itm12)))))                                                       
#Important: do not code as factor, see after calculation of sum score!

descT(v1_idsc_itm12)

##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1134 146 186  78  47  195  1786
## [2,] Percent   63.5 8.2 10.4 4.4 2.6 10.9 100

Item 13 (ordinal [0,1,2,3], v1_idsc_itm13)

v1_idsc_gew_abn<-c(v1_clin$v1_ids_c_s2_ids13_gewichtsabn,v1_con$v1_ids_c_s2_ids13_gewichtsabn)
v1_idsc_gew_zun<-c(v1_clin$v1_ids_c_s2_ids14_gewichtszun,v1_con$v1_ids_c_s2_ids14_gewichtszun)

v1_idsc_itm13<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm13<-ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==F, -999,                
                  ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==T, v1_idsc_gew_abn,          
                      ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_abn>v1_idsc_gew_zun), v1_idsc_gew_abn,                                                     ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_zun >= v1_idsc_gew_abn),-999,v1_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v1_idsc_itm13)

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 493  893 75  79  44  202  1786
## [2,] Percent   27.6 50  4.2 4.4 2.5 11.3 100

Item 14 (ordinal [0,1,2,3], v1_idsc_itm14)

v1_idsc_itm14<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm14<-ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==F,    
                         v1_idsc_gew_zun,                
                  ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==T, -999, 
                    ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_abn>v1_idsc_gew_zun), -999,                                            
                    ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_zun>=v1_idsc_gew_abn), v1_idsc_gew_zun,v1_idsc_itm14)))))                                                       
#Important: do not code as factor, see after calculation of sum score!

descT(v1_idsc_itm14)

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1091 202  116 101 74  202  1786
## [2,] Percent   61.1 11.3 6.5 5.7 4.1 11.3 100

Item 15 Concentration/decision making (ordinal [0,1,2,3], v1_idsc_itm15)

v1_idsc_itm15<-c(v1_clin$v1_ids_c_s2_ids15_konz_entscheid,v1_con$v1_ids_c_s2_ids15_konz_entscheid)
v1_idsc_itm15<-factor(v1_idsc_itm15, ordered=T)

descT(v1_idsc_itm15)

##                0    1    2    3   <NA>     
## [1,] No. cases 850  378  290  67  201  1786
## [2,] Percent   47.6 21.2 16.2 3.8 11.3 100

Item 16 Outlook (self) (ordinal [0,1,2,3], v1_idsc_itm16)

v1_idsc_itm16<-c(v1_clin$v1_ids_c_s2_ids16_selbstbild,v1_con$v1_ids_c_s2_ids16_selbstbild)
v1_idsc_itm16<-factor(v1_idsc_itm16, ordered=T)

descT(v1_idsc_itm16)

##                0    1    2   3   <NA>     
## [1,] No. cases 1137 241  105 109 194  1786
## [2,] Percent   63.7 13.5 5.9 6.1 10.9 100

Item 17 Outlook (future) (ordinal [0,1,2,3], v1_idsc_itm17)

v1_idsc_itm17<-c(v1_clin$v1_ids_c_s2_ids17_zukunftssicht,v1_con$v1_ids_c_s2_ids17_zukunftssicht)
v1_idsc_itm17<-factor(v1_idsc_itm17, ordered=T)

descT(v1_idsc_itm17)

##                0    1    2   3   <NA>     
## [1,] No. cases 998  407  155 30  196  1786
## [2,] Percent   55.9 22.8 8.7 1.7 11   100

Item 18 Suicidal ideation (ordinal [0,1,2,3], v1_idsc_itm18)

v1_idsc_itm18<-c(v1_clin$v1_ids_c_s2_ids18_selbstmordged,v1_con$v1_ids_c_s2_ids18_selbstmordged)
v1_idsc_itm18<-factor(v1_idsc_itm18, ordered=T)

descT(v1_idsc_itm18)

##                0    1   2   3   <NA>     
## [1,] No. cases 1423 93  70  9   191  1786
## [2,] Percent   79.7 5.2 3.9 0.5 10.7 100

Item 19 Involvement (ordinal [0,1,2,3], v1_idsc_itm19)

v1_idsc_itm19<-c(v1_clin$v1_ids_c_s2_ids19_interess_aktiv,v1_con$v1_ids_c_s2_ids19_interess_aktiv)
v1_idsc_itm19<-factor(v1_idsc_itm19, ordered=T)

descT(v1_idsc_itm19)

##                0    1    2   3   <NA>     
## [1,] No. cases 1207 284  58  44  193  1786
## [2,] Percent   67.6 15.9 3.2 2.5 10.8 100

Item 20 Energy/fatigability (ordinal [0,1,2,3], v1_idsc_itm20)

v1_idsc_itm20<-c(v1_clin$v1_ids_c_s2_ids20_energ_ermued,v1_con$v1_ids_c_s2_ids20_energ_ermued)
v1_idsc_itm20<-factor(v1_idsc_itm20, ordered=T)

descT(v1_idsc_itm20)

##                0    1    2    3   <NA>     
## [1,] No. cases 961  404  185  44  192  1786
## [2,] Percent   53.8 22.6 10.4 2.5 10.8 100

Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v1_idsc_itm21)

v1_idsc_itm21<-c(v1_clin$v1_ids_c_s3_ids21_vergn_genuss,v1_con$v1_ids_c_s3_ids21_vergn_genuss)
v1_idsc_itm21<-factor(v1_idsc_itm21, ordered=T)

descT(v1_idsc_itm21)

##                0    1    2   3   <NA>     
## [1,] No. cases 1243 247  75  24  197  1786
## [2,] Percent   69.6 13.8 4.2 1.3 11   100

Item 22 Sexual interest (ordinal [0,1,2,3], v1_idsc_itm22)

v1_idsc_itm22<-c(v1_clin$v1_ids_c_s3_ids22_sex_interesse,v1_con$v1_ids_c_s3_ids22_sex_interesse)
v1_idsc_itm22<-factor(v1_idsc_itm22, ordered=T)

descT(v1_idsc_itm22)

##                0    1   2   3   <NA>     
## [1,] No. cases 1107 110 215 151 203  1786
## [2,] Percent   62   6.2 12  8.5 11.4 100

Item 23 Psychomotor slowing (ordinal [0,1,2,3], v1_idsc_itm23)

v1_idsc_itm23<-c(v1_clin$v1_ids_c_s3_ids23_psymo_hemm,v1_con$v1_ids_c_s3_ids23_psymo_hemm)
v1_idsc_itm23<-factor(v1_idsc_itm23, ordered=T)

descT(v1_idsc_itm23)

##                0    1    2   3   <NA>     
## [1,] No. cases 1210 309  63  8   196  1786
## [2,] Percent   67.7 17.3 3.5 0.4 11   100

Item 24 Psychomotor agitation (ordinal [0,1,2,3], v1_idsc_itm24)

v1_idsc_itm24<-c(v1_clin$v1_ids_c_s3_ids24_psymo_agitht,v1_con$v1_ids_c_s3_ids24_psymo_agitht)
v1_idsc_itm24<-factor(v1_idsc_itm24, ordered=T)

descT(v1_idsc_itm24)

##                0    1    2   3  <NA>     
## [1,] No. cases 1265 219  84  18 200  1786
## [2,] Percent   70.8 12.3 4.7 1  11.2 100

Item 25 Somatic complaints (ordinal [0,1,2,3], v1_idsc_itm25)

v1_idsc_itm25<-c(v1_clin$v1_ids_c_s3_ids25_som_beschw,v1_con$v1_ids_c_s3_ids25_som_beschw)
v1_idsc_itm25<-factor(v1_idsc_itm25, ordered=T)

descT(v1_idsc_itm25)

##                0    1    2   3   <NA>     
## [1,] No. cases 1117 361  81  29  198  1786
## [2,] Percent   62.5 20.2 4.5 1.6 11.1 100

Item 26 Sympathetic arousal (ordinal [0,1,2,3], v1_idsc_itm26)

v1_idsc_itm26<-c(v1_clin$v1_ids_c_s3_ids26_veg_erreg,v1_con$v1_ids_c_s3_ids26_veg_erreg)
v1_idsc_itm26<-factor(v1_idsc_itm26, ordered=T)

descT(v1_idsc_itm26)

##                0    1    2   3   <NA>     
## [1,] No. cases 1148 333  88  20  197  1786
## [2,] Percent   64.3 18.6 4.9 1.1 11   100

Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v1_idsc_itm27)

v1_idsc_itm27<-c(v1_clin$v1_ids_c_s3_ids27_panik_phob,v1_con$v1_ids_c_s3_ids27_panik_phob)
v1_idsc_itm27<-factor(v1_idsc_itm27, ordered=T)

descT(v1_idsc_itm27)

##                0    1   2   3   <NA>     
## [1,] No. cases 1354 157 55  24  196  1786
## [2,] Percent   75.8 8.8 3.1 1.3 11   100

Item 28 Gastrointestinal (ordinal [0,1,2,3], v1_idsc_itm28)

v1_idsc_itm28<-c(v1_clin$v1_ids_c_s3_ids28_verdauung,v1_con$v1_ids_c_s3_ids28_verdauung)
v1_idsc_itm28<-factor(v1_idsc_itm28, ordered=T)

descT(v1_idsc_itm28)

##                0    1   2   3   <NA>     
## [1,] No. cases 1316 160 78  34  198  1786
## [2,] Percent   73.7 9   4.4 1.9 11.1 100

Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v1_idsc_itm29)

v1_idsc_itm29<-c(v1_clin$v1_ids_c_s3_ids29_pers_bezieh,v1_con$v1_ids_c_s3_ids29_pers_bezieh)
v1_idsc_itm29<-factor(v1_idsc_itm29, ordered=T)

descT(v1_idsc_itm29)

##                0    1    2   3   <NA>     
## [1,] No. cases 1262 218  80  31  195  1786
## [2,] Percent   70.7 12.2 4.5 1.7 10.9 100

Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v1_idsc_itm30)

v1_idsc_itm30<-c(v1_clin$v1_ids_c_s3_ids30_schwgf_k_energ,v1_con$v1_ids_c_s3_ids30_schwgf_k_energ)
v1_idsc_itm30<-factor(v1_idsc_itm30, ordered=T)

descT(v1_idsc_itm30)

##                0    1    2   3  <NA>     
## [1,] No. cases 1247 216  87  35 201  1786
## [2,] Percent   69.8 12.1 4.9 2  11.3 100

Create IDS-C₃₀ total score (continuous [0-84], v1_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.

v1_idsc_sum<-as.numeric.factor(v1_idsc_itm1)+
             as.numeric.factor(v1_idsc_itm2)+
             as.numeric.factor(v1_idsc_itm3)+
             as.numeric.factor(v1_idsc_itm4)+
             as.numeric.factor(v1_idsc_itm5)+
             as.numeric.factor(v1_idsc_itm6)+
             as.numeric.factor(v1_idsc_itm7)+
             as.numeric.factor(v1_idsc_itm8)+
             as.numeric.factor(v1_idsc_itm9)+
             as.numeric.factor(v1_idsc_itm10)+
  
 ifelse(is.na(v1_idsc_itm11)==T & is.na(v1_idsc_itm12)==T, NA, 
        ifelse((v1_idsc_itm11==-999 & v1_idsc_itm12!=-999), v1_idsc_itm12,                
        ifelse((v1_idsc_itm11!=-999 & v1_idsc_itm12==-999),v1_idsc_itm11, NA)))+
  
   ifelse(is.na(v1_idsc_itm13)==T & is.na(v1_idsc_itm14)==T, NA, 
        ifelse((v1_idsc_itm13==-999 & v1_idsc_itm14!=-999), v1_idsc_itm14,                
        ifelse((v1_idsc_itm13!=-999 & v1_idsc_itm14==-999),v1_idsc_itm13, NA)))+
                                                  
             as.numeric.factor(v1_idsc_itm15)+
             as.numeric.factor(v1_idsc_itm16)+
             as.numeric.factor(v1_idsc_itm17)+
             as.numeric.factor(v1_idsc_itm18)+
             as.numeric.factor(v1_idsc_itm19)+
             as.numeric.factor(v1_idsc_itm20)+
             as.numeric.factor(v1_idsc_itm21)+
             as.numeric.factor(v1_idsc_itm22)+
             as.numeric.factor(v1_idsc_itm23)+
             as.numeric.factor(v1_idsc_itm24)+
             as.numeric.factor(v1_idsc_itm25)+
             as.numeric.factor(v1_idsc_itm26)+
             as.numeric.factor(v1_idsc_itm27)+
             as.numeric.factor(v1_idsc_itm28)+
             as.numeric.factor(v1_idsc_itm29)+
             as.numeric.factor(v1_idsc_itm30)

summary(v1_idsc_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0     3.0     8.0    11.9    18.0    63.0     302

Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)

v1_idsc_itm11<-factor(v1_idsc_itm11,ordered=T)
v1_idsc_itm12<-factor(v1_idsc_itm12,ordered=T)
v1_idsc_itm13<-factor(v1_idsc_itm13,ordered=T)
v1_idsc_itm14<-factor(v1_idsc_itm14,ordered=T)

Create dataset

v1_symp_ids_c<-data.frame(v1_idsc_itm1,v1_idsc_itm2,v1_idsc_itm3,v1_idsc_itm4,v1_idsc_itm5,v1_idsc_itm6,v1_idsc_itm7,
                          v1_idsc_itm8,v1_idsc_itm9,v1_idsc_itm9a,v1_idsc_itm9b,v1_idsc_itm10,v1_idsc_itm11,v1_idsc_itm12,
                          v1_idsc_itm13,v1_idsc_itm14,v1_idsc_itm15,v1_idsc_itm16,v1_idsc_itm17,v1_idsc_itm18,v1_idsc_itm19,
                          v1_idsc_itm20,v1_idsc_itm21,v1_idsc_itm22,v1_idsc_itm23,v1_idsc_itm24,v1_idsc_itm25,v1_idsc_itm26,
                          v1_idsc_itm27,v1_idsc_itm28,v1_idsc_itm29,v1_idsc_itm30,v1_idsc_sum)

Young Mania Rating Scale (YMRS)

The YMRS (Young, Biggs, Ziegler, & Meyer, 1978) is an 11-item rating scale used to assess the severity of mania symptoms. Each item is rated on an ordinal scale, either from zero to four or from zero to eight with zero indicating absence of the respective symptom. The ratings refer to the past fourty-eight hours. On all items, higher scores mean more severe symptoms. Please find the items below.

Item 1 Elevated mood (ordinal [0,1,2,3,4], v1_ymrs_itm1)

v1_ymrs_itm1<-c(v1_clin$v1_ymrs_ymrs1_gehob_stimm,v1_con$v1_ymrs_ymrs1_gehob_stimm)
v1_ymrs_itm1<-factor(v1_ymrs_itm1, ordered=T)

descT(v1_ymrs_itm1)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 1269 159 84  21  5   248  1786
## [2,] Percent   71.1 8.9 4.7 1.2 0.3 13.9 100

Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v1_ymrs_itm2)

v1_ymrs_itm2<-c(v1_clin$v1_ymrs_ymrs2_gest_aktiv,v1_con$v1_ymrs_ymrs2_gest_aktiv)
v1_ymrs_itm2<-factor(v1_ymrs_itm2, ordered=T)

descT(v1_ymrs_itm2)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 1304 137 67  27  3   248  1786
## [2,] Percent   73   7.7 3.8 1.5 0.2 13.9 100

Item 3 Sexual interest (ordinal [0,1,2,3,4], v1_ymrs_itm3)

v1_ymrs_itm3<-c(v1_clin$v1_ymrs_ymrs3_sex_interesse,v1_con$v1_ymrs_ymrs3_sex_interesse)
v1_ymrs_itm3<-factor(v1_ymrs_itm3, ordered=T)

descT(v1_ymrs_itm3)

##                0    1   2   3   <NA>     
## [1,] No. cases 1431 61  31  10  253  1786
## [2,] Percent   80.1 3.4 1.7 0.6 14.2 100

Item 4 Sleep (ordinal [0,1,2,3,4], v1_ymrs_itm4)

v1_ymrs_itm4<-c(v1_clin$v1_ymrs_ymrs4_schlaf,v1_con$v1_ymrs_ymrs4_schlaf)
v1_ymrs_itm4<-factor(v1_ymrs_itm4, ordered=T)

descT(v1_ymrs_itm4)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 1363 87  44  42  1   249  1786
## [2,] Percent   76.3 4.9 2.5 2.4 0.1 13.9 100

Item 5 Irritability (ordinal [0,2,4,6,8], v1_ymrs_itm5)

v1_ymrs_itm5<-c(v1_clin$v1_ymrs_ymrs5_reizbarkeit,v1_con$v1_ymrs_ymrs5_reizbarkeit)
v1_ymrs_itm5<-factor(v1_ymrs_itm5, ordered=T)

descT(v1_ymrs_itm5)

##                0    2    4   6   <NA>     
## [1,] No. cases 1299 193  44  3   247  1786
## [2,] Percent   72.7 10.8 2.5 0.2 13.8 100

Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v1_ymrs_itm6)

v1_ymrs_itm6<-c(v1_clin$v1_ymrs_ymrs6_sprechweise,v1_con$v1_ymrs_ymrs6_sprechweise)
v1_ymrs_itm6<-factor(v1_ymrs_itm6, ordered=T)

descT(v1_ymrs_itm6)

##                0    2   4   6   8   <NA>     
## [1,] No. cases 1299 113 84  38  3   249  1786
## [2,] Percent   72.7 6.3 4.7 2.1 0.2 13.9 100

Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v1_ymrs_itm7)

v1_ymrs_itm7<-c(v1_clin$v1_ymrs_ymrs7_sprachstoer,v1_con$v1_ymrs_ymrs7_sprachstoer)
v1_ymrs_itm7<-factor(v1_ymrs_itm7, ordered=T)

descT(v1_ymrs_itm7)

##                0    1   2   3   <NA>     
## [1,] No. cases 1305 162 61  9   249  1786
## [2,] Percent   73.1 9.1 3.4 0.5 13.9 100

Item 8 Content (ordinal [0,2,4,6,8], v1_ymrs_itm8)

v1_ymrs_itm8<-c(v1_clin$v1_ymrs_ymrs8_inhalte,v1_con$v1_ymrs_ymrs8_inhalte)
v1_ymrs_itm8<-factor(v1_ymrs_itm8, ordered=T)

descT(v1_ymrs_itm8)

##                0    2   4   6   8   <NA>     
## [1,] No. cases 1409 69  15  21  21  251  1786
## [2,] Percent   78.9 3.9 0.8 1.2 1.2 14.1 100

Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v1_ymrs_itm9)

v1_ymrs_itm9<-c(v1_clin$v1_ymrs_ymrs9_exp_aggr_verh,v1_con$v1_ymrs_ymrs9_exp_aggr_verh)
v1_ymrs_itm9<-factor(v1_ymrs_itm9, ordered=T)

descT(v1_ymrs_itm9)

##                0    2   4   <NA>     
## [1,] No. cases 1462 66  5   253  1786
## [2,] Percent   81.9 3.7 0.3 14.2 100

Item 10 Appearance (ordinal [0,1,2,3,4], v1_ymrs_itm10)

v1_ymrs_itm10<-c(v1_clin$v1_ymrs_ymrs10_erscheinung,v1_con$v1_ymrs_ymrs10_erscheinung)
v1_ymrs_itm10<-factor(v1_ymrs_itm10, ordered=T)

descT(v1_ymrs_itm10)

##                0    1   2   3   <NA>     
## [1,] No. cases 1381 129 23  3   250  1786
## [2,] Percent   77.3 7.2 1.3 0.2 14   100

Item 11 Insight (ordinal [0,1,2,3,4], v1_ymrs_itm11)

v1_ymrs_itm11<-c(v1_clin$v1_ymrs_ymrs11_krkh_einsicht,v1_con$v1_ymrs_ymrs11_krkh_einsicht)
v1_ymrs_itm11<-factor(v1_ymrs_itm11, ordered=T)

descT(v1_ymrs_itm11)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 1424 52  29  11  8   262  1786
## [2,] Percent   79.7 2.9 1.6 0.6 0.4 14.7 100

Create YMRS total score (continuous [0-60], v1_ymrs_sum)

v1_ymrs_sum<-(as.numeric.factor(v1_ymrs_itm1)+
        as.numeric.factor(v1_ymrs_itm2)+
        as.numeric.factor(v1_ymrs_itm3)+
        as.numeric.factor(v1_ymrs_itm4)+
        as.numeric.factor(v1_ymrs_itm5)+
        as.numeric.factor(v1_ymrs_itm6)+
        as.numeric.factor(v1_ymrs_itm7)+
        as.numeric.factor(v1_ymrs_itm8)+
        as.numeric.factor(v1_ymrs_itm9)+
        as.numeric.factor(v1_ymrs_itm10)+
        as.numeric.factor(v1_ymrs_itm11))

summary(v1_ymrs_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   2.554   3.000  39.000     280

Create dataset

v1_symp_ymrs<-data.frame(v1_ymrs_itm1,
                         v1_ymrs_itm2,
                         v1_ymrs_itm3,
                         v1_ymrs_itm4,
                         v1_ymrs_itm5,
                         v1_ymrs_itm6,
                         v1_ymrs_itm7,
                         v1_ymrs_itm8,
                         v1_ymrs_itm9,
                         v1_ymrs_itm10,
                         v1_ymrs_itm11,
                         v1_ymrs_sum)

Clinical Global Impression (CGI) - illness severity scale (ordinal [1,2,3,4,5,6,7], v1_cgi_s)

The CGI (see e.g. Busner & Targum, 2007) measures illness severity. The degree of impairment is to be quantified on a scale from zero to seven. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “normal, not at all ill”-1 to “extremely ill”-7. Please note that in all other study visits, the improvement scale (whether or not there was improvement compared to the last study visit) is also assessed. All control subjects also have -999 in this variable.

v1_cgi_s<-c(v1_clin$v1_cgi_cgi1_schweregrad,rep(-999,dim(v1_con)[1]))
v1_cgi_s[v1_cgi_s==0]<- -999
v1_cgi_s<-factor(v1_cgi_s, ordered=T)

descT(v1_cgi_s)

##                -999 1  2   3    4    5   6   7   <NA>     
## [1,] No. cases 468  17 56  313  383  447 73  5   24   1786
## [2,] Percent   26.2 1  3.1 17.5 21.4 25  4.1 0.3 1.3  100

Global Assessment of Functioning (GAF) (continuous [1-100], v1_gaf)

The GAF scale is a rating scale that is used to measure an individual’s psychosocial functioning. The GAF was initially developed by Luborsky (1962) as Health-Sickness Rating Scale, revised by Endicott et al. (1976) under the name GAS of which a modified version was included in the DSM-III-R and, with minimal changes, also in the DSM-IV as GAF scale (Axis V). The scale is continuous and ranges from one to 100. Values of zero indicate lack of information. Such values were therefore set to “-999”. The following rating instructions are given:

• “No symptoms. Superior functioning in a wide range of activities, life’s problems never seem to get out of hand, is sought out by others because of his or her many positive qualities.”: 91-100

• “Absent or minimal symptoms (e.g., mild anxiety before an exam), good functioning in all areas, interested and involved in a wide range of activities, socially effective, generally satisfied with life, no more than everyday problems or concerns.”: 81-90

• “If symptoms are present, they are transient and expectable reactions to psychosocial stressors (e.g., difficulty concentrating after family argument); no more than slight impairment in social, occupational, or school functioning (e.g., temporarily falling behind in schoolwork).”: 71-80,

• “Some mild symptoms (e.g., depressed mood and mild insomnia) or some difficulty in social, occupational, or school functioning (e.g., occasional truancy, or theft within the household), but generally functioning pretty well, has some meaningful interpersonal relationships.”: 61-70

• “Moderate symptoms (e.g., flat affect and circumlocutory speech, occasional panic attacks) or moderate difficulty in social, occupational, or school functioning (e.g., few friends, conflicts with peers or co-workers)”: 51-60

• “Serious symptoms (e.g., suicidal ideation, severe obsessional rituals, frequent shoplifting) or any serious impairment in social, occupational, or school functioning (e.g., no friends, unable to keep a job, cannot work).”: 41-50

• “Some impairment in reality testing or communication (e.g., speech is at times illogical, obscure, or irrelevant) or major impairment in several areas, such as work or school, family relations, judgment, thinking, or mood (e.g., depressed adult avoids friends, neglects family, and is unable to work; child frequently beats up younger children, is defiant at home, and is failing at school).”: 31-40

• “Behavior is considerably influenced by delusions or hallucinations or serious impairment, in communication or judgment (e.g., sometimes incoherent, acts grossly inappropriately, suicidal preoccupation) or inability to function in almost all areas (e.g., stays in bed all day, no job, home, or friends)”: 21-30

• “Some danger of hurting self or others (e.g., suicide attempts without clear expectation of death; frequently violent; manic excitement) or occasionally fails to maintain minimal personal hygiene (e.g., smears feces) or gross impairment in communication (e.g., largely incoherent or mute).”: 11-20

• “Persistent danger of severely hurting self or others (e.g., recurrent violence) or persistent inability to maintain minimal personal hygiene or serious suicidal act with clear expectation of death.”: 1-10

According to the Endicott et al. (1976), “[m]ost outpatients will be rated 31 to 70, and most inpatients between 1 and 40.”.

The scale is continuous but, in the opinion of most experienced raters, rather has ordinal scale level.

In the present study, many raters used values at the margin of each category, such as 50, 60, 70, 80, 90, or 100 to rate patients, which lead to pronounced spikes in the distribution of GAF values (see histogram below).

Manually set entries of three probands to NA, which cannot be done in the original phenotype database

Combine clinical and control individuals

v1_gaf<-c(v1_clin$v1_gaf_gaf_code,v1_con$v1_gaf_gaf_code)
v1_gaf[v1_gaf==0]<- -999

summary(v1_gaf[v1_gaf>0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   50.00   60.00   62.48   75.00  100.00     191

Histogram of GAF scores of both CLINICAL and CONTROL study participants

hist(v1_gaf[v1_gaf>0],breaks=c(0:100))

Boxplot of GAF scores of both CLINICAL and CONTROL study participants

boxplot(v1_gaf[v1_gaf>0 & v1_stat=="CLINICAL"], v1_gaf[v1_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))

Create dataset

v1_ill_sev<-data.frame(v1_cgi_s, v1_gaf)

Visit 1: Neuropsychology (cognitive tests)

During the first study visit, the following neuropsychological tests are completed by the participant: Trail-Making-Test (parts A and B), Digit-Symbol-Test (taken fron HAWIE-R), Verbal Digit-span (forward and backward; “Zahlennachsprechen”, also from HAWIE-R), Multiple-Choice Vocabulary Intelligence Test (MWT-B). All are paper-and-pencil tests and are briefly explained below.

Please note: We have now also included test results from only partially completed tests

General comments on the testing (character [free text], v1_nrpsy_com) If there were no comments, this item was coded -999.

Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v1_nrpsy_lng)

v1_nrpsy_lng_pre<-c(v1_clin$v1_npu_np_sprach,v1_con$v1_npu_np_sprach)
v1_nrpsy_lng<-ifelse(v1_nrpsy_lng_pre==0, "mother tongue", 
                  ifelse(v1_nrpsy_lng_pre==1, "good",
                      ifelse(v1_nrpsy_lng_pre==2, "sufficient", 
                          ifelse(v1_nrpsy_lng_pre==3, "not sufficient", NA))))

v1_nrpsy_lng<-factor(v1_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))

descT(v1_nrpsy_lng)

##                mother tongue good sufficient not sufficient <NA>     
## [1,] No. cases 1556          130  33         3              64   1786
## [2,] Percent   87.1          7.3  1.8        0.2            3.6  100

Motivation of the participant (ordinal [“poor”,“average”,“good”], v1_nrpsy_mtv)

v1_nrpsy_mtv_pre<-c(v1_clin$v1_npu_np_mot,v1_con$v1_npu_np_mot)

v1_nrpsy_mtv<-ifelse(v1_nrpsy_mtv_pre==0, "poor", 
                  ifelse(v1_nrpsy_mtv_pre==1, "average", 
                      ifelse(v1_nrpsy_mtv_pre==2, "good", NA)))

v1_nrpsy_mtv<-factor(v1_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))

descT(v1_nrpsy_mtv)

##                poor average good <NA>     
## [1,] No. cases 27   145     1405 209  1786
## [2,] Percent   1.5  8.1     78.7 11.7 100

Executive function: Trail-Making-Test (TMT)

Using a pen, the participant is required to connect digits in increasing order (“1-2-3-4…”; part A) or connect digits and symbols alternately (“1-A-2-B-3-C…”; part B). The time taken to complete each part of the test is measured. While part A assesses psychomotor speed of the participant, part B assesses switching between two automated tasks (counting and reciting the alphabet). The time taken to complete the A form may be subtracted from the time taken to complete the B form to arrive at an estimate of the switching process. This test measures multiple cognitive domains which are difficult to disentangle (e.g. visual search etc.), but is a good estimator of executive function. The errors the participant made are also recorded (during the test, the participant is required by the interviewer to correct errors immediately). However, these errors are usually not evaluated separately, as any error the participant makes is supposed to be reflected in the time taken to complete the test.

TMT Part A, time (continuous [seconds], v1_nrpsy_tmt_A_rt)

v1_nrpsy_tmt_A_rt<-c(v1_clin$v1_npu_tmt_001,v1_con$v1_npu_np_tmt_001)
summary(v1_nrpsy_tmt_A_rt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    9.00   24.00   31.00   34.78   41.00  180.00      82

TMT Part A, errors (continuous [number of errors], v1_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see above)

v1_nrpsy_tmt_A_err<-c(v1_clin$v1_npu_tmt_af_001,v1_con$v1_npu_np_tmtfehler_001)
summary(v1_nrpsy_tmt_A_err)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   0.134   0.000   5.000      99

TMT Part B, time (continuous [seconds], v1_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.

v1_nrpsy_tmt_B_rt<-c(v1_clin$v1_npu_tmt_002,v1_con$v1_npu_np_tmt_002)
v1_nrpsy_tmt_B_rt[v1_nrpsy_tmt_B_rt>300]<-300

summary(v1_nrpsy_tmt_B_rt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.00   51.00   70.00   79.81   96.00  300.00     151

TMT Part B, errors (continuous [number of errors], v1_nrpsy_tmt_B_err) We did not impose any cut-off value to errors (see above)

v1_nrpsy_tmt_B_err<-c(v1_clin$v1_npu_tmt_af_002,v1_con$v1_npu_np_tmtfehler_002)
summary(v1_nrpsy_tmt_B_err)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.5986  1.0000 17.0000     164

Short-term and working memory: Verbal digit span

This test assesses short-term (forward digit-span) and working memory (backward digit-span). In the short-term memory task, the participant is asked to repeat strings of digits verbally presented by the interviewer. The initial length of the string is two items (“1-7”). If the participant is able to repeat two different strings of numbers of the same length (“1-7” and “6-3”, each assessed separately), the interviewer moves to a longer string, (“5-8-2”), which is also assessed two times separately (using different strings). For each correctly repeated string of digits, the subject receives one point. The test is repeated until the participant fails to repeat two presented strings of the same length. All points are added up in the end to receive the final score. The working memory task works exactly the same way, only that the subject has to repeat the string of digits presented by the interviewer in backward order. Briefly, the difference between short-term and working memory is that the latter involves mental manipulation.

Forward (continuous [number of items], v1_nrpsy_dgt_sp_frw)

v1_nrpsy_dgt_sp_frw<-c(v1_clin$v1_npu_zns_001,v1_con$v1_npu_np_wie_001)
summary(v1_nrpsy_dgt_sp_frw)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.000   8.000   9.000   9.445  11.000  16.000     125

Histogram

hist(v1_nrpsy_dgt_sp_frw,breaks=c(1:16), main="Digit-span forward", xlab="Score",ylab="Number of Individuals")

Backward (continuous [number of items], v1_nrpsy_dgt_sp_bck)

v1_nrpsy_dgt_sp_bck<-c(v1_clin$v1_npu_zns_002,v1_con$v1_npu_np_wie_002)
summary(v1_nrpsy_dgt_sp_bck)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   5.000   6.000   6.358   8.000  14.000     128

Histogram

hist(v1_nrpsy_dgt_sp_bck, breaks=c(1:14), xlim=c(0,14), main="Digit-span backward", xlab="Score",ylab="Number of Individuals")

Psychomotor speed: Digit-Symbol-Test (DST) (continuous [number of correct symbols], v1_nrpsy_dg_sym)

This test measures processing speed. The participant is presented with rows of numbers and an empty space below each number. In these empty spaces, the participant is asked to fill in symbols that match the number above it. The respective number-symbole association is given at the top of the test sheet. It is measured how many correct symbols the participant can fill in during a 120 second period. Participants that only partially completed the test were excluded and are coded as -999.

v1_introcheck3<-c(v1_clin$v1_npu_np_introcheck3,v1_con$v1_npu_np_hawier)
v1_nrpsy_dg_sym_pre<-c(v1_clin$v1_npu_zst_001,v1_con$v1_npu_np_hawier_001)

v1_nrpsy_dg_sym<-ifelse(v1_introcheck3==1, v1_nrpsy_dg_sym_pre, 
                           ifelse(v1_introcheck3==9,-999,
                                  ifelse(v1_introcheck3==0,NA,NA)))

summary(v1_nrpsy_dg_sym[v1_nrpsy_dg_sym>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   49.00   63.00   63.87   78.00  133.00     140

Histogram

hist(v1_nrpsy_dg_sym[v1_nrpsy_dg_sym>=0], breaks=c(1:133), main="Digit-Symbol-Test", xlab="Number of correct symbols")

Crystallized Intelligence: Multiple-Choice Vocabulary Intelligence Test (MWT-B) (continuous [score], v1_nrpsy_mwtb)

This test assesses crystallized intelligence. Crystallized intelligence rises with advancing age. In this test, subjects are presented with 37 sets of five words each. Four “words” of each set are artificial (i.e. do not exist in the German language), one word really exists. They are instructed that they may be familiar with one word in each set, and asked to cross out that word. The known words start with easy ones and their difficulty increases. The sum score of correctly identified real words is the final score.

Important: only persons with German as a native language and who completed the test are included in the present dataset, those who were excluded due to these criteria are coded -999.

v1_introcheck4<-c(v1_clin$v1_npu_np_introcheck4,v1_con$v1_npu_np_mwtb)
v1_nrpsy_mwtb_pre<-c(v1_clin$v1_npu_mwt_001,v1_con$v1_npu_np_mwtb_001)

v1_nrpsy_mwtb<-ifelse((v1_introcheck4=="1" & v1_nrpsy_lng=="mother tongue"),v1_nrpsy_mwtb_pre,-999)

#Set one participant with zero recognized words to NA - this person either misunderstood the instructions or
#gave wrong answers on purpose
v1_nrpsy_mwtb[v1_nrpsy_mwtb==0]<-NA

summary(v1_nrpsy_mwtb[v1_nrpsy_mwtb>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   26.00   29.00   28.45   32.00   37.00      63

Histogram

hist(v1_nrpsy_mwtb[v1_nrpsy_mwtb>=0], breaks=c(0:37), main="Multiple-Choice Vocabulary Intelligence Test", xlab="Score")

Create dataset

v1_nrpsy<-data.frame(v1_nrpsy_com,
                     v1_nrpsy_lng,
                     v1_nrpsy_mtv,
                     v1_nrpsy_tmt_A_rt,
                     v1_nrpsy_tmt_A_err,
                     v1_nrpsy_tmt_B_rt,
                     v1_nrpsy_tmt_B_err,
                     v1_nrpsy_dgt_sp_frw,
                     v1_nrpsy_dgt_sp_bck,
                     v1_nrpsy_dg_sym,
                     v1_nrpsy_mwtb)

Visit 1: Questionnaires (patient/proband rates her/himself)

All participants were asked to fill out questionnaires on the following topics: religious beliefs, current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, current quality of life (WHOQOL-BREF) and personality (big five). Additionally, control participants completed the CAPE-42 questionnaire (Community Assessment of Psycic Experiences) and the Short Form Health Survey (SF-12). Medication adherence (compliance) was only assessed in clinical participants. All questionnaires are briefly explained below. Importantly, there are items in our database assessing whether a questionnaire was filled out correctly. Questionnaires considered unusable are NOT included in this dataset (i.e. are NA).

Community Assessment Psychic Experiences-42 (CAPE-42)

The CAPE-42 was developed by Jim van Os, Hélène Verdoux and Manon Hanssen. It is based on the PDI-21 and PDI-40 developed by Emmanuelle Peters et al (2004). It asesses psychotic-like experiences and was only assessed in control subjects. All items have a part A (“Never”,“Sometimes”,“Often”,“Nearly always”; coded 0-3, repectively) and a part B, which is to be answered if the answer to the corresponding part A item was not “Never” and asks how distressed the participant was by this experience (“Not distressed”,“A bit distressed”,“Quite distressed” or “Very distressed”; coded 0-3, repectively).

“Do you ever feel sad?” (ordinal [0,1,2,3], v1_cape_itm1A)

v1_cape_recode(v1_con$v1_cape_cape_hsjtraugefa1,"v1_cape_itm1A")

##                -999 0   1    2  3   <NA>     
## [1,] No. cases 1320 7   256  35 1   167  1786
## [2,] Percent   73.9 0.4 14.3 2  0.1 9.4  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm1B)

v1_cape_recode(v1_con$v1_cape_cape_hsjtraugefb1,"v1_cape_itm1B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 124 143 25  1   173  1786
## [2,] Percent   73.9 6.9 8   1.4 0.1 9.7  100

“Do you ever feel as if people seem to drop hints about you or say things with a double meaning?” (ordinal [0,1,2,3], v1_cape_itm2A)

v1_cape_recode(v1_con$v1_cape_cape_anspapersa1,"v1_cape_itm2A")

##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1320 94  186  16  4   166  1786
## [2,] Percent   73.9 5.3 10.4 0.9 0.2 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm2B)

v1_cape_recode(v1_con$v1_cape_cape_anspapersb1,"v1_cape_itm2B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 59  111 28  8   260  1786
## [2,] Percent   73.9 3.3 6.2 1.6 0.4 14.6 100

“Do you ever feel that you are not a very animated person?” (ordinal [0,1,2,3], v1_cape_itm3A)

v1_cape_recode(v1_con$v1_cape_cape_nlebha1,"v1_cape_itm3A")

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1320 185  99  13  3   166  1786
## [2,] Percent   73.9 10.4 5.5 0.7 0.2 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm3B)

v1_cape_recode(v1_con$v1_cape_cape_nlebhb1,"v1_cape_itm3B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 59  47  9   351  1786
## [2,] Percent   73.9 3.3 2.6 0.5 19.7 100

“Do you ever feel that you are not much of a talker when you are conversing with other people?” (ordinal [0,1,2,3], v1_cape_itm4A)

v1_cape_recode(v1_con$v1_cape_cape_nsaga1,"v1_cape_itm4A")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 126 140 30  4   166  1786
## [2,] Percent   73.9 7.1 7.8 1.7 0.2 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm4B)

v1_cape_recode(v1_con$v1_cape_cape_nsagb1,"v1_cape_itm4B")

##                -999 0   1  2   <NA>     
## [1,] No. cases 1320 92  72 10  292  1786
## [2,] Percent   73.9 5.2 4  0.6 16.3 100

“Do you ever feel as if things in magazines or on TV were written especially for you?” (ordinal [0,1,2,3], v1_cape_itm5A)

v1_cape_recode(v1_con$v1_cape_cape_auszeita1,"v1_cape_itm5A")

##                -999 0    1  2   <NA>     
## [1,] No. cases 1320 258  36 6   166  1786
## [2,] Percent   73.9 14.4 2  0.3 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm5B)

v1_cape_recode(v1_con$v1_cape_cape_auszeitb1,"v1_cape_itm5B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 33  7   2   424  1786
## [2,] Percent   73.9 1.8 0.4 0.1 23.7 100

“Do you ever feel as if some people are not what they seem to be?” (ordinal [0,1,2,3], v1_cape_itm6A)

v1_cape_recode(v1_con$v1_cape_cape_geflnswsea1,"v1_cape_itm6A")

##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1320 40  184  70  6   166  1786
## [2,] Percent   73.9 2.2 10.3 3.9 0.3 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm6B)

v1_cape_recode(v1_con$v1_cape_cape_geflnswseb1,"v1_cape_itm6B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1320 108 127 18 6   207  1786
## [2,] Percent   73.9 6   7.1 1  0.3 11.6 100

“Do you ever feel as if you are being persecuted in some way?” (ordinal [0,1,2,3], v1_cape_itm7A)

v1_cape_recode(v1_con$v1_cape_cape_verfa1,"v1_cape_itm7A")

##                -999 0    1   2   <NA>     
## [1,] No. cases 1320 267  32  1   166  1786
## [2,] Percent   73.9 14.9 1.8 0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm7B)

v1_cape_recode(v1_con$v1_cape_cape_verfb1,"v1_cape_itm7B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 16  11  6   433  1786
## [2,] Percent   73.9 0.9 0.6 0.3 24.2 100

“Do you ever feel that you experience few or no emotions at important events?” (ordinal [0,1,2,3], v1_cape_itm8A)

v1_cape_recode(v1_con$v1_cape_cape_kgefa1,"v1_cape_itm8A")

##                -999 0    1  2   3   <NA>     
## [1,] No. cases 1320 195  89 13  2   167  1786
## [2,] Percent   73.9 10.9 5  0.7 0.1 9.4  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm8B)

v1_cape_recode(v1_con$v1_cape_cape_kgefb1,"v1_cape_itm8B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 59  40  4   1   362  1786
## [2,] Percent   73.9 3.3 2.2 0.2 0.1 20.3 100

“Do you ever feel pessimistic about everything?” (ordinal [0,1,2,3], v1_cape_itm9A)

v1_cape_recode(v1_con$v1_cape_cape_negseha1,"v1_cape_itm9A")

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1320 191  98  10  1   166  1786
## [2,] Percent   73.9 10.7 5.5 0.6 0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm9B)

v1_cape_recode(v1_con$v1_cape_cape_negsehb1,"v1_cape_itm9B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1320 21  66  17 5   357  1786
## [2,] Percent   73.9 1.2 3.7 1  0.3 20   100

“Do you ever feel as if there is a conspiracy against you?” (ordinal [0,1,2,3], v1_cape_itm10A)

v1_cape_recode(v1_con$v1_cape_cape_kompla1,"v1_cape_itm10A")

##                -999 0    1   2   <NA>     
## [1,] No. cases 1320 260  39  1   166  1786
## [2,] Percent   73.9 14.6 2.2 0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm10B)

v1_cape_recode(v1_con$v1_cape_cape_komplb1,"v1_cape_itm10B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 5   23  9   3   426  1786
## [2,] Percent   73.9 0.3 1.3 0.5 0.2 23.9 100

“Do you ever feel as if you are destined to be someone very important?” (ordinal [0,1,2,3], v1_cape_itm11A)

v1_cape_recode(v1_con$v1_cape_cape_bestwpa1,"v1_cape_itm11A")

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1320 230  61  9   1   165  1786
## [2,] Percent   73.9 12.9 3.4 0.5 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm11B)

v1_cape_recode(v1_con$v1_cape_cape_bestwpb1,"v1_cape_itm11B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 66  4   1   395  1786
## [2,] Percent   73.9 3.7 0.2 0.1 22.1 100

“Do you ever feel as if there is no future for you?” (ordinal [0,1,2,3], v1_cape_itm12A)

v1_cape_recode(v1_con$v1_cape_cape_keinza1,"v1_cape_itm12A")

##                -999 0    1   2   <NA>     
## [1,] No. cases 1320 244  51  5   166  1786
## [2,] Percent   73.9 13.7 2.9 0.3 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm12B)

v1_cape_recode(v1_con$v1_cape_cape_keinzb1,"v1_cape_itm12B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 11  30  10  5   410  1786
## [2,] Percent   73.9 0.6 1.7 0.6 0.3 23   100

“Do you ever feel that you are a very special or unusual person?” (ordinal [0,1,2,3], v1_cape_itm13A)

v1_cape_recode(v1_con$v1_cape_cape_gefaupersa1,"v1_cape_itm13A")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 169 98  29  5   165  1786
## [2,] Percent   73.9 9.5 5.5 1.6 0.3 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm13B)

v1_cape_recode(v1_con$v1_cape_cape_gefaupersb1,"v1_cape_itm13B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 111 16  3   336  1786
## [2,] Percent   73.9 6.2 0.9 0.2 18.8 100

“Do you ever feel as if you do not want to live anymore?” (ordinal [0,1,2,3], v1_cape_itm14A)

v1_cape_recode(v1_con$v1_cape_cape_nileba1,"v1_cape_itm14A")

##                -999 0    1   2   <NA>     
## [1,] No. cases 1320 234  63  2   167  1786
## [2,] Percent   73.9 13.1 3.5 0.1 9.4  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm14B)

v1_cape_recode(v1_con$v1_cape_cape_nileba1,"v1_cape_itm14B")

##                -999 0    1   2   <NA>     
## [1,] No. cases 1320 234  63  2   167  1786
## [2,] Percent   73.9 13.1 3.5 0.1 9.4  100

“Do you ever think that people can communicate telepathically?” (ordinal [0,1,2,3], v1_cape_itm15A)

v1_cape_recode(v1_con$v1_cape_cape_telea1,"v1_cape_itm15A")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 214 69  14  3   166  1786
## [2,] Percent   73.9 12  3.9 0.8 0.2 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm15B)

v1_cape_recode(v1_con$v1_cape_cape_teleb1,"v1_cape_itm15B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 81  4   1   380  1786
## [2,] Percent   73.9 4.5 0.2 0.1 21.3 100

“Do you ever feel that you have no interest to be with other people?” (ordinal [0,1,2,3], v1_cape_itm16A)

v1_cape_recode(v1_con$v1_cape_cape_kbedgesa1,"v1_cape_itm16A")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 97  179 23  2   165  1786
## [2,] Percent   73.9 5.4 10  1.3 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm16B)

v1_cape_recode(v1_con$v1_cape_cape_kbedgesb1,"v1_cape_itm16B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 179 20  5   262  1786
## [2,] Percent   73.9 10  1.1 0.3 14.7 100

“Do you ever feel as if electrical devices such as computers can influence the way you think?” (ordinal [0,1,2,3], v1_cape_itm17A)

v1_cape_recode(v1_con$v1_cape_cape_elegeggeda1,"v1_cape_itm17A")

##                -999 0    1  2   3   <NA>     
## [1,] No. cases 1320 278  18 4   1   165  1786
## [2,] Percent   73.9 15.6 1  0.2 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm17B)

v1_cape_recode(v1_con$v1_cape_cape_elegeggedb1,"v1_cape_itm17B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 8   10  4   444  1786
## [2,] Percent   73.9 0.4 0.6 0.2 24.9 100

“Do you ever feel that you are lacking in motivation to do things?” (ordinal [0,1,2,3], v1_cape_itm18A)

v1_cape_recode(v1_con$v1_cape_cape_motfehla1,"v1_cape_itm18A")

##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1320 55  201  39  6   165  1786
## [2,] Percent   73.9 3.1 11.3 2.2 0.3 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm18B)

v1_cape_recode(v1_con$v1_cape_cape_motfehlb1,"v1_cape_itm18B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 77  115 41  12  221  1786
## [2,] Percent   73.9 4.3 6.4 2.3 0.7 12.4 100

“Do you ever cry about nothing?” (ordinal [0,1,2,3], v1_cape_itm19A)

v1_cape_recode(v1_con$v1_cape_cape_ougewa1,"v1_cape_itm19A")

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1320 243  56  1   1   165  1786
## [2,] Percent   73.9 13.6 3.1 0.1 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm19B)

v1_cape_recode(v1_con$v1_cape_cape_ougewb1,"v1_cape_itm19B")

##                -999 0   1  2   <NA>     
## [1,] No. cases 1320 34  18 6   408  1786
## [2,] Percent   73.9 1.9 1  0.3 22.8 100

“Do you believe in the power of witchcraft, voodoo or the occult?” (ordinal [0,1,2,3], v1_cape_itm20A)

v1_cape_recode(v1_con$v1_cape_cape_hexvoa1,"v1_cape_itm20A")

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1320 247  39  8   7   165  1786
## [2,] Percent   73.9 13.8 2.2 0.4 0.4 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm20B)

v1_cape_recode(v1_con$v1_cape_cape_hexvob1,"v1_cape_itm20B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 46  5   1   1   413  1786
## [2,] Percent   73.9 2.6 0.3 0.1 0.1 23.1 100

“Do you ever feel that you are lacking in energy?” (ordinal [0,1,2,3], v1_cape_itm21A)

v1_cape_recode(v1_con$v1_cape_cape_energiela1,"v1_cape_itm21A")

##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1320 73  205  21  2   165  1786
## [2,] Percent   73.9 4.1 11.5 1.2 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm21B)

v1_cape_recode(v1_con$v1_cape_cape_energielb1,"v1_cape_itm21B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 79  106 32  10  239  1786
## [2,] Percent   73.9 4.4 5.9 1.8 0.6 13.4 100

“Do you ever feel that people look at you oddly because of your appearance?” (ordinal [0,1,2,3], v1_cape_itm22A)

v1_cape_recode(v1_con$v1_cape_cape_sonda1,"v1_cape_itm22A")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1320 163 119 18 1   165  1786
## [2,] Percent   73.9 9.1 6.7 1  0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm22B)

v1_cape_recode(v1_con$v1_cape_cape_sondb1,"v1_cape_itm22B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 65  59  11  2   329  1786
## [2,] Percent   73.9 3.6 3.3 0.6 0.1 18.4 100

“Do you ever feel that your mind is empty?” (ordinal [0,1,2,3], v1_cape_itm23A)

v1_cape_recode(v1_con$v1_cape_cape_kopfleera1,"v1_cape_itm23A")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 173 122 5   166  1786
## [2,] Percent   73.9 9.7 6.8 0.3 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm23B)

v1_cape_recode(v1_con$v1_cape_cape_kopfleerb1,"v1_cape_itm23B")

##                -999 0  1   2   3   <NA>     
## [1,] No. cases 1320 54 60  7   5   340  1786
## [2,] Percent   73.9 3  3.4 0.4 0.3 19   100

“Do you ever feel as if the thoughts in your head are being taken away from you?” (ordinal [0,1,2,3], v1_cape_itm24A)

v1_cape_recode(v1_con$v1_cape_cape_gedaka1,"v1_cape_itm24A")

##                -999 0    1   <NA>     
## [1,] No. cases 1320 290  11  165  1786
## [2,] Percent   73.9 16.2 0.6 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm24B)

v1_cape_recode(v1_con$v1_cape_cape_gedakb1,"v1_cape_itm24B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 6   4   1   455  1786
## [2,] Percent   73.9 0.3 0.2 0.1 25.5 100

“Do you ever feel that you are spending all your days doing nothing?” (ordinal [0,1,2,3], v1_cape_itm25A)

v1_cape_recode(v1_con$v1_cape_cape_tagoetuna1,"v1_cape_itm25A")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 151 132 16  2   165  1786
## [2,] Percent   73.9 8.5 7.4 0.9 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm25B)

v1_cape_recode(v1_con$v1_cape_cape_tagoetunb1,"v1_cape_itm25B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 61  55  26  7   317  1786
## [2,] Percent   73.9 3.4 3.1 1.5 0.4 17.7 100

“Do you ever feel as if the thoughts in your head are not your own?” (ordinal [0,1,2,3], v1_cape_itm26A)

v1_cape_recode(v1_con$v1_cape_cape_gedneiga1,"v1_cape_itm26A")

##                -999 0    1  2   <NA>     
## [1,] No. cases 1320 281  18 1   166  1786
## [2,] Percent   73.9 15.7 1  0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm26B)

v1_cape_recode(v1_con$v1_cape_cape_gedneigb1,"v1_cape_itm26B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 7   5   5   2   447  1786
## [2,] Percent   73.9 0.4 0.3 0.3 0.1 25   100

” Do you ever feel that your feelings are lacking in intensity?” (ordinal [0,1,2,3], v1_cape_itm27A)

v1_cape_recode(v1_con$v1_cape_cape_gefinta1,"v1_cape_itm27A")

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1320 207  85  8   1   165  1786
## [2,] Percent   73.9 11.6 4.8 0.4 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm27B)

v1_cape_recode(v1_con$v1_cape_cape_gefintb1,"v1_cape_itm27B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 39  40  12  3   372  1786
## [2,] Percent   73.9 2.2 2.2 0.7 0.2 20.8 100

“Have your thoughts ever been so vivid that you were worried other people would hear them?” (ordinal [0,1,2,3], v1_cape_itm28A)

v1_cape_recode(v1_con$v1_cape_cape_lebhfa1,"v1_cape_itm28A")

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1320 283  15  2   1   165  1786
## [2,] Percent   73.9 15.8 0.8 0.1 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm28B)

v1_cape_recode(v1_con$v1_cape_cape_lebhfb1,"v1_cape_itm28B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 13  4   1   448  1786
## [2,] Percent   73.9 0.7 0.2 0.1 25.1 100

“Do you ever feel that you are lacking in spontaneity?” (ordinal [0,1,2,3], v1_cape_itm29A)

v1_cape_recode(v1_con$v1_cape_cape_sponfehla1,"v1_cape_itm29A")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 158 125 16  2   165  1786
## [2,] Percent   73.9 8.8 7   0.9 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm29B)

v1_cape_recode(v1_con$v1_cape_cape_sponfehlb1,"v1_cape_itm29B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 50  77  13  2   324  1786
## [2,] Percent   73.9 2.8 4.3 0.7 0.1 18.1 100

“Do you ever hear your own thoughts being echoed back to you?” (ordinal [0,1,2,3], v1_cape_itm30A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa1,"v1_cape_itm30A")

##                -999 0   1   <NA>     
## [1,] No. cases 1320 285 16  165  1786
## [2,] Percent   73.9 16  0.9 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm30B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob1,"v1_cape_itm30B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 1320 11  4   1   450  1786
## [2,] Percent   73.9 0.6 0.2 0.1 25.2 100

“Do you ever feel as if you are under the control of some force or power other than yourself?” (ordinal [0,1,2,3], v1_cape_itm31A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa2,"v1_cape_itm31A")

##                -999 0    1   2   <NA>     
## [1,] No. cases 1320 287  11  2   166  1786
## [2,] Percent   73.9 16.1 0.6 0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm31B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob2,"v1_cape_itm31B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 5   4   3   1   453  1786
## [2,] Percent   73.9 0.3 0.2 0.2 0.1 25.4 100

“Do you ever feel that your emotions are blunted?” (ordinal [0,1,2,3], v1_cape_itm32A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa3,"v1_cape_itm32A")

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1320 206  83  8   3   166  1786
## [2,] Percent   73.9 11.5 4.6 0.4 0.2 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm32B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob3,"v1_cape_itm32B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 43  37  9   4   373  1786
## [2,] Percent   73.9 2.4 2.1 0.5 0.2 20.9 100

“Do you ever hear voices when you are alone?” (ordinal [0,1,2,3], v1_cape_itm33A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa4,"v1_cape_itm33A")

##                -999 0    1   2   <NA>     
## [1,] No. cases 1320 296  4   1   165  1786
## [2,] Percent   73.9 16.6 0.2 0.1 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm33B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob4,"v1_cape_itm33B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 1320 3   1   1   461  1786
## [2,] Percent   73.9 0.2 0.1 0.1 25.8 100

“Do you ever hear voices talking to each other when you are alone?” (ordinal [0,1,2,3], v1_cape_itm34A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa5,"v1_cape_itm34A")

##                -999 0    <NA>     
## [1,] No. cases 1320 300  166  1786
## [2,] Percent   73.9 16.8 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm34B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob5,"v1_cape_itm34B")

##                -999 <NA>     
## [1,] No. cases 1320 466  1786
## [2,] Percent   73.9 26.1 100

“Do you ever feel that you are neglecting your appearance or personal hygiene?” (ordinal [0,1,2,3], v1_cape_itm35A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa6,"v1_cape_itm35A")

##                -999 0    1   2   <NA>     
## [1,] No. cases 1320 243  52  6   165  1786
## [2,] Percent   73.9 13.6 2.9 0.3 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm35B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob6,"v1_cape_itm35B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 25  23  8   2   408  1786
## [2,] Percent   73.9 1.4 1.3 0.4 0.1 22.8 100

“Do you ever feel that you can never get things done?” (ordinal [0,1,2,3], v1_cape_itm36A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa7,"v1_cape_itm36A")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1320 153 128 17 3   165  1786
## [2,] Percent   73.9 8.6 7.2 1  0.2 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm36B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob7,"v1_cape_itm36B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 1320 28  74  29  17 318  1786
## [2,] Percent   73.9 1.6 4.1 1.6 1  17.8 100

“Do you ever feel that you have only few hobbies or interests?” (ordinal [0,1,2,3], v1_cape_itm37A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa8,"v1_cape_itm37A")

##                -999 0    1  2   <NA>     
## [1,] No. cases 1320 224  72 5   165  1786
## [2,] Percent   73.9 12.5 4  0.3 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm37B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob8,"v1_cape_itm37B")

##                -999 0  1  2   3   <NA>     
## [1,] No. cases 1320 35 36 5   1   389  1786
## [2,] Percent   73.9 2  2  0.3 0.1 21.8 100

“Do you ever feel guilty?” (ordinal [0,1,2,3], v1_cape_itm38A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa9,"v1_cape_itm38A")

##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1320 46  226  24  5   165  1786
## [2,] Percent   73.9 2.6 12.7 1.3 0.3 9.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm38B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob9,"v1_cape_itm38B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 58  128 48  21  211  1786
## [2,] Percent   73.9 3.2 7.2 2.7 1.2 11.8 100

“Do you ever feel like a failure?” (ordinal [0,1,2,3], v1_cape_itm39A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa10,"v1_cape_itm39A")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1320 156 128 14  2   166  1786
## [2,] Percent   73.9 8.7 7.2 0.8 0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm39B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob10,"v1_cape_itm39B")

##                -999 0  1   2   3  <NA>     
## [1,] No. cases 1320 35 65  26  18 322  1786
## [2,] Percent   73.9 2  3.6 1.5 1  18   100

“Do you ever feel tense?” (ordinal [0,1,2,3], v1_cape_itm40A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa11,"v1_cape_itm40A")

##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1320 68  188  42  2   166  1786
## [2,] Percent   73.9 3.8 10.5 2.4 0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm40B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob11,"v1_cape_itm40B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1320 116 93  18 3   236  1786
## [2,] Percent   73.9 6.5 5.2 1  0.2 13.2 100

“Do you ever feel as if a double has taken the place of a family member, friend or acquaintance?” (ordinal [0,1,2,3], v1_cape_itm41A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa12,"v1_cape_itm41A")

##                -999 0    1   <NA>     
## [1,] No. cases 1320 298  2   166  1786
## [2,] Percent   73.9 16.7 0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm41B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob12,"v1_cape_itm41B")

##                -999 0   2   <NA>     
## [1,] No. cases 1320 1   1   464  1786
## [2,] Percent   73.9 0.1 0.1 26   100

“Do you ever see objects, people or animals that other people cannot see?” (ordinal [0,1,2,3], v1_cape_itm42A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa13,"v1_cape_itm42A")

##                -999 0    1   3   <NA>     
## [1,] No. cases 1320 292  7   1   166  1786
## [2,] Percent   73.9 16.3 0.4 0.1 9.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm42B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob13,"v1_cape_itm42B")

##                -999 0   <NA>     
## [1,] No. cases 1320 8   458  1786
## [2,] Percent   73.9 0.4 25.6 100

Create dataset

v1_cape<-data.frame(v1_cape_itm1A,v1_cape_itm1B,
                    v1_cape_itm2A,v1_cape_itm2B,
                    v1_cape_itm3A,v1_cape_itm3B,
                    v1_cape_itm4A,v1_cape_itm4B,
                    v1_cape_itm5A,v1_cape_itm5B,
                    v1_cape_itm6A,v1_cape_itm6B,
                    v1_cape_itm7A,v1_cape_itm7B,
                    v1_cape_itm8A,v1_cape_itm8B,
                    v1_cape_itm9A,v1_cape_itm9B,
                    v1_cape_itm10A,v1_cape_itm10B,
                    v1_cape_itm11A,v1_cape_itm11B,
                    v1_cape_itm12A,v1_cape_itm12B,
                    v1_cape_itm13A,v1_cape_itm13B,
                    v1_cape_itm14A,v1_cape_itm14B,
                    v1_cape_itm15A,v1_cape_itm15B,
                    v1_cape_itm16A,v1_cape_itm16B,
                    v1_cape_itm17A,v1_cape_itm17B,
                    v1_cape_itm18A,v1_cape_itm18B,
                    v1_cape_itm19A,v1_cape_itm19B,
                    v1_cape_itm20A,v1_cape_itm20B,
                    v1_cape_itm21A,v1_cape_itm21B,
                    v1_cape_itm22A,v1_cape_itm22B,
                    v1_cape_itm23A,v1_cape_itm23B,
                    v1_cape_itm24A,v1_cape_itm24B,
                    v1_cape_itm25A,v1_cape_itm25B,
                    v1_cape_itm26A,v1_cape_itm26B,
                    v1_cape_itm27A,v1_cape_itm27B,
                    v1_cape_itm28A,v1_cape_itm28B,
                    v1_cape_itm29A,v1_cape_itm29B,
                    v1_cape_itm30A,v1_cape_itm30B,
                    v1_cape_itm31A,v1_cape_itm31B,
                    v1_cape_itm32A,v1_cape_itm32B,
                    v1_cape_itm33A,v1_cape_itm33B,
                    v1_cape_itm34A,v1_cape_itm34B,
                    v1_cape_itm35A,v1_cape_itm35B,
                    v1_cape_itm36A,v1_cape_itm36B,
                    v1_cape_itm37A,v1_cape_itm37B,
                    v1_cape_itm38A,v1_cape_itm38B,
                    v1_cape_itm39A,v1_cape_itm39B,
                    v1_cape_itm40A,v1_cape_itm40B,
                    v1_cape_itm41A,v1_cape_itm41B,
                    v1_cape_itm42A,v1_cape_itm42B)

Short Form Health Survey (SF-12)

The SF-12 is a short instrument to assess health-related quality of life.

“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v1_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.

v1_sf12_recode(v1_con$v1_sf12_sf_allgemein,"v1_sf12_itm0")

##                -999 1   2   3   4   5   6   7   8   9   10  <NA>     
## [1,] No. cases 1320 1   2   6   13  10  19  67  129 118 57  44   1786
## [2,] Percent   73.9 0.1 0.1 0.3 0.7 0.6 1.1 3.8 7.2 6.6 3.2 2.5  100

“In general, would you say your health is…” (ordinal [1,2,3,4,5], v1_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.

v1_sf12_recode(v1_con$v1_sf12_sf1,"v1_sf12_itm1")

##                -999 1  2    3   4   5   <NA>     
## [1,] No. cases 1320 90 227  122 13  1   13   1786
## [2,] Percent   73.9 5  12.7 6.8 0.7 0.1 0.7  100

“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”

“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v1_sf12_itm2) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v1_sf12_recode(v1_con$v1_sf12_sf2,"v1_sf12_itm2")

##                -999 1   2  3    <NA>     
## [1,] No. cases 1320 2   36 414  14   1786
## [2,] Percent   73.9 0.1 2  23.2 0.8  100

“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v1_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v1_sf12_recode(v1_con$v1_sf12_sf3,"v1_sf12_itm3")

##                -999 1   2   3    <NA>     
## [1,] No. cases 1320 3   49  401  13   1786
## [2,] Percent   73.9 0.2 2.7 22.5 0.7  100

During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?

“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v1_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.

v1_sf12_recode(v1_con$v1_sf12_sf4,"v1_sf12_itm4")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 55  397  14   1786
## [2,] Percent   73.9 3.1 22.2 0.8  100

“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v1_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.

v1_sf12_recode(v1_con$v1_sf12_sf5,"v1_sf12_itm5")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 32  416  18   1786
## [2,] Percent   73.9 1.8 23.3 1    100

During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?

“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v1_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.

v1_sf12_recode(v1_con$v1_sf12_sf6,"v1_sf12_itm6")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 28  425  13   1786
## [2,] Percent   73.9 1.6 23.8 0.7  100

“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v1_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.

v1_sf12_recode(v1_con$v1_sf12_sf7,"v1_sf12_itm7")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 22  430  14   1786
## [2,] Percent   73.9 1.2 24.1 0.8  100

“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v1_sf12_itm8) Answering alternatives are the following: “None”-1 to “Extremely”-6.

v1_sf12_recode(v1_con$v1_sf12_st8,"v1_sf12_itm8")

##                -999 1    2   3   4  5   6   <NA>     
## [1,] No. cases 1320 262  85  58  35 9   1   16   1786
## [2,] Percent   73.9 14.7 4.8 3.2 2  0.5 0.1 0.9  100

The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS

Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.

“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v1_sf12_itm9)

v1_sf12_recode(v1_con$v1_sf12_st9,"v1_sf12_itm9")

##                -999 1  2    3   4  5   6   <NA>     
## [1,] No. cases 1320 35 270  98  35 12  2   14   1786
## [2,] Percent   73.9 2  15.1 5.5 2  0.7 0.1 0.8  100

“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v1_sf12_itm10)

v1_sf12_recode(v1_con$v1_sf12_st10,"v1_sf12_itm10")

##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1320 27  173 150 75  20  3   18   1786
## [2,] Percent   73.9 1.5 9.7 8.4 4.2 1.1 0.2 1    100

“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v1_sf12_itm11)

v1_sf12_recode(v1_con$v1_sf12_st11,"v1_sf12_itm11")

##                -999 2   3   4   5    6   <NA>     
## [1,] No. cases 1320 2   10  67  230  140 17   1786
## [2,] Percent   73.9 0.1 0.6 3.8 12.9 7.8 1    100

“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [1,2,3,4,5], v1_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.

There is an error in the phenotype database regarding this item. The answering alternatives 3, 4, and 5 appear as 4, 5, and 6 in the database exports. These errors are corrected below.

v1_sf12_recode(v1_con$v1_sf12_st12,"v1_sf12_itm12")

##                -999 2   4   5  6    <NA>     
## [1,] No. cases 1320 7   29  90 327  13   1786
## [2,] Percent   73.9 0.4 1.6 5  18.3 0.7  100

#recode error in phenotype database
v1_sf12_itm12[v1_sf12_itm12==4]<-3
v1_sf12_itm12[v1_sf12_itm12==5]<-4
v1_sf12_itm12[v1_sf12_itm12==6]<-5

descT(v1_sf12_itm12)

##                -999 2   3   4  5    <NA>     
## [1,] No. cases 1320 7   29  90 327  13   1786
## [2,] Percent   73.9 0.4 1.6 5  18.3 0.7  100

Create dataset

v1_sf12<-data.frame(v1_sf12_itm0,
                    v1_sf12_itm1,
                    v1_sf12_itm2,
                    v1_sf12_itm3,
                    v1_sf12_itm4,
                    v1_sf12_itm5,
                    v1_sf12_itm6,
                    v1_sf12_itm7,
                    v1_sf12_itm8,
                    v1_sf12_itm9,
                    v1_sf12_itm10,
                    v1_sf12_itm11,
                    v1_sf12_itm12)

#INCLUDE v2_sf12_itm12 when issues are settled

Religious beliefs

This self-created questionnaire asks about whether the participant belongs to a certain belief system and how actively she or he practices this belief. The first two questions are about Christianity and Islam. In a third question, other belief systems such are Judaism, Hinduism, Buddhism, Other (specify) and No religious denomination are assessed. There are also mode fine-grained distinctions concerning Christianity and Islan, but these are not included in the present dataset. The second item assesses how actively the belief is practiced. Because this questionnaire was introduced after data collection started, it is included in Visit 4 as well for those participants that were not assessed in Visit 1. In control participants, the questionnaire is assessed in Visit 1.

Religion Christianity (dichotomous, v1_rel_chr)

v1_rel_chris<-c(v1_clin$v1_religion_christ,v1_con$v1_religion_christ_jn)
v1_rel_chr<-ifelse(v1_rel_chris==1, "Y","N")
descT(v1_rel_chr) 

##                N   Y    <NA>     
## [1,] No. cases 76  748  962  1786
## [2,] Percent   4.3 41.9 53.9 100

Religion Islam (dichotomous, v1_rel_isl)

v1_rel_islam<-c(v1_clin$v1_religion_islam_jn,v1_con$v1_religion_islam_jn)
v1_rel_isl<-ifelse(v1_rel_islam==1, "Y","N")
descT(v1_rel_isl) 

##                N    Y   <NA>     
## [1,] No. cases 211  24  1551 1786
## [2,] Percent   11.8 1.3 86.8 100

Other religion (categorical,[v1_rel_oth])

v1_rel_var<-c(v1_clin$v1_religion_religion,v1_con$v1_religion_religion)
v1_rel_oth<-ifelse(v1_rel_var==1, "Judaism",
                   ifelse(v1_rel_var==2, "Hinduism",
                          ifelse(v1_rel_var==3, "Buddhism",
                                 ifelse(v1_rel_var==4, "Other",
                                        ifelse(v1_rel_var==5, "No denomination",NA)))))
descT(v1_rel_oth) 

##                Buddhism Judaism No denomination Other <NA>     
## [1,] No. cases 15       1       280             6     1484 1786
## [2,] Percent   0.8      0.1     15.7            0.3   83.1 100

“How actively do you practice your belief?” (ordinal [1,2,3,4,5], v1_rel_act)
This is an ordinal item with the following answer possibilities and the assigned gradation: “not at all”-1,“little active”-2,“moderately active”-3,“rather active”-4,“very actively”-5.

v1_rel_act<-c(v1_clin$v1_religion_religion_aktiv,v1_con$v1_religion_aktiv)
descT(v1_rel_act)

##                1    2    3   4   5   <NA>     
## [1,] No. cases 364  301  176 88  51  806  1786
## [2,] Percent   20.4 16.9 9.9 4.9 2.9 45.1 100

Create dataset

v1_rlgn<-data.frame(v1_rel_chr,v1_rel_isl,v1_rel_oth,v1_rel_act)

Medication adherence (compliance)

This questionnaire asks whether psychopharmacological medication was taken as prescribed. We developed this questionnaire ourselves. The past seven days and the past six months are assessed. Both items have the following gradation: “everyday, exactly as prescribed”-1, “everyday, but not always as prescribed”-2, “regularly, but not every day”-3, “sometimes, but not regularly”-4, “seldom”-5, “not at all”-6. Control participants are coded -999.

Past seven days (ordinal [1,2,3,4,5,6], v1_med_pst_wk)

v1_med_chk<-c(v1_clin$v1_compl_verwer_fragebogen,rep(1,dim(v1_con)[1]))
v1_med_pst_wk_pre<-c(v1_clin$v1_compl_psychopharm_7_tag,rep(-999,dim(v1_con)[1]))
  
v1_med_pst_wk<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_med_pst_wk<-ifelse((is.na(v1_med_chk) | v1_med_chk!=2), 
                      v1_med_pst_wk_pre, v1_med_pst_wk)

descT(v1_med_pst_wk)

##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 466  988  88  21  5   3   14  201  1786
## [2,] Percent   26.1 55.3 4.9 1.2 0.3 0.2 0.8 11.3 100

Past six months (ordinal [1,2,3,4,5,6], v1_med_pst_sx_mths)

v1_med_pre<-c(v1_clin$v1_compl_psychopharm_6_mon,rep(-999,dim(v1_con)[1]))

v1_med_pst_sx_mths<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_med_pst_sx_mths<-ifelse((is.na(v1_med_chk) | v1_med_chk!=2),
                           v1_med_pre, v1_med_pst_sx_mths)

descT(v1_med_pst_sx_mths)

##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 466  730  149 107 44  24  49  217  1786
## [2,] Percent   26.1 40.9 8.3 6   2.5 1.3 2.7 12.2 100

Create dataset

v1_med_adh<-data.frame(v1_med_pst_wk,v1_med_pst_sx_mths)

Beck Depression Inventory (BDI-II)

The German translation of the BDI-II (Hautzinger, Keller, & Kühner, 2006) asesses depressive symptoms. Patients are supposed to pick the answer that best describes how they have been feeling during the past two weeks. Each item is rated from zero to three, except item 16 (sleep) and item 18 (apppetite), for which seven alternatives exist (described below). With all items, higher scores mean more depressive symptomatology. For clinically meaningful threshold values see sum score calculation at the end of thhis section.

1. Sadness (ordinal [0,1,2,3], v1_bdi2_itm1)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi1_traurigkeit,v1_con$v1_bdi2_s1_bdi1,"v1_bdi2_itm1")

##                0    1    2   3   <NA>     
## [1,] No. cases 990  486  64  29  217  1786
## [2,] Percent   55.4 27.2 3.6 1.6 12.2 100

2. Pessimism (ordinal [0,1,2,3], v1_bdi2_itm2)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi2_pessimismus,v1_con$v1_bdi2_s1_bdi2,"v1_bdi2_itm2")

##                0    1    2   3   <NA>     
## [1,] No. cases 1075 326  121 43  221  1786
## [2,] Percent   60.2 18.3 6.8 2.4 12.4 100

3. Past failure (ordinal [0,1,2,3], v1_bdi2_itm3)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi3_versagensgef,v1_con$v1_bdi2_s1_bdi3,"v1_bdi2_itm3")

##                0    1    2    3  <NA>     
## [1,] No. cases 940  328  248  54 216  1786
## [2,] Percent   52.6 18.4 13.9 3  12.1 100

4. Loss of pleasure (ordinal [0,1,2,3], v1_bdi2_itm4)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi4_verlust_freude,v1_con$v1_bdi2_s1_bdi4,"v1_bdi2_itm4")

##                0   1    2   3   <NA>     
## [1,] No. cases 857 491  163 58  217  1786
## [2,] Percent   48  27.5 9.1 3.2 12.2 100

5. Guilty feelings (ordinal [0,1,2,3], v1_bdi2_itm5)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi5_schuldgef,v1_con$v1_bdi2_s1_bdi5,"v1_bdi2_itm5")

##                0    1    2   3   <NA>     
## [1,] No. cases 1007 451  66  45  217  1786
## [2,] Percent   56.4 25.3 3.7 2.5 12.2 100

6. Punishment feelings (ordinal [0,1,2,3], v1_bdi2_itm6)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi6_bestrafungsgef,v1_con$v1_bdi2_s1_bdi6,"v1_bdi2_itm6")

##                0    1    2   3   <NA>     
## [1,] No. cases 1177 238  37  114 220  1786
## [2,] Percent   65.9 13.3 2.1 6.4 12.3 100

7. Self-dislike (ordinal [0,1,2,3], v1_bdi2_itm7)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi7_selbstablehnung,v1_con$v1_bdi2_s1_bdi7,"v1_bdi2_itm7")

##                0    1    2   3   <NA>     
## [1,] No. cases 1091 277  154 41  223  1786
## [2,] Percent   61.1 15.5 8.6 2.3 12.5 100

8. Self-criticalness (ordinal [0,1,2,3], v1_bdi2_itm8)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi8_selbstvorwuerfe,v1_con$v1_bdi2_s1_bdi8,"v1_bdi2_itm8")

##                0    1    2   3   <NA>     
## [1,] No. cases 901  458  152 51  224  1786
## [2,] Percent   50.4 25.6 8.5 2.9 12.5 100

9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v1_bdi2_itm9)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi9_selbstmordged,v1_con$v1_bdi2_s1_bdi9,"v1_bdi2_itm9")

##                0    1    2   3   <NA>     
## [1,] No. cases 1240 292  23  13  218  1786
## [2,] Percent   69.4 16.3 1.3 0.7 12.2 100

10. Crying (ordinal [0,1,2,3], v1_bdi2_itm10)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi10_weinen,v1_con$v1_bdi2_s1_bdi10,"v1_bdi2_itm10")

##                0    1    2   3   <NA>     
## [1,] No. cases 1118 228  66  154 220  1786
## [2,] Percent   62.6 12.8 3.7 8.6 12.3 100

11. Agitation (ordinal [0,1,2,3], v1_bdi2_itm11)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi11_unruhe,v1_con$v1_bdi2_s2_bdi11,"v1_bdi2_itm11")

##                0    1    2   3  <NA>     
## [1,] No. cases 986  423  88  53 236  1786
## [2,] Percent   55.2 23.7 4.9 3  13.2 100

12. Loss of interest (ordinal [0,1,2,3], v1_bdi2_itm12)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi12_interessverl,v1_con$v1_bdi2_s2_bdi12,"v1_bdi2_itm12")

##                0    1    2   3   <NA>     
## [1,] No. cases 1006 353  114 76  237  1786
## [2,] Percent   56.3 19.8 6.4 4.3 13.3 100

13. Indecisiveness (ordinal [0,1,2,3], v1_bdi2_itm13)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi13_entschlussunf,v1_con$v1_bdi2_s2_bdi13,"v1_bdi2_itm13")

##                0    1    2   3   <NA>     
## [1,] No. cases 927  408  130 87  234  1786
## [2,] Percent   51.9 22.8 7.3 4.9 13.1 100

14. Worthlessness (ordinal [0,1,2,3], v1_bdi2_itm14)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi14_wertlosigkeit,v1_con$v1_bdi2_s2_bdi14,"v1_bdi2_itm14")

##                0    1    2   3   <NA>     
## [1,] No. cases 1086 252  167 43  238  1786
## [2,] Percent   60.8 14.1 9.4 2.4 13.3 100

15. Loss of energy (ordinal [0,1,2,3], v1_bdi2_itm15)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi15_energieverlust,v1_con$v1_bdi2_s2_bdi15,"v1_bdi2_itm15")

##                0    1    2    3   <NA>     
## [1,] No. cases 738  599  183  26  240  1786
## [2,] Percent   41.3 33.5 10.2 1.5 13.4 100

16. Changes in sleeping pattern (ordinal [0,1,2,3], v1_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep”. There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.

v1_itm_bdi2_chk<-c(v1_clin$v1_bdi2_s1_verwer_fragebogen,v1_con$v1_bdi2_s1_bdi_korrekt)
v1_itm_bdi2_itm16_clin_con<-c(v1_clin$v1_bdi2_s2_bdi16_schlafgewohn,v1_con$v1_bdi2_s2_bdi16)

v1_bdi2_itm16<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])

v1_bdi2_itm16<-ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & v1_itm_bdi2_itm16_clin_con==0, 0,
                ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                           (v1_itm_bdi2_itm16_clin_con==1 | v1_itm_bdi2_itm16_clin_con==100), 1, 
                 ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                               (v1_itm_bdi2_itm16_clin_con==2 | v1_itm_bdi2_itm16_clin_con==200), 2, 
                  ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                               (v1_itm_bdi2_itm16_clin_con==3 | v1_itm_bdi2_itm16_clin_con==300), 3, v1_bdi2_itm16))))  

v1_bdi2_itm16<-factor(v1_bdi2_itm16,ordered=T)
descT(v1_bdi2_itm16)

##                0    1    2    3   <NA>     
## [1,] No. cases 665  577  191  115 238  1786
## [2,] Percent   37.2 32.3 10.7 6.4 13.3 100

17. Irritability (ordinal [0,1,2,3], v1_bdi2_itm17)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi17_reizbarkeit,v1_con$v1_bdi2_s2_bdi17,"v1_bdi2_itm17")

##                0    1    2   3   <NA>     
## [1,] No. cases 1143 335  50  23  235  1786
## [2,] Percent   64   18.8 2.8 1.3 13.2 100

18. Change in appetite (ordinal [0,1,2,3], v1_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.

v1_itm_bdi2_itm18_clin_con<-c(v1_clin$v1_bdi2_s2_bdi18_appetit,v1_con$v1_bdi2_s2_bdi18)
v1_bdi2_itm18<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])

v1_bdi2_itm18<-ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & v1_itm_bdi2_itm18_clin_con==0, 0,
                ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                           (v1_itm_bdi2_itm18_clin_con==1 | v1_itm_bdi2_itm18_clin_con==100), 1, 
                 ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                               (v1_itm_bdi2_itm18_clin_con==2 | v1_itm_bdi2_itm18_clin_con==200), 2, 
                  ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                               (v1_itm_bdi2_itm18_clin_con==3 | v1_itm_bdi2_itm18_clin_con==300), 3, v1_bdi2_itm18))))  

v1_bdi2_itm18<-factor(v1_bdi2_itm18,ordered=T)
descT(v1_bdi2_itm18)

##                0    1    2   3   <NA>     
## [1,] No. cases 908  474  108 58  238  1786
## [2,] Percent   50.8 26.5 6   3.2 13.3 100

19. Concentration difficulty (ordinal [0,1,2,3], v1_bdi2_itm19)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi19_konzschw,v1_con$v1_bdi2_s2_bdi19,"v1_bdi2_itm19")

##                0    1    2    3   <NA>     
## [1,] No. cases 807  448  265  31  235  1786
## [2,] Percent   45.2 25.1 14.8 1.7 13.2 100

20. Tiredness or fatigue (ordinal [0,1,2,3], v1_bdi2_itm20)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi20_ermued_ersch,v1_con$v1_bdi2_s2_bdi20,"v1_bdi2_itm20")

##                0    1   2   3   <NA>     
## [1,] No. cases 791  553 168 39  235  1786
## [2,] Percent   44.3 31  9.4 2.2 13.2 100

21. Loss of interest in sex (ordinal [0,1,2,3], v1_bdi2_itm21)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi21_sex_interess,v1_con$v1_bdi2_s2_bdi21,"v1_bdi2_itm21")

##                0    1    2   3   <NA>     
## [1,] No. cases 937  309  149 147 244  1786
## [2,] Percent   52.5 17.3 8.3 8.2 13.7 100

BDI-II sum score calculation (continuous [0-63], v1_bdi2_sum) The following cut-off values are generally considered to be meaningful:

• “0-10 = These ups and downs are considered normal”
• “11-16 = Mild mood disturbance”
• “17-20 = Borderline clinical depression”
• “21-30 = Moderate depression”
• “31-40 = Severe depression”
• “over 40 = Extreme depression”

Please note that if one or more of BDI-II items are missing, this will result in the sum score to become NA.

v1_bdi2_sum<-as.numeric.factor(v1_bdi2_itm1)+
              as.numeric.factor(v1_bdi2_itm2)+
              as.numeric.factor(v1_bdi2_itm3)+
              as.numeric.factor(v1_bdi2_itm4)+
              as.numeric.factor(v1_bdi2_itm5)+
              as.numeric.factor(v1_bdi2_itm6)+
              as.numeric.factor(v1_bdi2_itm7)+
              as.numeric.factor(v1_bdi2_itm8)+
              as.numeric.factor(v1_bdi2_itm9)+
              as.numeric.factor(v1_bdi2_itm10)+
              as.numeric.factor(v1_bdi2_itm11)+
              as.numeric.factor(v1_bdi2_itm12)+
              as.numeric.factor(v1_bdi2_itm13)+
              as.numeric.factor(v1_bdi2_itm14)+
              as.numeric.factor(v1_bdi2_itm15)+
              as.numeric.factor(v1_bdi2_itm16)+
              as.numeric.factor(v1_bdi2_itm17)+
              as.numeric.factor(v1_bdi2_itm18)+
              as.numeric.factor(v1_bdi2_itm19)+
              as.numeric.factor(v1_bdi2_itm20)+
              as.numeric.factor(v1_bdi2_itm21)

summary(v1_bdi2_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    2.00    8.00   11.16   17.00   59.00     313

Create dataset

v1_bdi2<-data.frame(v1_bdi2_itm1,v1_bdi2_itm2,v1_bdi2_itm3,v1_bdi2_itm4,v1_bdi2_itm5,
                    v1_bdi2_itm6,v1_bdi2_itm7,v1_bdi2_itm8,v1_bdi2_itm9,v1_bdi2_itm10,
                    v1_bdi2_itm11,v1_bdi2_itm12,v1_bdi2_itm13,v1_bdi2_itm14,
                    v1_bdi2_itm15,v1_bdi2_itm16,v1_bdi2_itm17,v1_bdi2_itm18,
                    v1_bdi2_itm19,v1_bdi2_itm20,v1_bdi2_itm21,v1_bdi2_sum)

Altman Self-Rating Mania Scala (ASRM)

The ASRM (Altman, Hedeker, Peterson, & Davis, 1997) assesses symptoms of mania during the past week. All items are scored from zero to four with higher scores indicating more mania symptoms.

1. Positive Mood (ordinal [0,1,2,3,4], v1_asrm_itm1)

v1_asrm_recode(v1_clin$v1_asrm_asrm1_gluecklich,v1_con$v1_asrm_asrm1,"v1_asrm_itm1")

##                0    1    2  3  4   <NA>     
## [1,] No. cases 993  366  89 53 12  273  1786
## [2,] Percent   55.6 20.5 5  3  0.7 15.3 100

2 Self-Confidence (ordinal [0,1,2,3,4], v1_asrm_itm2)

v1_asrm_recode(v1_clin$v1_asrm_asrm2_selbstbewusst,v1_con$v1_asrm_asrm2,"v1_asrm_itm2")

##                0    1    2   3   4  <NA>     
## [1,] No. cases 1031 318  97  48  18 274  1786
## [2,] Percent   57.7 17.8 5.4 2.7 1  15.3 100

3. Sleep (ordinal [0,1,2,3,4], v1_asrm_itm3)

v1_asrm_recode(v1_clin$v1_asrm_asrm3_schlaf,v1_con$v1_asrm_asrm3,"v1_asrm_itm3")

##                0    1    2   3   4   <NA>     
## [1,] No. cases 1169 234  56  37  16  274  1786
## [2,] Percent   65.5 13.1 3.1 2.1 0.9 15.3 100

4. Speech (ordinal [0,1,2,3,4], v1_asrm_itm4)

v1_asrm_recode(v1_clin$v1_asrm_asrm4_reden,v1_con$v1_asrm_asrm4,"v1_asrm_itm4")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 1084 304 77  37  14  270  1786
## [2,] Percent   60.7 17  4.3 2.1 0.8 15.1 100

5. Activity Level (ordinal [0,1,2,3,4], v1_asrm_itm5)

v1_asrm_recode(v1_clin$v1_asrm_asrm5_aktiv,v1_con$v1_asrm_asrm5,"v1_asrm_itm5")

##                0    1    2   3   4   <NA>     
## [1,] No. cases 1014 350  91  37  25  269  1786
## [2,] Percent   56.8 19.6 5.1 2.1 1.4 15.1 100

Create ASRM sum scoresum score (continuous [0-20],v1_asrm_sum)

v1_asrm_sum<-as.numeric.factor(v1_asrm_itm1)+
            as.numeric.factor(v1_asrm_itm2)+
            as.numeric.factor(v1_asrm_itm3)+
            as.numeric.factor(v1_asrm_itm4)+
            as.numeric.factor(v1_asrm_itm5)

summary(v1_asrm_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   1.000   2.216   3.000  20.000     283

Create dataset

v1_asrm<-data.frame(v1_asrm_itm1,v1_asrm_itm2,v1_asrm_itm3,v1_asrm_itm4,v1_asrm_itm5,v1_asrm_sum)

Manie-Selbstbeurteilungsskala (MSS)

Forty-eight statements, each of which asks for a mania symtom and should to be answered “Yes” or No” (Shugar, Schertzer, Toner, & Di Gasbarro, 1992). Measures mania symptoms during the past month. All questions have the same direction, “Yes” indicating mania symptom present.

1. “I had more energy” (dichotomous, v1_mss_itm1)

v1_mss_recode(v1_clin$v1_mss_s1_mss1_energie,v1_con$v1_mss_s1_mss1,"v1_mss_itm1")

##                N    Y   <NA>     
## [1,] No. cases 1129 375 282  1786
## [2,] Percent   63.2 21  15.8 100

2. “I had trouble sitting still” (dichotomous, v1_mss_itm2)

v1_mss_recode(v1_clin$v1_mss_s1_mss2_ruhig_sitzen,v1_con$v1_mss_s1_mss2,"v1_mss_itm2")

##                N    Y    <NA>     
## [1,] No. cases 1168 333  285  1786
## [2,] Percent   65.4 18.6 16   100

3. “I drove faster” (dichotomous, v1_mss_itm3)

v1_mss_recode(v1_clin$v1_mss_s1_mss3_auto_fahren,v1_con$v1_mss_s1_mss3,"v1_mss_itm3")

##                N    Y   <NA>     
## [1,] No. cases 1348 77  361  1786
## [2,] Percent   75.5 4.3 20.2 100

4. “I drank more alcoholic beverages” (dichotomous, v1_mss_itm4)

v1_mss_recode(v1_clin$v1_mss_s1_mss4_alkohol,v1_con$v1_mss_s1_mss4,"v1_mss_itm4")

##                N    Y   <NA>     
## [1,] No. cases 1350 136 300  1786
## [2,] Percent   75.6 7.6 16.8 100

5. “I changed clothes several times a day” (dichotomous, v1_mss_itm5)

v1_mss_recode(v1_clin$v1_mss_s1_mss5_umziehen, v1_con$v1_mss_s1_mss5,"v1_mss_itm5")

##                N    Y    <NA>     
## [1,] No. cases 1317 181  288  1786
## [2,] Percent   73.7 10.1 16.1 100

6. “I wore brighter clothes/make-up” (dichotomous, v1_mss_itm6)

v1_mss_recode(v1_clin$v1_mss_s1_mss6_bunter,v1_con$v1_mss_s1_mss6,"v1_mss_itm6")

##                N    Y   <NA>     
## [1,] No. cases 1374 122 290  1786
## [2,] Percent   76.9 6.8 16.2 100

7. “I played music louder” (dichotomous, v1_mss_itm7)

v1_mss_recode(v1_clin$v1_mss_s1_mss7_musik_lauter,v1_con$v1_mss_s1_mss7,"v1_mss_itm7")

##                N    Y   <NA>     
## [1,] No. cases 1218 285 283  1786
## [2,] Percent   68.2 16  15.8 100

8. “I ate faster than usual” (dichotomous, v1_mss_itm8)

v1_mss_recode(v1_clin$v1_mss_s1_mss8_hastiger_essen,v1_con$v1_mss_s1_mss8,"v1_mss_itm8")

##                N    Y    <NA>     
## [1,] No. cases 1263 242  281  1786
## [2,] Percent   70.7 13.5 15.7 100

9. “I ate more than usual” (dichotomous, v1_mss_itm9)

v1_mss_recode(v1_clin$v1_mss_s1_mss9_mehr_essen,v1_con$v1_mss_s1_mss9,"v1_mss_itm9")

##                N    Y    <NA>     
## [1,] No. cases 1132 371  283  1786
## [2,] Percent   63.4 20.8 15.8 100

10. “I slept fewer hours than usual” (dichotomous, v1_mss_itm10)

v1_mss_recode(v1_clin$v1_mss_s1_mss10_weniger_schlaf,v1_con$v1_mss_s1_mss10,"v1_mss_itm10")

##                N    Y    <NA>     
## [1,] No. cases 1243 254  289  1786
## [2,] Percent   69.6 14.2 16.2 100

11. “I started things that I didn’t finish” (dichotomous, v1_mss_itm11)

v1_mss_recode(v1_clin$v1_mss_s1_mss11_unbeendet,v1_con$v1_mss_s1_mss11,"v1_mss_itm11")

##                N    Y    <NA>     
## [1,] No. cases 1090 413  283  1786
## [2,] Percent   61   23.1 15.8 100

12. “I gave away my own possessions” (dichotomous, v1_mss_itm12)

v1_mss_recode(v1_clin$v1_mss_s1_mss12_weggeben,v1_con$v1_mss_s1_mss12,"v1_mss_itm12")

##                N    Y    <NA>     
## [1,] No. cases 1290 213  283  1786
## [2,] Percent   72.2 11.9 15.8 100

13. “I bought gifts for people” (dichotomous, v1_mss_itm13)

v1_mss_recode(v1_clin$v1_mss_s1_mss13_geschenke,v1_con$v1_mss_s1_mss13,"v1_mss_itm13")

##                N    Y   <NA>     
## [1,] No. cases 1325 177 284  1786
## [2,] Percent   74.2 9.9 15.9 100

14. “I spent money more freely” (dichotomous, v1_mss_itm14)

v1_mss_recode(v1_clin$v1_mss_s1_mss14_mehr_geld,v1_con$v1_mss_s1_mss14,"v1_mss_itm14")

##                N    Y    <NA>     
## [1,] No. cases 1107 398  281  1786
## [2,] Percent   62   22.3 15.7 100

15. “I accumulated debts” (dichotomous, v1_mss_itm15)

v1_mss_recode(v1_clin$v1_mss_s1_mss15_schulden,v1_con$v1_mss_s1_mss15,"v1_mss_itm15")

##                N    Y   <NA>     
## [1,] No. cases 1380 124 282  1786
## [2,] Percent   77.3 6.9 15.8 100

16. “I made unwise business decisions” (dichotomous, v1_mss_itm16)

v1_mss_recode(v1_clin$v1_mss_s1_mss16_unkluge_entsch,v1_con$v1_mss_s1_mss16,"v1_mss_itm16")

##                N    Y   <NA>     
## [1,] No. cases 1421 80  285  1786
## [2,] Percent   79.6 4.5 16   100

17. “I partied more” (dichotomous, v1_mss_itm17)

v1_mss_recode(v1_clin$v1_mss_s1_mss17_parties,v1_con$v1_mss_s1_mss17,"v1_mss_itm17")

##                N    Y   <NA>     
## [1,] No. cases 1393 110 283  1786
## [2,] Percent   78   6.2 15.8 100

18. “I enjoyed flirting” (dichotomous, v1_mss_itm18)

v1_mss_recode(v1_clin$v1_mss_s1_mss18_flirten,v1_con$v1_mss_s1_mss18,"v1_mss_itm18")

##                N    Y   <NA>     
## [1,] No. cases 1340 167 279  1786
## [2,] Percent   75   9.4 15.6 100

19. “I masturbated more often” (dichotomous, v1_mss_itm19)

v1_mss_recode(v1_clin$v1_mss_s2_mss19_selbstbefried,v1_con$v1_mss_s2_mss19,"v1_mss_itm19")

##                N    Y   <NA>     
## [1,] No. cases 1359 115 312  1786
## [2,] Percent   76.1 6.4 17.5 100

20. “I was more interested in sex than usual” (dichotomous, v1_mss_itm20)

v1_mss_recode(v1_clin$v1_mss_s2_mss20_sex_interess,v1_con$v1_mss_s2_mss20,"v1_mss_itm20")

##                N    Y   <NA>     
## [1,] No. cases 1301 175 310  1786
## [2,] Percent   72.8 9.8 17.4 100

21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v1_mss_itm21)

v1_mss_recode(v1_clin$v1_mss_s2_mss21_sexpartner,v1_con$v1_mss_s2_mss21,"v1_mss_itm21")

##                N    Y   <NA>     
## [1,] No. cases 1431 45  310  1786
## [2,] Percent   80.1 2.5 17.4 100

22. “I spent more time on the phone” (dichotomous, v1_mss_itm22)

v1_mss_recode(v1_clin$v1_mss_s2_mss22_mehr_telefon,v1_con$v1_mss_s2_mss22,"v1_mss_itm22")

##                N    Y    <NA>     
## [1,] No. cases 1209 276  301  1786
## [2,] Percent   67.7 15.5 16.9 100

23. “I spoke louder than usual” (dichotomous, v1_mss_itm23)

v1_mss_recode(v1_clin$v1_mss_s2_mss23_sprache_lauter,v1_con$v1_mss_s2_mss23,"v1_mss_itm23")

##                N    Y   <NA>     
## [1,] No. cases 1289 197 300  1786
## [2,] Percent   72.2 11  16.8 100

24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v1_mss_itm24)

v1_mss_recode(v1_clin$v1_mss_s2_mss24_spr_schneller,v1_con$v1_mss_s2_mss24,"v1_mss_itm24")

##                N    Y   <NA>     
## [1,] No. cases 1321 163 302  1786
## [2,] Percent   74   9.1 16.9 100

25. “1 enjoyed punning or rhyming” (dichotomous, v1_mss_itm25)

v1_mss_recode(v1_clin$v1_mss_s2_mss25_witze,v1_con$v1_mss_s2_mss25,"v1_mss_itm25")

##                N    Y    <NA>     
## [1,] No. cases 1293 192  301  1786
## [2,] Percent   72.4 10.8 16.9 100

26. “I butted into conversations” (dichotomous, v1_mss_itm26)

v1_mss_recode(v1_clin$v1_mss_s2_mss26_einmischen,v1_con$v1_mss_s2_mss26,"v1_mss_itm26")

##                N    Y   <NA>     
## [1,] No. cases 1331 155 300  1786
## [2,] Percent   74.5 8.7 16.8 100

27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v1_mss_itm27)

v1_mss_recode(v1_clin$v1_mss_s2_mss27_red_pausenlos,v1_con$v1_mss_s2_mss27,"v1_mss_itm27")

##                N    Y   <NA>     
## [1,] No. cases 1399 84  303  1786
## [2,] Percent   78.3 4.7 17   100

28. “I enjoyed being the centre of attention” (dichotomous, v1_mss_itm28)

v1_mss_recode(v1_clin$v1_mss_s2_mss28_mittelpunkt,v1_con$v1_mss_s2_mss28,"v1_mss_itm28")

##                N    Y   <NA>     
## [1,] No. cases 1332 152 302  1786
## [2,] Percent   74.6 8.5 16.9 100

29. “I liked to joke and laugh” (dichotomous, v1_mss_itm29)

v1_mss_recode(v1_clin$v1_mss_s2_mss29_herumalbern,v1_con$v1_mss_s2_mss29,"v1_mss_itm29")

##                N    Y    <NA>     
## [1,] No. cases 1216 267  303  1786
## [2,] Percent   68.1 14.9 17   100

30. “People found me entertaining” (dichotomous, v1_mss_itm30)

v1_mss_recode(v1_clin$v1_mss_s2_mss30_unterhaltsamer,v1_con$v1_mss_s2_mss30,"v1_mss_itm30")

##                N    Y    <NA>     
## [1,] No. cases 1273 202  311  1786
## [2,] Percent   71.3 11.3 17.4 100

31. “I felt as if I was on top of the world” (dichotomous, v1_mss_itm31)

v1_mss_recode(v1_clin$v1_mss_s2_mss31_obenauf,v1_con$v1_mss_s2_mss31,"v1_mss_itm31")

##                N    Y    <NA>     
## [1,] No. cases 1302 180  304  1786
## [2,] Percent   72.9 10.1 17   100

32. “I was more cheerful than my usual self” (dichotomous, v1_mss_itm32)

v1_mss_recode(v1_clin$v1_mss_s2_mss32_froehlicher,v1_con$v1_mss_s2_mss32,"v1_mss_itm32")

##                N    Y    <NA>     
## [1,] No. cases 1154 329  303  1786
## [2,] Percent   64.6 18.4 17   100

33. “Other people got on my nerves” (dichotomous, v1_mss_itm33)

v1_mss_recode(v1_clin$v1_mss_s2_mss33_ungeduldiger,v1_con$v1_mss_s2_mss33,"v1_mss_itm33")

##                N    Y    <NA>     
## [1,] No. cases 1054 430  302  1786
## [2,] Percent   59   24.1 16.9 100

34. “I was getting into arguments” (dichotomous, v1_mss_itm34)

v1_mss_recode(v1_clin$v1_mss_s2_mss34_streiten,v1_con$v1_mss_s2_mss34,"v1_mss_itm34")

##                N    Y    <NA>     
## [1,] No. cases 1284 194  308  1786
## [2,] Percent   71.9 10.9 17.2 100

35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v1_mss_itm35)

v1_mss_recode(v1_clin$v1_mss_s2_mss35_ideen,v1_con$v1_mss_s2_mss35,"v1_mss_itm35")

##                N    Y    <NA>     
## [1,] No. cases 1134 349  303  1786
## [2,] Percent   63.5 19.5 17   100

36. “My thoughts raced through my mind” (dichotomous, v1_mss_itm36)

v1_mss_recode(v1_clin$v1_mss_s2_mss36_gedanken,v1_con$v1_mss_s2_mss36,"v1_mss_itm36")

##                N    Y    <NA>     
## [1,] No. cases 992  491  303  1786
## [2,] Percent   55.5 27.5 17   100

37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v1_mss_itm37)

v1_mss_recode(v1_clin$v1_mss_s2_mss37_konzentration,v1_con$v1_mss_s2_mss37,"v1_mss_itm37")

##                N    Y    <NA>     
## [1,] No. cases 1182 298  306  1786
## [2,] Percent   66.2 16.7 17.1 100

38. “I thought I was an especially important person” (dichotomous, v1_mss_itm38)

v1_mss_recode(v1_clin$v1_mss_s2_mss38_etw_besonderes,v1_con$v1_mss_s2_mss38,"v1_mss_itm38")

##                N    Y   <NA>     
## [1,] No. cases 1323 163 300  1786
## [2,] Percent   74.1 9.1 16.8 100

39. “I thought I could change the world” (dichotomous, v1_mss_itm39)

v1_mss_recode(v1_clin$v1_mss_s2_mss39_welt_veraender,v1_con$v1_mss_s2_mss39,"v1_mss_itm39")

##                N    Y   <NA>     
## [1,] No. cases 1338 143 305  1786
## [2,] Percent   74.9 8   17.1 100

40. “I thought I was right most of the time” (dichotomous, v1_mss_itm40)

v1_mss_recode(v1_clin$v1_mss_s2_mss40_recht_haben,v1_con$v1_mss_s2_mss40,"v1_mss_itm40")

##                N    Y   <NA>     
## [1,] No. cases 1355 129 302  1786
## [2,] Percent   75.9 7.2 16.9 100

41. “I thought I was superior to others” (dichotomous, v1_mss_itm41)

v1_mss_recode(v1_clin$v1_mss_s3_mss41_ueberlegen,v1_con$v1_mss_s3_mss41,"v1_mss_itm41")

##                N    Y   <NA>     
## [1,] No. cases 1380 94  312  1786
## [2,] Percent   77.3 5.3 17.5 100

42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v1_mss_itm42)

v1_mss_recode(v1_clin$v1_mss_s3_mss42_uebermut,v1_con$v1_mss_s3_mss42,"v1_mss_itm42")

##                N    Y   <NA>     
## [1,] No. cases 1315 158 313  1786
## [2,] Percent   73.6 8.8 17.5 100

43. “I thought I knew what other people were thinking” (dichotomous, v1_mss_itm43)

v1_mss_recode(v1_clin$v1_mss_s3_mss43_ged_lesen_akt,v1_con$v1_mss_s3_mss43,"v1_mss_itm43")

##                N    Y   <NA>     
## [1,] No. cases 1295 178 313  1786
## [2,] Percent   72.5 10  17.5 100

44. “I thought other people knew what I was thinking” (dichotomous, v1_mss_itm44)

v1_mss_recode(v1_clin$v1_mss_s3_mss44_ged_lesen_pas,v1_con$v1_mss_s3_mss44,"v1_mss_itm44")

##                N    Y   <NA>     
## [1,] No. cases 1345 126 315  1786
## [2,] Percent   75.3 7.1 17.6 100

45. “I thought someone wanted to harm me” (dichotomous, v1_mss_itm45)

v1_mss_recode(v1_clin$v1_mss_s3_mss45_etw_antun,v1_con$v1_mss_s3_mss45,"v1_mss_itm45")

##                N    Y   <NA>     
## [1,] No. cases 1347 126 313  1786
## [2,] Percent   75.4 7.1 17.5 100

46. “I heard voices when people weren’t there” (dichotomous, v1_mss_itm46)

v1_mss_recode(v1_clin$v1_mss_s3_mss46_stimmen,v1_con$v1_mss_s3_mss46,"v1_mss_itm46")

##                N    Y   <NA>     
## [1,] No. cases 1332 141 313  1786
## [2,] Percent   74.6 7.9 17.5 100

47. “I had false beliefs concerning who I was” (dichotomous, v1_mss_itm47)

v1_mss_recode(v1_clin$v1_mss_s3_mss47_jmd_anders,v1_con$v1_mss_s3_mss47,"v1_mss_itm47")

##                N    Y   <NA>     
## [1,] No. cases 1412 62  312  1786
## [2,] Percent   79.1 3.5 17.5 100

48. “I knew I was getting ill” (dichotomous, v1_mss_itm48)

v1_mss_recode(v1_clin$v1_mss_s3_mss48_krank_einsicht,v1_con$v1_mss_s3_mss48,"v1_mss_itm48")

##                N    Y    <NA>     
## [1,] No. cases 1142 317  327  1786
## [2,] Percent   63.9 17.7 18.3 100

Create MSS sum score (continuous [0-48],v1_mss_sum) Please note that if one or more of MSS items are missing, this will result in the sum score to become NA.

v1_mss_sum<-ifelse(v1_mss_itm1=="Y",1,0)+
            ifelse(v1_mss_itm2=="Y",1,0)+
            ifelse(v1_mss_itm3=="Y",1,0)+
            ifelse(v1_mss_itm4=="Y",1,0)+
            ifelse(v1_mss_itm5=="Y",1,0)+
            ifelse(v1_mss_itm6=="Y",1,0)+
            ifelse(v1_mss_itm7=="Y",1,0)+
            ifelse(v1_mss_itm8=="Y",1,0)+
            ifelse(v1_mss_itm9=="Y",1,0)+
            ifelse(v1_mss_itm10=="Y",1,0)+
            ifelse(v1_mss_itm11=="Y",1,0)+
            ifelse(v1_mss_itm12=="Y",1,0)+
            ifelse(v1_mss_itm13=="Y",1,0)+
            ifelse(v1_mss_itm14=="Y",1,0)+
            ifelse(v1_mss_itm15=="Y",1,0)+
            ifelse(v1_mss_itm16=="Y",1,0)+
            ifelse(v1_mss_itm17=="Y",1,0)+
            ifelse(v1_mss_itm18=="Y",1,0)+
            ifelse(v1_mss_itm19=="Y",1,0)+
            ifelse(v1_mss_itm20=="Y",1,0)+
            ifelse(v1_mss_itm21=="Y",1,0)+
            ifelse(v1_mss_itm22=="Y",1,0)+
            ifelse(v1_mss_itm23=="Y",1,0)+
            ifelse(v1_mss_itm24=="Y",1,0)+
            ifelse(v1_mss_itm25=="Y",1,0)+
            ifelse(v1_mss_itm26=="Y",1,0)+
            ifelse(v1_mss_itm27=="Y",1,0)+
            ifelse(v1_mss_itm28=="Y",1,0)+
            ifelse(v1_mss_itm29=="Y",1,0)+
            ifelse(v1_mss_itm30=="Y",1,0)+
            ifelse(v1_mss_itm31=="Y",1,0)+
            ifelse(v1_mss_itm32=="Y",1,0)+
            ifelse(v1_mss_itm33=="Y",1,0)+
            ifelse(v1_mss_itm34=="Y",1,0)+
            ifelse(v1_mss_itm35=="Y",1,0)+
            ifelse(v1_mss_itm36=="Y",1,0)+
            ifelse(v1_mss_itm37=="Y",1,0)+
            ifelse(v1_mss_itm38=="Y",1,0)+
            ifelse(v1_mss_itm39=="Y",1,0)+
            ifelse(v1_mss_itm40=="Y",1,0)+
            ifelse(v1_mss_itm41=="Y",1,0)+
            ifelse(v1_mss_itm42=="Y",1,0)+
            ifelse(v1_mss_itm43=="Y",1,0)+
            ifelse(v1_mss_itm44=="Y",1,0)+
            ifelse(v1_mss_itm45=="Y",1,0)+
            ifelse(v1_mss_itm46=="Y",1,0)+
            ifelse(v1_mss_itm47=="Y",1,0)+
            ifelse(v1_mss_itm48=="Y",1,0)

summary(v1_mss_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   4.000   5.905   8.000  39.000     562

Create dataset

v1_mss<-data.frame(v1_mss_itm1,v1_mss_itm2,v1_mss_itm3,v1_mss_itm4,v1_mss_itm5,v1_mss_itm6,
                   v1_mss_itm7,v1_mss_itm8,v1_mss_itm9,v1_mss_itm10,v1_mss_itm11,
                   v1_mss_itm12,v1_mss_itm13,v1_mss_itm14,v1_mss_itm15,v1_mss_itm16,
                   v1_mss_itm17,v1_mss_itm18,v1_mss_itm19,v1_mss_itm20,v1_mss_itm21,
                   v1_mss_itm22,v1_mss_itm23,v1_mss_itm24,v1_mss_itm25,v1_mss_itm26,
                   v1_mss_itm27,v1_mss_itm28,v1_mss_itm29,v1_mss_itm30,v1_mss_itm31,
                   v1_mss_itm32,v1_mss_itm33,v1_mss_itm34,v1_mss_itm35,v1_mss_itm36,
                   v1_mss_itm37,v1_mss_itm38,v1_mss_itm39,v1_mss_itm40,v1_mss_itm41,
                   v1_mss_itm42,v1_mss_itm43,v1_mss_itm44,v1_mss_itm45,v1_mss_itm46,
                   v1_mss_itm47,v1_mss_itm48, v1_mss_sum)

Life Events Questionnaire (LEQ)

In this questionnaire (Norbeck, 1984; Sarason, Johnson, & Siegel, 1978) many possible life events are listed (e.g. “Difficulties finding work”) from the following areas: health, work, school, residence, love and marriage, family and close friends, parenting, personal or social, financial, crime and legal matters, and other. Participants are supposed to answer only to those life events which they have experienced during the past six months. For these particular events, participants were asked to rate:

1. the nature of the influence (good/bad) and
2. the impact of the event in question on her/his life (0-3).

As participants usually only experience relatively few life events during the follow-up period, most of the items are not filled out. We have coded empty items as “-999” in people that filled out the questionnaire correctly. In participants that did not fill out the questionaire at all or filled it out obviously wrong (e.g. answering every question, regardless whether they experienced the life event or not), all items are “NA”.

The questionaire is divided in ten separate sections (A-“Health”, B-“Work”, C-“School”, D-“Residence”, E-“Love and marriage”, F-“Family and close friends”, G-“Parenting”, H-“Personal or social”, I-“Financial”, J-“Crime and legal matters”). The respective sections are contained in the item name.

Health

1. “Major personal illness or injury”

1A Nature (dichotomous [“good”,“bad”], v1_leq_A_1A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq1a_schw_krankh,v1_con$v1_leq_a_leq1a,"v1_leq_A_1A")

##                -999 bad  good <NA>     
## [1,] No. cases 932  431  85   338  1786
## [2,] Percent   52.2 24.1 4.8  18.9 100

1B Impact (ordinal [0,1,2,3], v1_leq_A_1B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq1e_schw_krankh,v1_con$v1_leq_a_leq1e,"v1_leq_A_1B")

##                -999 0   1   2   3    <NA>     
## [1,] No. cases 922  25  39  118 344  338  1786
## [2,] Percent   51.6 1.4 2.2 6.6 19.3 18.9 100

2. “Major change in eating habits”

2A Nature (dichotomous [“good”,“bad”], v1_leq_A_2A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq2a_ernaehrung,v1_con$v1_leq_a_leq2a,"v1_leq_A_2A")

##                -999 bad  good <NA>     
## [1,] No. cases 1005 204  239  338  1786
## [2,] Percent   56.3 11.4 13.4 18.9 100

2B Impact (ordinal [0,1,2,3], v1_leq_A_2B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq2e_ernaehrung,v1_con$v1_leq_a_leq2e,"v1_leq_A_2B")

##                -999 0   1   2    3   <NA>     
## [1,] No. cases 992  39  92  186  139 338  1786
## [2,] Percent   55.5 2.2 5.2 10.4 7.8 18.9 100

3. “Major change in sleeping habits”

3A Nature (dichotomous [“good”,“bad”], v1_leq_A_3A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq3a_schlaf,v1_con$v1_leq_a_leq3a,"v1_leq_A_3A")

##                -999 bad  good <NA>     
## [1,] No. cases 938  344  166  338  1786
## [2,] Percent   52.5 19.3 9.3  18.9 100

3B Impact (ordinal [0,1,2,3], v1_leq_A_3B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq3e_schlaf,v1_con$v1_leq_a_leq3e,"v1_leq_A_3B")

##                -999 0   1   2    3    <NA>     
## [1,] No. cases 926  27  111 182  202  338  1786
## [2,] Percent   51.8 1.5 6.2 10.2 11.3 18.9 100

4. “Major change in usual type and/or amount of recreation”

4A Nature (dichotomous [“good”,“bad”], v1_leq_A_4A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq4a_freizeit,v1_con$v1_leq_a_leq4a,"v1_leq_A_4A")

##                -999 bad  good <NA>     
## [1,] No. cases 885  269  294  338  1786
## [2,] Percent   49.6 15.1 16.5 18.9 100

4B Impact (ordinal [0,1,2,3], v1_leq_A_4B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq4e_freizeit,v1_con$v1_leq_a_leq4e,"v1_leq_A_4B")

##                -999 0  1   2    3    <NA>     
## [1,] No. cases 874  35 109 228  202  338  1786
## [2,] Percent   48.9 2  6.1 12.8 11.3 18.9 100

5. “Major dental work”

5A Nature (dichotomous [“good”,“bad”], v1_leq_A_5A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq5a_zahnarzt,v1_con$v1_leq_a_leq5a,"v1_leq_A_5A")

##                -999 bad good <NA>     
## [1,] No. cases 1222 93  133  338  1786
## [2,] Percent   68.4 5.2 7.4  18.9 100

5B Impact (ordinal [0,1,2,3], v1_leq_A_5B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq5e_zahnarzt,v1_con$v1_leq_a_leq5e,"v1_leq_A_5B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1205 66  52  68  57  338  1786
## [2,] Percent   67.5 3.7 2.9 3.8 3.2 18.9 100

6. “(Female) Pregnancy”

6A Nature (dichotomous [“good”,“bad”], v1_leq_A_6A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq6a_schwanger,v1_con$v1_leq_a_leq6a,"v1_leq_A_6A")

##                -999 bad good <NA>     
## [1,] No. cases 1423 7   18   338  1786
## [2,] Percent   79.7 0.4 1    18.9 100

6B Impact (ordinal [0,1,2,3], v1_leq_A_6B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq6e_schwanger,v1_con$v1_leq_a_leq6e,"v1_leq_A_6B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 1421 9   3   15  338  1786
## [2,] Percent   79.6 0.5 0.2 0.8 18.9 100

7. “(Female) Miscarriage or abortion”

7A Nature (dichotomous [“good”,“bad”], v1_leq_A_7A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq7a_fehlg_abtr,v1_con$v1_leq_a_leq7a,"v1_leq_A_7A")

##                -999 bad good <NA>     
## [1,] No. cases 1435 9   4    338  1786
## [2,] Percent   80.3 0.5 0.2  18.9 100

7B Impact (ordinal [0,1,2,3], v1_leq_A_7B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq7e_fehlg_abtr,v1_con$v1_leq_a_leq7e,"v1_leq_A_7B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1433 6   2   1   6   338  1786
## [2,] Percent   80.2 0.3 0.1 0.1 0.3 18.9 100

8. “(Female) Started menopause”

8A Nature (dichotomous [“good”,“bad”], v1_leq_A_8A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq8a_wechseljahre,v1_con$v1_leq_a_leq8a,"v1_leq_A_8A")

##                -999 bad good <NA>     
## [1,] No. cases 1391 38  19   338  1786
## [2,] Percent   77.9 2.1 1.1  18.9 100

8B Impact (ordinal [0,1,2,3], v1_leq_A_8B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq8e_wechseljahre,v1_con$v1_leq_a_leq8e,"v1_leq_A_8B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 1387 12  10  21  18 338  1786
## [2,] Percent   77.7 0.7 0.6 1.2 1  18.9 100

9. “Major difficulties with birth control pills or devices”

9A Nature (dichotomous [“good”,“bad”], v1_leq_A_9A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq9a_verhuetung,v1_con$v1_leq_a_leq9a,"v1_leq_A_9A")

##                -999 bad good <NA>     
## [1,] No. cases 1396 38  14   338  1786
## [2,] Percent   78.2 2.1 0.8  18.9 100

9B Impact (ordinal [0,1,2,3], v1_leq_A_9B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq9e_verhuetung,v1_con$v1_leq_a_leq9e,"v1_leq_A_9B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1392 16  12  9   19  338  1786
## [2,] Percent   77.9 0.9 0.7 0.5 1.1 18.9 100

Create dataset

v1_leq_A<-data.frame(v1_leq_A_1A,v1_leq_A_1B,v1_leq_A_2A,v1_leq_A_2B,v1_leq_A_3A,
                     v1_leq_A_3B,v1_leq_A_4A,v1_leq_A_4B,v1_leq_A_5A,v1_leq_A_5B,
                     v1_leq_A_6A,v1_leq_A_6B,v1_leq_A_7A,v1_leq_A_7B,v1_leq_A_8A,
                     v1_leq_A_8B,v1_leq_A_9A,v1_leq_A_9B)

Work

10. “Difficulty finding a job”

10A Nature (dichotomous [“good”,“bad”], v1_leq_B_10A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq10a_arbeitssuche,v1_con$v1_leq_b_leq10a,"v1_leq_B_10A")

##                -999 bad  good <NA>     
## [1,] No. cases 1193 204  51   338  1786
## [2,] Percent   66.8 11.4 2.9  18.9 100

10B Impact (ordinal [0,1,2,3], v1_leq_B_10B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq10e_arbeitssuche,v1_con$v1_leq_b_leq10e,"v1_leq_B_10B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1184 25  46  68  125 338  1786
## [2,] Percent   66.3 1.4 2.6 3.8 7   18.9 100

11. “Beginning work outside the home”

11A Nature (dichotomous [“good”,“bad”], v1_leq_B_11A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq11a_arbeit_aussen,v1_con$v1_leq_b_leq11a,"v1_leq_B_11A")

##                -999 bad good <NA>     
## [1,] No. cases 1225 73  150  338  1786
## [2,] Percent   68.6 4.1 8.4  18.9 100

11B Impact (ordinal [0,1,2,3], v1_leq_B_11B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq11e_arbeit_aussen,v1_con$v1_leq_b_leq11e,"v1_leq_B_11B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1220 25  42  66  95  338  1786
## [2,] Percent   68.3 1.4 2.4 3.7 5.3 18.9 100

12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v1_leq_B_12A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq12a_arbeitswechs,v1_con$v1_leq_b_leq12a,"v1_leq_B_12A")

##                -999 bad good <NA>     
## [1,] No. cases 1196 62  190  338  1786
## [2,] Percent   67   3.5 10.6 18.9 100

12B Impact (ordinal [0,1,2,3], v1_leq_B_12B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq12e_arbeitswechs,v1_con$v1_leq_b_leq12e,"v1_leq_B_12B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1193 14  48  87  106 338  1786
## [2,] Percent   66.8 0.8 2.7 4.9 5.9 18.9 100

13. “Changing your work hours or conditions”

13A Nature (dichotomous [“good”,“bad”], v1_leq_B_13A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq13a_veraend_arb,v1_con$v1_leq_b_leq13a,"v1_leq_B_13A")

##                -999 bad good <NA>     
## [1,] No. cases 1149 113 186  338  1786
## [2,] Percent   64.3 6.3 10.4 18.9 100

13B Impact (ordinal [0,1,2,3], v1_leq_B_13B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq13e_veraend_arb,v1_con$v1_leq_b_leq13e,"v1_leq_B_13B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1146 14  75  102 111 338  1786
## [2,] Percent   64.2 0.8 4.2 5.7 6.2 18.9 100

14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v1_leq_B_14A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq14a_veraend_ba,v1_con$v1_leq_b_leq14a,"v1_leq_B_14A")

##                -999 bad good <NA>     
## [1,] No. cases 1141 92  215  338  1786
## [2,] Percent   63.9 5.2 12   18.9 100

14B Impact (ordinal [0,1,2,3], v1_leq_B_14B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq14e_veraend_ba,v1_con$v1_leq_b_leq14e,"v1_leq_B_14B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1140 19  67  102 120 338  1786
## [2,] Percent   63.8 1.1 3.8 5.7 6.7 18.9 100

15. “Troubles at work with your employer or co-worker”

15A Nature (dichotomous [“good”,“bad”], v1_leq_B_15A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq15a_schw_arbeit,v1_con$v1_leq_b_leq15a,"v1_leq_B_15A")

##                -999 bad  good <NA>     
## [1,] No. cases 1223 201  24   338  1786
## [2,] Percent   68.5 11.3 1.3  18.9 100

15B Impact (ordinal [0,1,2,3], v1_leq_B_15B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq15e_schw_arbeit,v1_con$v1_leq_b_leq15e,"v1_leq_B_15B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1220 27  55  64  82  338  1786
## [2,] Percent   68.3 1.5 3.1 3.6 4.6 18.9 100

16. “Major business readjustment”

16A Nature (dichotomous [“good”,“bad”], v1_leq_B_16A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq16a_betr_reorg,v1_con$v1_leq_b_leq16a,"v1_leq_B_16A")

##                -999 bad good <NA>     
## [1,] No. cases 1374 41  33   338  1786
## [2,] Percent   76.9 2.3 1.8  18.9 100

16B Impact (ordinal [0,1,2,3], v1_leq_B_16B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq16e_betr_reorg,v1_con$v1_leq_b_leq16e,"v1_leq_B_16B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1370 13  23  19  23  338  1786
## [2,] Percent   76.7 0.7 1.3 1.1 1.3 18.9 100

17. “Being fired or laid off from work”

17A Nature (dichotomous [“good”,“bad”], v1_leq_B_17A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq17a_kuendigung,v1_con$v1_leq_b_leq17a,"v1_leq_B_17A")

##                -999 bad good <NA>     
## [1,] No. cases 1314 97  37   338  1786
## [2,] Percent   73.6 5.4 2.1  18.9 100

17B Impact (ordinal [0,1,2,3], v1_leq_B_17B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq17e_kuendigung,v1_con$v1_leq_b_leq17e,"v1_leq_B_17B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 1312 15  17 31  73  338  1786
## [2,] Percent   73.5 0.8 1  1.7 4.1 18.9 100

18. “Retirement from work”

18A Nature (dichotomous [“good”,“bad”], v1_leq_B_18A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq18a_ende_beruf,v1_con$v1_leq_b_leq18a,"v1_leq_B_18A")

##                -999 bad good <NA>     
## [1,] No. cases 1367 47  34   338  1786
## [2,] Percent   76.5 2.6 1.9  18.9 100

18B Impact (ordinal [0,1,2,3], v1_leq_B_18B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq18e_ende_beruf,v1_con$v1_leq_b_leq18e,"v1_leq_B_18B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1364 11  7   16  50  338  1786
## [2,] Percent   76.4 0.6 0.4 0.9 2.8 18.9 100

19. “Taking courses by mail or studying at home to help you in your work”

19A Nature (dichotomous [“good”,“bad”], v1_leq_B_19A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq19a_fortbildung,v1_con$v1_leq_b_leq19a,"v1_leq_B_19A")

##                -999 bad good <NA>     
## [1,] No. cases 1331 21  96   338  1786
## [2,] Percent   74.5 1.2 5.4  18.9 100

19B Impact (ordinal [0,1,2,3], v1_leq_B_19B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq19e_fortbildung,v1_con$v1_leq_b_leq19e,"v1_leq_B_19B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 1328 20  17 41  42  338  1786
## [2,] Percent   74.4 1.1 1  2.3 2.4 18.9 100

v1_leq_B<-data.frame(v1_leq_B_10A,v1_leq_B_10B,v1_leq_B_11A,v1_leq_B_11B,v1_leq_B_12A,
                     v1_leq_B_12B,v1_leq_B_13A,v1_leq_B_13B,v1_leq_B_14A,v1_leq_B_14B,
                     v1_leq_B_15A,v1_leq_B_15B,v1_leq_B_16A,v1_leq_B_16B,v1_leq_B_17A,
                     v1_leq_B_17B,v1_leq_B_18A,v1_leq_B_18B,v1_leq_B_19A,v1_leq_B_19B)

School

20. “Beginning or ceasing school, college, or training program”

20A Nature (dichotomous [“good”,“bad”], v1_leq_C_20A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq20a_beginn_ende,v1_con$v1_leq_c_d_leq20a,"v1_leq_C_20A")

##                -999 bad good <NA>     
## [1,] No. cases 1306 37  105  338  1786
## [2,] Percent   73.1 2.1 5.9  18.9 100

20B Impact (ordinal [0,1,2,3], v1_leq_C_20B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq20e_beginn_ende,v1_con$v1_leq_c_d_leq20e,"v1_leq_C_20B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1304 9   20  37  78  338  1786
## [2,] Percent   73   0.5 1.1 2.1 4.4 18.9 100

21. “Change of school, college, or training program”

21A Nature (dichotomous [“good”,“bad”], v1_leq_C_21A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq21a_schulwechsel,v1_con$v1_leq_c_d_leq21a,"v1_leq_C_21A")

##                -999 bad good <NA>     
## [1,] No. cases 1398 13  37   338  1786
## [2,] Percent   78.3 0.7 2.1  18.9 100

21B Impact (ordinal [0,1,2,3], v1_leq_C_21B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq21e_schulwechsel,v1_con$v1_leq_c_d_leq21e,"v1_leq_C_21B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1395 7   8   18 20  338  1786
## [2,] Percent   78.1 0.4 0.4 1  1.1 18.9 100

22. “Change in career goal or academic major”

A Nature (dichotomous [“good”,“bad”], v1_leq_C_22A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq22a_aend_karriere,v1_con$v1_leq_c_d_leq22a,"v1_leq_C_22A")

##                -999 bad good <NA>     
## [1,] No. cases 1339 22  87   338  1786
## [2,] Percent   75   1.2 4.9  18.9 100

B Impact (ordinal [0,1,2,3], v1_leq_C_22B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq22e_aend_karriere,v1_con$v1_leq_c_d_leq22e,"v1_leq_C_22B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 1336 8   19  31  54 338  1786
## [2,] Percent   74.8 0.4 1.1 1.7 3  18.9 100

23. “Problem in school, college, or training program”

23A Nature (dichotomous [“good”,“bad”], v1_leq_C_23A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq23a_schulprob,v1_con$v1_leq_c_d_leq23a,"v1_leq_C_23A")

##                -999 bad good <NA>     
## [1,] No. cases 1355 82  11   338  1786
## [2,] Percent   75.9 4.6 0.6  18.9 100

23B Impact (ordinal [0,1,2,3], v1_leq_C_23B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq23e_schulprob,v1_con$v1_leq_c_d_leq23e,"v1_leq_C_23B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1353 9   20  29  37  338  1786
## [2,] Percent   75.8 0.5 1.1 1.6 2.1 18.9 100

Create dataset

v1_leq_C<-data.frame(v1_leq_C_20A,v1_leq_C_20B,v1_leq_C_21A,v1_leq_C_21B,v1_leq_C_22A,v1_leq_C_22B,v1_leq_C_23A,v1_leq_C_23B)

Residence

24. “Difficulty finding housing”

24A Nature (dichotomous [“good”,“bad”], v1_leq_D_24A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq24a_schw_wsuche,v1_con$v1_leq_c_d_leq24a,"v1_leq_D_24A")

##                -999 bad good <NA>     
## [1,] No. cases 1265 149 34   338  1786
## [2,] Percent   70.8 8.3 1.9  18.9 100

24B Impact (ordinal [0,1,2,3], v1_leq_D_24B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq24e_schw_wsuche,v1_con$v1_leq_c_d_leq24e,"v1_leq_D_24B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1260 23  46  55  64  338  1786
## [2,] Percent   70.5 1.3 2.6 3.1 3.6 18.9 100

25. “Changing residence within the same town or city”

A Nature (dichotomous [“good”,“bad”], v1_leq_D_25A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq25a_umzug_nah,v1_con$v1_leq_c_d_leq25a,"v1_leq_D_25A")

##                -999 bad good <NA>     
## [1,] No. cases 1290 36  122  338  1786
## [2,] Percent   72.2 2   6.8  18.9 100

B Impact (ordinal [0,1,2,3], v1_leq_D_25B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq25e_umzug_nah,v1_con$v1_leq_c_d_leq25e,"v1_leq_D_25B")

##                -999 0  1   2   3   <NA>     
## [1,] No. cases 1288 17 30  46  67  338  1786
## [2,] Percent   72.1 1  1.7 2.6 3.8 18.9 100

26. “Moving to a different town, city, state, or country”

26A Nature (dichotomous [“good”,“bad”], v1_leq_D_26A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq26a_umzug_fern,v1_con$v1_leq_c_d_leq26a,"v1_leq_D_26A")

##                -999 bad good <NA>     
## [1,] No. cases 1321 41  86   338  1786
## [2,] Percent   74   2.3 4.8  18.9 100

26B Impact (ordinal [0,1,2,3], v1_leq_D_26B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq26e_umzug_fern,v1_con$v1_leq_c_d_leq26e,"v1_leq_D_26B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1315 15  12  36 70  338  1786
## [2,] Percent   73.6 0.8 0.7 2  3.9 18.9 100

27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”

27A Nature (dichotomous [“good”,“bad”], v1_leq_D_27A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq27a_veraend_lu,v1_con$v1_leq_c_d_leq27a,"v1_leq_D_27A")

##                -999 bad good <NA>     
## [1,] No. cases 1166 122 160  338  1786
## [2,] Percent   65.3 6.8 9    18.9 100

27B Impact (ordinal [0,1,2,3], v1_leq_D_27B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq27e_veraend_lu,v1_con$v1_leq_c_d_leq27e,"v1_leq_D_27B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1159 22  56  79  132 338  1786
## [2,] Percent   64.9 1.2 3.1 4.4 7.4 18.9 100

Create dataset

v1_leq_D<-data.frame(v1_leq_D_24A,v1_leq_D_24B,v1_leq_D_25A,v1_leq_D_25B,v1_leq_D_26A,
                     v1_leq_D_26B,v1_leq_D_27A,v1_leq_D_27B)

Love and marriage

28. “Began a new, close, personal relationship”

28A Nature (dichotomous [“good”,“bad”], v1_leq_E_28A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq28a_neue_bez,v1_con$v1_leq_e_leq28a,"v1_leq_E_28A")

##                -999 bad good <NA>     
## [1,] No. cases 1243 32  173  338  1786
## [2,] Percent   69.6 1.8 9.7  18.9 100

28B Impact (ordinal [0,1,2,3], v1_leq_E_28B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq28e_neue_bez,v1_con$v1_leq_e_leq28e,"v1_leq_E_28B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1239 12  32  52  113 338  1786
## [2,] Percent   69.4 0.7 1.8 2.9 6.3 18.9 100

29. “Became engaged”

29A Nature (dichotomous [“good”,“bad”], v1_leq_E_29A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq29a_verlobung,v1_con$v1_leq_e_leq29a,"v1_leq_E_29A")

##                -999 bad good <NA>     
## [1,] No. cases 1408 10  30   338  1786
## [2,] Percent   78.8 0.6 1.7  18.9 100

29B Impact (ordinal [0,1,2,3], v1_leq_E_29B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq29e_verlobung,v1_con$v1_leq_e_leq29e,"v1_leq_E_29B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1405 6   4   11  22  338  1786
## [2,] Percent   78.7 0.3 0.2 0.6 1.2 18.9 100

30. “Girlfriend or boyfriend problems”

30A Nature (dichotomous [“good”,“bad”], v1_leq_E_30A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq30a_prob_partner,v1_con$v1_leq_e_leq30a,"v1_leq_E_30A")

##                -999 bad  good <NA>     
## [1,] No. cases 1202 219  27   338  1786
## [2,] Percent   67.3 12.3 1.5  18.9 100

30B Impact (ordinal [0,1,2,3], v1_leq_E_30B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq30e_prob_partner,v1_con$v1_leq_e_leq30e,"v1_leq_E_30B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1199 10  47  95  97  338  1786
## [2,] Percent   67.1 0.6 2.6 5.3 5.4 18.9 100

31. “Breaking up with a girlfriend or breaking an engagement”

31A Nature (dichotomous [“good”,“bad”], v1_leq_E_31A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq31a_trennung,v1_con$v1_leq_e_leq31a,"v1_leq_E_31A")

##                -999 bad good <NA>     
## [1,] No. cases 1295 116 37   338  1786
## [2,] Percent   72.5 6.5 2.1  18.9 100

31B Impact (ordinal [0,1,2,3], v1_leq_E_31B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq31e_trennung,v1_con$v1_leq_e_leq31e,"v1_leq_E_31B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1290 12  26  50  70  338  1786
## [2,] Percent   72.2 0.7 1.5 2.8 3.9 18.9 100

32. “(Male) Wife or girlfriend’s pregnancy”

32A Nature (dichotomous [“good”,“bad”], v1_leq_E_32A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq32a_schwanger_p,v1_con$v1_leq_e_leq32a,"v1_leq_E_32A")

##                -999 bad good <NA>     
## [1,] No. cases 1433 7   8    338  1786
## [2,] Percent   80.2 0.4 0.4  18.9 100

32B Impact (ordinal [0,1,2,3], v1_leq_E_32B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq32e_schwanger_p,v1_con$v1_leq_e_leq32e,"v1_leq_E_32B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1431 2   3   3   9   338  1786
## [2,] Percent   80.1 0.1 0.2 0.2 0.5 18.9 100

33. “(Male) Wife or girlfriend having a miscarriage or abortion”

33A Nature (dichotomous [“good”,“bad”], v1_leq_E_33A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq33a_fehlg_abtr_p,v1_con$v1_leq_e_leq33a,"v1_leq_E_33A")

##                -999 bad good <NA>     
## [1,] No. cases 1436 10  2    338  1786
## [2,] Percent   80.4 0.6 0.1  18.9 100

33B Impact (ordinal [0,1,2,3], v1_leq_E_33B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq33e_fehlg_abtr_p,v1_con$v1_leq_e_leq33e,"v1_leq_E_33B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1435 5   2   1   5   338  1786
## [2,] Percent   80.3 0.3 0.1 0.1 0.3 18.9 100

34. “Getting married (or beginning to live with someone)”

34A Nature (dichotomous [“good”,“bad”], v1_leq_E_34A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq34a_heirat,v1_con$v1_leq_e_leq34a,"v1_leq_E_34A")

##                -999 bad good <NA>     
## [1,] No. cases 1404 5   39   338  1786
## [2,] Percent   78.6 0.3 2.2  18.9 100

34B Impact (ordinal [0,1,2,3], v1_leq_E_34B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq34e_heirat,v1_con$v1_leq_e_leq34e,"v1_leq_E_34B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1399 4   4   14  27  338  1786
## [2,] Percent   78.3 0.2 0.2 0.8 1.5 18.9 100

35. “A change in closeness with your partner”

35A Nature (dichotomous [“good”,“bad”], v1_leq_E_35A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq35a_veraend_naehe,v1_con$v1_leq_e_leq35a,"v1_leq_E_35A")

##                -999 bad good <NA>     
## [1,] No. cases 1223 129 96   338  1786
## [2,] Percent   68.5 7.2 5.4  18.9 100

35B Impact (ordinal [0,1,2,3], v1_leq_E_35B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq35e_veraend_naehe,v1_con$v1_leq_e_leq35e,"v1_leq_E_35B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 1217 7   36 82  106 338  1786
## [2,] Percent   68.1 0.4 2  4.6 5.9 18.9 100

36. “Infidelity”

36A Nature (dichotomous [“good”,“bad”], v1_leq_E_36A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq36a_untreue,v1_con$v1_leq_e_leq36a,"v1_leq_E_36A")

##                -999 bad good <NA>     
## [1,] No. cases 1383 52  13   338  1786
## [2,] Percent   77.4 2.9 0.7  18.9 100

36B Impact (ordinal [0,1,2,3], v1_leq_E_36B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq36e_untreue,v1_con$v1_leq_e_leq36e,"v1_leq_E_36B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1380 14  12  9   33  338  1786
## [2,] Percent   77.3 0.8 0.7 0.5 1.8 18.9 100

37. “Trouble with in-laws”

37A Nature (dichotomous [“good”,“bad”], v1_leq_E_37A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq37a_konf_schwiege,v1_con$v1_leq_e_leq37a,"v1_leq_E_37A")

##                -999 bad good <NA>     
## [1,] No. cases 1379 60  9    338  1786
## [2,] Percent   77.2 3.4 0.5  18.9 100

37B Impact (ordinal [0,1,2,3], v1_leq_E_37B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq37e_konf_schwiege,v1_con$v1_leq_e_leq37e,"v1_leq_E_37B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 1377 5   21  28  17 338  1786
## [2,] Percent   77.1 0.3 1.2 1.6 1  18.9 100

38. “Separation from spouse or partner due to conflict”

38A Nature (dichotomous [“good”,“bad”], v1_leq_E_38A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq38a_trennung_str,v1_con$v1_leq_e_leq38a,"v1_leq_E_38A")

##                -999 bad good <NA>     
## [1,] No. cases 1379 51  18   338  1786
## [2,] Percent   77.2 2.9 1    18.9 100

38B Impact (ordinal [0,1,2,3], v1_leq_E_38B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq38e_trennung_str,v1_con$v1_leq_e_leq38e,"v1_leq_E_38B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1379 4   5   21  39  338  1786
## [2,] Percent   77.2 0.2 0.3 1.2 2.2 18.9 100

39. “Separation from spouse or partner due to work, travel, etc.”

39A Nature (dichotomous [“good”,“bad”], v1_leq_E_39A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq39a_trennung_ber,v1_con$v1_leq_e_leq39a,"v1_leq_E_39A")

##                -999 bad good <NA>     
## [1,] No. cases 1428 19  1    338  1786
## [2,] Percent   80   1.1 0.1  18.9 100

39B Impact (ordinal [0,1,2,3], v1_leq_E_39B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq39e_trennung_ber,v1_con$v1_leq_e_leq39e,"v1_leq_E_39B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1426 7   1   6   8   338  1786
## [2,] Percent   79.8 0.4 0.1 0.3 0.4 18.9 100

40. “Reconciliation with spouse or partner”

40A Nature (dichotomous [“good”,“bad”], v1_leq_E_40A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq40a_versoehnung,v1_con$v1_leq_e_leq40a,"v1_leq_E_40A")

##                -999 bad good <NA>     
## [1,] No. cases 1369 7   72   338  1786
## [2,] Percent   76.7 0.4 4    18.9 100

40B Impact (ordinal [0,1,2,3], v1_leq_E_40B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq40e_versoehnung,v1_con$v1_leq_e_leq40e,"v1_leq_E_40B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1368 5   14  22  39  338  1786
## [2,] Percent   76.6 0.3 0.8 1.2 2.2 18.9 100

41. “Divorce”

41A Nature (dichotomous [“good”,“bad”], v1_leq_E_41A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq41a_scheidung,v1_con$v1_leq_e_leq41a,"v1_leq_E_41A")

##                -999 bad good <NA>     
## [1,] No. cases 1422 18  8    338  1786
## [2,] Percent   79.6 1   0.4  18.9 100

41B Impact (ordinal [0,1,2,3], v1_leq_E_41B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq41e_scheidung,v1_con$v1_leq_e_leq41e,"v1_leq_E_41B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1421 5   1   6   15  338  1786
## [2,] Percent   79.6 0.3 0.1 0.3 0.8 18.9 100

42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”

42A Nature (dichotomous [“good”,“bad”], v1_leq_E_42A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq42a_veraend_taet,v1_con$v1_leq_e_leq42a,"v1_leq_E_42A")

##                -999 bad good <NA>     
## [1,] No. cases 1363 34  51   338  1786
## [2,] Percent   76.3 1.9 2.9  18.9 100

42B Impact (ordinal [0,1,2,3], v1_leq_E_42B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq42e_veraend_taet,v1_con$v1_leq_e_leq42e,"v1_leq_E_42B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1361 10  15  32  30  338  1786
## [2,] Percent   76.2 0.6 0.8 1.8 1.7 18.9 100

Create dataset

v1_leq_E<-data.frame(v1_leq_E_28A,v1_leq_E_28B,v1_leq_E_29A,v1_leq_E_29B,v1_leq_E_30A,
                     v1_leq_E_30B,v1_leq_E_31A,v1_leq_E_31B,v1_leq_E_32A,v1_leq_E_32B,
                     v1_leq_E_33A,v1_leq_E_33B,v1_leq_E_34A,v1_leq_E_34B,v1_leq_E_35A,
                     v1_leq_E_35B,v1_leq_E_36A,v1_leq_E_36B,v1_leq_E_37A,v1_leq_E_37B,
                     v1_leq_E_38A,v1_leq_E_38B,v1_leq_E_39A,v1_leq_E_39B,v1_leq_E_40A,
                     v1_leq_E_40B,v1_leq_E_41A,v1_leq_E_41B,v1_leq_E_42A,v1_leq_E_42B)

Family and close friends

43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”

43A Nature (dichotomous [“good”,“bad”], v1_leq_F_43A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq43a_neu_fmitglied,v1_con$v1_leq_f_g_leq43a,"v1_leq_F_43A")

##                -999 bad good <NA>     
## [1,] No. cases 1326 14  108  338  1786
## [2,] Percent   74.2 0.8 6    18.9 100

43B Impact (ordinal [0,1,2,3], v1_leq_F_43B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq43e_neu_fmitglied,v1_con$v1_leq_f_g_leq43e,"v1_leq_F_43B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 1324 10  35 30  49  338  1786
## [2,] Percent   74.1 0.6 2  1.7 2.7 18.9 100

44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”

44A Nature (dichotomous [“good”,“bad”], v1_leq_F_44A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq44a_auszug_fm,v1_con$v1_leq_f_g_leq44a,"v1_leq_F_44A")

##                -999 bad good <NA>     
## [1,] No. cases 1382 30  36   338  1786
## [2,] Percent   77.4 1.7 2    18.9 100

44B Impact (ordinal [0,1,2,3], v1_leq_F_44B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq44e_auszug_fm,v1_con$v1_leq_f_g_leq44e,"v1_leq_F_44B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1379 8   14  25  22  338  1786
## [2,] Percent   77.2 0.4 0.8 1.4 1.2 18.9 100

45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”

45A Nature (dichotomous [“good”,“bad”], v1_leq_F_45A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq45a_gz_verh_fm,v1_con$v1_leq_f_g_leq45a,"v1_leq_F_45A")

##                -999 bad  good <NA>     
## [1,] No. cases 1176 247  25   338  1786
## [2,] Percent   65.8 13.8 1.4  18.9 100

45B Impact (ordinal [0,1,2,3], v1_leq_F_45B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq45e_gz_verh_fm,v1_con$v1_leq_f_g_leq45e,"v1_leq_F_45B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1175 6   44  102 121 338  1786
## [2,] Percent   65.8 0.3 2.5 5.7 6.8 18.9 100

46. “Death of spouse or partner”

46A Nature (dichotomous [“good”,“bad”], v1_leq_F_46A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq46a_tod_partner,v1_con$v1_leq_f_g_leq46a,"v1_leq_F_46A")

##                -999 bad good <NA>     
## [1,] No. cases 1432 15  1    338  1786
## [2,] Percent   80.2 0.8 0.1  18.9 100

46B Impact (ordinal [0,1,2,3], v1_leq_F_46B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq46e_tod_partner,v1_con$v1_leq_f_g_leq46e,"v1_leq_F_46B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 1431 3   4   10  338  1786
## [2,] Percent   80.1 0.2 0.2 0.6 18.9 100

47. “Death of a child”

47A Nature (dichotomous [“good”,“bad”], v1_leq_F_47A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq47a_tod_kind,v1_con$v1_leq_f_g_leq47a,"v1_leq_F_47A")

##                -999 bad good <NA>     
## [1,] No. cases 1434 13  1    338  1786
## [2,] Percent   80.3 0.7 0.1  18.9 100

47B Impact (ordinal [0,1,2,3], v1_leq_F_47B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq47e_tod_kind,v1_con$v1_leq_f_g_leq47e,"v1_leq_F_47B")

##                -999 0   3   <NA>     
## [1,] No. cases 1434 3   11  338  1786
## [2,] Percent   80.3 0.2 0.6 18.9 100

48. “Death of family member or close friend”

48A Nature (dichotomous [“good”,“bad”], v1_leq_F_48A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq48a_tod_fm_ef,v1_con$v1_leq_f_g_leq48a,"v1_leq_F_48A")

##                -999 bad good <NA>     
## [1,] No. cases 1326 118 4    338  1786
## [2,] Percent   74.2 6.6 0.2  18.9 100

48B Impact (ordinal [0,1,2,3], v1_leq_F_48B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq48e_tod_fm_ef,v1_con$v1_leq_f_g_leq48e,"v1_leq_F_48B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1324 11  25  40  48  338  1786
## [2,] Percent   74.1 0.6 1.4 2.2 2.7 18.9 100

49. “Birth of a grandchild”

49A Nature (dichotomous [“good”,“bad”], v1_leq_F_49A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq49a_geb_enkel,v1_con$v1_leq_f_g_leq49a,"v1_leq_F_49A")

##                -999 bad good <NA>     
## [1,] No. cases 1413 4   31   338  1786
## [2,] Percent   79.1 0.2 1.7  18.9 100

49B Impact (ordinal [0,1,2,3], v1_leq_F_49B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq49e_geb_enkel,v1_con$v1_leq_f_g_leq49e,"v1_leq_F_49B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1411 6   6   3   22  338  1786
## [2,] Percent   79   0.3 0.3 0.2 1.2 18.9 100

50. “Change in marital status of your parents”

50A Nature (dichotomous [“good”,“bad”], v1_leq_F_50A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq50a_fstand_eltern,v1_con$v1_leq_f_g_leq50a,"v1_leq_F_50A")

##                -999 bad good <NA>     
## [1,] No. cases 1412 28  8    338  1786
## [2,] Percent   79.1 1.6 0.4  18.9 100

50B Impact (ordinal [0,1,2,3], v1_leq_F_50B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq50e_fstand_eltern,v1_con$v1_leq_f_g_leq50e,"v1_leq_F_50B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1411 5   7   12  13  338  1786
## [2,] Percent   79   0.3 0.4 0.7 0.7 18.9 100

Create dataset

v1_leq_F<-data.frame(v1_leq_F_43A,v1_leq_F_43B,v1_leq_F_44A,v1_leq_F_44B,v1_leq_F_45A,
                     v1_leq_F_45B,v1_leq_F_46A,v1_leq_F_46B,v1_leq_F_47A,v1_leq_F_47B,
                     v1_leq_F_48A,v1_leq_F_48B,v1_leq_F_49A,v1_leq_F_49B,v1_leq_F_50A,
                     v1_leq_F_50B)

Parenting

51. “Change in child care arrangements”

51A Nature (dichotomous [“good”,“bad”], v1_leq_G_51A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq51a_kindbetr,v1_con$v1_leq_f_g_leq51a,"v1_leq_G_51A")

##                -999 bad good <NA>     
## [1,] No. cases 1405 20  23   338  1786
## [2,] Percent   78.7 1.1 1.3  18.9 100

51B Impact (ordinal [0,1,2,3], v1_leq_G_51B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq51e_kindbetr,v1_con$v1_leq_f_g_leq51e,"v1_leq_G_51B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1404 6   5   14  19  338  1786
## [2,] Percent   78.6 0.3 0.3 0.8 1.1 18.9 100

52. “Conflicts with spouse or partner about parenting”

52A Nature (dichotomous [“good”,“bad”], v1_leq_G_52A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq52a_konf_eschaft,v1_con$v1_leq_f_g_leq52a,"v1_leq_G_52A")

##                -999 bad good <NA>     
## [1,] No. cases 1403 38  7    338  1786
## [2,] Percent   78.6 2.1 0.4  18.9 100

52B Impact (ordinal [0,1,2,3], v1_leq_G_52B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq52e_konf_eschaft,v1_con$v1_leq_f_g_leq52e,"v1_leq_G_52B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1403 5   12  17 11  338  1786
## [2,] Percent   78.6 0.3 0.7 1  0.6 18.9 100

53. “Conflicts with child’s grandparents (or other important person) about parenting”

53A Nature (dichotomous [“good”,“bad”], v1_leq_G_53A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq53a_konf_geltern,v1_con$v1_leq_f_g_leq53a,"v1_leq_G_53A")

##                -999 bad good <NA>     
## [1,] No. cases 1427 17  4    338  1786
## [2,] Percent   79.9 1   0.2  18.9 100

53B Impact (ordinal [0,1,2,3], v1_leq_G_53B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq53e_konf_geltern,v1_con$v1_leq_f_g_leq53e,"v1_leq_G_53B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1427 3   7   3   8   338  1786
## [2,] Percent   79.9 0.2 0.4 0.2 0.4 18.9 100

54. “Taking on full responsibility for parenting as a single parent”

54A Nature (dichotomous [“good”,“bad”], v1_leq_G_54A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq54a_alleinerz,v1_con$v1_leq_f_g_leq54a,"v1_leq_G_54A")

##                -999 bad good <NA>     
## [1,] No. cases 1426 12  10   338  1786
## [2,] Percent   79.8 0.7 0.6  18.9 100

54B Impact (ordinal [0,1,2,3], v1_leq_G_54B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq54e_alleinerz,v1_con$v1_leq_f_g_leq54e,"v1_leq_G_54B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1426 3   2   8   9   338  1786
## [2,] Percent   79.8 0.2 0.1 0.4 0.5 18.9 100

55. “Custody battles with former spouse or partner”

55A Nature (dichotomous [“good”,“bad”], v1_leq_G_55A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq55a_sorgerecht,v1_con$v1_leq_f_g_leq55a,"v1_leq_G_55A")

##                -999 bad good <NA>     
## [1,] No. cases 1416 28  4    338  1786
## [2,] Percent   79.3 1.6 0.2  18.9 100

55B Impact (ordinal [0,1,2,3], v1_leq_G_55B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq55e_sorgerecht,v1_con$v1_leq_f_g_leq55e,"v1_leq_G_55B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1414 3   11  7   13  338  1786
## [2,] Percent   79.2 0.2 0.6 0.4 0.7 18.9 100

Create dataset

v1_leq_G<-data.frame(v1_leq_G_51A,
                     v1_leq_G_51B,
                     v1_leq_G_52A,
                     v1_leq_G_52B,
                     v1_leq_G_53A,
                     v1_leq_G_53B,
                     v1_leq_G_54A,
                     v1_leq_G_54B,
                     v1_leq_G_55A,
                     v1_leq_G_55B)

Personal or social

56. “Major personal achievement”

56A Nature (dichotomous [“good”,“bad”], v1_leq_H_56A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq56a_pers_leistung,v1_con$v1_leq_h_leq56a,"v1_leq_H_56A")

##                -999 bad good <NA>     
## [1,] No. cases 1099 55  294  338  1786
## [2,] Percent   61.5 3.1 16.5 18.9 100

56B Impact (ordinal [0,1,2,3], v1_leq_H_56B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq56e_pers_leistung,v1_con$v1_leq_h_leq56e,"v1_leq_H_56B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1095 13  61  134 145 338  1786
## [2,] Percent   61.3 0.7 3.4 7.5 8.1 18.9 100

57. “Major decision regarding your immediate future”

57A Nature (dichotomous [“good”,“bad”], v1_leq_H_57A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq57a_wicht_entsch,v1_con$v1_leq_h_leq57a,"v1_leq_H_57A")

##                -999 bad good <NA>     
## [1,] No. cases 894  122 432  338  1786
## [2,] Percent   50.1 6.8 24.2 18.9 100

57B Impact (ordinal [0,1,2,3], v1_leq_H_57B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq57e_wicht_entsch,v1_con$v1_leq_h_leq57e,"v1_leq_H_57B")

##                -999 0   1   2    3    <NA>     
## [1,] No. cases 883  12  69  194  290  338  1786
## [2,] Percent   49.4 0.7 3.9 10.9 16.2 18.9 100

58. “Change in your personal habits (your dress, life-style, hobbies, etc.)”

58A Nature (dichotomous [“good”,“bad”], v1_leq_H_58A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq58a_pers_gewohn,v1_con$v1_leq_h_leq58a,"v1_leq_H_58A")

##                -999 bad good <NA>     
## [1,] No. cases 1060 104 284  338  1786
## [2,] Percent   59.4 5.8 15.9 18.9 100

58B Impact (ordinal [0,1,2,3], v1_leq_H_58B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq58e_pers_gewohn,v1_con$v1_leq_h_leq58e,"v1_leq_H_58B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1055 13  92  156 132 338  1786
## [2,] Percent   59.1 0.7 5.2 8.7 7.4 18.9 100

59. “Change in your religious beliefs”

59A Nature (dichotomous [“good”,“bad”], v1_leq_H_59A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq59a_relig_ueberz,v1_con$v1_leq_h_leq59a,"v1_leq_H_59A")

##                -999 bad good <NA>     
## [1,] No. cases 1341 17  90   338  1786
## [2,] Percent   75.1 1   5    18.9 100

59B Impact (ordinal [0,1,2,3], v1_leq_H_59B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq59e_relig_ueberz,v1_con$v1_leq_h_leq59e,"v1_leq_H_59B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 1333 22  30  28  35 338  1786
## [2,] Percent   74.6 1.2 1.7 1.6 2  18.9 100

60. “Change in your political beliefs”

60A Nature (dichotomous [“good”,“bad”], v1_leq_H_60A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq60a_pol_ansichten,v1_con$v1_leq_h_leq60a,"v1_leq_H_60A")

##                -999 bad good <NA>     
## [1,] No. cases 1350 18  80   338  1786
## [2,] Percent   75.6 1   4.5  18.9 100

60B Impact (ordinal [0,1,2,3], v1_leq_H_60B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq60e_pol_ansichten,v1_con$v1_leq_h_leq60e,"v1_leq_H_60B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1341 27  29  36 15  338  1786
## [2,] Percent   75.1 1.5 1.6 2  0.8 18.9 100

61. “Loss or damage of personal property”

61A Nature (dichotomous [“good”,“bad”], v1_leq_H_61A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq61a_pers_eigent,v1_con$v1_leq_h_leq61a,"v1_leq_H_61A")

##                -999 bad good <NA>     
## [1,] No. cases 1301 126 21   338  1786
## [2,] Percent   72.8 7.1 1.2  18.9 100

61B Impact (ordinal [0,1,2,3], v1_leq_H_61B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq61e_pers_eigent,v1_con$v1_leq_h_leq61e,"v1_leq_H_61B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1295 25  46  40  42  338  1786
## [2,] Percent   72.5 1.4 2.6 2.2 2.4 18.9 100

62. “Took a vacation”

62A Nature (dichotomous [“good”,“bad”], v1_leq_H_62A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq62a_erholungsurl,v1_con$v1_leq_h_leq62a,"v1_leq_H_62A")

##                -999 bad good <NA>     
## [1,] No. cases 1040 22  386  338  1786
## [2,] Percent   58.2 1.2 21.6 18.9 100

62B Impact (ordinal [0,1,2,3], v1_leq_H_62B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq62e_erholungsurl,v1_con$v1_leq_h_leq62e,"v1_leq_H_62B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1031 39  79  157 142 338  1786
## [2,] Percent   57.7 2.2 4.4 8.8 8   18.9 100

63. “Took a trip other than a vacation”

63A Nature (dichotomous [“good”,“bad”], v1_leq_H_63A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq63a_reise_andere,v1_con$v1_leq_h_leq63a,"v1_leq_H_63A")

##                -999 bad good <NA>     
## [1,] No. cases 1226 16  206  338  1786
## [2,] Percent   68.6 0.9 11.5 18.9 100

63B Impact (ordinal [0,1,2,3], v1_leq_H_63B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq63e_reise_andere,v1_con$v1_leq_h_leq63e,"v1_leq_H_63B")

##                -999 0   1  2   3  <NA>     
## [1,] No. cases 1222 22  54 79  71 338  1786
## [2,] Percent   68.4 1.2 3  4.4 4  18.9 100

64. “Change in family get-togethers”

64A Nature (dichotomous [“good”,“bad”], v1_leq_H_64A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq64a_familientreff,v1_con$v1_leq_h_leq64a,"v1_leq_H_64A")

##                -999 bad good <NA>     
## [1,] No. cases 1291 61  96   338  1786
## [2,] Percent   72.3 3.4 5.4  18.9 100

64B Impact (ordinal [0,1,2,3], v1_leq_H_64B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq64e_familientreff,v1_con$v1_leq_h_leq64e,"v1_leq_H_64B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1287 14  47  57  43  338  1786
## [2,] Percent   72.1 0.8 2.6 3.2 2.4 18.9 100

65. “Change in your social activities (clubs, movies, visiting)”

65A Nature (dichotomous [“good”,“bad”], v1_leq_H_65A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq65a_ges_unternehm,v1_con$v1_leq_h_leq65a,"v1_leq_H_65A")

##                -999 bad good <NA>     
## [1,] No. cases 1233 74  141  338  1786
## [2,] Percent   69   4.1 7.9  18.9 100

65B Impact (ordinal [0,1,2,3], v1_leq_H_65B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq65e_ges_unternehm,v1_con$v1_leq_h_leq65e,"v1_leq_H_65B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1229 12  63  86  58  338  1786
## [2,] Percent   68.8 0.7 3.5 4.8 3.2 18.9 100

66. “Made new friends”

66A Nature (dichotomous [“good”,“bad”], v1_leq_H_66A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq66a_neue_freunds,v1_con$v1_leq_h_leq66a,"v1_leq_H_66A")

##                -999 bad good <NA>     
## [1,] No. cases 980  20  448  338  1786
## [2,] Percent   54.9 1.1 25.1 18.9 100

66B Impact (ordinal [0,1,2,3], v1_leq_H_66B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq66e_neue_freunds,v1_con$v1_leq_h_leq66e,"v1_leq_H_66B")

##                -999 0   1   2    3   <NA>     
## [1,] No. cases 973  19  116 209  131 338  1786
## [2,] Percent   54.5 1.1 6.5 11.7 7.3 18.9 100

67. “Broke up with a friend”

67A Nature (dichotomous [“good”,“bad”], v1_leq_H_67A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq67a_ende_freunds,v1_con$v1_leq_h_leq67a,"v1_leq_H_67A")

##                -999 bad good <NA>     
## [1,] No. cases 1235 151 62   338  1786
## [2,] Percent   69.1 8.5 3.5  18.9 100

67B Impact (ordinal [0,1,2,3], v1_leq_H_67B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq67e_ende_freunds,v1_con$v1_leq_h_leq67e,"v1_leq_H_67B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 1232 16  53 79  68  338  1786
## [2,] Percent   69   0.9 3  4.4 3.8 18.9 100

68. “Acquired or lost a pet”

68A Nature (dichotomous [“good”,“bad”], v1_leq_H_68A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq68a_haustier,v1_con$v1_leq_h_leq68a,"v1_leq_H_68A")

##                -999 bad good <NA>     
## [1,] No. cases 1314 64  70   338  1786
## [2,] Percent   73.6 3.6 3.9  18.9 100

68B Impact (ordinal [0,1,2,3], v1_leq_H_68B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq68e_haustier,v1_con$v1_leq_h_leq68e,"v1_leq_H_68B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1311 15  22  37  63  338  1786
## [2,] Percent   73.4 0.8 1.2 2.1 3.5 18.9 100

Create dataset

v1_leq_H<-data.frame(v1_leq_H_56A,
                     v1_leq_H_56B,
                     v1_leq_H_57A,
                     v1_leq_H_57B,
                     v1_leq_H_58A,
                     v1_leq_H_58B,
                     v1_leq_H_59A,
                     v1_leq_H_59B,
                     v1_leq_H_60A,
                     v1_leq_H_60B,
                     v1_leq_H_61A,
                     v1_leq_H_61B,
                     v1_leq_H_62A,
                     v1_leq_H_62B,
                     v1_leq_H_63A,
                     v1_leq_H_63B,
                     v1_leq_H_64A,
                     v1_leq_H_64B,
                     v1_leq_H_65A,
                     v1_leq_H_65B,
                     v1_leq_H_66A,
                     v1_leq_H_66B,
                     v1_leq_H_67A,
                     v1_leq_H_67B,
                     v1_leq_H_68A,
                     v1_leq_H_68B)

Financial

69. “Major change in finances (increased or decreased income)”

69A Nature (dichotomous [“good”,“bad”], v1_leq_I_69A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq69a_finanz_sit,v1_con$v1_leq_i_j_k_leq69a,"v1_leq_I_69A")

##                -999 bad  good <NA>     
## [1,] No. cases 1004 256  188  338  1786
## [2,] Percent   56.2 14.3 10.5 18.9 100

69B Impact (ordinal [0,1,2,3], v1_leq_I_69B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq69e_finanz_sit,v1_con$v1_leq_i_j_k_leq69e,"v1_leq_I_69B")

##                -999 0   1   2   3    <NA>     
## [1,] No. cases 1001 16  105 135 191  338  1786
## [2,] Percent   56   0.9 5.9 7.6 10.7 18.9 100

70. “Took on a moderate purchase, such as TV, car, freezer, etc.”

70A Nature (dichotomous [“good”,“bad”], v1_leq_I_70A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq70a_finanz_verpfl,v1_con$v1_leq_i_j_k_leq70a,"v1_leq_I_70A")

##                -999 bad good <NA>     
## [1,] No. cases 1287 63  98   338  1786
## [2,] Percent   72.1 3.5 5.5  18.9 100

70B Impact (ordinal [0,1,2,3], v1_leq_I_70B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq70e_finanz_verpfl,v1_con$v1_leq_i_j_k_leq70e,"v1_leq_I_70B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1286 22  52  58  30  338  1786
## [2,] Percent   72   1.2 2.9 3.2 1.7 18.9 100

71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”

71A Nature (dichotomous [“good”,“bad”], v1_leq_I_71A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq71a_hypothek,v1_con$v1_leq_i_j_k_leq71a,"v1_leq_I_71A")

##                -999 bad good <NA>     
## [1,] No. cases 1389 34  25   338  1786
## [2,] Percent   77.8 1.9 1.4  18.9 100

71B Impact (ordinal [0,1,2,3], v1_leq_I_71B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq71e_hypothek,v1_con$v1_leq_i_j_k_leq71e,"v1_leq_I_71B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1386 10  15  17 20  338  1786
## [2,] Percent   77.6 0.6 0.8 1  1.1 18.9 100

72. “Experienced a foreclosure on a mortgage or loan”

72A Nature (dichotomous [“good”,“bad”], v1_leq_I_72A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq72a_hypoth_kuend,v1_con$v1_leq_i_j_k_leq72a,"v1_leq_I_72A")

##                -999 bad good <NA>     
## [1,] No. cases 1416 13  19   338  1786
## [2,] Percent   79.3 0.7 1.1  18.9 100

72B Impact (ordinal [0,1,2,3], v1_leq_I_72B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq72e_hypoth_kuend,v1_con$v1_leq_i_j_k_leq72e,"v1_leq_I_72B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1416 7   4   6   15  338  1786
## [2,] Percent   79.3 0.4 0.2 0.3 0.8 18.9 100

73. “Credit rating difficulties”

73A Nature (dichotomous [“good”,“bad”], v1_leq_I_73A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq73a_kreditwuerdk,v1_con$v1_leq_i_j_k_leq73a,"v1_leq_I_73A")

##                -999 bad good <NA>     
## [1,] No. cases 1351 91  6    338  1786
## [2,] Percent   75.6 5.1 0.3  18.9 100

73B Impact (ordinal [0,1,2,3], v1_leq_I_73B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq73e_kreditwuerdk,v1_con$v1_leq_i_j_k_leq73e,"v1_leq_I_73B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1349 12  21  24  42  338  1786
## [2,] Percent   75.5 0.7 1.2 1.3 2.4 18.9 100

Create dataset

v1_leq_I<-data.frame(v1_leq_I_69A,v1_leq_I_69B,v1_leq_I_70A,v1_leq_I_70B,v1_leq_I_71A,
                     v1_leq_I_71B,v1_leq_I_72A,v1_leq_I_72B,v1_leq_I_73A,v1_leq_I_73B)

Crime and legal matters

74. “Being robbed or victim of identity theft”

74A Nature (dichotomous [“good”,“bad”], v1_leq_J_74A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq74a_opf_diebstahl,v1_con$v1_leq_i_j_k_leq74a,"v1_leq_J_74A")

##                -999 bad good <NA>     
## [1,] No. cases 1361 78  9    338  1786
## [2,] Percent   76.2 4.4 0.5  18.9 100

74B Impact (ordinal [0,1,2,3], v1_leq_J_74B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq74e_opf_diebstahl,v1_con$v1_leq_i_j_k_leq74e,"v1_leq_J_74B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1358 16  24  20  30  338  1786
## [2,] Percent   76   0.9 1.3 1.1 1.7 18.9 100

75. “Being a victim of a violent act (rape, assault, etc.)”

75A Nature (dichotomous [“good”,“bad”], v1_leq_J_75A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq75a_opf_gewalttat,v1_con$v1_leq_i_j_k_leq75a,"v1_leq_J_75A")

##                -999 bad good <NA>     
## [1,] No. cases 1404 40  4    338  1786
## [2,] Percent   78.6 2.2 0.2  18.9 100

75B Impact (ordinal [0,1,2,3], v1_leq_J_75B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq75e_opf_gewalttat,v1_con$v1_leq_i_j_k_leq75e,"v1_leq_J_75B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1402 8   3   6   29  338  1786
## [2,] Percent   78.5 0.4 0.2 0.3 1.6 18.9 100

76. “Involved in an accident”

76A Nature (dichotomous [“good”,“bad”], v1_leq_J_76A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq76a_unfall,v1_con$v1_leq_i_j_k_leq76a,"v1_leq_J_76A")

##                -999 bad good <NA>     
## [1,] No. cases 1384 57  7    338  1786
## [2,] Percent   77.5 3.2 0.4  18.9 100

76B Impact (ordinal [0,1,2,3], v1_leq_J_76B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq76e_unfall,v1_con$v1_leq_i_j_k_leq76e,"v1_leq_J_76B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1378 11  22  13  24  338  1786
## [2,] Percent   77.2 0.6 1.2 0.7 1.3 18.9 100

77. “Involved in a law suit”

77A Nature (dichotomous [“good”,“bad”], v1_leq_J_77A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq77a_rechtsstreit,v1_con$v1_leq_i_j_k_leq77a,"v1_leq_J_77A")

##                -999 bad good <NA>     
## [1,] No. cases 1338 91  19   338  1786
## [2,] Percent   74.9 5.1 1.1  18.9 100

77B Impact (ordinal [0,1,2,3], v1_leq_J_77B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq77e_rechtsstreit,v1_con$v1_leq_i_j_k_leq77e,"v1_leq_J_77B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1337 13  27  24  47  338  1786
## [2,] Percent   74.9 0.7 1.5 1.3 2.6 18.9 100

78. “Involved in a minor violation of the law (traffic tickets, disturbing the peace, etc)”

78A Nature (dichotomous [“good”,“bad”], v1_leq_J_78A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq78a_owi,v1_con$v1_leq_i_j_k_leq78a,"v1_leq_J_78A")

##                -999 bad good <NA>     
## [1,] No. cases 1342 99  7    338  1786
## [2,] Percent   75.1 5.5 0.4  18.9 100

78B Impact (ordinal [0,1,2,3], v1_leq_J_78B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq78e_owi,v1_con$v1_leq_i_j_k_leq78e,"v1_leq_J_78B")

##                -999 0   1  2  3   <NA>     
## [1,] No. cases 1341 32  36 18 21  338  1786
## [2,] Percent   75.1 1.8 2  1  1.2 18.9 100

79. “Legal troubles resulting in your being arrested or held in jail”

79A Nature (dichotomous [“good”,“bad”], v1_leq_J_79A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq79a_konf_gesetz,v1_con$v1_leq_i_j_k_leq79a,"v1_leq_J_79A")

##                -999 bad good <NA>     
## [1,] No. cases 1421 21  6    338  1786
## [2,] Percent   79.6 1.2 0.3  18.9 100

79B Impact (ordinal [0,1,2,3], v1_leq_J_79B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq79e_konf_gesetz,v1_con$v1_leq_i_j_k_leq79e,"v1_leq_J_79B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 1420 8   9   11  338  1786
## [2,] Percent   79.5 0.4 0.5 0.6 18.9 100

Create dataset

v1_leq_J<-data.frame(v1_leq_J_74A,v1_leq_J_74B,v1_leq_J_75A,v1_leq_J_75B,v1_leq_J_76A,
                     v1_leq_J_76B,v1_leq_J_77A,v1_leq_J_77B,v1_leq_J_78A,v1_leq_J_78B,
                     v1_leq_J_79A,v1_leq_J_79B)

Create LEQ dataset

v1_leq<-data.frame(v1_leq_A,v1_leq_B,v1_leq_C,v1_leq_D,v1_leq_E,v1_leq_F,v1_leq_G,
                   v1_leq_H,v1_leq_I,v1_leq_J)

Quality of Life Questionnaire (WHOQOL-BREF)

The WHOQOL-BREF instrument comprises 26 items, which measure the following broad domains: physical health, psychological health, social relationships, and environment. The past two weeks are assessed. All items are on a five-point scale with the following gradations:
Items 1, 15: “Very poor”-1, “Poor”-2, “Neither poor nor good”-3, “Good”-4, “Very good”-5 Items 2, 16-25: “Very dissatisfied”-1, “dissatisfied”-2, “Neither satisfied nor dissatisfied”-3, “satisfied”-4, “Very satisfied”-5 Items 3-14: “Not at all”-1, “A little”-2, “A moderate amount”-3, “Very much”-4, “An extreme amount”-5 Items 26: “Never”-1, “Seldom”-2, “Quite often”-3, “Very often”-4, “Always”-5

The coding of items number three, four and 26 has been reversed to keep directionality (see below). For all items higher scores now mean higher quality of life. Please see below for subscales (domain scores) of this questionnaire.

1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v1_whoqol_itm1)

v1_quol_recode(v1_clin$v1_whoqol_bref_who1_lebensqualitaet,v1_con$v1_whoqol_bref_who1,"v1_whoqol_itm1",0)

##                1  2   3    4    5    NA's     
## [1,] No. cases 53 159 413  605  307  249  1786
## [2,] Percent   3  8.9 23.1 33.9 17.2 13.9 100

2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v1_whoqol_itm2)”

v1_quol_recode(v1_clin$v1_whoqol_bref_who2_gesundheit,v1_con$v1_whoqol_bref_who2,"v1_whoqol_itm2",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 97  326  326  541  240  256  1786
## [2,] Percent   5.4 18.3 18.3 30.3 13.4 14.3 100

3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v1_whoqol_itm3)

Coding reversed so that higher scores mean less impairment by pain.

v1_quol_recode(v1_clin$v1_whoqol_bref_who3_schmerzen,v1_con$v1_whoqol_bref_who3,"v1_whoqol_itm3",1)

##                1   2   3   4    5    NA's     
## [1,] No. cases 26  108 135 336  924  257  1786
## [2,] Percent   1.5 6   7.6 18.8 51.7 14.4 100

4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v1_whoqol_itm4)”

Coding reversed so that higher scores mean less dependence on medical treatment.

v1_quol_recode(v1_clin$v1_whoqol_bref_who4_med_behand,v1_con$v1_whoqol_bref_who4,"v1_whoqol_itm4",1)

##                1   2    3    4    5    NA's     
## [1,] No. cases 170 300  223  248  587  258  1786
## [2,] Percent   9.5 16.8 12.5 13.9 32.9 14.4 100

5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v1_whoqol_itm5)

v1_quol_recode(v1_clin$v1_whoqol_bref_who5_lebensgenuss,v1_con$v1_whoqol_bref_who5,"v1_whoqol_itm5",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 78  236  401  545  262  264  1786
## [2,] Percent   4.4 13.2 22.5 30.5 14.7 14.8 100

6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v1_whoqol_itm6)

v1_quol_recode(v1_clin$v1_whoqol_bref_who6_lebenssinn,v1_con$v1_whoqol_bref_who6,"v1_whoqol_itm6",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 111 163 301  513  432  266  1786
## [2,] Percent   6.2 9.1 16.9 28.7 24.2 14.9 100

7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v1_whoqol_itm7)

v1_quol_recode(v1_clin$v1_whoqol_bref_who7_konzentration,v1_con$v1_whoqol_bref_who7,"v1_whoqol_itm7",0)

##                1   2    3    4    5   NA's     
## [1,] No. cases 46  242  538  579  125 256  1786
## [2,] Percent   2.6 13.5 30.1 32.4 7   14.3 100

8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v1_whoqol_itm8)

v1_quol_recode(v1_clin$v1_whoqol_bref_who8_sicherheit,v1_con$v1_whoqol_bref_who8,"v1_whoqol_itm8",0)

##                1   2   3    4   5    NA's     
## [1,] No. cases 67  155 344  625 335  260  1786
## [2,] Percent   3.8 8.7 19.3 35  18.8 14.6 100

9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v1_whoqol_itm9)

v1_quol_recode(v1_clin$v1_whoqol_bref_who9_umweltbed,v1_con$v1_whoqol_bref_who9,"v1_whoqol_itm9",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 28  63  331  723  381  260  1786
## [2,] Percent   1.6 3.5 18.5 40.5 21.3 14.6 100

10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v1_whoqol_itm10)

v1_quol_recode(v1_clin$v1_whoqol_bref_who10_energie,v1_con$v1_whoqol_bref_who10,"v1_whoqol_itm10",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 48  177 374  550  375 262  1786
## [2,] Percent   2.7 9.9 20.9 30.8 21  14.7 100

11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v1_whoqol_itm11)

v1_quol_recode(v1_clin$v1_whoqol_bref_who11_aussehen,v1_con$v1_whoqol_bref_who11,"v1_whoqol_itm11",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 49  138 320  602  408  269  1786
## [2,] Percent   2.7 7.7 17.9 33.7 22.8 15.1 100

12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v1_whoqol_itm12)

v1_quol_recode(v1_clin$v1_whoqol_bref_who12_genug_geld,v1_con$v1_whoqol_bref_who12,"v1_whoqol_itm12",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 93  219  360  453  399  262  1786
## [2,] Percent   5.2 12.3 20.2 25.4 22.3 14.7 100

13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v1_whoqol_itm13)

v1_quol_recode(v1_clin$v1_whoqol_bref_who13_infozugang,v1_con$v1_whoqol_bref_who13,"v1_whoqol_itm13",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 11  50  184  528  748  265  1786
## [2,] Percent   0.6 2.8 10.3 29.6 41.9 14.8 100

14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v1_whoqol_itm14)

v1_quol_recode(v1_clin$v1_whoqol_bref_who14_freizeitaktiv,v1_con$v1_whoqol_bref_who14,"v1_whoqol_itm14",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 19  146 307  494  557  263  1786
## [2,] Percent   1.1 8.2 17.2 27.7 31.2 14.7 100

15. “How well are you able to get around? (ordinal [1,2,3,4,5], v1_whoqol_itm15)”

v1_quol_recode(v1_clin$v1_whoqol_bref_who15_fortbewegung,v1_con$v1_whoqol_bref_who15,"v1_whoqol_itm15",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 8   69  225  527  693  264  1786
## [2,] Percent   0.4 3.9 12.6 29.5 38.8 14.8 100

16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v1_whoqol_itm16)

v1_quol_recode(v1_clin$v1_whoqol_bref_who16_schlaf,v1_con$v1_whoqol_bref_who16,"v1_whoqol_itm16",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 74  266  279  655  271  241  1786
## [2,] Percent   4.1 14.9 15.6 36.7 15.2 13.5 100

17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v1_whoqol_itm17)

v1_quol_recode(v1_clin$v1_whoqol_bref_who17_alltag,v1_con$v1_whoqol_bref_who17,"v1_whoqol_itm17",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 60  248  266  619  350  243  1786
## [2,] Percent   3.4 13.9 14.9 34.7 19.6 13.6 100

18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v1_whoqol_itm18)

v1_quol_recode(v1_clin$v1_whoqol_bref_who18_arbeitsfhgk,v1_con$v1_whoqol_bref_who18,"v1_whoqol_itm18",0)

##                1   2    3   4    5    NA's     
## [1,] No. cases 169 297  303 469  289  259  1786
## [2,] Percent   9.5 16.6 17  26.3 16.2 14.5 100

19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v1_whoqol_itm19)

v1_quol_recode(v1_clin$v1_whoqol_bref_who19_selbstzufried,v1_con$v1_whoqol_bref_who19,"v1_whoqol_itm19",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 94  222  356  634  224  256  1786
## [2,] Percent   5.3 12.4 19.9 35.5 12.5 14.3 100

20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v1_whoqol_itm20)

v1_quol_recode(v1_clin$v1_whoqol_bref_who20_pers_bezieh,v1_con$v1_whoqol_bref_who20,"v1_whoqol_itm20",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 68  190  283  689  298  258  1786
## [2,] Percent   3.8 10.6 15.8 38.6 16.7 14.4 100

21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v1_whoqol_itm21)

v1_quol_recode(v1_clin$v1_whoqol_bref_who21_sexualleben,v1_con$v1_whoqol_bref_who21,"v1_whoqol_itm21",0)

##                1    2    3    4    5    NA's     
## [1,] No. cases 206  264  437  412  201  266  1786
## [2,] Percent   11.5 14.8 24.5 23.1 11.3 14.9 100

22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v1_whoqol_itm22)

v1_quol_recode(v1_clin$v1_whoqol_bref_who22_freunde,v1_con$v1_whoqol_bref_who22,"v1_whoqol_itm22",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 66  121 308  646  399  246  1786
## [2,] Percent   3.7 6.8 17.2 36.2 22.3 13.8 100

23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v1_whoqol_itm23)

v1_quol_recode(v1_clin$v1_whoqol_bref_who23_wohnbeding,v1_con$v1_whoqol_bref_who23,"v1_whoqol_itm23",0)

##                1  2   3    4    5    NA's     
## [1,] No. cases 90 144 230  594  486  242  1786
## [2,] Percent   5  8.1 12.9 33.3 27.2 13.5 100

24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v1_whoqol_itm24)

v1_quol_recode(v1_clin$v1_whoqol_bref_who24_gesundhdiens,v1_con$v1_whoqol_bref_who24,"v1_whoqol_itm24",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 30  44  222  681  567  242  1786
## [2,] Percent   1.7 2.5 12.4 38.1 31.7 13.5 100

25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v1_whoqol_itm25)

v1_quol_recode(v1_clin$v1_whoqol_bref_who25_transport,v1_con$v1_whoqol_bref_who25,"v1_whoqol_itm25",0)

##                1  2   3    4    5    NA's     
## [1,] No. cases 35 79  207  628  587  250  1786
## [2,] Percent   2  4.4 11.6 35.2 32.9 14   100

26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v1_whoqol_itm26)

Coding reversed so that higher scores mean symptoms less often.

v1_quol_recode(v1_clin$v1_whoqol_bref_who26_neg_gefuehle,v1_con$v1_whoqol_bref_who26,"v1_whoqol_itm26",1)

##                1   2    3    4    5    NA's     
## [1,] No. cases 66  298  366  539  249  268  1786
## [2,] Percent   3.7 16.7 20.5 30.2 13.9 15   100

WHOQOL-BREF domain scores

Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).

Global (continuous [4-20],v1_whoqol_dom_glob)

v1_whoqol_dom_glob_df<-data.frame(as.numeric(v1_whoqol_itm1),as.numeric(v1_whoqol_itm2))

v1_who_glob_no_nas<-rowSums(is.na(v1_whoqol_dom_glob_df))

v1_whoqol_dom_glob<-ifelse((v1_who_glob_no_nas==0) | (v1_who_glob_no_nas==1), 
                            rowMeans(v1_whoqol_dom_glob_df,na.rm=T)*4,NA)

v1_whoqol_dom_glob<-round(v1_whoqol_dom_glob,2)

summary(v1_whoqol_dom_glob)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     4.0    10.0    14.0    13.9    16.0    20.0     243

Physical Health (continuous [4-20],v1_whoqol_dom_phys)

v1_whoqol_dom_phys_df<-data.frame(as.numeric(v1_whoqol_itm3),as.numeric(v1_whoqol_itm10),as.numeric(v1_whoqol_itm16),as.numeric(v1_whoqol_itm15),as.numeric(v1_whoqol_itm17),as.numeric(v1_whoqol_itm4),as.numeric(v1_whoqol_itm18))

v1_who_phys_no_nas<-rowSums(is.na(v1_whoqol_dom_phys_df))

v1_whoqol_dom_phys<-ifelse((v1_who_phys_no_nas==0) | (v1_who_phys_no_nas==1), 
                            rowMeans(v1_whoqol_dom_phys_df,na.rm=T)*4,NA)

v1_whoqol_dom_phys<-round(v1_whoqol_dom_phys,2)

summary(v1_whoqol_dom_phys)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.14   12.57   15.43   14.92   17.71   20.00     262

Psychological (continuous [4-20],v1_whoqol_dom_psy)

v1_whoqol_dom_psy_df<-data.frame(as.numeric(v1_whoqol_itm5),as.numeric(v1_whoqol_itm7),as.numeric(v1_whoqol_itm19),as.numeric(v1_whoqol_itm11),as.numeric(v1_whoqol_itm26),as.numeric(v1_whoqol_itm6))

v1_who_psy_no_nas<-rowSums(is.na(v1_whoqol_dom_psy_df))

v1_whoqol_dom_psy<-ifelse((v1_who_psy_no_nas==0) | (v1_who_psy_no_nas==1), 
                            rowMeans(v1_whoqol_dom_psy_df,na.rm=T)*4,NA)

v1_whoqol_dom_psy<-round(v1_whoqol_dom_psy,2)

summary(v1_whoqol_dom_psy)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.67   11.33   14.67   14.03   16.67   20.00     266

Social relationships (continuous [4-20],v1_whoqol_dom_soc)

v1_whoqol_dom_soc_df<-data.frame(as.numeric(v1_whoqol_itm20),as.numeric(v1_whoqol_itm22),as.numeric(v1_whoqol_itm21))

v1_who_soc_no_nas<-rowSums(is.na(v1_whoqol_dom_soc_df))

v1_whoqol_dom_soc<-ifelse((v1_who_soc_no_nas==0) | (v1_who_soc_no_nas==1), 
                            rowMeans(v1_whoqol_dom_soc_df,na.rm=T)*4,NA)

v1_whoqol_dom_soc<-round(v1_whoqol_dom_soc,2)

summary(v1_whoqol_dom_soc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.01   16.00   20.00     248

Environment (continuous [4-20],v1_whoqol_dom_env)

v1_whoqol_dom_env_df<-data.frame(as.numeric(v1_whoqol_itm8),as.numeric(v1_whoqol_itm23),as.numeric(v1_whoqol_itm12),as.numeric(v1_whoqol_itm24),as.numeric(v1_whoqol_itm13),as.numeric(v1_whoqol_itm14),as.numeric(v1_whoqol_itm9),as.numeric(v1_whoqol_itm25))

v1_who_env_no_nas<-rowSums(is.na(v1_whoqol_dom_env_df))

v1_whoqol_dom_env<-ifelse((v1_who_env_no_nas==0) | (v1_who_env_no_nas==1), 
                            rowMeans(v1_whoqol_dom_env_df,na.rm=T)*4,NA)

v1_whoqol_dom_env<-round(v1_whoqol_dom_env,2)

summary(v1_whoqol_dom_env)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.50   14.00   16.00   15.67   17.71   20.00     265

Create dataset

v1_whoqol<-data.frame(v1_whoqol_itm1,v1_whoqol_itm2,v1_whoqol_itm3,v1_whoqol_itm4,
                      v1_whoqol_itm5,v1_whoqol_itm6,v1_whoqol_itm7,v1_whoqol_itm8,
                      v1_whoqol_itm9,v1_whoqol_itm10,v1_whoqol_itm11,v1_whoqol_itm12,
                      v1_whoqol_itm13,v1_whoqol_itm14,v1_whoqol_itm15,v1_whoqol_itm16,
                      v1_whoqol_itm17,v1_whoqol_itm18,v1_whoqol_itm19,v1_whoqol_itm20,
                      v1_whoqol_itm21,v1_whoqol_itm22,v1_whoqol_itm23,v1_whoqol_itm24,
                      v1_whoqol_itm25,v1_whoqol_itm26,v1_whoqol_dom_glob,
                      v1_whoqol_dom_phys,v1_whoqol_dom_psy,v1_whoqol_dom_soc,
                      v1_whoqol_dom_env)

Personality

This is a 10-item questionnaire measuring personality (Rammstedt & John, 2007). It is based on the well-known ‘Big Five’ model of personality. The five dimensions are the following: extraversion, neuroticism, conscientiousness, agreeableness, openness. Instruction: How well do the following statements describe your personality? Each statement starts with: “I see myself as someone who…”. Each item is to be rated on a five point scale (“disagree strongly”-1, “disagree a little”-2,“neither agree nor disagree”-3, “agree a little”-4,“agree strongly”-5). The coding of some items has been reversed so that higher scores on each item mean higher scores on the respective personality dimension. Below, we calculate sumscores for each personality dimension.

1. “…is reserved” (ordinal [1,2,3,4,5], v1_big_five_itm1)
Personality dimension: extraversion, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi1_reserviert,v1_con$v1_bfi_10_bfi1,"v1_big_five_itm1",1)

##                1   2    3    4    5    NA's     
## [1,] No. cases 171 528  241  405  195  246  1786
## [2,] Percent   9.6 29.6 13.5 22.7 10.9 13.8 100

2. “… is generally trusting” (ordinal [1,2,3,4,5], v1_big_five_itm2)
Personality dimension: agreeableness.

big_five_recode(v1_clin$v1_bfi_10_bfi2_vertrauen,v1_con$v1_bfi_10_bfi2,"v1_big_five_itm2",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 50  236  258  749  248  245  1786
## [2,] Percent   2.8 13.2 14.4 41.9 13.9 13.7 100

3. “…tends to be lazy” (ordinal [1,2,3,4,5], v1_big_five_itm3)
Personality dimension: conscientiousness, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi3_bequem,v1_con$v1_bfi_10_bfi3,"v1_big_five_itm3",1)

##                1  2    3    4    5    NA's     
## [1,] No. cases 89 387  298  471  292  249  1786
## [2,] Percent   5  21.7 16.7 26.4 16.3 13.9 100

4. “…is relaxed, handles stress well” (ordinal [1,2,3,4,5], v1_big_five_itm4)
Personality dimension: neuroticism, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi4_stress,v1_con$v1_bfi_10_bfi4,"v1_big_five_itm4",1)

##                1   2    3    4    5   NA's     
## [1,] No. cases 126 484  282  481  167 246  1786
## [2,] Percent   7.1 27.1 15.8 26.9 9.4 13.8 100

5. “… has few artistic interests” (ordinal [1,2,3,4,5], v1_big_five_itm5)
Personality dimension: openness, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi5_kunst,v1_con$v1_bfi_10_bfi5,"v1_big_five_itm5",1)

##                1   2   3    4    5    NA's     
## [1,] No. cases 152 303 201  461  417  252  1786
## [2,] Percent   8.5 17  11.3 25.8 23.3 14.1 100

6. “…is outgoing, sociable” (ordinal [1,2,3,4,5], v1_big_five_itm6)
Personality dimension: extraversion.

big_five_recode(v1_clin$v1_bfi_10_bfi6_gesellig,v1_con$v1_bfi_10_bfi6,"v1_big_five_itm6",0)

##                1   2    3    4    5    NA's     
## [1,] No. cases 96  336  292  569  247  246  1786
## [2,] Percent   5.4 18.8 16.3 31.9 13.8 13.8 100

7. “…tends to find fault with others” (ordinal [1,2,3,4,5], v1_big_five_itm7)
Personality dimension: agreeableness, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi7_kritik,v1_con$v1_bfi_10_bfi7,"v1_big_five_itm7",1)

##                1   2    3   4    5    NA's     
## [1,] No. cases 47  351  410 507  223  248  1786
## [2,] Percent   2.6 19.7 23  28.4 12.5 13.9 100

8. “… does a thorough job” (ordinal [1,2,3,4,5], v1_big_five_itm8)
Personality dimension: conscientiousness.

big_five_recode(v1_clin$v1_bfi_10_bfi8_gruendlich,v1_con$v1_bfi_10_bfi8,"v1_big_five_itm8",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 13  113 176 783  454  247  1786
## [2,] Percent   0.7 6.3 9.9 43.8 25.4 13.8 100

9. “…gets nervous easily” (ordinal [1,2,3,4,5], v1_big_five_itm9)
Personality dimension: neuroticism.

big_five_recode(v1_clin$v1_bfi_10_bfi9_unsicher1,v1_con$v1_bfi_10_bfi9,"v1_big_five_itm9",0)

##                1   2   3    4   5   NA's     
## [1,] No. cases 168 446 315  465 150 242  1786
## [2,] Percent   9.4 25  17.6 26  8.4 13.5 100

10. “…habe eine aktive Vorstellungskraft, bin phantasievoll.” (ordinal [1,2,3,4,5], v1_big_five_itm10)
Personality dimension: openness.

big_five_recode(v1_clin$v1_bfi_10_bfi10_phantasie,v1_con$v1_bfi_10_bfi10,"v1_big_five_itm10",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 50  171 222  690  409  244  1786
## [2,] Percent   2.8 9.6 12.4 38.6 22.9 13.7 100

Big-Five personality dimension

The scoring instructions are described in Rammstedt et al. (2012).

Extraversion (continuous, [1,2,3,4,5], v1_big_five_extra)

v1_big_five_extra<-(as.numeric(v1_big_five_itm1)+as.numeric(v1_big_five_itm6))/2
summary(v1_big_five_extra)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.000   3.000   3.148   4.000   5.000     253

Neuroticism (continuous, [1,2,3,4,5], v1_big_five_neuro)

v1_big_five_neuro<-(as.numeric(v1_big_five_itm4)+as.numeric(v1_big_five_itm9))/2
summary(v1_big_five_neuro)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.500   3.000   3.019   4.000   5.000     248

Openness (continuous, [1,2,3,4,5], v1_big_five_openn)

v1_big_five_openn<-(as.numeric(v1_big_five_itm5)+as.numeric(v1_big_five_itm10))/2
summary(v1_big_five_openn)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   3.000   4.000   3.627   4.500   5.000     257

Conscientiousness (continuous, [1,2,3,4,5], v1_big_five_consc)

v1_big_five_consc<-(as.numeric(v1_big_five_itm3)+as.numeric(v1_big_five_itm8))/2
summary(v1_big_five_consc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   3.000   3.500   3.664   4.500   5.000     257

Agreeableness (continuous, [1,2,3,4,5], v1_big_five_agree)

v1_big_five_agree<-(as.numeric(v1_big_five_itm2)+as.numeric(v1_big_five_itm7))/2
summary(v1_big_five_agree)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   3.000   3.500   3.458   4.000   5.000     253

Create dataset

v1_pers<-data.frame(v1_big_five_itm1,v1_big_five_itm2,v1_big_five_itm3,v1_big_five_itm4,
                    v1_big_five_itm5,v1_big_five_itm6,v1_big_five_itm7,v1_big_five_itm8,
                    v1_big_five_itm9,v1_big_five_itm10,v1_big_five_extra,v1_big_five_neuro,
                    v1_big_five_openn,v1_big_five_consc,v1_big_five_agree)

Visit 1: Create dataframe

v1_df<-data.frame(v1_id,
                  v1_rec,
                  v1_dem,
                  v1_eth,
                  v1_psy_trtmt,
                  v1_med,
                  v1_fam_hist,
                  v1_som_dsrdr,
                  v1_subst,
                  v1_scid,
                  v1_symp_panss,
                  v1_symp_ids_c,
                  v1_symp_ymrs,
                  v1_ill_sev,
                  v1_nrpsy,
                  v1_rlgn,
                  v1_cape,
                  v1_sf12,
                  v1_med_adh,
                  v1_bdi2,
                  v1_asrm,
                  v1_mss,
                  v1_leq,
                  v1_whoqol,
                  v1_pers)

Visit 2: Data preparation

Read in data of clinical participants

## [1] 1320

Read in data of control participants

## [1] 466

Modify column names

Only include subjects for which data for the first visit is present

v2_clin<-subset(v2_clin, as.character(v2_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v2_clin)[1]

## [1] 1320

v2_con<-subset(v2_con, as.character(v2_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v2_con)[1]

## [1] 466

Participant identity column (categorical [id], v2_id)

v2_id<-as.factor(c(as.character(v2_clin$mnppsd),as.character(v2_con$mnppsd)))                               

Visit 2: Recruitment data

Correct date of interview in some participants

In some participants, an incorrect date of interview was entered into the original phenotype database, which I correct here.

## [1] 20151216

## [1] "20141216"

## [1] "20100602"

## [1] "20170602"

## [1] "20150620"

## [1] "20130620"

## [1] "20150925"

## [1] "20130925"

## [1] "20150724"

## [1] "20140724"

## [1] "20140211"

## [1] "20150211"

## [1] "20150807"

## [1] "20140807"

## [1] "20181106"

## [1] "20171106"

Date of interview (categorical [year-month-day], v2_interv_date)

v2_interv_date<-c(as.Date(as.character(v2_clin$v2_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v2_con$v2_rekru_visit_rekr_datum), "%Y%m%d"))

Age at second interview (continuous [years], v2_age)

v2_age_years_clin<-as.numeric(substr(v2_clin$v2_ausschluss1_rekr_datum,1,4))-
  as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))

v2_age_years_con<-as.numeric(substr(v2_con$v2_rekru_visit_rekr_datu,1,4))-
  as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))

v2_age_years<-c(v2_age_years_clin,v2_age_years_con)

v2_age<-ifelse(c(as.numeric(substr(v2_clin$v2_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v2_con$v2_rekru_visit_rekr_datu,5,6)))<
                 c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
                   v2_age_years-1,v2_age_years)
summary(v2_age) 

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   18.00   30.00   44.00   42.97   53.00   86.00     710

Create dataset

v2_rec<-data.frame(v2_age,v2_interv_date)

Visit 2: Illness episodes between study visits in clinical participants

Clinical study participant are asked whether an acute illness episode occurred since the last study visit. Possible answers are “Y”-yes, “N”-no and “C”-chronic symptoms. The latter category is for people which continually experience symptoms. If the answer was yes, additional questions were asked about the episodes, if not these are omitted. For participants with chronic symptomatology, the participant is asked about the nature of the chronic symptomatology (manic/depressive/mixed/psychotic) and answers are coded in the questions “Did you experience … symptoms during this illness episode?” (first illness episode).
Importantly, if the participant experienced multiple illness episodes since the last study visit, a set of questions (see below) was supposed to be answered for each illness episode. As most interviewers answered these questions only for a maximum of two illness episodes and few participants experienced more than two illness episodes, data are included only for the first two illness episodes.

Illness episodes since last study visit (only in clinical participants)

“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v2_clin_ill_ep_snc_lst)

v2_clin_ill_ep_snc_lst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_ill_ep_snc_lst<-ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==1,"Y",
                          ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==2,"N",
                            ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==3,"C",v2_clin_ill_ep_snc_lst)))

v2_clin_ill_ep_snc_lst<-factor(v2_clin_ill_ep_snc_lst)                         
descT(v2_clin_ill_ep_snc_lst)

##                -999 C   N    Y    <NA>     
## [1,] No. cases 466  91  452  242  535  1786
## [2,] Percent   26.1 5.1 25.3 13.5 30   100

“If yes, how many illness episodes? (continuous [no. illness episodes], v2_clin_no_ep)”

v2_clin_no_ep<-ifelse(v2_clin_ill_ep_snc_lst=="Y",c(v2_clin$v2_aktu_situat_anzahl_episoden,rep(-999,dim(v2_con)[1])),-999)
descT(v2_clin_no_ep)

##                -999 1    2   3   4   5   99  <NA>     
## [1,] No. cases 1009 184  32  8   3   3   1   546  1786
## [2,] Percent   56.5 10.3 1.8 0.4 0.2 0.2 0.1 30.6 100

In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).

First illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_man)

v2_clin_fst_ill_ep_man<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_man<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", 
                          -999)

descT(v2_clin_fst_ill_ep_man)

##                -999 Y   <NA>     
## [1,] No. cases 1233 37  516  1786
## [2,] Percent   69   2.1 28.9 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_dep)

v2_clin_fst_ill_ep_dep<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_dep<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", 
                          -999)

descT(v2_clin_fst_ill_ep_dep)

##                -999 Y   <NA>     
## [1,] No. cases 1119 151 516  1786
## [2,] Percent   62.7 8.5 28.9 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v2_clin_fst_ill_ep_mx<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_mx<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", 
                         -999)

descT(v2_clin_fst_ill_ep_mx)

##                -999 Y   <NA>     
## [1,] No. cases 1257 14  515  1786
## [2,] Percent   70.4 0.8 28.8 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_psy)

v2_clin_fst_ill_ep_psy<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_psy<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", 
                          -999)

descT(v2_clin_fst_ill_ep_psy)

##                -999 Y   <NA>     
## [1,] No. cases 1207 64  515  1786
## [2,] Percent   67.6 3.6 28.8 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v2_clin_fst_ill_ep_dur)

v2_clin_fst_ill_ep_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_dur<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",
                               ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==2,"two to four weeks", 
                                      ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
                                             ifelse(v2_clin_ill_ep_snc_lst=="N" | v2_clin_ill_ep_snc_lst=="C",-999,v2_clin_fst_ill_ep_dur))))

v2_clin_fst_ill_ep_dur<-ordered(v2_clin_fst_ill_ep_dur, 
                               levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_fst_ill_ep_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1009 39                  60                133                 
## [2,] Percent   56.5 2.2                 3.4               7.4                 
##      <NA>     
## [1,] 545  1786
## [2,] 30.5 100

“During this episode, were you hospitalized?” (dichotomous, v2_clin_fst_ill_ep_hsp)

v2_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="N"| v2_clin_ill_ep_snc_lst=="C",-999,
                          ifelse(v2_clin_ill_ep_snc_lst=="Y" &
                            c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v2_con)[1]))==2,"N",
                              ifelse(v2_clin_ill_ep_snc_lst=="Y" &
                                c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", v2_clin_fst_ill_ep_hsp)))

descT(v2_clin_fst_ill_ep_hsp)

##                -999 N   Y   <NA>     
## [1,] No. cases 1009 114 122 541  1786
## [2,] Percent   56.5 6.4 6.8 30.3 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v2_clin_fst_ill_ep_hsp_dur)

v2_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_hsp_dur<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",  
                              ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
                                ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==3,"more than four weeks", v2_clin_fst_ill_ep_hsp_dur)))

v2_clin_fst_ill_ep_hsp_dur<-ifelse((v2_clin_ill_ep_snc_lst=="Y" & is.na(c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1])))) | 
                                 (v2_clin_ill_ep_snc_lst=="N" | v2_clin_ill_ep_snc_lst=="C"),-999, v2_clin_fst_ill_ep_hsp_dur)

v2_clin_fst_ill_ep_hsp_dur<-ordered(v2_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_fst_ill_ep_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1133 19                  25                72                  
## [2,] Percent   63.4 1.1                 1.4               4                   
##      <NA>     
## [1,] 537  1786
## [2,] 30.1 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):

Reason for hospitalization: symptom worsensing (checkbox [Y], v2_clin_fst_ill_ep_symp_wrs)

v2_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_symp_wrs<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y",-999)
  
descT(v2_clin_fst_ill_ep_symp_wrs)

##                -999 Y   <NA>     
## [1,] No. cases 1169 101 516  1786
## [2,] Percent   65.5 5.7 28.9 100

Reason for hospitalization: self-endangerment (checkbox [Y], v2_clin_fst_ill_ep_slf_end)

v2_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_slf_end<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y", 
                              -999)

descT(v2_clin_fst_ill_ep_slf_end)

##                -999 Y   <NA>     
## [1,] No. cases 1257 14  515  1786
## [2,] Percent   70.4 0.8 28.8 100

Reason for hospitalization: suicidality (checkbox [Y], v2_clin_fst_ill_ep_suic)

v2_clin_fst_ill_ep_suic<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_suic<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y", 
                           -999)

descT(v2_clin_fst_ill_ep_suic)

##                -999 Y   <NA>     
## [1,] No. cases 1251 20  515  1786
## [2,] Percent   70   1.1 28.8 100

Reason for hospitalization: endangerment of others (checkbox [Y], v2_clin_fst_ill_ep_oth_end)

v2_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_oth_end<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)

descT(v2_clin_fst_ill_ep_oth_end)

##                -999 Y   <NA>     
## [1,] No. cases 1267 4   515  1786
## [2,] Percent   70.9 0.2 28.8 100

Reason for hospitalization: medication change (checkbox [Y], v2_clin_fst_ill_ep_med_chg)

v2_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_med_chg<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)

descT(v2_clin_fst_ill_ep_med_chg)

##                -999 Y  <NA>     
## [1,] No. cases 1253 17 516  1786
## [2,] Percent   70.2 1  28.9 100

Reason for hospitalization: other (checkbox [Y], v2_clin_fst_ill_ep_othr)

v2_clin_fst_ill_ep_othr<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_othr<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
 
descT(v2_clin_fst_ill_ep_othr)

##                -999 Y   <NA>     
## [1,] No. cases 1243 28  515  1786
## [2,] Percent   69.6 1.6 28.8 100

Second illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_man)

v2_clin_sec_ill_ep_man<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_man<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",-999)

descT(v2_clin_sec_ill_ep_man)

##                -999 Y   <NA>     
## [1,] No. cases 1042 5   739  1786
## [2,] Percent   58.3 0.3 41.4 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_dep)

v2_clin_sec_ill_ep_dep<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_dep<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",
                          -999)

descT(v2_clin_sec_ill_ep_dep)

##                -999 Y   <NA>     
## [1,] No. cases 1023 24  739  1786
## [2,] Percent   57.3 1.3 41.4 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v2_clin_sec_ill_ep_mx<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_mx<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y", 
                         -999)

descT(v2_clin_sec_ill_ep_mx)

##                -999 Y   <NA>     
## [1,] No. cases 1044 3   739  1786
## [2,] Percent   58.5 0.2 41.4 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_psy)

v2_clin_sec_ill_ep_psy<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_psy<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y", 
                          -999)

descT(v2_clin_sec_ill_ep_psy)

##                -999 Y   <NA>     
## [1,] No. cases 1039 8   739  1786
## [2,] Percent   58.2 0.4 41.4 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v2_clin_sec_ill_ep_dur)

v2_clin_sec_ill_ep_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_dur<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==1,"less than two weeks", 
                           ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
                              ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==3,"more than four weeks", v2_clin_sec_ill_ep_dur)))

v2_clin_sec_ill_ep_dur<-ifelse((v2_clin_ill_ep_snc_lst=="Y" & is.na(c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1])))) | 
                                 v2_clin_ill_ep_snc_lst=="N" | v2_clin_ill_ep_snc_lst=="C",-999, v2_clin_sec_ill_ep_dur)

v2_clin_sec_ill_ep_dur<-ordered(v2_clin_sec_ill_ep_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_sec_ill_ep_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1213 8                   14                16                  
## [2,] Percent   67.9 0.4                 0.8               0.9                 
##      <NA>     
## [1,] 535  1786
## [2,] 30   100

“During this episode, were you hospitalized?” (dichotomous, v2_clin_sec_ill_ep_hsp)

v2_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="N"| v2_clin_ill_ep_snc_lst=="C",-999,
                          ifelse(v2_clin_ill_ep_snc_lst=="Y" &
                            c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v2_con)[1]))==2,"N",
                              ifelse(v2_clin_ill_ep_snc_lst=="Y" &
                                c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y", v2_clin_sec_ill_ep_hsp)))

v2_clin_sec_ill_ep_hsp<-factor(v2_clin_sec_ill_ep_hsp)                         
descT(v2_clin_sec_ill_ep_hsp)

##                -999 N   Y  <NA>     
## [1,] No. cases 1009 19  18 740  1786
## [2,] Percent   56.5 1.1 1  41.4 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v2_clin_sec_ill_ep_hsp_dur)

v2_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_hsp_dur<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",  
                              ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
                                ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",    
                                            -999)))

v2_clin_sec_ill_ep_hsp_dur<-ifelse((v2_clin_ill_ep_snc_lst=="Y" & is.na(c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1])))) | 
                                 (v2_clin_ill_ep_snc_lst=="N" | v2_clin_ill_ep_snc_lst=="C"),-999, v2_clin_sec_ill_ep_hsp_dur)


v2_clin_sec_ill_ep_hsp_dur<-ordered(v2_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v2_clin_sec_ill_ep_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1237 1                   4                 9                   
## [2,] Percent   69.3 0.1                 0.2               0.5                 
##      <NA>     
## [1,] 535  1786
## [2,] 30   100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):

Reason for hospitalization: symptom worsensing (checkbox [Y], v2_clin_sec_ill_ep_symp_wrs)

v2_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_symp_wrs<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",-999)
  
descT(v2_clin_sec_ill_ep_symp_wrs)

##                -999 Y   <NA>     
## [1,] No. cases 1036 11  739  1786
## [2,] Percent   58   0.6 41.4 100

Reason for hospitalization: self-endangerment (checkbox [Y], v2_clin_sec_ill_ep_slf_end)

v2_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_slf_end<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y", 
                              -999)

descT(v2_clin_sec_ill_ep_slf_end)

##                -999 Y   <NA>     
## [1,] No. cases 1046 1   739  1786
## [2,] Percent   58.6 0.1 41.4 100

Reason for hospitalization: suicidality (checkbox [Y], v2_clin_sec_ill_ep_suic)

v2_clin_sec_ill_ep_suic<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_suic<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y", 
                           -999)

descT(v2_clin_sec_ill_ep_suic)

##                -999 Y   <NA>     
## [1,] No. cases 1044 3   739  1786
## [2,] Percent   58.5 0.2 41.4 100

Reason for hospitalization: endangerment of others (checkbox [Y], v2_clin_sec_ill_ep_oth_end)

v2_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_oth_end<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)

descT(v2_clin_sec_ill_ep_oth_end)

##                -999 <NA>     
## [1,] No. cases 1047 739  1786
## [2,] Percent   58.6 41.4 100

Reason for hospitalization: medication change (checkbox [Y], v2_clin_sec_ill_ep_med_chg)

v2_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_med_chg<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)

descT(v2_clin_sec_ill_ep_med_chg)

##                -999 Y   <NA>     
## [1,] No. cases 1046 1   739  1786
## [2,] Percent   58.6 0.1 41.4 100

Reason for hospitalization: other (checkbox [Y], v2_clin_sec_ill_ep_othr)

v2_clin_sec_ill_ep_othr<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_othr<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
 
descT(v2_clin_sec_ill_ep_othr)

##                -999 Y   <NA>     
## [1,] No. cases 1042 5   739  1786
## [2,] Percent   58.3 0.3 41.4 100

Additional psychiatric hospitalization as in- or daypatient? (dichotomous, v2_clin_add_oth_hsp)

v2_clin_add_oth_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_add_oth_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_aufent,rep(-999,dim(v2_con)[1]))==1,"Y","N")

descT(v2_clin_add_oth_hsp)

##                N    Y   <NA>     
## [1,] No. cases 1211 26  549  1786
## [2,] Percent   67.8 1.5 30.7 100

If yes, how many other hospitalizations? (continous [no. of hospitalizations], v2_clin_oth_hsp_nmb)

v2_clin_oth_hsp_nmb<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_oth_hsp_nmb<-ifelse(v2_clin_add_oth_hsp=="Y",
          c(v2_clin$v2_aktu_situat_aenderung_anzahl,rep(-999,dim(v2_con)[1])),-999)

descT(v2_clin_oth_hsp_nmb)

##                -999 1   2   3   <NA>     
## [1,] No. cases 1211 21  1   1   552  1786
## [2,] Percent   67.8 1.2 0.1 0.1 30.9 100

If yes, duration of other hospitalizations? (ordinal, v2_clin_oth_hsp_dur)

v2_clin_oth_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_oth_hsp_dur<-
  ifelse(v2_clin_add_oth_hsp=="Y" & 
           c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==1,"less than two weeks", 
   ifelse(v2_clin_add_oth_hsp=="Y" & 
            c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
    ifelse(v2_clin_add_oth_hsp=="Y" & 
             c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
     ifelse(v2_clin_add_oth_hsp=="N",-999,v2_clin_add_oth_hsp))))

v2_clin_oth_hsp_dur<-ifelse((v2_clin_add_oth_hsp=="Y" & is.na(c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1])))),-999, v2_clin_sec_ill_ep_hsp_dur)

v2_clin_oth_hsp_dur<-ordered(v2_clin_oth_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_oth_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1    0                   0                 0                   
## [2,] Percent   0.1  0                   0                 0                   
##      <NA>     
## [1,] 1785 1786
## [2,] 99.9 100

If yes, reason for other hospitalization(s) medication change? (checkbox [Y], v2_clin_othr_psy_med)

v2_clin_othr_psy_med<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_othr_psy_med<-ifelse(v2_clin_add_oth_hsp=="Y" & v2_clin_add_oth_hsp=="Y" & 
      c(v2_clin$v2_aktu_situat_aenderung_medikament,rep(-999,dim(v2_con)[1]))==1,"Y",
                        ifelse(v2_clin_add_oth_hsp=="N",-999,v2_clin_othr_psy_med))

descT(v2_clin_othr_psy_med)

##                -999 Y   <NA>     
## [1,] No. cases 1211 2   573  1786
## [2,] Percent   67.8 0.1 32.1 100

Current psychiatric treatment of both clinical and control participants (ordinal [1,2,3,4], v2_cur_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.

v2_clin_cur_psy_trm<-rep(NA,dim(v2_clin)[1])
v2_con_cur_psy_trm<-rep(NA,dim(v2_con)[1])

v2_clin_cur_psy_trm<-ifelse(v2_clin$v2_aktu_situat_psybehandlung==0,"1",
                        ifelse(v2_clin$v2_aktu_situat_psybehandlung==3,"2", 
                          ifelse(v2_clin$v2_aktu_situat_psybehandlung==2,"3",
                            ifelse(v2_clin$v2_aktu_situat_psybehandlung==1,"4",v2_clin_cur_psy_trm)))) 

v2_con_cur_psy_trm<-ifelse(v2_con$v2_bildung_beruf_psybehandlung==0,"1",
                      ifelse(v2_con$v2_bildung_beruf_psybehandlung==3,"2",
                        ifelse(v2_con$v2_bildung_beruf_psybehandlung==2,"3",
                          ifelse(v2_con$v2_bildung_beruf_psybehandlung==1,"4",v2_con_cur_psy_trm))))

v2_cur_psy_trm<-factor(c(v2_clin_cur_psy_trm,v2_con_cur_psy_trm),ordered=T)
descT(v2_cur_psy_trm)

##                1    2    3   4   <NA>     
## [1,] No. cases 333  655  11  39  748  1786
## [2,] Percent   18.6 36.7 0.6 2.2 41.9 100

Create dataset

v2_clin_ill_ep<-data.frame(v2_clin_ill_ep_snc_lst,
                           v2_clin_no_ep,
                           v2_clin_fst_ill_ep_man,
                           v2_clin_fst_ill_ep_dep,
                           v2_clin_fst_ill_ep_mx,
                           v2_clin_fst_ill_ep_psy,
                           v2_clin_fst_ill_ep_dur,
                           v2_clin_fst_ill_ep_hsp,
                           v2_clin_fst_ill_ep_hsp_dur,
                           v2_clin_fst_ill_ep_symp_wrs,
                           v2_clin_fst_ill_ep_slf_end,
                           v2_clin_fst_ill_ep_suic,
                           v2_clin_fst_ill_ep_oth_end,
                           v2_clin_fst_ill_ep_med_chg,
                           v2_clin_fst_ill_ep_othr,
                           v2_clin_sec_ill_ep_man,
                           v2_clin_sec_ill_ep_dep,
                           v2_clin_sec_ill_ep_mx,
                           v2_clin_sec_ill_ep_psy,
                           v2_clin_sec_ill_ep_dur,
                           v2_clin_sec_ill_ep_hsp,
                           v2_clin_sec_ill_ep_hsp_dur,
                           v2_clin_sec_ill_ep_symp_wrs,
                           v2_clin_sec_ill_ep_slf_end,
                           v2_clin_sec_ill_ep_suic,
                           v2_clin_sec_ill_ep_oth_end,
                           v2_clin_sec_ill_ep_med_chg,
                           v2_clin_sec_ill_ep_othr,
                           v2_clin_add_oth_hsp,
                           v2_clin_oth_hsp_nmb,
                           v2_clin_oth_hsp_dur,
                           v2_clin_othr_psy_med,
                           v2_cur_psy_trm)

Visit 2: Emotional, psychological, alcohol-related or similar problems between study visits (only in control participants)

“Did you experience emotional, psychological, alcohol-related or similar problems since the last study visit?” (categorical [Y,N], v2_con_problems_snc_lst)

v2_con_problems_snc_lst<-c(rep(-999,dim(v2_clin)[1]),rep(NA,dim(v2_con)[1]))

v2_con_problems_snc_lst<-ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_stand)==1,"Y",
                          ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_stand)==2,"N",v2_con_problems_snc_lst))

v2_con_problems_snc_lst<-factor(v2_con_problems_snc_lst)                        
descT(v2_con_problems_snc_lst)

##                -999 N    Y   <NA>     
## [1,] No. cases 1320 259  27  180  1786
## [2,] Percent   73.9 14.5 1.5 10.1 100

“Since the last study visit, were there psychiatric hospitalizations” (categorical [Y,N], v2_con_psy_snc_lst) This item is -999 in patients.

v2_con_psy_snc_lst<-c(rep(-999,dim(v2_clin)[1]),rep(NA,dim(v2_con)[1]))
v2_con_psy_snc_lst<-ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_tstbehand)==1,"Y",
                           ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_tstbehand)==2,"N",v2_con_psy_snc_lst))
v2_con_psy_snc_lst<-factor(v2_con_psy_snc_lst)

descT(v2_con_psy_snc_lst)

##                -999 N    <NA>     
## [1,] No. cases 1320 280  186  1786
## [2,] Percent   73.9 15.7 10.4 100

“If yes, how many?” (continuous, [number of hospitalizations] ,v2_con_no_psy_hosp) This item is -999 in patients and -999 in controls that negated the previous question.

v2_con_no_psy_hosp<-ifelse(v2_con_psy_snc_lst==-999,-999,
                           ifelse(v2_con_psy_snc_lst=="N",-999,
                                  ifelse(v2_con_psy_snc_lst=="Y",c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_anzahl),NA)))
descT(v2_con_no_psy_hosp)

##                -999 <NA>     
## [1,] No. cases 1600 186  1786
## [2,] Percent   89.6 10.4 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v2_con_psy_hosp_dur)

v2_con_psy_hosp_dur<-c(ifelse((v2_con_psy_snc_lst==-999) | (v2_con_psy_snc_lst=="N"),-999,
                        ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_dauer)==1,"less than two weeks",
                          ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_dauer)==2,"two to four weeks",
                            ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_dauer)==3,"more than four weeks",NA)))))

v2_con_psy_hosp_dur<-as.factor(v2_con_psy_hosp_dur)
descT(v2_con_psy_hosp_dur)

##                -999 <NA>     
## [1,] No. cases 1600 186  1786
## [2,] Percent   89.6 10.4 100

“If yes, due to medication change?” (checkbox [Y], v2_con_psy_hosp_why_medchg)

v2_con_psy_hosp_why_medchg<-ifelse((v2_con_psy_snc_lst==-999) | (v2_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_medikament)==1,"Y",-999))
                          
#v2_con_psy_hosp_why_medchg<-as.factor(v2_con_psy_hosp_why_medchg)
descT(v2_con_psy_hosp_why_medchg)

##                -999 <NA>     
## [1,] No. cases 1600 186  1786
## [2,] Percent   89.6 10.4 100

“If yes, due to other circumstances?” (checkbox [Y], v2_con_psy_hosp_why_othr)

v2_con_psy_hosp_why_othr<-ifelse((v2_con_psy_snc_lst==-999) | (v2_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_sonstiges)==1,"Y",-999))
                          
#v2_con_psy_hosp_why_othr<-as.factor(v2_con_psy_hosp_why_othr)
descT(v2_con_psy_hosp_why_othr)

##                -999 <NA>     
## [1,] No. cases 1600 186  1786
## [2,] Percent   89.6 10.4 100

“If yes and due to other circumstances, which circumstances?” (character [text], v2_con_psy_hosp_why_othr_txt)

v2_con_psy_hosp_why_othr_txt<-ifelse((v2_con_psy_snc_lst==-999) | (v2_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v2_clin)[1]),v2_con$v2_bildung_beruf_psy_grund)==1,"Y",-999))
                          
v2_con_psy_hosp_why_othr_txt<-as.factor(v2_con_psy_hosp_why_othr_txt)
descT(v2_con_psy_hosp_why_othr_txt)

##                -999 <NA>     
## [1,] No. cases 1600 186  1786
## [2,] Percent   89.6 10.4 100

Create dataset

v2_con_problems<-data.frame(v2_con_problems_snc_lst,v2_con_psy_snc_lst,v2_con_no_psy_hosp,
                            v2_con_psy_hosp_dur,v2_con_psy_hosp_why_medchg,v2_con_psy_hosp_why_othr,
                            v2_con_psy_hosp_why_othr_txt)

Visit 2: Demographic information

See Visit 1 marital status item for general explanation of the next two items.

Did your marital status change since the last study visit? (dichotomous, v2_cng_mar_stat)

v2_clin_cng_mar_stat<-rep(NA,dim(v2_clin)[1]) 
v2_clin_cng_mar_stat<-ifelse(v2_clin$v2_aktu_situat_fam_stand==1, "Y", 
                        ifelse(v2_clin$v2_aktu_situat_fam_stand==2, "N", v2_clin_cng_mar_stat))

v2_con_cng_mar_stat<-rep(NA,dim(v2_con)[1]) 
v2_con_cng_mar_stat<-ifelse(v2_con$v2_famil_wohn_fam_stand==1, "Y", 
                        ifelse(v2_con$v2_famil_wohn_fam_stand==2, "N", v2_con_cng_mar_stat))

v2_cng_mar_stat<-factor(c(v2_clin_cng_mar_stat,v2_con_cng_mar_stat))

Marital status (categorical [married, married but living separately, single, divorced, widowed], v2_marital_stat)

v2_clin_marital_stat<-rep(NA,dim(v2_clin)[1])
v2_clin_marital_stat<-ifelse(v2_clin$v2_aktu_situat_fam_familienstand==1,"Married", 
                 ifelse(v2_clin$v2_aktu_situat_fam_familienstand==2,"Married_living_sep",
                 ifelse(v2_clin$v2_aktu_situat_fam_familienstand==3,"Single",
                 ifelse(v2_clin$v2_aktu_situat_fam_familienstand==4,"Divorced",
                 ifelse(v2_clin$v2_aktu_situat_fam_familienstand==5,"Widowed",v2_clin_marital_stat)))))

v2_con_marital_stat<-rep(NA,dim(v2_con)[1])
v2_con_marital_stat<-ifelse(v2_con$v2_famil_wohn_fam_famstand==1,"Married", 
                 ifelse(v2_con$v2_famil_wohn_fam_famstand==2,"Married_living_sep",
                 ifelse(v2_con$v2_famil_wohn_fam_famstand==3,"Single",
                 ifelse(v2_con$v2_famil_wohn_fam_famstand==4,"Divorced",
                 ifelse(v2_con$v2_famil_wohn_fam_famstand==5,"Widowed",v2_con_marital_stat)))))

v2_marital_stat<-factor(c(v2_clin_marital_stat,v2_con_marital_stat))
desc(v2_marital_stat)

##                Divorced Married Married_living_sep Single Widowed NA's     
## [1,] No. cases 139      260     40                 603    20      724  1786
## [2,] Percent   7.8      14.6    2.2                33.8   1.1     40.5 100

Relationship status

“Do you currently have a partner?” (dichotomous, v2_partner)

v2_clin_partner<-rep(NA,dim(v2_clin)[1])
v2_clin_partner<-ifelse(v2_clin$v2_aktu_situat_fam_fest_partner==1,"Y",
            ifelse(v2_clin$v2_aktu_situat_fam_fest_partner==2,"N",v2_clin_partner))

v2_con_partner<-rep(NA,dim(v2_con)[1])
v2_con_partner<-ifelse(v2_con$v2_famil_wohn_fam_partner==1,"Y",
            ifelse(v2_con$v2_famil_wohn_fam_partner==2,"N",v2_con_partner))


v2_partner<-factor(c(v2_clin_partner,v2_con_partner))
descT(v2_partner)

##                N    Y    <NA>     
## [1,] No. cases 502  540  744  1786
## [2,] Percent   28.1 30.2 41.7 100

Children

Biological (continuous [number], v2_no_bio_chld)

v2_no_bio_chld<-c(v2_clin$v2_aktu_situat_fam_kind_gesamt,v2_con$v2_famil_wohn_fam_lkind)
descT(v2_no_bio_chld)

##                0    1    2   3   4   5   <NA>     
## [1,] No. cases 650  191  138 60  16  5   726  1786
## [2,] Percent   36.4 10.7 7.7 3.4 0.9 0.3 40.6 100

Non-biological

Adoptive children (continuous [number], v2_no_adpt_chld)

v2_no_adpt_chld<-c(v2_clin$v2_aktu_situat_fam_adopt_gesamt,v2_con$v2_famil_wohn_fam_adkind)
descT(v2_no_adpt_chld)  

##                0    1   2   <NA>     
## [1,] No. cases 1037 2   2   745  1786
## [2,] Percent   58.1 0.1 0.1 41.7 100

Step children (continuous [number], v2_stp_chld)

v2_stp_chld<-c(v2_clin$v2_aktu_situat_fam_stift_gesamt,v2_con$v2_famil_wohn_fam_skind)
descT(v2_stp_chld)      

##                0    1   2  3   4   <NA>     
## [1,] No. cases 961  50  18 4   3   750  1786
## [2,] Percent   53.8 2.8 1  0.2 0.2 42   100

Change in housing situation since last study visit? (dichotomous, v2_chg_hsng)

v2_clin_chg_hsng<-rep(NA,dim(v2_clin)[1])
v2_clin_chg_hsng<-ifelse(v2_clin$v2_wohnsituation_wohn_aenderung==1,"Y",
                  ifelse(v2_clin$v2_wohnsituation_wohn_aenderung==2,"N",v2_clin_chg_hsng))  

v2_con_chg_hsng<-rep(NA,dim(v2_con)[1])
v2_con_chg_hsng<-ifelse(v2_con$v2_famil_wohn_wohn_stand==1,"Y",
                 ifelse(v2_con$v2_famil_wohn_wohn_stand==2,"N",v2_con_chg_hsng))

v2_chg_hsng<-factor(c(v2_clin_chg_hsng,v2_con_chg_hsng))
descT(v2_chg_hsng)

##                N    Y   <NA>     
## [1,] No. cases 925  143 718  1786
## [2,] Percent   51.8 8   40.2 100

Living alone (dichotomous, v2_liv_aln)

v2_clin_liv_aln<-rep(NA,dim(v2_clin)[1])
v2_clin_liv_aln<-ifelse(v2_clin$v2_wohnsituation_wohn_allein==1,"Y",    
                 ifelse(v2_clin$v2_wohnsituation_wohn_allein==0,"N",v2_clin_liv_aln))   

v2_con_liv_aln<-rep(NA,dim(v2_con)[1])
v2_con_liv_aln<-ifelse(v2_con$v2_famil_wohn_wohn_allein==1,"Y", 
                 ifelse(v2_con$v2_famil_wohn_wohn_allein==0,"N",v2_con_liv_aln))
                 
v2_liv_aln<-factor(c(v2_clin_liv_aln,v2_con_liv_aln))
descT(v2_liv_aln)

##                N    Y    <NA>     
## [1,] No. cases 691  404  691  1786
## [2,] Percent   38.7 22.6 38.7 100

Employment

Did your employment situation change since the last study visit?

v2_clin_chg_empl_stat<-rep(NA,dim(v2_clin)[1]) 
v2_clin_chg_empl_stat<-ifelse(v2_clin$v2_wohnsituation_erwerb_aenderung==1, "Y", 
                  ifelse(v2_clin$v2_wohnsituation_erwerb_aenderung==2, "N",v2_clin_chg_empl_stat))

v2_con_chg_empl_stat<-rep(NA,dim(v2_con)[1]) 
v2_con_chg_empl_stat<-ifelse(v2_con$v2_bildung_beruf_bild_stand==1, "Y", 
                  ifelse(v2_con$v2_bildung_beruf_bild_stand==2, "N",v2_con_chg_empl_stat))

v2_chg_empl_stat<-factor(c(v2_clin_chg_empl_stat,v2_con_chg_empl_stat))
descT(v2_chg_empl_stat)

##                N    Y   <NA>     
## [1,] No. cases 906  155 725  1786
## [2,] Percent   50.7 8.7 40.6 100

Currently paid employment (dichotomous, v2_curr_paid_empl)

Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.

v2_clin_curr_paid_empl<-rep(NA,dim(v2_clin)[1])
v2_clin_curr_paid_empl<-ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==1,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==2,"Y",   
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==3,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==4,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==5,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==6,-999,  
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==7,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==8,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==9,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==10,"Y",  
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==11,"N",v2_clin_curr_paid_empl)))))))))))

v2_con_curr_paid_empl<-rep(NA,dim(v2_con)[1])
v2_con_curr_paid_empl<-ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==1,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==2,"Y",    
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==3,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==4,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==5,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==6,-999,   
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==7,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==8,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==9,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==10,"Y",   
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==11,"N",v2_con_curr_paid_empl)))))))))))

v2_curr_paid_empl<-factor(c(v2_clin_curr_paid_empl,v2_con_curr_paid_empl))
descT(v2_curr_paid_empl)

##                -999 N    Y    <NA>     
## [1,] No. cases 26   492  543  725  1786
## [2,] Percent   1.5  27.5 30.4 40.6 100

Disability pension due to psychological/psychiatric illness (dichotomous, v2_disabl_pens)

NB: Not available (-999) in control participants

v2_clin_disabl_pens<-rep(NA,dim(v2_clin)[1])
v2_clin_disabl_pens<-ifelse(v2_clin$v2_wohnsituation_rente_psych==1,"Y",        
                     ifelse(v2_clin$v2_wohnsituation_rente_psych==2,"N",v2_clin_disabl_pens))       

v2_con_disabl_pens<-rep(-999,dim(v2_con)[1])

v2_disabl_pens<-factor(c(v2_clin_disabl_pens,v2_con_disabl_pens))
descT(v2_disabl_pens)

##                -999 N   Y    <NA>     
## [1,] No. cases 466  357 279  684  1786
## [2,] Percent   26.1 20  15.6 38.3 100

Employed in workshop for handicapped persons (dichotomous, v2_spec_emp)

v2_clin_spec_emp<-rep(NA,dim(v2_clin)[1])
v2_clin_spec_emp<-ifelse(v2_clin$v2_wohnsituation_erwerb_werk==1,"Y",           
                  ifelse(v2_clin$v2_wohnsituation_erwerb_werk==2,"N",v2_clin_spec_emp))         

v2_con_spec_emp<-rep(NA,dim(v2_con)[1])
v2_con_spec_emp<-ifelse(v2_con$v2_bildung_beruf_erwerb_wfbm==1,"Y",         
                 ifelse(v2_con$v2_bildung_beruf_erwerb_wfbm==2,"N",v2_con_spec_emp))            


v2_spec_emp<-factor(c(v2_clin_spec_emp,v2_con_spec_emp))
descT(v2_spec_emp)

##                N    Y   <NA>     
## [1,] No. cases 413  66  1307 1786
## [2,] Percent   23.1 3.7 73.2 100

Weeks of work absence due to psychological distress in past six months (continuous [weeks], v2_wrk_abs_pst_6_mths)

Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.

v2_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v2_clin)[1])
v2_clin_wrk_abs_pst_6_mths<-ifelse((v2_clin$v2_wohnsituation_erwerb_unbekannt==1 | v2_clin$v2_wohnsituation_erwerb_rente==1 |  
                                 v2_clin$v2_wohnsituation_erwerb_fehlen>26),-999, v2_clin$v2_wohnsituation_erwerb_fehlen)

v2_con_wrk_abs_pst_6_mths<-rep(NA,dim(v2_con)[1])
v2_con_wrk_abs_pst_6_mths<-ifelse((v2_con$v2_bildung_beruf_erwerb_ausfallu==1 | v2_con$v2_bildung_beruf_erwerb_rente==1 |  
                                 v2_con$v2_bildung_beruf_erwerb_ausfallm>26),-999, v2_con$v2_bildung_beruf_erwerb_ausfallm)

v2_wrk_abs_pst_6_mths<-c(v2_clin_wrk_abs_pst_6_mths,v2_con_wrk_abs_pst_6_mths)
descT(v2_wrk_abs_pst_6_mths)

##                -999 0   1   2   3   4   5   6   7   8   10  12  13  14  15  16 
## [1,] No. cases 372  321 10  10  10  14  5   11  1   12  4   8   2   2   1   6  
## [2,] Percent   20.8 18  0.6 0.6 0.6 0.8 0.3 0.6 0.1 0.7 0.2 0.4 0.1 0.1 0.1 0.3
##      18  19  20  24 25  26  <NA>     
## [1,] 1   1   8   35 4   16  932  1786
## [2,] 0.1 0.1 0.4 2  0.2 0.9 52.2 100

Currently impaired by psychological/psychiatric symptoms in exercising profession (dichotomous, v2_cur_work_restr)

Important: if receiving pension, this question refers to impairments in the household

v2_clin_cur_work_restr<-rep(NA,dim(v2_clin)[1])
v2_clin_cur_work_restr<-ifelse(v2_clin$v2_wohnsituation_erwerb_psysymptom==1,"Y",   
                    ifelse(v2_clin$v2_wohnsituation_erwerb_psysymptom==2,"N",v2_clin_cur_work_restr))    

v2_con_cur_work_restr<-rep(NA,dim(v2_con)[1])
v2_con_cur_work_restr<-ifelse(v2_con$v2_bildung_beruf_erwerb_psyeinsch==1,"Y",  
                    ifelse(v2_con$v2_bildung_beruf_erwerb_psyeinsch==2,"N",v2_con_cur_work_restr))   

v2_cur_work_restr<-factor(c(v2_clin_cur_work_restr,v2_con_cur_work_restr))
descT(v2_cur_work_restr)

##                N    Y   <NA>     
## [1,] No. cases 626  358 802  1786
## [2,] Percent   35.1 20  44.9 100

Self-reported Weight (continuous [kilograms], v2_weight)

v2_weight<-c(v2_clin$v2_wohnsituation_erwerb_gewicht,v2_con$v2_bildung_beruf_erwerb_gewicht)
summary(v2_weight)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   43.00   69.00   80.00   83.29   95.00  171.00     744

Waist circumference (continouos [centimeters], v2_waist)

This item was only recorded in a subset of individuals, because the question was introduced while the study was running.

v2_clin_waist<-v2_clin$v2_wohnsituation_erwerb_tailumf
v2_con_waist<-v2_con$v2_bildung_beruf_erwerb_taille

v2_waist<-c(v2_clin_waist,v2_con_waist)
summary(v2_waist)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   52.00   76.00   86.00   89.12  100.00  164.00    1423

BMI (continuous [BMI], v2_bmi)

We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.

v2_bmi<-v2_weight/(v1_height/100)^2
summary(v2_bmi)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.90   23.15   26.47   27.51   30.82   66.17     749

Create dataset

v2_dem<-data.frame(v2_cng_mar_stat,v2_marital_stat,v2_partner,v2_no_bio_chld,v2_no_adpt_chld,v2_stp_chld,v2_chg_hsng,v2_liv_aln,
                    v2_chg_empl_stat,v2_curr_paid_empl,v2_disabl_pens,v2_spec_emp,v2_wrk_abs_pst_6_mths,v2_cur_work_restr,
                    v2_weight,v2_waist,v2_bmi)

Visit 2: Events precipitating first illness episode (categorical [N,U,Y], v2_evnt_prcp_illn)

The participant is asked the following question: “Before your first illness episode, was there a special event that could have triggered the disease? If yes, please describe.” The following answering alternatives were given: N-“No”, U-“Unclear if trigger, namely”, Y-“yes, namely”.

v2_evnt_prcp_illn<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_evnt_prcp_illn<-ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==1,"N",
                   ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==2,"U",
                   ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==3,"Y",
                   ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==-999,-999,v2_evnt_prcp_illn))))

v2_evnt_prcp_illn<-factor(v2_evnt_prcp_illn)
descT(v2_evnt_prcp_illn)

##                -999 N   U   Y    <NA>     
## [1,] No. cases 466  176 103 330  711  1786
## [2,] Percent   26.1 9.9 5.8 18.5 39.8 100

If unclear or yes, find the event described in the following item (categorical [text], v2_evnt_prcp_illn_txt) Output masked, as this is very sensitive information.

Create dataset

v2_ev_prc_fst_ep<-data.frame(v2_evnt_prcp_illn,v2_evnt_prcp_illn_txt)

Visit 2: Life events precipitating illness episode between study visits (only in clinical participants)

If there occurred one or more illness episodes between study visits, participants were asked whether one of the life events they experienced and coded in the LEQ may have precipitated the episode.

To systematically evaluate this, each life event coded by the participant in the LEQ was afterwards evaluated if it:

A Occurred before the episode B Was, in the opinion of the participant, a precipitating factor for the illness episode C Which ife event, expressed as the corresponding item of the LEQ (“item number…”) she or he experienced

As these items were answered by only a fraction of the patients, these were autmatically recoded using a for loop and descriptive statistics on each item are not given here. The items are, however, included in the dataset as “v2_evnt_prcp_b4_1” to “v2_evnt_prcp_b4_31” (A), “v2_evnt_prcp_it_1” to “v2_evnt_prcp_it_31” (B), and “v2_evnt_prcp_it_1” to “v2_evnt_prcp_it_31” (C).

**Life events: Occurred before illness episode? (dichotomous, v2_evnt_prcp_b4_*)**

for(i in 1:length(grep("v2_ergaenz_leq_leq_zeit_31055_",names(v2_clin)))){
  b4_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_zeit_31055_",names(v2_clin))[i]],
                   paste("v2_evnt_prcp_b4_",i,sep=""))
}

**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v2_evnt_prcp_f_*)**

for(i in 1:length(grep("v2_ergaenz_leq_leq_ausloeser_31055_",names(v2_clin)))){
  prcp_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_ausloeser_31055_",names(v2_clin))[i]],
                   paste("v2_evnt_prcp_f_",i,sep=""))
}

**Life events: LEQ item number (categorical [LEQ item number], v2_evnt_prcp_it_*)**

for(i in 1:length(grep("v2_ergaenz_leq_leq_item_31055_",names(v2_clin)))){
  leq_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_item_31055_",names(v2_clin))[i]],
                   paste("v2_evnt_prcp_it_",i,sep=""))
}

Create dataset

v2_leprcp<-data.frame(v2_evnt_prcp_it_1,v2_evnt_prcp_b4_1,v2_evnt_prcp_f_1,
                      v2_evnt_prcp_it_2,v2_evnt_prcp_b4_2,v2_evnt_prcp_f_2,
                      v2_evnt_prcp_it_3,v2_evnt_prcp_b4_3,v2_evnt_prcp_f_3,
                      v2_evnt_prcp_it_4,v2_evnt_prcp_b4_4,v2_evnt_prcp_f_4,
                      v2_evnt_prcp_it_5,v2_evnt_prcp_b4_5,v2_evnt_prcp_f_5,
                      v2_evnt_prcp_it_6,v2_evnt_prcp_b4_6,v2_evnt_prcp_f_6,
                      v2_evnt_prcp_it_7,v2_evnt_prcp_b4_7,v2_evnt_prcp_f_7,
                      v2_evnt_prcp_it_8,v2_evnt_prcp_b4_8,v2_evnt_prcp_f_8,
                      v2_evnt_prcp_it_9,v2_evnt_prcp_b4_9,v2_evnt_prcp_f_9,
                      v2_evnt_prcp_it_10,v2_evnt_prcp_b4_10,v2_evnt_prcp_f_10,
                      v2_evnt_prcp_it_11,v2_evnt_prcp_b4_11,v2_evnt_prcp_f_11,
                      v2_evnt_prcp_it_12,v2_evnt_prcp_b4_12,v2_evnt_prcp_f_12,
                      v2_evnt_prcp_it_13,v2_evnt_prcp_b4_13,v2_evnt_prcp_f_13,
                      v2_evnt_prcp_it_14,v2_evnt_prcp_b4_14,v2_evnt_prcp_f_14,
                      v2_evnt_prcp_it_15,v2_evnt_prcp_b4_15,v2_evnt_prcp_f_15,
                      v2_evnt_prcp_it_16,v2_evnt_prcp_b4_16,v2_evnt_prcp_f_16,
                      v2_evnt_prcp_it_17,v2_evnt_prcp_b4_17,v2_evnt_prcp_f_17,
                      v2_evnt_prcp_it_18,v2_evnt_prcp_b4_18,v2_evnt_prcp_f_18,
                      v2_evnt_prcp_it_19,v2_evnt_prcp_b4_19,v2_evnt_prcp_f_19,
                      v2_evnt_prcp_it_20,v2_evnt_prcp_b4_20,v2_evnt_prcp_f_20,
                      v2_evnt_prcp_it_21,v2_evnt_prcp_b4_21,v2_evnt_prcp_f_21,
                      v2_evnt_prcp_it_22,v2_evnt_prcp_b4_22,v2_evnt_prcp_f_22,
                      v2_evnt_prcp_it_23,v2_evnt_prcp_b4_23,v2_evnt_prcp_f_23,
                      v2_evnt_prcp_it_24,v2_evnt_prcp_b4_24,v2_evnt_prcp_f_24,
                      v2_evnt_prcp_it_25,v2_evnt_prcp_b4_25,v2_evnt_prcp_f_25,
                      v2_evnt_prcp_it_26,v2_evnt_prcp_b4_26,v2_evnt_prcp_f_26,
                      v2_evnt_prcp_it_27,v2_evnt_prcp_b4_27,v2_evnt_prcp_f_27,
                      v2_evnt_prcp_it_28,v2_evnt_prcp_b4_28,v2_evnt_prcp_f_28,
                      v2_evnt_prcp_it_29,v2_evnt_prcp_b4_29,v2_evnt_prcp_f_29,
                      v2_evnt_prcp_it_30,v2_evnt_prcp_b4_30,v2_evnt_prcp_f_30,
                      v2_evnt_prcp_it_31,v2_evnt_prcp_b4_31,v2_evnt_prcp_f_31)

Visit 2: Suicide attempts and suicidal ideation since last study visit

Here, we used a modified version of section X of the SCID, that was assessed at visit 1 (lifetime assessment of suicidality). Specifically, we slightly changed the wording of the items, so that they covered the time window from the last study visit until the current assessment.

Suicidal ideation

Suicidal ideation since last study visit (dichotomous, v2_suic_ide_snc_lst_vst)

Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.

v2_suic_ide_snc_lst_vst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_suic_ide_snc_lst_vst<-ifelse(c(v2_clin$v2_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v2_con)[1]))==1, "N", 
                            ifelse(c(v2_clin$v2_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v2_con)[1]))==3, "Y",                                                 v2_suic_ide_snc_lst_vst))

v2_suic_ide_snc_lst_vst<-factor(v2_suic_ide_snc_lst_vst)
descT(v2_suic_ide_snc_lst_vst)

##                -999 N    Y    <NA>     
## [1,] No. cases 466  560  204  556  1786
## [2,] Percent   26.1 31.4 11.4 31.1 100

Suicidal ideation detailed (ordinal [1,2,3,4], v2_scid_suic_ide)

This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v2_scid_suic_ide<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_scid_suic_ide<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==1, "1",
                         ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==2, "2",
                                ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==3, "3",
                                       ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==4, "4",-999))))

v2_scid_suic_ide<-factor(v2_scid_suic_ide,ordered=T)                                   
descT(v2_scid_suic_ide)

##                -999 1   2   3   4  <NA>     
## [1,] No. cases 1026 116 29  39  18 558  1786
## [2,] Percent   57.4 6.5 1.6 2.2 1  31.2 100

Thoughts about methods (ordinal [1,2,3], v2_scid_suic_thght_mth)

This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.

v2_scid_suic_thght_mth<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_scid_suic_thght_mth<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==1, "1",
                         ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==2, "2",
                                ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==3, "3",-999)))

v2_scid_suic_thght_mth<-factor(v2_scid_suic_thght_mth,ordered=T)                                   
descT(v2_scid_suic_thght_mth)

##                -999 1   2   3   <NA>     
## [1,] No. cases 1026 103 67  31  559  1786
## [2,] Percent   57.4 5.8 3.8 1.7 31.3 100

Suicidal ideation: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v2_scid_suic_note_thgts)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v2_scid_suic_note_thgts<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_scid_suic_note_thgts<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==1, "1",
                         ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==2, "2",
                                ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==3, "3",
                                       ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==4, "4",-999))))

v2_scid_suic_note_thgts<-factor(v2_scid_suic_note_thgts,ordered=T)                                   
descT(v2_scid_suic_note_thgts)

##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1026 179 12  4   5   560  1786
## [2,] Percent   57.4 10  0.7 0.2 0.3 31.4 100

Suicide attemps

Suicide attempt since last study visit (ordinal [1,2,3], v2_suic_attmpt_snc_lst_vst)

This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.

v2_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_suic_attmpt_snc_lst_vst<-ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==1, "1",
                         ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==2, "2",
                                ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==3, "3",-999)))

v2_suic_attmpt_snc_lst_vst<-factor(v2_suic_attmpt_snc_lst_vst,ordered=T)                                   
descT(v2_suic_attmpt_snc_lst_vst)

##                -999 1    2   3   <NA>     
## [1,] No. cases 466  736  2   14  568  1786
## [2,] Percent   26.1 41.2 0.1 0.8 31.8 100

Number of suicide attempts (ordinal [1,2,3,4,5,6], v2_no_suic_attmpt)

This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.

v2_no_suic_attmpt<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_no_suic_attmpt<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999, ifelse(v2_suic_attmpt_snc_lst_vst>1, c(v2_clin$v2_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v2_con)[1])),v2_no_suic_attmpt))

v2_no_suic_attmpt<-factor(v2_no_suic_attmpt,ordered=T)
descT(v2_no_suic_attmpt)

##                -999 1   3   <NA>     
## [1,] No. cases 1202 13  1   570  1786
## [2,] Percent   67.3 0.7 0.1 31.9 100

Preparation of suicide attempt (ordinal [1,2,3,4], v2_prep_suic_attp_ord)

This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.

v2_prep_suic_attp_ord<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_prep_suic_attp_ord<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999, 
                              ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==1, "1",
                              ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==2, "2",             
                              ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==3, "3",
                              ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==4, "4",
                              v2_prep_suic_attp_ord))))) 

v2_prep_suic_attp_ord<-factor(v2_prep_suic_attp_ord,ordered=T)
descT(v2_prep_suic_attp_ord)

##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1202 5   2   4   4   569  1786
## [2,] Percent   67.3 0.3 0.1 0.2 0.2 31.9 100

Suicidal attempt: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v2_suic_note_attmpt)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v2_suic_note_attmpt<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_suic_note_attmpt<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999, 
                            ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==1, "1",
                            ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==2, "2",
                            ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==3, "3",
                            ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==4, "4",
                            v2_suic_note_attmpt))))) 

v2_suic_note_attmpt<-factor(v2_suic_note_attmpt,ordered=T)
descT(v2_suic_note_attmpt)

##                -999 1   3   4   <NA>     
## [1,] No. cases 1202 8   2   3   571  1786
## [2,] Percent   67.3 0.4 0.1 0.2 32   100

Create dataset

v2_suic<-data.frame(v2_suic_ide_snc_lst_vst,v2_scid_suic_ide,v2_scid_suic_thght_mth,v2_scid_suic_note_thgts,
                    v2_suic_attmpt_snc_lst_vst,v2_no_suic_attmpt,v2_prep_suic_attp_ord,
                    v2_suic_note_attmpt)

Visit 2: Medication

As in the fist visit, the code below creates the following variables that summarize the medication of each individual:

Number of antidepressants prescribed (continuous [number], v2_Antidepressants)
Number of antipsychotics prescribed (continuous [number], v2_Antipsychotics)
Number of mood stabilizers prescribed (continuous [number], v2_Mood_stabilizers)
Number of tranquilizers prescribed (continuous [number], v2_Tranquilizers)
Number of other psychiatric medications (continuous [number], v2_Other_psychiatric)

Clinical participants

#get the following variables from v2_clin
#1. Medication name     ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_1"]
#6. Bedarf category     ["_bedarf_kategorie_1"]

v2_clin_medication_variables_1<-as.data.frame(v2_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v2_clin))])
dim(v2_clin_medication_variables_1) 

## [1] 1320   61

#recode the variables that are coded as characters/logicals in the "v2_clin_medication_variables_1" as factors
v2_clin_medication_variables_1$v2_medikabehand3_depot_medi_200170_3<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_depot_medi_200170_3)

v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_9<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_9)

v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_9)

v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_10<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_10)

v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_10)

#make the duplicated data frame
v2_clin_medications_duplicated_1<-as.data.frame(t(apply(v2_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v2_clin_medications_duplicated_1)

## [1] 1320   30

#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_clin, not as character
v2_clin_medication_variables_1[,!c(TRUE, FALSE)][v2_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v2_clin_medication_variables_1) 

## [1] 1320   61

#bind columns id and medication names, but not categories together 
v2_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v2_clin_medication_variables_1[,1], v2_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v2_clin_medication_name_1) 

## [1] 1320   31

#get the medication categories from the "_medication_variables_1" dataframe
v2_clin_medication_categories_1<-as.data.frame(v2_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v2_clin_medication_categories_1) 

## [1] 1320   31

#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_clin, not as character
#Important: v2_clin_medication_name_1=="NA" replaced with is.na(v2_clin_medication_name_1)
v2_clin_medication_categories_1[is.na(v2_clin_medication_name_1)] <- NA
#write.csv(v2_clin_medication_categories_1, file="v2_clin_medication_group_1.csv") 

#Make a count table of medications
v2_clin_med_table<-data.frame("mnppsd"=v2_clin$mnppsd)
v2_clin_med_table$v2_Antidepressants<-rowSums(v2_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v2_clin_med_table$v2_Antipsychotics<-rowSums(v2_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v2_clin_med_table$v2_Mood_stabilizers<-rowSums(v2_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v2_clin_med_table$v2_Tranquilizers<-rowSums(v2_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v2_clin_med_table$v2_Other_psychiatric<-rowSums(v2_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Control participants

#get the following variables from v2_con
#1. Medication name     ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_2"]
#6. Bedarf category     ["_bedarf_kategorie_2"]

v2_con_medication_variables_1<-as.data.frame(v2_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v2_con))])
dim(v2_con_medication_variables_1) #[1] 320 29 

## [1] 466  29

#recode the variables that are coded as characters/logicals in the "v2_con_medication_variables_1" as factors
v2_con_medication_variables_1$v2_medikabehand3_med_medi_200705_8<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_med_medi_200705_8)

v2_con_medication_variables_1$v2_medikabehand3_med_kategorie_200705_8<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_med_kategorie_200705_8)

v2_con_medication_variables_1$v2_medikabehand3_depot_medi_201224_2<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_depot_medi_201224_2)

v2_con_medication_variables_1$v2_medikabehand3_depot_kategorie_201224_2<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_depot_kategorie_201224_2)

v2_con_medication_variables_1$v2_medikabehand3_bedarf_medi_201187_4<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_bedarf_medi_201187_4)

v2_con_medication_variables_1$v2_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_bedarf_kategorie_201187_4)

#make the duplicated data frame
v2_con_medications_duplicated_1<-as.data.frame(t(apply(v2_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v2_con_medications_duplicated_1) 

## [1] 466  14

#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_con, not as character
v2_con_medication_variables_1[,!c(TRUE, FALSE)][v2_con_medications_duplicated_1=="TRUE"] <- NA
dim(v2_con_medication_variables_1) 

## [1] 466  29

#bind columns id and medication names, but not categories together 
v2_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v2_con_medication_variables_1[,1], v2_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v2_con_medication_name_1) 

## [1] 466  15

#get the medication categories from the "_medication_variables_1" dataframe
v2_con_medication_categories_1<-as.data.frame(v2_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v2_con_medication_categories_1) 

## [1] 466  15

#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_con, not as character
#Important: v2_con_medication_name_1=="NA" replaced with is.na(v2_con_medication_name_1)
v2_con_medication_categories_1[is.na(v2_con_medication_name_1)] <- NA
#write.csv(v2_con_medication_categories_1, file="v2_con_medication_group_1.csv")

#Make a count table of medications
v2_con_med_table<-data.frame("mnppsd"=v2_con$mnppsd)
v2_con_med_table$v2_Antidepressants<-rowSums(v2_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v2_con_med_table$v2_Antipsychotics<-rowSums(v2_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v2_con_med_table$v2_Mood_stabilizers<-rowSums(v2_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v2_con_med_table$v2_Tranquilizers<-rowSums(v2_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v2_con_med_table$v2_Other_psychiatric<-rowSums(v2_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Bind v2_clin and v2_con together by rows

v2_drugs<-rbind(v2_clin_med_table,v2_con_med_table)
dim(v2_drugs) 

## [1] 1786    6

#check if the id column of v2_drugs and v1_id match
table(v2_drugs[,1]==v1_id)

## 
## TRUE 
## 1786

Adverse events under current medication (only in clinical participants) (dichotomous, v2_adv)

v2_clin_adv<-ifelse(v2_clin$v2_medikabehand_medi2_nebenwirk==1,"Y","N")
v2_con_adv<-rep("-999",dim(v2_con)[1])
v2_adv<-factor(c(v2_clin_adv,v2_con_adv))
descT(v2_adv)

##                -999 N    Y    <NA>     
## [1,] No. cases 466  219  354  747  1786
## [2,] Percent   26.1 12.3 19.8 41.8 100

Psychiatric medication change during the past six months (only in clinical participants) (dichotomous, v2_medchange)

v2_clin_medchange<-rep(NA,dim(v2_clin)[1])
v2_clin_medchange<-ifelse(v2_clin$v2_medikabehand_medi3_mediaenderung==1,"Y","N")
v2_con_medchange<-rep("-999",dim(v2_con)[1])

v2_medchange<-as.factor(c(v2_clin_medchange,v2_con_medchange))
descT(v2_medchange)

##                -999 N    Y    <NA>     
## [1,] No. cases 466  192  381  747  1786
## [2,] Percent   26.1 10.8 21.3 41.8 100

Lithium (only in clinical participants)

Please see the section in Visit 1 for explanation.

“Did you ever take lithium?” (dichotomous, v2_lith)

v2_clin_lith<-rep(NA,dim(v2_clin)[1])
v2_clin_lith<-ifelse(v2_clin$v2_medikabehand_med_zusatz_lithium==1,"Y","N")
v2_con_lith<-rep("-999",dim(v2_con)[1])

v2_lith<-as.factor(c(v2_clin_lith,v2_con_lith))
v2_lith<-as.factor(v2_lith)

descT(v2_lith)

##                -999 N   Y   <NA>     
## [1,] No. cases 466  215 135 970  1786
## [2,] Percent   26.1 12  7.6 54.3 100

“If yes, for how long?” (only in clinical participants) (dichotomous, v2_lith_prd)

Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.

v2_clin_lith_prd<-rep(NA,dim(v2_clin)[1])
v2_con_lith_prd<-rep(-999,dim(v2_con)[1])

v2_clin_lith_prd<-ifelse(v2_clin_lith=="N", -999, ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==2,1,
                  ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==1,2,    
                  ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==0,3,NA))))
                                                     
v2_lith_prd<-factor(c(v2_clin_lith_prd,v2_con_lith_prd))
descT(v2_lith_prd)

##                -999 1  2   3   <NA>     
## [1,] No. cases 681  54 19  62  970  1786
## [2,] Percent   38.1 3  1.1 3.5 54.3 100

Create dataset

v2_med<-data.frame(v2_drugs[,2:6],v2_adv,v2_medchange,v2_lith,v2_lith_prd)

Create datasets with raw medication information

Here, separate datasets for clinical and control participants are created that contain the raw medication information at visit 2, as specified in the phenotype database.

For each medication that the individual took at visit 2 (including non-psychiatric drugs), the information given below is assessed.

The last character of each variable name always refers to the medication in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.

The medications were not assessed in any specific order, i.e. the order was determined by the individual participant (whatever she or he mentioned first). To classify medications, we used a catalogue, from which the categories and subcategories a medication belongs to were selected (see below).

Below, the variable names of clinical/control participants, respectively, are given in quotes, and the coding is explained in the parentheses.

1.Was the individual treated with any medication? (-1-not assessed, 1-yes, 2-no, 99-unknown)
“v2_medikabehand3_keine_med”/“v2_medikabehand3_keine_med”

2. Regular medication: Name of the medication (character)
   “v2_medikabehand3_med_medi_199998”/“v2_medikabehand3_med_medi_200705”

3. Regular medication: Category to which the medication belongs (character)
   “v2_medikabehand3_med_kategorie_199998”/“v2_medikabehand3_med_kategorie_200705”

4. Regular medication: Subcategory to which the medication belongs (character)
   “v2_medikabehand3_med_kategorie_sub_199998”/“v2_medikabehand3_med_kategorie_sub_200705”

5. Regular medication: Psychiatric medication? (0-no, 1-yes) “v2_medikabehand3_med_zusatz_199998”/“v2_medikabehand3_med_zusatz_200705”

6. Regular medication: Dose in the morning (unitless)
   “v2_medikabehand3_s_medi1_morgens_199998”/“v2_medikabehand3_s_medi1_morgens_200705”

7. Regular medication: Dose at midday (unitless)
   “v2_medikabehand3_smedi1_mittags_199998”/“v2_medikabehand3_smedi1_mittags_200705”

8. Regular medication: Dose in the evening (unitless)
   “v2_medikabehand3_smedi1_abends_199998”/“v2_medikabehand3_smedi1_abends_200705”

9. Regular medication: Dose at night (unitless)
   “v2_medikabehand3_smedi1_nachts_199998”/“v2_medikabehand3_smedi1_nachts_200705”

10. Regular medication: Unit of the medication asked in the last four questions (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
    “v2_medikabehand3_smedi1_einheit_199998”/“v2_medikabehand3_smedi1_einheit_200705”

11. Regular medication: Total dose of the medication per day (unitless)
    “v2_medikabehand3_smedi1_gesamtdosis_199998”/“v2_medikabehand3_smedi1_gesamtdosis_200705”

12. Regular medication: Unit of the medication asked in the last question (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
    “v2_medikabehand3_smedi1_einheit1_199998”/“v2_medikabehand3_smedi1_einheit1_200705”

13. Regular medication: Medication name, if not contained in our catalog (character)
    “v2_medikabehand3_medikament_text_199998”/“v2_medikabehand3_medikament_text_200705”

14. Depot medication: Name of the medication (character) “v2_medikabehand3_depot_medi_200170”/“v2_medikabehand3_depot_medi_201224

15. Depot medication: Category to which the medication belongs (character) “v2_medikabehand3_depot_kategorie_200170”/“v2_medikabehand3_depot_kategorie_201224

16. Depot medication: Subcategory to which the medication belongs (character)
    “v2_medikabehand3_depot_kategorie_sub_200170”/“v2_medikabehand3_depot_kategorie_sub_201224

17. Depot medication: Psychiatric medication? (0-no, 1-yes) “v2_medikabehand3_depot_zusatz_200170”/“v2_medikabehand3_depot_zusatz_201224”

18. Depot medication: Total Dose (unitless) “v2_medikabehand3_s_depot_gesamtdosis_200170”/“v2_medikabehand3_s_depot_gesamtdosis_201224”

19. Depot medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v2_medikabehand3_s_depot_einheit_200170”/ “v2_medikabehand3_s_depot_einheit_201224”

20. Interval, at which the depot medication is given (days) “v2_medikabehand3_s_depot_tage_200170”/“v2_medikabehand3_s_depot_tage_201224”

21. Medication name, if not contained in our catalog (character) “v2_medikabehand3_medikament_text_200170”/“v2_medikabehand3_medikament_text_201224”

22. Pro re nata (PRN) medication: Name of the medication (character) “v2_medikabehand3_bedarf_medi_199584”/“v2_medikabehand3_bedarf_medi_201187”

23. Pro re nata (PRN) medication: Category to which the medication belongs (character)
    “v2_medikabehand3_bedarf_kategorie_199584”/“v2_medikabehand3_bedarf_kategorie_201187”

24. Pro re nata (PRN) medication: Subcategory to which the medication belongs (character) “v2_medikabehand3_bedarf_kategorie_sub_199584”/“v2_medikabehand3_bedarf_kategorie_sub_201187”

25. Pro re nata (PRN) medication: Psychiatric medication? (0-no, 1-yes) “v2_medikabehand3_bedarf_zusatz_199584”/“v2_medikabehand3_bedarf_zusatz_201187”

26. Pro re nata (PRN) medication: Total dose up to (unitless) “v2_medikabehand3_s_bedarf_gesamtdosis_199584”/“v2_medikabehand3_s_bedarf_kommentar_201187

27. Pro re nata (PRN) medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v2_medikabehand3_s_bedarf_einheit1_199584”/“v2_medikabehand3_s_bedarf_einheit1_201187”

28. Pro re nata (PRN) medication: Comment (character) “v2_medikabehand3_s_bedarf_kommentar_199584”/“v2_medikabehand3_s_bedarf_kommentar_201187”

29. Pro re nata (PRN) medication: Medication name, if not contained in our catalog (character) “v2_medikabehand3_medikament_text_199584”/“v2_medikabehand3_medikament_text_201187”

Make datasets containing only information on medication

v2_med_clin_orig<-data.frame(v2_clin$mnppsd,v2_clin[,147:455])
names(v2_med_clin_orig)[1]<-"v1_id"

v2_med_con_orig<-data.frame(v2_con$mnppsd,v2_con[,75:219])
names(v2_med_con_orig)[1]<-"v1_id"

Save raw medication datasets of visit 2

save(v2_med_clin_orig, file="230614_v6.0_psycourse_clin_raw_med_visit2.RData")
save(v2_med_con_orig, file="230614_v6.0_psycourse_con_raw_med_visit2.RData")

Write .csv file

write.table(v2_med_clin_orig,file="230614_v6.0_psycourse_clin_raw_med_visit2.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v2_med_con_orig,file="230614_v6.0_psycourse_con_raw_med_visit2.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Visit 2: Substance abuse

Tobacco

For more explanation, see Visit 1

“Did you start or stop smoking during the past six months?” (categorical [NS,NN,YSP,YST], v2_smk_strt_stp)

This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.

v2_clin_smk_strt_stp<-rep(NA,dim(v2_clin)[1])
v2_clin_smk_strt_stp<-ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==1,"NS",
                        ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==2,"NN",
                          ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==3,"YSP",
                            ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==4,"YST",v2_clin_smk_strt_stp))))
       
#ATTENTION: answering alternative: e-cigarette only in controls
v2_con_smk_strt_stp<-rep(NA,dim(v2_clin)[1])
v2_con_smk_strt_stp<-ifelse(v2_con$v2_tabalk_folge_tabak1==1 | v2_con$v2_tabalk_folge_tabak1==2,"NS",
                        ifelse(v2_con$v2_tabalk_folge_tabak1==3,"NN",
                          ifelse(v2_con$v2_tabalk_folge_tabak1==4,"YSP",
                            ifelse(v2_con$v2_tabalk_folge_tabak1==5,"YST",v2_con_smk_strt_stp))))
                        
v2_smk_strt_stp<-c(v2_clin_smk_strt_stp,v2_con_smk_strt_stp)
descT(v2_smk_strt_stp)

##                NN   NS   YSP YST <NA>     
## [1,] No. cases 374  663  23  15  711  1786
## [2,] Percent   20.9 37.1 1.3 0.8 39.8 100

“How many cigarettes do you presently smoke on average?” (continuous [number cigarettes], v2_no_cig)

In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.

Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.

v2_no_cig<-c(rep(NA,dim(v2_clin)[1]),rep(NA,dim(v2_con)[1]))

v2_no_cig<-ifelse((v2_smk_strt_stp=="NN" | v2_smk_strt_stp=="YSP"), -999, 
            ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") & 
                     c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==1,                                
                     c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*365,
            ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") & 
                     c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==2,                                
                     c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*52,
            ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") & 
                     c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==3,                                
                     c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*12,
                      v2_no_cig))))

summary(v2_no_cig[v2_no_cig>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0    3650    6022    6172    7300   23725     945

Alcohol

“How often did you consume alcoholic beverages during the past six months?” (ordinal [1,2,3,4,5,6,7], v2_alc_pst6_mths)

This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.

v2_alc_pst6_mths<-c(v2_clin$v2_tabalk1_ta9_alkkonsum,v2_con$v2_tabalk_folge_alkohol4)
v2_alc_pst6_mths<-factor(v2_alc_pst6_mths, ordered=T)

descT(v2_alc_pst6_mths)

##                1    2    3   4    5   6   7   <NA>     
## [1,] No. cases 270  216  111 255  130 46  39  719  1786
## [2,] Percent   15.1 12.1 6.2 14.3 7.3 2.6 2.2 40.3 100

“On how many occasions during the past six months did you drink FIVE (men)/FOUR (women) or more alcoholic beverages?”” (ordinal [1,2,3,4,5,6,7,8,9], v2_alc_5orm)

This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.

v2_alc_5orm<-ifelse(v2_alc_pst6_mths<4,-999,
                    ifelse(is.na(c(v2_clin$v2_tabalk1_ta10_alk_haeufigk_m1,v2_con$v2_tabalk_folge_alkohol5))==T,   
                            c(v2_clin$v2_tabalk1_ta11_alk_haeufigk_f1,v2_con$v2_tabalk_folge_alkohol6),
                            c(v2_clin$v2_tabalk1_ta10_alk_haeufigk_m1,v2_con$v2_tabalk_folge_alkohol5)))

v2_alc_5orm<-factor(v2_alc_5orm, ordered=T)

descT(v2_alc_5orm)

##                -999 1    2   3   4   5   6   7   8   9   <NA>     
## [1,] No. cases 597  213  88  59  21  29  33  15  3   7   721  1786
## [2,] Percent   33.4 11.9 4.9 3.3 1.2 1.6 1.8 0.8 0.2 0.4 40.4 100

Illicit drugs

On follow-up visits, participant were asked whether they had consumed illicit drugs since the last visit. If yes, the following information was collected:

• The name of each illicit drug consumed since the last visit
• The category of each illicit drug consumed since the last visit
• The frequency of consumption since the last visit
• Whether the individual developed tolerance to the drug

In the PsyCourse dataset, only the information on whether, since the last study visit, the participant consumed any illicit drugs is contained. A separated dataset, containing the raw illicit drug information, is created below.

“During the past six months, did you take ANY illicit drugs?” (dichotomous, v2_pst6_ill_drg)

v2_pst6_ill_drg<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_pst6_ill_drg<-ifelse(c(v2_clin$v2_drogen1_dg1_konsum,v2_con$v2_drogen_folge_drogenkonsum)==2, "Y", "N")

descT(v2_pst6_ill_drg)

##                N    Y   <NA>     
## [1,] No. cases 989  81  716  1786
## [2,] Percent   55.4 4.5 40.1 100

Create dataset

v2_subst<-data.frame(v2_smk_strt_stp,
                     v2_no_cig,
                     v2_alc_pst6_mths,
                     v2_alc_5orm,
                     v2_pst6_ill_drg)

Create dataset with raw illicit drug information

Here, separate datasets for clinical and control participants are created that contain the raw information on illicit drugs at visit 2, exactly as specified in the phenotype database.

For each illicit drug ever taken, the information given below is assessed.

The last character of each variable name always refers to the drug in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.

The drugs are not assessed in any specific order, i.e. the order is determined by the individual participant (whatever she or he mentions first).

Below, the variable names of clinical/control participants are given in quotes, and the coding is explained in the parentheses.

1. Whether the individual consumed illicit drugs since the last visit. (Coding: 1-no, 2-yes) “v2_drogen1_dg1_konsum”/“v2_drogen_folge_drogenkonsum”/

2. The name of the drug: (character) “v2_drogen1_s_dg_droge_28483”/v2_drogen_folge_droge_117794”

The category to which the drug belongs (each item below is a checkbox: 0-not checked, 1-checked):
3. Stimulants: “v2_drogen1_s_dg_drogekt1_28483”/“v2_drogen_folge_droge1_117794
4. Cannabis:”v2_drogen2_s_dg_drogekt1_28483”/“v2_drogen_folge_droge2_117794”
5. Opiates and pain reliefers: “v2_drogen3_s_dg_drogekt1_28483”/“v2_drogen_folge_droge3_117794”
6. Cocaine: “v2_drogen4_s_dg_drogekt1_28483”/“v2_drogen_folge_droge4_117794”
7. Hallucinogens: “v2_drogen5_s_dg_drogekt1_28483”/“v2_drogen_folge_droge5_117794”
8. Inhalants: “v2_drogen6_s_dg_drogekt1_28483”/“v2_drogen_folge_droge6_117794”
9. Tranquilizers: “v2_drogen7_s_dg_drogekt1_28483”/“v2_drogen_folge_droge7_117794”
10. Other: “v2_drogen8_s_dg_drogekt1_28483”/“v2_drogen_folge_droge8_117794”

11. “Referring to the time since the last study visit, how often did you consume it?” “v2_drogen1_s_dga_haeufigk_28483”/“v2_drogen_folge_droge_haeufig_117794”

The coding is given below:
1 - tried 1 time
2 - less than once a month
3 - about once a month
4 - at least 2 times but less than 10 times a month
5 - at least 10 times a month

12. “Referring to the period of time since the last study visit, did you have to take more of the drug to achieve the same effect?” (Coding: 1-no, 2-yes). “v2_drogen1_s_dgf_l6m_dosis_28483”/“v2_drogen_folge_droge_dosis_117794”

Important: There is an error in the original phenotype database, that affects the coding of item 10 (above). In all drugs the exports of the phenotype database do not reflect the input into the graphical user interface. Below, the incorrect variable is replaced with the corrected one

Make datasets containing only information on illicit drugs

v2_drg_clin<-v2_clin[,725:780]
v2_drg_con<-v2_con[,315:392]

Clinical participants

v2_clin_ill_drugs_orig<-data.frame(v2_clin$mnppsd,v2_drg_clin)
names(v2_clin_ill_drugs_orig)[1]<-"v1_id"

#recode wrongly coded item 10
for(i in c(0:4)){

v2_clin_ill_drugs_orig[,12+i*11]<-ifelse(v2_clin_ill_drugs_orig[,12+i*11]==5,1,
                               ifelse(v2_clin_ill_drugs_orig[,12+i*11]==4,5,
                                ifelse(v2_clin_ill_drugs_orig[,12+i*11]==3,4,
                                 ifelse(v2_clin_ill_drugs_orig[,12+i*11]==2,3,
                                  ifelse(v2_clin_ill_drugs_orig[,12+i*11]==1,2,NA)))))}

Control participants

v2_con_ill_drugs_orig<-data.frame(v2_con$mnppsd,v2_drg_con)
names(v2_con_ill_drugs_orig)[1]<-"v1_id"

#recode wrongly coded item 10
for(i in c(0:6)){

v2_con_ill_drugs_orig[,12+i*11]<-ifelse(v2_con_ill_drugs_orig[,12+i*11]==5,1,
                               ifelse(v2_con_ill_drugs_orig[,12+i*11]==4,5,
                                ifelse(v2_con_ill_drugs_orig[,12+i*11]==3,4,
                                 ifelse(v2_con_ill_drugs_orig[,12+i*11]==2,3,
                                  ifelse(v2_con_ill_drugs_orig[,12+i*11]==1,2,NA)))))}

Save raw illicit drug dataset from visit 2

save(v2_clin_ill_drugs_orig, file="230614_v6.0_psycourse_clin_raw_ill_drg_visit2.RData")
save(v2_con_ill_drugs_orig, file="230614_v6.0_psycourse_con_raw_ill_drg_visit2.RData")

Write .csv file

write.table(v2_clin_ill_drugs_orig,file="230614_v6.0_psycourse_clin_raw_ill_drg_visit2.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v2_con_ill_drugs_orig,file="230614_v6.0_psycourse_con_raw_ill_drg_visit2.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Visit 2: Symptom rating scales (interviewer rates patient)

PANSS

For more information on the scale, please see Visit 1

P1 Delusions (ordinal [1,2,3,4,5,6,7], v2_panss_p1)

v2_panss_p1<-c(v2_clin$v2_panss_p_p1_wahnideen,v2_con$v2_panss_p_p1_wahnideen)
v2_panss_p1<-factor(v2_panss_p1, ordered=T)

descT(v2_panss_p1)

##                1    2   3   4   5  6   <NA>     
## [1,] No. cases 832  58  63  28  17 11  777  1786
## [2,] Percent   46.6 3.2 3.5 1.6 1  0.6 43.5 100

P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v2_panss_p2)

v2_panss_p2<-c(v2_clin$v2_panss_p_p2_form_denkst,v2_con$v2_panss_p_p2_form_denkst)
v2_panss_p2<-factor(v2_panss_p2, ordered=T)

descT(v2_panss_p2)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 748  95  109 46  8   3   777  1786
## [2,] Percent   41.9 5.3 6.1 2.6 0.4 0.2 43.5 100

P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v2_panss_p3)

v2_panss_p3<-c(v2_clin$v2_panss_p_p3_halluz,v2_con$v2_panss_p_p3_halluz)
v2_panss_p3<-factor(v2_panss_p3, ordered=T)

descT(v2_panss_p3)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 899  33  28  31  13  6   776  1786
## [2,] Percent   50.3 1.8 1.6 1.7 0.7 0.3 43.4 100

P4 Excitement (ordinal [1,2,3,4,5,6,7], v2_panss_p4)

v2_panss_p4<-c(v2_clin$v2_panss_p_p4_erregung,v2_con$v2_panss_p_p4_erregung)
v2_panss_p4<-factor(v2_panss_p4, ordered=T)

descT(v2_panss_p4)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 787  81  112 22  4   3   777  1786
## [2,] Percent   44.1 4.5 6.3 1.2 0.2 0.2 43.5 100

P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v2_panss_p5)

v2_panss_p5<-c(v2_clin$v2_panss_p_p5_groessenideen,v2_con$v2_panss_p_p5_groessenideen)
v2_panss_p5<-factor(v2_panss_p5, ordered=T)

descT(v2_panss_p5)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 942  28  28  6   4   1   777  1786
## [2,] Percent   52.7 1.6 1.6 0.3 0.2 0.1 43.5 100

P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v2_panss_p6)

v2_panss_p6<-c(v2_clin$v2_panss_p_p6_misstr_verfolg,v2_con$v2_panss_p_p6_misstr_verfolg)
v2_panss_p6<-factor(v2_panss_p6, ordered=T)

descT(v2_panss_p6)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 829  61  77  22  16  4   1   776  1786
## [2,] Percent   46.4 3.4 4.3 1.2 0.9 0.2 0.1 43.4 100

P7 Hostility (ordinal [1,2,3,4,5,6,7], v2_panss_p7)

v2_panss_p7<-c(v2_clin$v2_panss_p_p7_feindseligkeit,v2_con$v2_panss_p_p7_feindseligkeit)
v2_panss_p7<-factor(v2_panss_p7, ordered=T)

descT(v2_panss_p7)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 922  43  34  8   1   1   777  1786
## [2,] Percent   51.6 2.4 1.9 0.4 0.1 0.1 43.5 100

PANSS Positive sum score (continuous [7-49], v2_panss_sum_pos)

v2_panss_sum_pos<-as.numeric.factor(v2_panss_p1)+
                  as.numeric.factor(v2_panss_p2)+
                  as.numeric.factor(v2_panss_p3)+
                  as.numeric.factor(v2_panss_p4)+
                  as.numeric.factor(v2_panss_p5)+
                  as.numeric.factor(v2_panss_p6)+
                  as.numeric.factor(v2_panss_p7)

summary(v2_panss_sum_pos)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    7.00    9.18   10.00   32.00     780

Negative subscale

N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v2_panss_n1)

v2_panss_n1<-c(v2_clin$v2_panss_n_n1_affektverflachung,v2_con$v2_panss_n_n1_affektverflachung)
v2_panss_n1<-factor(v2_panss_n1, ordered=T)

descT(v2_panss_n1)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 613  109 133 94  49  8   2   778  1786
## [2,] Percent   34.3 6.1 7.4 5.3 2.7 0.4 0.1 43.6 100

N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v2_panss_n2)

v2_panss_n2<-c(v2_clin$v2_panss_n_n2_emot_rueckzug,v2_con$v2_panss_n_n2_emot_rueckzug)
v2_panss_n2<-factor(v2_panss_n2, ordered=T)

descT(v2_panss_n2)

##                1   2   3   4   5   6   <NA>     
## [1,] No. cases 696 100 110 77  22  5   776  1786
## [2,] Percent   39  5.6 6.2 4.3 1.2 0.3 43.4 100

N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v2_panss_n3)

v2_panss_n3<-c(v2_clin$v2_panss_n_n3_mang_aff_rapp,v2_con$v2_panss_n_n3_mang_aff_rapp)
v2_panss_n3<-factor(v2_panss_n3, ordered=T)

descT(v2_panss_n3)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 759  88  107 40  11  4   777  1786
## [2,] Percent   42.5 4.9 6   2.2 0.6 0.2 43.5 100

N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v2_panss_n4)

v2_panss_n4<-c(v2_clin$v2_panss_n_n4_soz_pass_apath,v2_con$v2_panss_n_n4_soz_pass_apath)
v2_panss_n4<-factor(v2_panss_n4, ordered=T)

descT(v2_panss_n4)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 709  80  136 57  25  3   776  1786
## [2,] Percent   39.7 4.5 7.6 3.2 1.4 0.2 43.4 100

N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v2_panss_n5)

v2_panss_n5<-c(v2_clin$v2_panss_n_n5_abstr_denken,v2_con$v2_panss_n_n5_abstr_denken)
v2_panss_n5<-factor(v2_panss_n5, ordered=T)

descT(v2_panss_n5)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 686  101 140 60  15  5   779  1786
## [2,] Percent   38.4 5.7 7.8 3.4 0.8 0.3 43.6 100

N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v2_panss_n6)

v2_panss_n6<-c(v2_clin$v2_panss_n_n6_spon_fl_sprache,v2_con$v2_panss_n_n6_spon_fl_sprache)
v2_panss_n6<-factor(v2_panss_n6, ordered=T)

descT(v2_panss_n6)

##                1    2   3  4  5   6   <NA>     
## [1,] No. cases 789  75  89 35 16  2   780  1786
## [2,] Percent   44.2 4.2 5  2  0.9 0.1 43.7 100

N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v2_panss_n7)

v2_panss_n7<-c(v2_clin$v2_panss_n_n7_stereotyp_ged,v2_con$v2_panss_n_n7_stereotyp_ged)
v2_panss_n7<-factor(v2_panss_n7, ordered=T)

descT(v2_panss_n7)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 849  70  62  21  4   780  1786
## [2,] Percent   47.5 3.9 3.5 1.2 0.2 43.7 100

PANSS Negative sum score (continuous [7-49], v2_panss_sum_neg)

v2_panss_sum_neg<-as.numeric.factor(v2_panss_n1)+
                  as.numeric.factor(v2_panss_n2)+
                  as.numeric.factor(v2_panss_n3)+
                  as.numeric.factor(v2_panss_n4)+
                  as.numeric.factor(v2_panss_n5)+
                  as.numeric.factor(v2_panss_n6)+
                  as.numeric.factor(v2_panss_n7)

summary(v2_panss_sum_neg)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    9.00   10.98   13.00   39.00     783

General psychopathology subscale

G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v2_panss_g1)

v2_panss_g1<-c(v2_clin$v2_panss_g_g1_sorge_gesundh,v2_con$v2_panss_g_g1_sorge_gesundh)
v2_panss_g1<-factor(v2_panss_g1, ordered=T)

descT(v2_panss_g1)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 722  122 104 49  11  2   1   775  1786
## [2,] Percent   40.4 6.8 5.8 2.7 0.6 0.1 0.1 43.4 100

G2 Anxiety (ordinal [1,2,3,4,5,6,7], v2_panss_g2)

v2_panss_g2<-c(v2_clin$v2_panss_g_g2_angst,v2_con$v2_panss_g_g2_angst)
v2_panss_g2<-factor(v2_panss_g2, ordered=T)

descT(v2_panss_g2)

##                1    2   3    4   5   6   7   <NA>     
## [1,] No. cases 656  81  199  49  25  1   1   774  1786
## [2,] Percent   36.7 4.5 11.1 2.7 1.4 0.1 0.1 43.3 100

G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v2_panss_g3)

v2_panss_g3<-c(v2_clin$v2_panss_g_g3_schuldgefuehle,v2_con$v2_panss_g_g3_schuldgefuehle)
v2_panss_g3<-factor(v2_panss_g3, ordered=T)

descT(v2_panss_g3)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 761  81  101 45  16  4   778  1786
## [2,] Percent   42.6 4.5 5.7 2.5 0.9 0.2 43.6 100

G4 Tension (ordinal [1,2,3,4,5,6,7], v2_panss_g4)

v2_panss_g4<-c(v2_clin$v2_panss_g_g4_anspannung,v2_con$v2_panss_g_g4_anspannung)
v2_panss_g4<-factor(v2_panss_g4, ordered=T)

descT(v2_panss_g4)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 666  113 165 52  11  4   1   774  1786
## [2,] Percent   37.3 6.3 9.2 2.9 0.6 0.2 0.1 43.3 100

G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v2_panss_g5)

v2_panss_g5<-c(v2_clin$v2_panss_g_g5_manier_koerperh,v2_con$v2_panss_g_g5_manier_koerperh)
v2_panss_g5<-factor(v2_panss_g5, ordered=T)

descT(v2_panss_g5)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 926  38  32  8   6   1   775  1786
## [2,] Percent   51.8 2.1 1.8 0.4 0.3 0.1 43.4 100

G6 Depression (ordinal [1,2,3,4,5,6,7], v2_panss_g6)

v2_panss_g6<-c(v2_clin$v2_panss_g_g6_depression,v2_con$v2_panss_g_g6_depression)
v2_panss_g6<-factor(v2_panss_g6, ordered=T)

descT(v2_panss_g6)

##                1    2   3    4   5   6   <NA>     
## [1,] No. cases 573  83  183  100 57  15  775  1786
## [2,] Percent   32.1 4.6 10.2 5.6 3.2 0.8 43.4 100

G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v2_panss_g7)

v2_panss_g7<-c(v2_clin$v2_panss_g_g7_mot_verlangs,v2_con$v2_panss_g_g7_mot_verlangs)
v2_panss_g7<-factor(v2_panss_g7, ordered=T)

descT(v2_panss_g7)

##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 706  89 143 63  7   2   776  1786
## [2,] Percent   39.5 5  8   3.5 0.4 0.1 43.4 100

G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v2_panss_g8)

v2_panss_g8<-c(v2_clin$v2_panss_g_g8_unkoop_verh,v2_con$v2_panss_g_g8_unkoop_verh)
v2_panss_g8<-factor(v2_panss_g8, ordered=T)

descT(v2_panss_g8)

##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 942  28  35 4   1   1   775  1786
## [2,] Percent   52.7 1.6 2  0.2 0.1 0.1 43.4 100

G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v2_panss_g9)

v2_panss_g9<-c(v2_clin$v2_panss_g_g9_ungew_denkinh,v2_con$v2_panss_g_g9_ungew_denkinh)
v2_panss_g9<-factor(v2_panss_g9, ordered=T)

descT(v2_panss_g9)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 847  50  77  21  13  4   774  1786
## [2,] Percent   47.4 2.8 4.3 1.2 0.7 0.2 43.3 100

G10 Disorientation (ordinal [1,2,3,4,5,6,7], v2_panss_g10)

v2_panss_g10<-c(v2_clin$v2_panss_g_g10_desorient,v2_con$v2_panss_g_g10_desorient)
v2_panss_g10<-factor(v2_panss_g10, ordered=T)

descT(v2_panss_g10)

##                1    2   3  4   5   <NA>     
## [1,] No. cases 951  40  17 2   2   774  1786
## [2,] Percent   53.2 2.2 1  0.1 0.1 43.3 100

G11 Poor attention (ordinal [1,2,3,4,5,6,7], v2_panss_g11)

v2_panss_g11<-c(v2_clin$v2_panss_g_g11_mang_aufmerks,v2_con$v2_panss_g_g11_mang_aufmerks)
v2_panss_g11<-factor(v2_panss_g11, ordered=T)

descT(v2_panss_g11)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 637  102 196 63  8   2   778  1786
## [2,] Percent   35.7 5.7 11  3.5 0.4 0.1 43.6 100

G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v2_panss_g12)

v2_panss_g12<-c(v2_clin$v2_panss_g_g12_mang_urt_einsi,v2_con$v2_panss_g_g12_mang_urt_einsi)

v2_panss_g12<-factor(v2_panss_g12, ordered=T)
descT(v2_panss_g12)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 887  51  51  8   12  2   775  1786
## [2,] Percent   49.7 2.9 2.9 0.4 0.7 0.1 43.4 100

G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v2_panss_g13)

v2_panss_g13<-c(v2_clin$v2_panss_g_g13_willensschwae,v2_con$v2_panss_g_g13_willensschwae)
v2_panss_g13<-factor(v2_panss_g13, ordered=T)

descT(v2_panss_g13)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 891  42  61  15  1   1   775  1786
## [2,] Percent   49.9 2.4 3.4 0.8 0.1 0.1 43.4 100

G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v2_panss_g14)

v2_panss_g14<-c(v2_clin$v2_panss_g_g14_mang_impulsk,v2_con$v2_panss_g_g14_mang_impulsk)
v2_panss_g14<-factor(v2_panss_g14, ordered=T)

descT(v2_panss_g14)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 896  32  70  12  1   775  1786
## [2,] Percent   50.2 1.8 3.9 0.7 0.1 43.4 100

G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v2_panss_g15)

v2_panss_g15<-c(v2_clin$v2_panss_g_g15_selbstbezog,v2_con$v2_panss_g_g15_selbstbezog)
v2_panss_g15<-factor(v2_panss_g15, ordered=T)

descT(v2_panss_g15)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 907  55  30  16  2   776  1786
## [2,] Percent   50.8 3.1 1.7 0.9 0.1 43.4 100

G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v2_panss_g16)

v2_panss_g16<-c(v2_clin$v2_panss_g_g16_aktsoz_vermeid,v2_con$v2_panss_g_g16_aktsoz_vermeid)
v2_panss_g16<-factor(v2_panss_g16, ordered=T)

descT(v2_panss_g16)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 783  75  102 29  20  2   775  1786
## [2,] Percent   43.8 4.2 5.7 1.6 1.1 0.1 43.4 100

PANSS General Psychopathology sum score (continuous [16-112], v2_panss_sum_gen)

v2_panss_sum_gen<-as.numeric.factor(v2_panss_g1)+
                  as.numeric.factor(v2_panss_g2)+
                  as.numeric.factor(v2_panss_g3)+
                  as.numeric.factor(v2_panss_g4)+
                  as.numeric.factor(v2_panss_g5)+
                  as.numeric.factor(v2_panss_g6)+
                  as.numeric.factor(v2_panss_g7)+
                  as.numeric.factor(v2_panss_g8)+
                  as.numeric.factor(v2_panss_g9)+
                  as.numeric.factor(v2_panss_g10)+
                  as.numeric.factor(v2_panss_g11)+
                  as.numeric.factor(v2_panss_g12)+
                  as.numeric.factor(v2_panss_g13)+
                  as.numeric.factor(v2_panss_g14)+
                  as.numeric.factor(v2_panss_g15)+
                  as.numeric.factor(v2_panss_g16)

summary(v2_panss_sum_gen)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.00   16.00   20.00   22.71   27.00   68.00     789

Create PANSS Total score (continuous [30-210], v2_panss_sum_tot)

v2_panss_sum_tot<-v2_panss_sum_pos+v2_panss_sum_neg+v2_panss_sum_gen
summary(v2_panss_sum_tot)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   30.00   31.00   38.00   42.87   50.00  137.00     800

Create dataset

v2_symp_panss<-data.frame(v2_panss_p1,v2_panss_p2,v2_panss_p3,v2_panss_p4,v2_panss_p5,v2_panss_p6,v2_panss_p7,
                          v2_panss_n1,v2_panss_n2,v2_panss_n3,v2_panss_n4,v2_panss_n5,v2_panss_n6,v2_panss_n7,
                          v2_panss_g1,v2_panss_g2,v2_panss_g3,v2_panss_g4,v2_panss_g5,v2_panss_g6,v2_panss_g7,
                          v2_panss_g8,v2_panss_g9,v2_panss_g10,v2_panss_g11,v2_panss_g12,v2_panss_g13,v2_panss_g14,
                          v2_panss_g15,v2_panss_g16,v2_panss_sum_pos,v2_panss_sum_neg,v2_panss_sum_gen,
                          v2_panss_sum_tot)

IDS-C₃₀

For more information on the scale, please see Visit 1

Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v2_idsc_itm1)

v2_idsc_itm1<-c(v2_clin$v2_ids_c_s1_ids1_einschlafschw,v2_con$v2_ids_c_s1_ids1_einschlafschw)
v2_idsc_itm1<-factor(v2_idsc_itm1, ordered=T)

descT(v2_idsc_itm1)

##                0    1   2   3  <NA>     
## [1,] No. cases 722  130 85  71 778  1786
## [2,] Percent   40.4 7.3 4.8 4  43.6 100

Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v2_idsc_itm2)

v2_idsc_itm2<-c(v2_clin$v2_ids_c_s1_ids2_naechtl_aufw,v2_con$v2_ids_c_s1_ids2_naechtl_aufw)
v2_idsc_itm2<-factor(v2_idsc_itm2, ordered=T)

descT(v2_idsc_itm2)

##                0    1   2   3   <NA>     
## [1,] No. cases 621  153 156 78  778  1786
## [2,] Percent   34.8 8.6 8.7 4.4 43.6 100

Item 3 Early morning insomnia (ordinal [0,1,2,3], v2_idsc_itm3)

v2_idsc_itm3<-c(v2_clin$v2_ids_c_s1_ids3_frueh_aufw,v2_con$v2_ids_c_s1_ids3_frueh_aufw)
v2_idsc_itm3<-factor(v2_idsc_itm3, ordered=T)

descT(v2_idsc_itm3)

##                0    1   2   3   <NA>     
## [1,] No. cases 855  65  48  38  780  1786
## [2,] Percent   47.9 3.6 2.7 2.1 43.7 100

Item 4 Hypersomnia (ordinal [0,1,2,3], v2_idsc_itm4)

v2_idsc_itm4<-c(v2_clin$v2_ids_c_s1_ids4_hypersomnie,v2_con$v2_ids_c_s1_ids4_hypersomnie)
v2_idsc_itm4<-factor(v2_idsc_itm4, ordered=T)

descT(v2_idsc_itm4)

##                0    1    2   3   <NA>     
## [1,] No. cases 638  252  98  20  778  1786
## [2,] Percent   35.7 14.1 5.5 1.1 43.6 100

Item 5 Mood (sad) (ordinal [0,1,2,3], v2_idsc_itm5)

v2_idsc_itm5<-c(v2_clin$v2_ids_c_s1_ids5_stimmung_trgk,v2_con$v2_ids_c_s1_ids5_stimmung_trgk)
v2_idsc_itm5<-factor(v2_idsc_itm5, ordered=T)

descT(v2_idsc_itm5)

##                0    1    2   3   <NA>     
## [1,] No. cases 622  226  103 55  780  1786
## [2,] Percent   34.8 12.7 5.8 3.1 43.7 100

Item 6 Mood (irritable) (ordinal [0,1,2,3], v2_idsc_itm6)

v2_idsc_itm6<-c(v2_clin$v2_ids_c_s1_ids6_stimmung_grzt,v2_con$v2_ids_c_s1_ids6_stimmung_grzt)
v2_idsc_itm6<-factor(v2_idsc_itm6, ordered=T)

descT(v2_idsc_itm6)

##                0    1    2   3   <NA>     
## [1,] No. cases 708  223  61  15  779  1786
## [2,] Percent   39.6 12.5 3.4 0.8 43.6 100

Item 7 Mood (anxious) (ordinal [0,1,2,3], v2_idsc_itm7)

v2_idsc_itm7<-c(v2_clin$v2_ids_c_s1_ids7_stimmung_agst,v2_con$v2_ids_c_s1_ids7_stimmung_agst)
v2_idsc_itm7<-factor(v2_idsc_itm7, ordered=T)

descT(v2_idsc_itm7)

##                0    1   2   3   <NA>     
## [1,] No. cases 668  215 91  32  780  1786
## [2,] Percent   37.4 12  5.1 1.8 43.7 100

Item 8 Reactivity of mood (ordinal [0,1,2,3], v2_idsc_itm8)

v2_idsc_itm8<-c(v2_clin$v2_ids_c_s1_ids8_reakt_stimmung,v2_con$v2_ids_c_s1_ids8_reakt_stimmung)
v2_idsc_itm8<-factor(v2_idsc_itm8, ordered=T)

descT(v2_idsc_itm8)

##                0   1   2   3   <NA>     
## [1,] No. cases 804 119 57  25  781  1786
## [2,] Percent   45  6.7 3.2 1.4 43.7 100

Item 9 Mood Variation (ordinal [0,1,2,3], v2_idsc_itm9)

v2_idsc_itm9<-c(v2_clin$v2_ids_c_s1_ids9_stimmungsschw,v2_con$v2_ids_c_s1_ids9_stimmungsschw)
v2_idsc_itm9<-factor(v2_idsc_itm9, ordered=T)

descT(v2_idsc_itm9)

##                0   1   2   3   <NA>     
## [1,] No. cases 768 87  46  106 779  1786
## [2,] Percent   43  4.9 2.6 5.9 43.6 100

Item 9A (categorical [M, A, N], v2_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).

v2_idsc_itm9a_pre<-c(v2_clin$v2_ids_c_s1_ids9a_stimmungsschw,v2_con$v2_ids_c_s1_ids9a_stimmungsschw)

v2_idsc_itm9a<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==1, "M", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==2, "A", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==3, "N", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-factor(v2_idsc_itm9a, ordered=F)

descT(v2_idsc_itm9a)

##                -999 A   M   N   <NA>     
## [1,] No. cases 768  16  133 37  832  1786
## [2,] Percent   43   0.9 7.4 2.1 46.6 100

Item 9B (dichotomous, v2_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.

v2_idsc_itm9b_pre<-c(v2_clin$v2_ids_c_s1_ids9b_stimmungsschw,v2_con$v2_ids_c_s1_ids9b_stimmungsschw)

v2_idsc_itm9b<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm9b<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9b_pre==0, "N", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9b))
v2_idsc_itm9b<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9b_pre==1, "Y", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9b))
v2_idsc_itm9b<-factor(v2_idsc_itm9b, ordered=F)

descT(v2_idsc_itm9b)

##                -999 N   Y   <NA>     
## [1,] No. cases 768  88  60  870  1786
## [2,] Percent   43   4.9 3.4 48.7 100

Item 10 Quality of mood (ordinal [0,1,2,3], v2_idsc_itm10)

v2_idsc_itm10<-c(v2_clin$v2_ids_c_s1_ids10_quali_stimmung,v2_con$v2_ids_c_s1_ids10_quali_stimmung)
v2_idsc_itm10<-factor(v2_idsc_itm10, ordered=T)

descT(v2_idsc_itm10)

##                0    1   2   3   <NA>     
## [1,] No. cases 842  60  41  59  784  1786
## [2,] Percent   47.1 3.4 2.3 3.3 43.9 100

Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.

The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.

Item 11 (ordinal [0,1,2,3], v2_idsc_itm11)

v2_idsc_app_verm<-c(v2_clin$v2_ids_c_s2_ids11_appetit_verm,v2_con$v2_ids_c_s2_ids11_appetit_verm)
v2_idsc_app_gest<-c(v2_clin$v2_ids_c_s2_ids12_appetit_steig,v2_con$v2_ids_c_s2_ids12_appetit_steig)

v2_idsc_itm11<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm11<-ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==F, -999,                
                  ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==T,          
                         v2_idsc_app_verm, 
                      ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &    
                     (v2_idsc_app_verm>v2_idsc_app_gest), v2_idsc_app_verm,                                            ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &                                                         (v2_idsc_app_gest>=v2_idsc_app_verm),-999,v2_idsc_itm11)))))

#Important: do not code as factor, see after calculation of sum score!
descT(v2_idsc_itm11)

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 300  596  82  25  3   780  1786
## [2,] Percent   16.8 33.4 4.6 1.4 0.2 43.7 100

Item 12 (ordinal [0,1,2,3], v2_idsc_itm12)

v2_idsc_itm12<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm12<-ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==F,    
                         v2_idsc_app_gest,                
                  ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==T,          
                         -999, 
                      ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &    
                     (v2_idsc_app_verm>v2_idsc_app_gest), -999,                                            
                     ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &                                                         (v2_idsc_app_gest>=v2_idsc_app_verm),
                            v2_idsc_app_gest,v2_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v2_idsc_itm12)

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 706  120 111 45  24  780  1786
## [2,] Percent   39.5 6.7 6.2 2.5 1.3 43.7 100

Item 13 (ordinal [0,1,2,3], v2_idsc_itm13)

v2_idsc_gew_abn<-c(v2_clin$v2_ids_c_s2_ids13_gewichtsabn,v2_con$v2_ids_c_s2_ids13_gewichtsabn)
v2_idsc_gew_zun<-c(v2_clin$v2_ids_c_s2_ids14_gewichtszun,v2_con$v2_ids_c_s2_ids14_gewichtszun)

v2_idsc_itm13<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm13<-ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==F, -999,                
                  ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==T,          
                         v2_idsc_gew_abn, 
                      ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F &    
                     (v2_idsc_gew_abn>v2_idsc_gew_zun), v2_idsc_gew_abn,                                            ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F & (v2_idsc_gew_zun >= v2_idsc_gew_abn),-999,v2_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v2_idsc_itm13)

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 328  571 51  38  21  777  1786
## [2,] Percent   18.4 32  2.9 2.1 1.2 43.5 100

Item 14 (ordinal [0,1,2,3], v2_idsc_itm14)

v2_idsc_itm14<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm14<-ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==F,    
                         v2_idsc_gew_zun,                
                  ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==T,          
                         -999, 
                      ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F &    
                     (v2_idsc_gew_abn>v2_idsc_gew_zun), -999,                                            
                     ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F &                                                         (v2_idsc_gew_zun>=v2_idsc_gew_abn),
                            v2_idsc_gew_zun,v2_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v2_idsc_itm14)

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 681  180  73  50  25  777  1786
## [2,] Percent   38.1 10.1 4.1 2.8 1.4 43.5 100

Item 15 Concentration/decision making (ordinal [0,1,2,3], v2_idsc_itm15)

v2_idsc_itm15<-c(v2_clin$v2_ids_c_s2_ids15_konz_entscheid,v2_con$v2_ids_c_s2_ids15_konz_entscheid)
v2_idsc_itm15<-factor(v2_idsc_itm15, ordered=T)

descT(v2_idsc_itm15)

##                0    1    2   3   <NA>     
## [1,] No. cases 582  260  146 20  778  1786
## [2,] Percent   32.6 14.6 8.2 1.1 43.6 100

Item 16 Outlook (self) (ordinal [0,1,2,3], v2_idsc_itm16)

v2_idsc_itm16<-c(v2_clin$v2_ids_c_s2_ids16_selbstbild,v2_con$v2_ids_c_s2_ids16_selbstbild)
v2_idsc_itm16<-factor(v2_idsc_itm16, ordered=T)

descT(v2_idsc_itm16)

##                0    1   2   3   <NA>     
## [1,] No. cases 729  165 60  52  780  1786
## [2,] Percent   40.8 9.2 3.4 2.9 43.7 100

Item 17 Outlook (future) (ordinal [0,1,2,3], v2_idsc_itm17)

v2_idsc_itm17<-c(v2_clin$v2_ids_c_s2_ids17_zukunftssicht,v2_con$v2_ids_c_s2_ids17_zukunftssicht)
v2_idsc_itm17<-factor(v2_idsc_itm17, ordered=T)

descT(v2_idsc_itm17)

##                0    1    2   3   <NA>     
## [1,] No. cases 656  244  93  12  781  1786
## [2,] Percent   36.7 13.7 5.2 0.7 43.7 100

Item 18 Suicidal ideation (ordinal [0,1,2,3], v2_idsc_itm18)

v2_idsc_itm18<-c(v2_clin$v2_ids_c_s2_ids18_selbstmordged,v2_con$v2_ids_c_s2_ids18_selbstmordged)
v2_idsc_itm18<-factor(v2_idsc_itm18, ordered=T)

descT(v2_idsc_itm18)

##                0    1   2  3   <NA>     
## [1,] No. cases 899  51  53 4   779  1786
## [2,] Percent   50.3 2.9 3  0.2 43.6 100

Item 19 Involvement (ordinal [0,1,2,3], v2_idsc_itm19)

v2_idsc_itm19<-c(v2_clin$v2_ids_c_s2_ids19_interess_aktiv,v2_con$v2_ids_c_s2_ids19_interess_aktiv)
v2_idsc_itm19<-factor(v2_idsc_itm19, ordered=T)

descT(v2_idsc_itm19)

##                0    1   2   3  <NA>     
## [1,] No. cases 806  153 31  18 778  1786
## [2,] Percent   45.1 8.6 1.7 1  43.6 100

Item 20 Energy/fatigability (ordinal [0,1,2,3], v2_idsc_itm20)

v2_idsc_itm20<-c(v2_clin$v2_ids_c_s2_ids20_energ_ermued,v2_con$v2_ids_c_s2_ids20_energ_ermued)
v2_idsc_itm20<-factor(v2_idsc_itm20, ordered=T)

descT(v2_idsc_itm20)

##                0    1    2   3   <NA>     
## [1,] No. cases 621  249  123 16  777  1786
## [2,] Percent   34.8 13.9 6.9 0.9 43.5 100

Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v2_idsc_itm21)

v2_idsc_itm21<-c(v2_clin$v2_ids_c_s3_ids21_vergn_genuss,v2_con$v2_ids_c_s3_ids21_vergn_genuss)
v2_idsc_itm21<-factor(v2_idsc_itm21, ordered=T)

descT(v2_idsc_itm21)

##                0   1   2   3   <NA>     
## [1,] No. cases 822 127 51  8   778  1786
## [2,] Percent   46  7.1 2.9 0.4 43.6 100

Item 22 Sexual interest (ordinal [0,1,2,3], v2_idsc_itm22)

v2_idsc_itm22<-c(v2_clin$v2_ids_c_s3_ids22_sex_interesse,v2_con$v2_ids_c_s3_ids22_sex_interesse)
v2_idsc_itm22<-factor(v2_idsc_itm22, ordered=T)

descT(v2_idsc_itm22)

##                0    1   2   3  <NA>     
## [1,] No. cases 718  85  111 89 783  1786
## [2,] Percent   40.2 4.8 6.2 5  43.8 100

Item 23 Psychomotor slowing (ordinal [0,1,2,3], v2_idsc_itm23)

v2_idsc_itm23<-c(v2_clin$v2_ids_c_s3_ids23_psymo_hemm,v2_con$v2_ids_c_s3_ids23_psymo_hemm)
v2_idsc_itm23<-factor(v2_idsc_itm23, ordered=T)

descT(v2_idsc_itm23)

##                0    1   2   3   <NA>     
## [1,] No. cases 787  172 48  1   778  1786
## [2,] Percent   44.1 9.6 2.7 0.1 43.6 100

Item 24 Psychomotor agitation (ordinal [0,1,2,3], v2_idsc_itm24)

v2_idsc_itm24<-c(v2_clin$v2_ids_c_s3_ids24_psymo_agitht,v2_con$v2_ids_c_s3_ids24_psymo_agitht)
v2_idsc_itm24<-factor(v2_idsc_itm24, ordered=T)

descT(v2_idsc_itm24)

##                0    1   2   3   <NA>     
## [1,] No. cases 836  115 52  5   778  1786
## [2,] Percent   46.8 6.4 2.9 0.3 43.6 100

Item 25 Somatic complaints (ordinal [0,1,2,3], v2_idsc_itm25)

v2_idsc_itm25<-c(v2_clin$v2_ids_c_s3_ids25_som_beschw,v2_con$v2_ids_c_s3_ids25_som_beschw)
v2_idsc_itm25<-factor(v2_idsc_itm25, ordered=T)

descT(v2_idsc_itm25)

##                0    1    2   3   <NA>     
## [1,] No. cases 671  263  51  23  778  1786
## [2,] Percent   37.6 14.7 2.9 1.3 43.6 100

Item 26 Sympathetic arousal (ordinal [0,1,2,3], v2_idsc_itm26)

v2_idsc_itm26<-c(v2_clin$v2_ids_c_s3_ids26_veg_erreg,v2_con$v2_ids_c_s3_ids26_veg_erreg)
v2_idsc_itm26<-factor(v2_idsc_itm26, ordered=T)

descT(v2_idsc_itm26)

##                0    1    2   3   <NA>     
## [1,] No. cases 719  234  47  7   779  1786
## [2,] Percent   40.3 13.1 2.6 0.4 43.6 100

Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v2_idsc_itm27)

v2_idsc_itm27<-c(v2_clin$v2_ids_c_s3_ids27_panik_phob,v2_con$v2_ids_c_s3_ids27_panik_phob)
v2_idsc_itm27<-factor(v2_idsc_itm27, ordered=T)

descT(v2_idsc_itm27)

##                0    1   2   3   <NA>     
## [1,] No. cases 889  78  32  9   778  1786
## [2,] Percent   49.8 4.4 1.8 0.5 43.6 100

Item 28 Gastrointestinal (ordinal [0,1,2,3], v2_idsc_itm28)

v2_idsc_itm28<-c(v2_clin$v2_ids_c_s3_ids28_verdauung,v2_con$v2_ids_c_s3_ids28_verdauung)
v2_idsc_itm28<-factor(v2_idsc_itm28, ordered=T)

descT(v2_idsc_itm28)

##                0    1   2   3   <NA>     
## [1,] No. cases 837  103 52  15  779  1786
## [2,] Percent   46.9 5.8 2.9 0.8 43.6 100

Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v2_idsc_itm29)

v2_idsc_itm29<-c(v2_clin$v2_ids_c_s3_ids29_pers_bezieh,v2_con$v2_ids_c_s3_ids29_pers_bezieh)
v2_idsc_itm29<-factor(v2_idsc_itm29, ordered=T)

descT(v2_idsc_itm29)

##                0    1   2  3  <NA>     
## [1,] No. cases 816  122 53 17 778  1786
## [2,] Percent   45.7 6.8 3  1  43.6 100

Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v2_idsc_itm30)

v2_idsc_itm30<-c(v2_clin$v2_ids_c_s3_ids30_schwgf_k_energ,v2_con$v2_ids_c_s3_ids30_schwgf_k_energ)
v2_idsc_itm30<-factor(v2_idsc_itm30, ordered=T)

descT(v2_idsc_itm30)

##                0    1   2  3   <NA>     
## [1,] No. cases 829  126 36 16  779  1786
## [2,] Percent   46.4 7.1 2  0.9 43.6 100

Create IDS-C₃₀ total score (continuous [0-84], v2_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.

v2_idsc_sum<-as.numeric.factor(v2_idsc_itm1)+
             as.numeric.factor(v2_idsc_itm2)+
             as.numeric.factor(v2_idsc_itm3)+
             as.numeric.factor(v2_idsc_itm4)+
             as.numeric.factor(v2_idsc_itm5)+
             as.numeric.factor(v2_idsc_itm6)+
             as.numeric.factor(v2_idsc_itm7)+
             as.numeric.factor(v2_idsc_itm8)+
             as.numeric.factor(v2_idsc_itm9)+
             as.numeric.factor(v2_idsc_itm10)+
  
 ifelse(is.na(v2_idsc_itm11)==T & is.na(v2_idsc_itm12)==T, NA, 
        ifelse((v2_idsc_itm11==-999 & v2_idsc_itm12!=-999), v2_idsc_itm12,                
              ifelse((v2_idsc_itm11!=-999 & v2_idsc_itm12==-999),v2_idsc_itm11, NA)))+
  
   ifelse(is.na(v2_idsc_itm13)==T & is.na(v2_idsc_itm14)==T, NA, 
        ifelse((v2_idsc_itm13==-999 & v2_idsc_itm14!=-999), v2_idsc_itm14,                
              ifelse((v2_idsc_itm13!=-999 & v2_idsc_itm14==-999),v2_idsc_itm13, NA)))+
                                                  
             as.numeric.factor(v2_idsc_itm15)+
             as.numeric.factor(v2_idsc_itm16)+
             as.numeric.factor(v2_idsc_itm17)+
             as.numeric.factor(v2_idsc_itm18)+
             as.numeric.factor(v2_idsc_itm19)+
             as.numeric.factor(v2_idsc_itm20)+
             as.numeric.factor(v2_idsc_itm21)+
             as.numeric.factor(v2_idsc_itm22)+
             as.numeric.factor(v2_idsc_itm23)+
             as.numeric.factor(v2_idsc_itm24)+
             as.numeric.factor(v2_idsc_itm25)+
             as.numeric.factor(v2_idsc_itm26)+
             as.numeric.factor(v2_idsc_itm27)+
             as.numeric.factor(v2_idsc_itm28)+
             as.numeric.factor(v2_idsc_itm29)+
             as.numeric.factor(v2_idsc_itm30)

summary(v2_idsc_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    3.00    8.00   10.95   16.00   55.00     832

Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)

v2_idsc_itm11<-factor(v2_idsc_itm11,ordered=T)
v2_idsc_itm12<-factor(v2_idsc_itm12,ordered=T)
v2_idsc_itm13<-factor(v2_idsc_itm13,ordered=T)
v2_idsc_itm14<-factor(v2_idsc_itm14,ordered=T)

Create dataset

v2_symp_ids_c<-data.frame(v2_idsc_itm1,v2_idsc_itm2,v2_idsc_itm3,v2_idsc_itm4,v2_idsc_itm5,v2_idsc_itm6,v2_idsc_itm7,
                          v2_idsc_itm8,v2_idsc_itm9,v2_idsc_itm9a,v2_idsc_itm9b,v2_idsc_itm10,v2_idsc_itm11,v2_idsc_itm12,
                          v2_idsc_itm13,v2_idsc_itm14,v2_idsc_itm15,v2_idsc_itm16,v2_idsc_itm17,v2_idsc_itm18,v2_idsc_itm19,
                          v2_idsc_itm20,v2_idsc_itm21,v2_idsc_itm22,v2_idsc_itm23,v2_idsc_itm24,v2_idsc_itm25,v2_idsc_itm26,
                          v2_idsc_itm27,v2_idsc_itm28,v2_idsc_itm29,v2_idsc_itm30,v2_idsc_sum)

YMRS

For more information on the scale, please see Visit 1

Item 1 Elevated mood (ordinal [0,1,2,3,4], v2_ymrs_itm1)

v2_ymrs_itm1<-c(v2_clin$v2_ymrs_ymrs1_gehob_stimm,v2_con$v2_ymrs_ymrs1_gehob_stimm)
v2_ymrs_itm1<-factor(v2_ymrs_itm1, ordered=T)

descT(v2_ymrs_itm1)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 845  120 38  4   1   778  1786
## [2,] Percent   47.3 6.7 2.1 0.2 0.1 43.6 100

Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v2_ymrs_itm2)

v2_ymrs_itm2<-c(v2_clin$v2_ymrs_ymrs2_gest_aktiv,v2_con$v2_ymrs_ymrs2_gest_aktiv)
v2_ymrs_itm2<-factor(v2_ymrs_itm2, ordered=T)

descT(v2_ymrs_itm2)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 885  84  33  4   1   779  1786
## [2,] Percent   49.6 4.7 1.8 0.2 0.1 43.6 100

Item 3 Sexual interest (ordinal [0,1,2,3,4], v2_ymrs_itm3)

v2_ymrs_itm3<-c(v2_clin$v2_ymrs_ymrs3_sex_interesse,v2_con$v2_ymrs_ymrs3_sex_interesse)
v2_ymrs_itm3<-factor(v2_ymrs_itm3, ordered=T)

descT(v2_ymrs_itm3)

##                0    1   2   3   <NA>     
## [1,] No. cases 960  30  15  1   780  1786
## [2,] Percent   53.8 1.7 0.8 0.1 43.7 100

Item 4 Sleep (ordinal [0,1,2,3,4], v2_ymrs_itm4)

v2_ymrs_itm4<-c(v2_clin$v2_ymrs_ymrs4_schlaf,v2_con$v2_ymrs_ymrs4_schlaf)
v2_ymrs_itm4<-factor(v2_ymrs_itm4, ordered=T)

descT(v2_ymrs_itm4)

##                0    1   2   3   <NA>     
## [1,] No. cases 932  37  25  13  779  1786
## [2,] Percent   52.2 2.1 1.4 0.7 43.6 100

Item 5 Irritability (ordinal [0,2,4,6,8], v2_ymrs_itm5)

v2_ymrs_itm5<-c(v2_clin$v2_ymrs_ymrs5_reizbarkeit,v2_con$v2_ymrs_ymrs5_reizbarkeit)
v2_ymrs_itm5<-factor(v2_ymrs_itm5, ordered=T)

descT(v2_ymrs_itm5)

##                0    2   4  6   <NA>     
## [1,] No. cases 862  123 17 5   779  1786
## [2,] Percent   48.3 6.9 1  0.3 43.6 100

Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v2_ymrs_itm6)

v2_ymrs_itm6<-c(v2_clin$v2_ymrs_ymrs6_sprechweise,v2_con$v2_ymrs_ymrs6_sprechweise)
v2_ymrs_itm6<-factor(v2_ymrs_itm6, ordered=T)

descT(v2_ymrs_itm6)

##                0    2   4   6   <NA>     
## [1,] No. cases 873  82  43  9   779  1786
## [2,] Percent   48.9 4.6 2.4 0.5 43.6 100

Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v2_ymrs_itm7)

v2_ymrs_itm7<-c(v2_clin$v2_ymrs_ymrs7_sprachstoer,v2_con$v2_ymrs_ymrs7_sprachstoer)
v2_ymrs_itm7<-factor(v2_ymrs_itm7, ordered=T)

descT(v2_ymrs_itm7)

##                0   1   2   3   <NA>     
## [1,] No. cases 893 93  16  4   780  1786
## [2,] Percent   50  5.2 0.9 0.2 43.7 100

Item 8 Content (ordinal [0,2,4,6,8], v2_ymrs_itm8)

v2_ymrs_itm8<-c(v2_clin$v2_ymrs_ymrs8_inhalte,v2_con$v2_ymrs_ymrs8_inhalte)
v2_ymrs_itm8<-factor(v2_ymrs_itm8, ordered=T)

descT(v2_ymrs_itm8)

##                0    2   4   6   8   <NA>     
## [1,] No. cases 961  26  4   7   8   780  1786
## [2,] Percent   53.8 1.5 0.2 0.4 0.4 43.7 100

Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v2_ymrs_itm9)

v2_ymrs_itm9<-c(v2_clin$v2_ymrs_ymrs9_exp_aggr_verh,v2_con$v2_ymrs_ymrs9_exp_aggr_verh)
v2_ymrs_itm9<-factor(v2_ymrs_itm9, ordered=T)

descT(v2_ymrs_itm9)

##                0    2   4   6   <NA>     
## [1,] No. cases 967  30  5   1   783  1786
## [2,] Percent   54.1 1.7 0.3 0.1 43.8 100

Item 10 Appearance (ordinal [0,1,2,3,4], v2_ymrs_itm10)

v2_ymrs_itm10<-c(v2_clin$v2_ymrs_ymrs10_erscheinung,v2_con$v2_ymrs_ymrs10_erscheinung)
v2_ymrs_itm10<-factor(v2_ymrs_itm10, ordered=T)

descT(v2_ymrs_itm10)

##                0   1   2   3   4   <NA>     
## [1,] No. cases 910 78  14  2   1   781  1786
## [2,] Percent   51  4.4 0.8 0.1 0.1 43.7 100

Item 11 Insight (ordinal [0,1,2,3,4], v2_ymrs_itm11)

v2_ymrs_itm11<-c(v2_clin$v2_ymrs_ymrs11_krkh_einsicht,v2_con$v2_ymrs_ymrs11_krkh_einsicht)
v2_ymrs_itm11<-factor(v2_ymrs_itm11, ordered=T)

descT(v2_ymrs_itm11)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 970  16  9   4   4   783  1786
## [2,] Percent   54.3 0.9 0.5 0.2 0.2 43.8 100

Create YMRS total score (continuous [0-60], v2_ymrs_sum)

v2_ymrs_sum<-(as.numeric.factor(v2_ymrs_itm1)+
        as.numeric.factor(v2_ymrs_itm2)+
        as.numeric.factor(v2_ymrs_itm3)+
        as.numeric.factor(v2_ymrs_itm4)+
        as.numeric.factor(v2_ymrs_itm5)+
        as.numeric.factor(v2_ymrs_itm6)+
        as.numeric.factor(v2_ymrs_itm7)+
        as.numeric.factor(v2_ymrs_itm8)+
        as.numeric.factor(v2_ymrs_itm9)+
        as.numeric.factor(v2_ymrs_itm10)+
        as.numeric.factor(v2_ymrs_itm11))

summary(v2_ymrs_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   1.866   2.000  36.000     786

Create dataset

v2_symp_ymrs<-data.frame(v2_ymrs_itm1,
                         v2_ymrs_itm2,
                         v2_ymrs_itm3,
                         v2_ymrs_itm4,
                         v2_ymrs_itm5,
                         v2_ymrs_itm6,
                         v2_ymrs_itm7,
                         v2_ymrs_itm8,
                         v2_ymrs_itm9,
                         v2_ymrs_itm10,
                         v2_ymrs_itm11,
                         v2_ymrs_sum)

CGI

Please see Visit 1 for more details and explicit rating instructions. A zero on this scale means “not assessable” (both of the following two items) and was coded as “-999”, as were all control participants.

Illness severity (ordinal [1,2,3,4,5,6,7], v2_cgi_s)

v2_cgi_s<-c(v2_clin$v2_cgi1_cgi1_schweregrad,rep(-999,dim(v2_con)[1]))

v2_cgi_s[v2_cgi_s==0]<- -999
v2_cgi_s<-factor(v2_cgi_s, ordered=T)

descT(v2_cgi_s)

##                -999 1   2   3    4    5   6   7   <NA>     
## [1,] No. cases 470  30  79  234  252  139 40  1   541  1786
## [2,] Percent   26.3 1.7 4.4 13.1 14.1 7.8 2.2 0.1 30.3 100

Change since last study visit (ordinal [1,2,3,4,5,6,7], v2_cgi_c)

During follow-up visits, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. These range from “very much improved”-1 to “very much worse”-7.

v2_cgi_c<-c(v2_clin$v2_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v2_con)[1]))

v2_cgi_c[v2_cgi_c==0]<- -999
v2_cgi_c<-factor(v2_cgi_c, ordered=T)

descT(v2_cgi_c)

##                -999 1   2   3    4    5   6   7   <NA>     
## [1,] No. cases 486  29  147 201  229  87  20  2   585  1786
## [2,] Percent   27.2 1.6 8.2 11.3 12.8 4.9 1.1 0.1 32.8 100

GAF (continuous [1-100], v2_gaf)

Please see Visit 1 for more details and explicit rating instructions. A zero on this scale means “not assessable” and was coded as “-999”.

v2_gaf<-c(v2_clin$v2_gaf_gaf_code,v2_con$v2_gaf_gaf_code)
v2_gaf[v2_gaf==0]<- -999

summary(v2_gaf[v2_gaf>0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   20.00   55.00   68.00   67.48   82.00  100.00     772

Boxplot of GAF scores of both CLINICAL and CONTROL study participants

boxplot(v2_gaf[v2_gaf>0 & v1_stat=="CLINICAL"], v2_gaf[v2_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))

Create dataset

v2_ill_sev<-data.frame(v2_cgi_s,v2_cgi_c,v2_gaf)

Visit 2: Neuropsychology (cognitive tests)

There are two differences compared to the test battery assessed in Visit 1:

1. The “Verbaler Lern- und Merkfähigkeitstest” is added (assesses learning and memory). This test is also implemented in all following visits. Parallel versions of the test are alternately used to avoid recall bias.

2. The MWT-B is omitted as results of this test are not expected to vary much during the timeframe of the study.

General comments on the testing (character, v2_nrpsy_com) If there were no comments, this item was coded -999.

Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v2_nrpsy_lng)

v2_nrpsy_lng<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_nrpsy_lng<-ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==0, "mother tongue", 
                     ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==1, "good", 
                            ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==2, "sufficient", 
                              ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==3, "not sufficient",v2_nrpsy_lng))))
                            
v2_nrpsy_lng<-factor(v2_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))

descT(v2_nrpsy_lng)

##                mother tongue good sufficient not sufficient <NA>     
## [1,] No. cases 977           65   7          2              735  1786
## [2,] Percent   54.7          3.6  0.4        0.1            41.2 100

Motivation of the participant (ordinal [“poor”,“average”,“good”], v2_nrpsy_mtv)

v2_nrpsy_mtv_pre<-c(v2_clin$v2_npu1_np_mot,v2_con$v2_npu_folge_np_mot)

v2_nrpsy_mtv<-ifelse(v2_nrpsy_mtv_pre==0, "poor", 
                  ifelse(v2_nrpsy_mtv_pre==1, "average", 
                      ifelse(v2_nrpsy_mtv_pre==2, "good", NA)))

v2_nrpsy_mtv<-factor(v2_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))
descT(v2_nrpsy_mtv)

##                poor average good <NA>     
## [1,] No. cases 12   89      936  749  1786
## [2,] Percent   0.7  5       52.4 41.9 100

Learning and memory: Verbaler Lern- und Merkfähigkeitstest (VLMT)

The VLMT (Helmstaedter, Lendt, & Lux, 2001) assesses learning and memory. A list of 15 words (list 1) is verbally presented to the participant for five times. After each presentation, the subject is required to recall as many words from the list as he remembers and the interviewer writes those down. After the fifth time, another list of words (list 2; distraction) is presented to the subject, with the same instruction (“recall as many words as possible from the list after I read it to you”). After writing down the recalled words from list 2, the interviewer asks the participant to recall the words from list 1 and writes those down. After a time interval of 25-30 minutes, during which other tests are performed, the interviewer asks the participant again and writes down the recalled words form list 1. Following this free recall phase, the interviewer tests recognition of the words from list 1 by verbally presenting 50 words (from list 1, list 2 and completely new words) and asking the participant whether each word belongs to list 1 or not.

Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.

VLMT_introcheck (categorical [0, 1, 9], v2_nrpsy_vlmt_check) This variable indicates whether a test was:

• Completed -> “1”
• Aborted -> “9”
• Not assessed -> “0”

In contrast to previous versions of the dataset, data are not filtered according to this item but all tests are included.

v2_nrpsy_vlmt_check<-c(v2_clin$v2_vlmt_vlmt_introcheck1,v2_con$v2_npu_folge_np_vlmt)
descT(v2_nrpsy_vlmt_check)

##                0   1    9   <NA>     
## [1,] No. cases 81  939  46  720  1786
## [2,] Percent   4.5 52.6 2.6 40.3 100

Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v2_nrpsy_vlmt_corr)

v2_nrpsy_vlmt_corr<-c(v2_clin$v2_vlmt_vlmt3_sw_a5d,v2_con$v2_npu_folge_np_vlmt_gl)
summary(v2_nrpsy_vlmt_corr)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   40.00   50.00   49.12   59.00   75.00     801

Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v2_nrpsy_vlmt_lss_d)

v2_nrpsy_vlmt_lss_d<-c(v2_clin$v2_vlmt_vlmt5_aw_ilsd6,v2_con$v2_npu_folge_np_vlmt_vni)
summary(v2_nrpsy_vlmt_lss_d)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -6.000   0.000   2.000   1.747   3.000   9.000     805

Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v2_nrpsy_vlmt_lss_t)

v2_nrpsy_vlmt_lss_t<-c(v2_clin$v2_vlmt_vlmt6_aw_vwd7,v2_con$v2_npu_folge_np_vlmt_vnzv)
summary(v2_nrpsy_vlmt_lss_t)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -5.000   0.000   2.000   1.867   3.000  13.000     818

Recognition performance (corrected for falsely recognized words) (continuous [number of words], v2_nrpsy_vlmt_rec)

v2_nrpsy_vlmt_rec<-c(v2_clin$v2_vlmt_vlmt8_kwl,v2_con$v2_npu_folge_np_vlmt_kw)
summary(v2_nrpsy_vlmt_rec)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -12.00   10.00   13.00   11.71   15.00   15.00     827

TMT

For a description of the test, see Visit 1.

TMT Part A, time (continuous [seconds], v2_nrpsy_tmt_A_rt)

v2_nrpsy_tmt_A_rt<-c(v2_clin$v2_npu1_tmt_001,v2_con$v2_npu_folge_np_tmt_001)
summary(v2_nrpsy_tmt_A_rt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   21.00   28.00   31.94   38.00  142.00     733

TMT Part A, errors (continuous [number of errors], v2_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).

v2_nrpsy_tmt_A_err<-c(v2_clin$v2_npu1_tmt_af_001,v2_con$v2_npu_folge_np_tmtfehler_001)
summary(v2_nrpsy_tmt_A_err)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.1418  0.0000  4.0000     742

TMT Part B, time (continuous [seconds], v2_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.

v2_nrpsy_tmt_B_rt<-c(v2_clin$v2_npu1_tmt_002,v2_con$v2_npu_folge_tmt_002)
v2_nrpsy_tmt_B_rt[v2_nrpsy_tmt_B_rt>300]<-300

summary(v2_nrpsy_tmt_B_rt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   22.00   49.00   64.00   73.77   87.00  300.00     785

TMT Part B, errors (continuous [number of errors], v2_nrpsy_tmt_B_err)

v2_nrpsy_tmt_B_err<-c(v2_clin$v2_npu1_tmt_af_002,v2_con$v2_npu_folge_tmt_af_002)
summary(v2_nrpsy_tmt_B_err)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.4924  1.0000 20.0000     793

Verbal digit span

For a description of the test, see Visit 1.

Forward (continuous [number of items], v2_nrpsy_dgt_sp_frw)

v2_nrpsy_dgt_sp_frw<-c(v2_clin$v2_npu1_zns_001,v2_con$v2_npu_folge_np_wie_001)
summary(v2_nrpsy_dgt_sp_frw)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   4.000   8.000  10.000   9.695  11.000  16.000     743

Backward (continuous [number of items], v2_nrpsy_dgt_sp_bck)

v2_nrpsy_dgt_sp_bck<-c(v2_clin$v2_npu1_zns_002,v2_con$v2_npu_folge_np_wie_002)
summary(v2_nrpsy_dgt_sp_bck)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.000   6.000   6.552   8.000  14.000     746

DST (continuous [number of correct symbols], v2_nrpsy_dg_sym)

For a description of the test and the coding of incompletet tests, see Visit 1.

v2_introcheck3<-c(v2_clin$v2_npu1_np_introcheck3,v2_con$v2_npu_folge_np_hawier)
v2_nrpsy_dg_sym_pre<-c(v2_clin$v2_npu1_zst_001,v2_con$v2_npu_folge_np_hawier_001)

v2_nrpsy_dg_sym<-ifelse(v2_introcheck3==1, v2_nrpsy_dg_sym_pre, 
                           ifelse(v2_introcheck3==9,-999,
                                  ifelse(v2_introcheck3==0,NA,NA)))

summary(subset(v2_nrpsy_dg_sym,v2_nrpsy_dg_sym>=0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.00   52.00   66.00   66.59   82.00  133.00

Create dataset

v2_nrpsy<-data.frame(v2_nrpsy_com,
                     v2_nrpsy_lng,
                     v2_nrpsy_mtv,
                     v2_nrpsy_vlmt_check,
                     v2_nrpsy_vlmt_corr,
                     v2_nrpsy_vlmt_lss_d,
                     v2_nrpsy_vlmt_lss_t,
                     v2_nrpsy_vlmt_rec,
                     v2_nrpsy_tmt_A_rt,
                     v2_nrpsy_tmt_A_err,
                     v2_nrpsy_tmt_B_rt,
                     v2_nrpsy_tmt_B_err,
                     v2_nrpsy_dgt_sp_frw,
                     v2_nrpsy_dgt_sp_bck,
                     v2_nrpsy_dg_sym)

Visit 2: Questionnaires (patient rates her/himself)

All participants were asked to fill out questionnaires on the following topics: current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months (LEQ), and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 1 and 2) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1, all questionnaires were checked on whether they were filled out correctly or not and only those correctly filled-out are included in this dataset.

SF-12

For explanation, please refer to the section in Visit 1

“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v2_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.

v2_sf12_recode(v2_con$v2_sf12_sf_allgemein,"v2_sf12_itm0")

##                -999 1   2   3   4   5   6   7   8   9  10 <NA>     
## [1,] No. cases 1320 1   2   6   6   8   10  34  101 72 35 191  1786
## [2,] Percent   73.9 0.1 0.1 0.3 0.3 0.4 0.6 1.9 5.7 4  2  10.7 100

“In general, would you say your health is…” (ordinal [1,2,3,4,5], v2_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.

v2_sf12_recode(v2_con$v2_sf12_sf1,"v2_sf12_itm1")

##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1320 56  128 91  11  180  1786
## [2,] Percent   73.9 3.1 7.2 5.1 0.6 10.1 100

“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”

“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v2_sf12_itm2)

Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v2_sf12_recode(v2_con$v2_sf12_sf2,"v2_sf12_itm2")

##                -999 1   2   3    <NA>     
## [1,] No. cases 1320 2   33  251  180  1786
## [2,] Percent   73.9 0.1 1.8 14.1 10.1 100

“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v2_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v2_sf12_recode(v2_con$v2_sf12_sf3,"v2_sf12_itm3")

##                -999 1   2  3    <NA>     
## [1,] No. cases 1320 6   36 244  180  1786
## [2,] Percent   73.9 0.3 2  13.7 10.1 100

During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?

“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v2_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.

v2_sf12_recode(v2_con$v2_sf12_sf4,"v2_sf12_itm4")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 39  244  183  1786
## [2,] Percent   73.9 2.2 13.7 10.2 100

“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v2_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.

v2_sf12_recode(v2_con$v2_sf12_sf5,"v2_sf12_itm5")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 23  260  183  1786
## [2,] Percent   73.9 1.3 14.6 10.2 100

During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?

“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v2_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.

v2_sf12_recode(v2_con$v2_sf12_sf6,"v2_sf12_itm6")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 25  260  181  1786
## [2,] Percent   73.9 1.4 14.6 10.1 100

“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v2_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.

v2_sf12_recode(v2_con$v2_sf12_sf7,"v2_sf12_itm7")

##                -999 1  2   <NA>     
## [1,] No. cases 1320 17 268 181  1786
## [2,] Percent   73.9 1  15  10.1 100

“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v2_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.

v2_sf12_recode(v2_con$v2_sf12_st8,"v2_sf12_itm8")

##                -999 1   2   3  4   5   6   <NA>     
## [1,] No. cases 1320 149 61  35 27  9   2   183  1786
## [2,] Percent   73.9 8.3 3.4 2  1.5 0.5 0.1 10.2 100

The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS

Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.

“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v2_sf12_itm9)

v2_sf12_recode(v2_con$v2_sf12_st9,"v2_sf12_itm9")

##                -999 1  2    3   4  5   6   <NA>     
## [1,] No. cases 1320 18 186  55  17 8   1   181  1786
## [2,] Percent   73.9 1  10.4 3.1 1  0.4 0.1 10.1 100

“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v2_sf12_itm10)

v2_sf12_recode(v2_con$v2_sf12_st10,"v2_sf12_itm10")

##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1320 13  115 88  55  13  1   181  1786
## [2,] Percent   73.9 0.7 6.4 4.9 3.1 0.7 0.1 10.1 100

“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v2_sf12_itm11)

v2_sf12_recode(v2_con$v2_sf12_st11,"v2_sf12_itm11")

##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1320 1   4   9   32  134 104 182  1786
## [2,] Percent   73.9 0.1 0.2 0.5 1.8 7.5 5.8 10.2 100

“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v2_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.

There is an error in the phenotype database regarding this item. The answering alternatives 3, 4, and 5 appear as 4, 5, and 6 in the database exports. These errors are corrected below.

v2_sf12_recode(v2_con$v2_sf12_st12,"v2_sf12_itm12")

##                -999 2   4   5   6    <NA>     
## [1,] No. cases 1320 3   19  65  198  181  1786
## [2,] Percent   73.9 0.2 1.1 3.6 11.1 10.1 100

#recode error in phenotype database
v2_sf12_itm12[v2_sf12_itm12==4]<-3
v2_sf12_itm12[v2_sf12_itm12==5]<-4
v2_sf12_itm12[v2_sf12_itm12==6]<-5

descT(v2_sf12_itm12)

##                -999 2   3   4   5    <NA>     
## [1,] No. cases 1320 3   19  65  198  181  1786
## [2,] Percent   73.9 0.2 1.1 3.6 11.1 10.1 100

Create dataset

v2_sf12<-data.frame(v2_sf12_itm0,
                    v2_sf12_itm1,
                    v2_sf12_itm2,
                    v2_sf12_itm3,
                    v2_sf12_itm4,
                    v2_sf12_itm5,
                    v2_sf12_itm6,
                    v2_sf12_itm7,
                    v2_sf12_itm8,
                    v2_sf12_itm9,
                    v2_sf12_itm10,
                    v2_sf12_itm11,
                    v2_sf12_itm12)

Medication adherence (compliance)

For a description of the questionnaire, see Visit 1.

Past seven days (ordinal [1,2,3,4,5,6], v2_med_pst_wk)

v2_med_chk<-c(v2_clin$v2_compl_verwer_fragebogen,rep(1,dim(v2_con)[1]))
v2_med_pst_wk_pre<-c(v2_clin$v2_compl_psychopharm_7_tag,rep(-999,dim(v2_con)[1]))
  
v2_med_pst_wk<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_med_pst_wk<-ifelse((is.na(v2_med_chk) | v2_med_chk!=2), 
                      v2_med_pst_wk_pre, v2_med_pst_wk)

descT(v2_med_pst_wk)

##                -999 1    2   3   4   5   6  <NA>     
## [1,] No. cases 466  615  75  28  3   1   18 580  1786
## [2,] Percent   26.1 34.4 4.2 1.6 0.2 0.1 1  32.5 100

Past six months (ordinal [1,2,3,4,5,6], v2_med_pst_sx_mths)

v2_med_pre<-c(v2_clin$v2_compl_psychopharm_6_mon,rep(-999,dim(v2_con)[1]))

v2_med_pst_sx_mths<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_med_pst_sx_mths<-ifelse((is.na(v2_med_chk) | v2_med_chk!=2),
                           v2_med_pre, v2_med_pst_sx_mths)

descT(v2_med_pst_sx_mths)

##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 466  547  109 63  8   5   10  578  1786
## [2,] Percent   26.1 30.6 6.1 3.5 0.4 0.3 0.6 32.4 100

Create dataset

v2_med_adh<-data.frame(v2_med_pst_wk,v2_med_pst_sx_mths)

BDI-II

For explanation, please refer to the section in Visit 1

1. Sadness (ordinal [0,1,2,3], v2_bdi2_itm1)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi1_traurigkeit,v2_con$v2_bdi2_s1_bdi1,"v2_bdi2_itm1")

##                0    1    2   3   <NA>     
## [1,] No. cases 726  260  22  23  755  1786
## [2,] Percent   40.6 14.6 1.2 1.3 42.3 100

2. Pessimism (ordinal [0,1,2,3], v2_bdi2_itm2)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi2_pessimismus,v2_con$v2_bdi2_s1_bdi2,"v2_bdi2_itm2")

##                0    1   2   3   <NA>     
## [1,] No. cases 789  135 92  14  756  1786
## [2,] Percent   44.2 7.6 5.2 0.8 42.3 100

3. Past failure (ordinal [0,1,2,3], v2_bdi2_itm3)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi3_versagensgef,v2_con$v2_bdi2_s1_bdi3,"v2_bdi2_itm3")

##                0    1   2   3   <NA>     
## [1,] No. cases 690  177 142 20  757  1786
## [2,] Percent   38.6 9.9 8   1.1 42.4 100

4. Loss of pleasure (ordinal [0,1,2,3], v2_bdi2_itm4)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi4_verlust_freude,v2_con$v2_bdi2_s1_bdi4,"v2_bdi2_itm4")

##                0   1   2  3   <NA>     
## [1,] No. cases 643 286 72 24  761  1786
## [2,] Percent   36  16  4  1.3 42.6 100

5. Guilty feelings (ordinal [0,1,2,3], v2_bdi2_itm5)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi5_schuldgef,v2_con$v2_bdi2_s1_bdi5,"v2_bdi2_itm5")

##                0    1    2   3   <NA>     
## [1,] No. cases 736  251  21  20  758  1786
## [2,] Percent   41.2 14.1 1.2 1.1 42.4 100

6. Punishment feelings (ordinal [0,1,2,3], v2_bdi2_itm6)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi6_bestrafungsgef,v2_con$v2_bdi2_s1_bdi6,"v2_bdi2_itm6")

##                0   1   2   3   <NA>     
## [1,] No. cases 857 111 14  48  756  1786
## [2,] Percent   48  6.2 0.8 2.7 42.3 100

7. Self-dislike (ordinal [0,1,2,3], v2_bdi2_itm7)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi7_selbstablehnung,v2_con$v2_bdi2_s1_bdi7,"v2_bdi2_itm7")

##                0    1   2  3  <NA>     
## [1,] No. cases 771  148 90 17 760  1786
## [2,] Percent   43.2 8.3 5  1  42.6 100

8. Self-criticalness (ordinal [0,1,2,3], v2_bdi2_itm8)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi8_selbstvorwuerfe,v2_con$v2_bdi2_s1_bdi8,"v2_bdi2_itm8")

##                0    1    2   3   <NA>     
## [1,] No. cases 672  253  80  23  758  1786
## [2,] Percent   37.6 14.2 4.5 1.3 42.4 100

9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v2_bdi2_itm9)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi9_selbstmordged,v2_con$v2_bdi2_s1_bdi9,"v2_bdi2_itm9")

##                0    1   2   3   <NA>     
## [1,] No. cases 842  175 12  1   756  1786
## [2,] Percent   47.1 9.8 0.7 0.1 42.3 100

10. Crying (ordinal [0,1,2,3], v2_bdi2_itm10)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi10_weinen,v2_con$v2_bdi2_s1_bdi10,"v2_bdi2_itm10")

##                0   1  2   3   <NA>     
## [1,] No. cases 840 90 22  78  756  1786
## [2,] Percent   47  5  1.2 4.4 42.3 100

11. Agitation (ordinal [0,1,2,3], v2_bdi2_itm11)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi11_unruhe,v2_con$v2_bdi2_s2_bdi11,"v2_bdi2_itm11")

##                0    1    2   3   <NA>     
## [1,] No. cases 729  247  28  16  766  1786
## [2,] Percent   40.8 13.8 1.6 0.9 42.9 100

12. Loss of interest (ordinal [0,1,2,3], v2_bdi2_itm12)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi12_interessverl,v2_con$v2_bdi2_s2_bdi12,"v2_bdi2_itm12")

##                0    1    2   3   <NA>     
## [1,] No. cases 717  223  50  29  767  1786
## [2,] Percent   40.1 12.5 2.8 1.6 42.9 100

13. Indecisiveness (ordinal [0,1,2,3], v2_bdi2_itm13)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi13_entschlussunf,v2_con$v2_bdi2_s2_bdi13,"v2_bdi2_itm13")

##                0    1    2   3   <NA>     
## [1,] No. cases 695  231  55  38  767  1786
## [2,] Percent   38.9 12.9 3.1 2.1 42.9 100

14. Worthlessness (ordinal [0,1,2,3], v2_bdi2_itm14)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi14_wertlosigkeit,v2_con$v2_bdi2_s2_bdi14,"v2_bdi2_itm14")

##                0    1   2   3   <NA>     
## [1,] No. cases 796  134 66  24  766  1786
## [2,] Percent   44.6 7.5 3.7 1.3 42.9 100

15. Loss of energy (ordinal [0,1,2,3], v2_bdi2_itm15)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi15_energieverlust,v2_con$v2_bdi2_s2_bdi15,"v2_bdi2_itm15")

##                0    1    2   3   <NA>     
## [1,] No. cases 574  335  96  16  765  1786
## [2,] Percent   32.1 18.8 5.4 0.9 42.8 100

16. Changes in sleeping pattern (ordinal [0,1,2,3], v2_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep”. There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.

v2_itm_bdi2_chk<-c(v2_clin$v2_bdi2_s1_verwer_fragebogen,v2_con$v2_bdi2_s1_bdi_korrekt)
v2_itm_bdi2_itm16_clin_con<-c(v2_clin$v2_bdi2_s2_bdi16_schlafgewohn,v2_con$v2_bdi2_s2_bdi16)

v2_bdi2_itm16<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])

v2_bdi2_itm16<-ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & v2_itm_bdi2_itm16_clin_con==0, 0,
                ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                           (v2_itm_bdi2_itm16_clin_con==1 | v2_itm_bdi2_itm16_clin_con==100), 1, 
                 ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                               (v2_itm_bdi2_itm16_clin_con==2 | v2_itm_bdi2_itm16_clin_con==200), 2, 
                  ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                               (v2_itm_bdi2_itm16_clin_con==3 | v2_itm_bdi2_itm16_clin_con==300), 3, v2_bdi2_itm16))))  

v2_bdi2_itm16<-factor(v2_bdi2_itm16,ordered=T)
descT(v2_bdi2_itm16)

##                0    1   2   3   <NA>     
## [1,] No. cases 538  357 81  43  767  1786
## [2,] Percent   30.1 20  4.5 2.4 42.9 100

17. Irritability (ordinal [0,1,2,3], v2_bdi2_itm17)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi17_reizbarkeit,v2_con$v2_bdi2_s2_bdi17,"v2_bdi2_itm17")

##                0    1   2  3   <NA>     
## [1,] No. cases 802  171 36 11  766  1786
## [2,] Percent   44.9 9.6 2  0.6 42.9 100

18. Change in appetite (ordinal [0,1,2,3], v2_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.

v2_itm_bdi2_itm18_clin_con<-c(v2_clin$v2_bdi2_s2_bdi18_appetit,v2_con$v2_bdi2_s2_bdi18)
v2_bdi2_itm18<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])

v2_bdi2_itm18<-ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & v2_itm_bdi2_itm18_clin_con==0, 0,
                ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                           (v2_itm_bdi2_itm18_clin_con==1 | v2_itm_bdi2_itm18_clin_con==100), 1, 
                 ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                               (v2_itm_bdi2_itm18_clin_con==2 | v2_itm_bdi2_itm18_clin_con==200), 2, 
                  ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                               (v2_itm_bdi2_itm18_clin_con==3 | v2_itm_bdi2_itm18_clin_con==300), 3, v2_bdi2_itm18))))  

v2_bdi2_itm18<-factor(v2_bdi2_itm18,ordered=T)
descT(v2_bdi2_itm18)

##                0    1    2   3   <NA>     
## [1,] No. cases 680  265  50  25  766  1786
## [2,] Percent   38.1 14.8 2.8 1.4 42.9 100

19. Concentration difficulty (ordinal [0,1,2,3], v2_bdi2_itm19)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi19_konzschw,v2_con$v2_bdi2_s2_bdi19,"v2_bdi2_itm19")

##                0    1    2   3   <NA>     
## [1,] No. cases 602  274  133 9   768  1786
## [2,] Percent   33.7 15.3 7.4 0.5 43   100

20. Tiredness or fatigue (ordinal [0,1,2,3], v2_bdi2_itm20)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi20_ermued_ersch,v2_con$v2_bdi2_s2_bdi20,"v2_bdi2_itm20")

##                0    1    2   3   <NA>     
## [1,] No. cases 597  320  83  20  766  1786
## [2,] Percent   33.4 17.9 4.6 1.1 42.9 100

21. Loss of interest in sex (ordinal [0,1,2,3], v2_bdi2_itm21)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi21_sex_interess,v2_con$v2_bdi2_s2_bdi21,"v2_bdi2_itm21")

##                0    1    2   3   <NA>     
## [1,] No. cases 705  180  46  84  771  1786
## [2,] Percent   39.5 10.1 2.6 4.7 43.2 100

BDI-II sum score calculation (continuous [0-63], v2_bdi2_sum)

v2_bdi2_sum<-as.numeric.factor(v2_bdi2_itm1)+
              as.numeric.factor(v2_bdi2_itm2)+
              as.numeric.factor(v2_bdi2_itm3)+
              as.numeric.factor(v2_bdi2_itm4)+
              as.numeric.factor(v2_bdi2_itm5)+
              as.numeric.factor(v2_bdi2_itm6)+
              as.numeric.factor(v2_bdi2_itm7)+
              as.numeric.factor(v2_bdi2_itm8)+
              as.numeric.factor(v2_bdi2_itm9)+
              as.numeric.factor(v2_bdi2_itm10)+
              as.numeric.factor(v2_bdi2_itm11)+
              as.numeric.factor(v2_bdi2_itm12)+
              as.numeric.factor(v2_bdi2_itm13)+
              as.numeric.factor(v2_bdi2_itm14)+
              as.numeric.factor(v2_bdi2_itm15)+
              as.numeric.factor(v2_bdi2_itm16)+
              as.numeric.factor(v2_bdi2_itm17)+
              as.numeric.factor(v2_bdi2_itm18)+
              as.numeric.factor(v2_bdi2_itm19)+
              as.numeric.factor(v2_bdi2_itm20)+
              as.numeric.factor(v2_bdi2_itm21)

summary(v2_bdi2_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   5.000   8.677  13.000  54.000     803

Create dataset

v2_bdi2<-data.frame(v2_bdi2_itm1,v2_bdi2_itm2,v2_bdi2_itm3,v2_bdi2_itm4,v2_bdi2_itm5,
                    v2_bdi2_itm6,v2_bdi2_itm7,v2_bdi2_itm8,v2_bdi2_itm9,v2_bdi2_itm10,
                    v2_bdi2_itm11,v2_bdi2_itm12,v2_bdi2_itm13,v2_bdi2_itm14,
                    v2_bdi2_itm15,v2_bdi2_itm16,v2_bdi2_itm17,v2_bdi2_itm18,
                    v2_bdi2_itm19,v2_bdi2_itm20,v2_bdi2_itm21, v2_bdi2_sum)

ASRM

For explanation, please refer to the section in Visit 1

1. Positive Mood (ordinal [0,1,2,3,4], v2_asrm_itm1)

v2_asrm_recode(v2_clin$v2_asrm_asrm1_gluecklich,v2_con$v2_asrm_asrm1,"v2_asrm_itm1")

##                0    1    2   3   4   <NA>     
## [1,] No. cases 722  228  45  23  10  758  1786
## [2,] Percent   40.4 12.8 2.5 1.3 0.6 42.4 100

2 Self-Confidence (ordinal [0,1,2,3,4], v2_asrm_itm2)

v2_asrm_recode(v2_clin$v2_asrm_asrm2_selbstbewusst,v2_con$v2_asrm_asrm2,"v2_asrm_itm2")

##                0    1    2   3   4   <NA>     
## [1,] No. cases 763  204  41  14  8   756  1786
## [2,] Percent   42.7 11.4 2.3 0.8 0.4 42.3 100

3. Sleep (ordinal [0,1,2,3,4], v2_asrm_itm3)

v2_asrm_recode(v2_clin$v2_asrm_asrm3_schlaf,v2_con$v2_asrm_asrm3,"v2_asrm_itm3")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 864  118 28  10  10  756  1786
## [2,] Percent   48.4 6.6 1.6 0.6 0.6 42.3 100

4. Speech (ordinal [0,1,2,3,4], v2_asrm_itm4)

v2_asrm_recode(v2_clin$v2_asrm_asrm4_reden,v2_con$v2_asrm_asrm4,"v2_asrm_itm4")

##                0    1    2   3   4   <NA>     
## [1,] No. cases 798  194  24  11  2   757  1786
## [2,] Percent   44.7 10.9 1.3 0.6 0.1 42.4 100

5. Activity Level (ordinal [0,1,2,3,4], v2_asrm_itm5)

v2_asrm_recode(v2_clin$v2_asrm_asrm5_aktiv,v2_con$v2_asrm_asrm5,"v2_asrm_itm5")

##                0    1    2   3   4   <NA>     
## [1,] No. cases 752  209  44  14  10  757  1786
## [2,] Percent   42.1 11.7 2.5 0.8 0.6 42.4 100

Create ASRM sum score (continuous [0-20],v2_asrm_sum)

v2_asrm_sum<-as.numeric.factor(v2_asrm_itm1)+
            as.numeric.factor(v2_asrm_itm2)+
            as.numeric.factor(v2_asrm_itm3)+
            as.numeric.factor(v2_asrm_itm4)+
            as.numeric.factor(v2_asrm_itm5)

summary(v2_asrm_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   1.000   1.637   3.000  16.000     760

Create dataset

v2_asrm<-data.frame(v2_asrm_itm1,v2_asrm_itm2,v2_asrm_itm3,v2_asrm_itm4,v2_asrm_itm5,v2_asrm_sum)

MSS

For explanation, please refer to the section in Visit 1

1. “I had more energy” (dichotomous, v2_mss_itm1)

v2_mss_recode(v2_clin$v2_mss_s1_mss1_energie,v2_con$v2_mss_s1_mss1,"v2_mss_itm1")

##                N    Y    <NA>     
## [1,] No. cases 811  206  769  1786
## [2,] Percent   45.4 11.5 43.1 100

2. “I had trouble sitting still” (dichotomous, v2_mss_itm2)

v2_mss_recode(v2_clin$v2_mss_s1_mss2_ruhig_sitzen,v2_con$v2_mss_s1_mss2,"v2_mss_itm2")

##                N    Y   <NA>     
## [1,] No. cases 871  142 773  1786
## [2,] Percent   48.8 8   43.3 100

3. “I drove faster” (dichotomous, v2_mss_itm3)

v2_mss_recode(v2_clin$v2_mss_s1_mss3_auto_fahren,v2_con$v2_mss_s1_mss3,"v2_mss_itm3")

##                N    Y  <NA>     
## [1,] No. cases 952  36 798  1786
## [2,] Percent   53.3 2  44.7 100

4. “I drank more alcoholic beverages” (dichotomous, v2_mss_itm4)

v2_mss_recode(v2_clin$v2_mss_s1_mss4_alkohol,v2_con$v2_mss_s1_mss4,"v2_mss_itm4")

##                N    Y   <NA>     
## [1,] No. cases 940  74  772  1786
## [2,] Percent   52.6 4.1 43.2 100

5. “I changed clothes several times a day” (dichotomous, v2_mss_itm5)

v2_mss_recode(v2_clin$v2_mss_s1_mss5_umziehen, v2_con$v2_mss_s1_mss5,"v2_mss_itm5")

##                N    Y   <NA>     
## [1,] No. cases 949  65  772  1786
## [2,] Percent   53.1 3.6 43.2 100

6. “I wore brighter clothes/make-up” (dichotomous, v2_mss_itm6)

v2_mss_recode(v2_clin$v2_mss_s1_mss6_bunter,v2_con$v2_mss_s1_mss6,"v2_mss_itm6")

##                N    Y  <NA>     
## [1,] No. cases 960  53 773  1786
## [2,] Percent   53.8 3  43.3 100

7. “I played music louder” (dichotomous, v2_mss_itm7)

v2_mss_recode(v2_clin$v2_mss_s1_mss7_musik_lauter,v2_con$v2_mss_s1_mss7,"v2_mss_itm7")

##                N    Y   <NA>     
## [1,] No. cases 885  132 769  1786
## [2,] Percent   49.6 7.4 43.1 100

8. “I ate faster than usual” (dichotomous, v2_mss_itm8)

v2_mss_recode(v2_clin$v2_mss_s1_mss8_hastiger_essen,v2_con$v2_mss_s1_mss8,"v2_mss_itm8")

##                N    Y   <NA>     
## [1,] No. cases 892  122 772  1786
## [2,] Percent   49.9 6.8 43.2 100

9. “I ate more than usual” (dichotomous, v2_mss_itm9)

v2_mss_recode(v2_clin$v2_mss_s1_mss9_mehr_essen,v2_con$v2_mss_s1_mss9,"v2_mss_itm9")

##                N    Y    <NA>     
## [1,] No. cases 790  224  772  1786
## [2,] Percent   44.2 12.5 43.2 100

10. “I slept fewer hours than usual” (dichotomous, v2_mss_itm10)

v2_mss_recode(v2_clin$v2_mss_s1_mss10_weniger_schlaf,v2_con$v2_mss_s1_mss10,"v2_mss_itm10")

##                N    Y   <NA>     
## [1,] No. cases 909  100 777  1786
## [2,] Percent   50.9 5.6 43.5 100

11. “I started things that I didn’t finish” (dichotomous, v2_mss_itm11)

v2_mss_recode(v2_clin$v2_mss_s1_mss11_unbeendet,v2_con$v2_mss_s1_mss11,"v2_mss_itm11")

##                N    Y    <NA>     
## [1,] No. cases 810  206  770  1786
## [2,] Percent   45.4 11.5 43.1 100

12. “I gave away my own possessions” (dichotomous, v2_mss_itm12)

v2_mss_recode(v2_clin$v2_mss_s1_mss12_weggeben,v2_con$v2_mss_s1_mss12,"v2_mss_itm12")

##                N    Y   <NA>     
## [1,] No. cases 916  98  772  1786
## [2,] Percent   51.3 5.5 43.2 100

13. “I bought gifts for people” (dichotomous, v2_mss_itm13)

v2_mss_recode(v2_clin$v2_mss_s1_mss13_geschenke,v2_con$v2_mss_s1_mss13,"v2_mss_itm13")

##                N    Y   <NA>     
## [1,] No. cases 921  93  772  1786
## [2,] Percent   51.6 5.2 43.2 100

14. “I spent money more freely” (dichotomous, v2_mss_itm14)

v2_mss_recode(v2_clin$v2_mss_s1_mss14_mehr_geld,v2_con$v2_mss_s1_mss14,"v2_mss_itm14")

##                N    Y    <NA>     
## [1,] No. cases 794  222  770  1786
## [2,] Percent   44.5 12.4 43.1 100

15. “I accumulated debts” (dichotomous, v2_mss_itm15)

v2_mss_recode(v2_clin$v2_mss_s1_mss15_schulden,v2_con$v2_mss_s1_mss15,"v2_mss_itm15")

##                N   Y   <NA>     
## [1,] No. cases 964 52  770  1786
## [2,] Percent   54  2.9 43.1 100

16. “I made unwise business decisions” (dichotomous, v2_mss_itm16)

v2_mss_recode(v2_clin$v2_mss_s1_mss16_unkluge_entsch,v2_con$v2_mss_s1_mss16,"v2_mss_itm16")

##                N    Y   <NA>     
## [1,] No. cases 974  37  775  1786
## [2,] Percent   54.5 2.1 43.4 100

17. “I partied more” (dichotomous, v2_mss_itm17)

v2_mss_recode(v2_clin$v2_mss_s1_mss17_parties,v2_con$v2_mss_s1_mss17,"v2_mss_itm17")

##                N    Y   <NA>     
## [1,] No. cases 953  60  773  1786
## [2,] Percent   53.4 3.4 43.3 100

18. “I enjoyed flirting” (dichotomous, v2_mss_itm18)

v2_mss_recode(v2_clin$v2_mss_s1_mss18_flirten,v2_con$v2_mss_s1_mss18,"v2_mss_itm18")

##                N    Y   <NA>     
## [1,] No. cases 935  79  772  1786
## [2,] Percent   52.4 4.4 43.2 100

19. “I masturbated more often” (dichotomous, v2_mss_itm19)

v2_mss_recode(v2_clin$v2_mss_s2_mss19_selbstbefried,v2_con$v2_mss_s2_mss19,"v2_mss_itm19")

##                N    Y   <NA>     
## [1,] No. cases 963  42  781  1786
## [2,] Percent   53.9 2.4 43.7 100

20. “I was more interested in sex than usual” (dichotomous, v2_mss_itm20)

v2_mss_recode(v2_clin$v2_mss_s2_mss20_sex_interess,v2_con$v2_mss_s2_mss20,"v2_mss_itm20")

##                N    Y   <NA>     
## [1,] No. cases 917  81  788  1786
## [2,] Percent   51.3 4.5 44.1 100

21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v2_mss_itm21)

v2_mss_recode(v2_clin$v2_mss_s2_mss21_sexpartner,v2_con$v2_mss_s2_mss21,"v2_mss_itm21")

##                N    Y   <NA>     
## [1,] No. cases 990  15  781  1786
## [2,] Percent   55.4 0.8 43.7 100

22. “I spent more time on the phone” (dichotomous, v2_mss_itm22)

v2_mss_recode(v2_clin$v2_mss_s2_mss22_mehr_telefon,v2_con$v2_mss_s2_mss22,"v2_mss_itm22")

##                N    Y   <NA>     
## [1,] No. cases 890  117 779  1786
## [2,] Percent   49.8 6.6 43.6 100

23. “I spoke louder than usual” (dichotomous, v2_mss_itm23)

v2_mss_recode(v2_clin$v2_mss_s2_mss23_sprache_lauter,v2_con$v2_mss_s2_mss23,"v2_mss_itm23")

##                N    Y   <NA>     
## [1,] No. cases 921  80  785  1786
## [2,] Percent   51.6 4.5 44   100

24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v2_mss_itm24)

v2_mss_recode(v2_clin$v2_mss_s2_mss24_spr_schneller,v2_con$v2_mss_s2_mss24,"v2_mss_itm24")

##                N   Y   <NA>     
## [1,] No. cases 946 61  779  1786
## [2,] Percent   53  3.4 43.6 100

25. “1 enjoyed punning or rhyming” (dichotomous, v2_mss_itm25)

v2_mss_recode(v2_clin$v2_mss_s2_mss25_witze,v2_con$v2_mss_s2_mss25,"v2_mss_itm25")

##                N   Y   <NA>     
## [1,] No. cases 929 78  779  1786
## [2,] Percent   52  4.4 43.6 100

26. “I butted into conversations” (dichotomous, v2_mss_itm26)

v2_mss_recode(v2_clin$v2_mss_s2_mss26_einmischen,v2_con$v2_mss_s2_mss26,"v2_mss_itm26")

##                N    Y   <NA>     
## [1,] No. cases 944  65  777  1786
## [2,] Percent   52.9 3.6 43.5 100

27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v2_mss_itm27)

v2_mss_recode(v2_clin$v2_mss_s2_mss27_red_pausenlos,v2_con$v2_mss_s2_mss27,"v2_mss_itm27")

##                N    Y   <NA>     
## [1,] No. cases 981  26  779  1786
## [2,] Percent   54.9 1.5 43.6 100

28. “I enjoyed being the centre of attention” (dichotomous, v2_mss_itm28)

v2_mss_recode(v2_clin$v2_mss_s2_mss28_mittelpunkt,v2_con$v2_mss_s2_mss28,"v2_mss_itm28")

##                N    Y   <NA>     
## [1,] No. cases 956  52  778  1786
## [2,] Percent   53.5 2.9 43.6 100

29. “I liked to joke and laugh” (dichotomous, v2_mss_itm29)

v2_mss_recode(v2_clin$v2_mss_s2_mss29_herumalbern,v2_con$v2_mss_s2_mss29,"v2_mss_itm29")

##                N    Y   <NA>     
## [1,] No. cases 869  136 781  1786
## [2,] Percent   48.7 7.6 43.7 100

30. “People found me entertaining” (dichotomous, v2_mss_itm30)

v2_mss_recode(v2_clin$v2_mss_s2_mss30_unterhaltsamer,v2_con$v2_mss_s2_mss30,"v2_mss_itm30")

##                N   Y   <NA>     
## [1,] No. cases 928 77  781  1786
## [2,] Percent   52  4.3 43.7 100

31. “I felt as if I was on top of the world” (dichotomous, v2_mss_itm31)

v2_mss_recode(v2_clin$v2_mss_s2_mss31_obenauf,v2_con$v2_mss_s2_mss31,"v2_mss_itm31")

##                N    Y   <NA>     
## [1,] No. cases 921  85  780  1786
## [2,] Percent   51.6 4.8 43.7 100

32. “I was more cheerful than my usual self” (dichotomous, v2_mss_itm32)

v2_mss_recode(v2_clin$v2_mss_s2_mss32_froehlicher,v2_con$v2_mss_s2_mss32,"v2_mss_itm32")

##                N    Y    <NA>     
## [1,] No. cases 826  181  779  1786
## [2,] Percent   46.2 10.1 43.6 100

33. “Other people got on my nerves” (dichotomous, v2_mss_itm33)

v2_mss_recode(v2_clin$v2_mss_s2_mss33_ungeduldiger,v2_con$v2_mss_s2_mss33,"v2_mss_itm33")

##                N    Y   <NA>     
## [1,] No. cases 792  215 779  1786
## [2,] Percent   44.3 12  43.6 100

34. “I was getting into arguments” (dichotomous, v2_mss_itm34)

v2_mss_recode(v2_clin$v2_mss_s2_mss34_streiten,v2_con$v2_mss_s2_mss34,"v2_mss_itm34")

##                N    Y  <NA>     
## [1,] No. cases 932  72 782  1786
## [2,] Percent   52.2 4  43.8 100

35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v2_mss_itm35)

v2_mss_recode(v2_clin$v2_mss_s2_mss35_ideen,v2_con$v2_mss_s2_mss35,"v2_mss_itm35")

##                N    Y   <NA>     
## [1,] No. cases 829  178 779  1786
## [2,] Percent   46.4 10  43.6 100

36. “My thoughts raced through my mind” (dichotomous, v2_mss_itm36)

v2_mss_recode(v2_clin$v2_mss_s2_mss36_gedanken,v2_con$v2_mss_s2_mss36,"v2_mss_itm36")

##                N    Y    <NA>     
## [1,] No. cases 756  251  779  1786
## [2,] Percent   42.3 14.1 43.6 100

37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v2_mss_itm37)

v2_mss_recode(v2_clin$v2_mss_s2_mss37_konzentration,v2_con$v2_mss_s2_mss37,"v2_mss_itm37")

##                N    Y   <NA>     
## [1,] No. cases 866  141 779  1786
## [2,] Percent   48.5 7.9 43.6 100

38. “I thought I was an especially important person” (dichotomous, v2_mss_itm38)

v2_mss_recode(v2_clin$v2_mss_s2_mss38_etw_besonderes,v2_con$v2_mss_s2_mss38,"v2_mss_itm38")

##                N    Y   <NA>     
## [1,] No. cases 942  61  783  1786
## [2,] Percent   52.7 3.4 43.8 100

39. “I thought I could change the world” (dichotomous, v2_mss_itm39)

v2_mss_recode(v2_clin$v2_mss_s2_mss39_welt_veraender,v2_con$v2_mss_s2_mss39,"v2_mss_itm39")

##                N    Y   <NA>     
## [1,] No. cases 953  55  778  1786
## [2,] Percent   53.4 3.1 43.6 100

40. “I thought I was right most of the time” (dichotomous, v2_mss_itm40)

v2_mss_recode(v2_clin$v2_mss_s2_mss40_recht_haben,v2_con$v2_mss_s2_mss40,"v2_mss_itm40")

##                N   Y   <NA>     
## [1,] No. cases 965 44  777  1786
## [2,] Percent   54  2.5 43.5 100

41. “I thought I was superior to others” (dichotomous, v2_mss_itm41)

v2_mss_recode(v2_clin$v2_mss_s3_mss41_ueberlegen,v2_con$v2_mss_s3_mss41,"v2_mss_itm41")

##                N    Y   <NA>     
## [1,] No. cases 980  29  777  1786
## [2,] Percent   54.9 1.6 43.5 100

42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v2_mss_itm42)

v2_mss_recode(v2_clin$v2_mss_s3_mss42_uebermut,v2_con$v2_mss_s3_mss42,"v2_mss_itm42")

##                N    Y   <NA>     
## [1,] No. cases 940  70  776  1786
## [2,] Percent   52.6 3.9 43.4 100

43. “I thought I knew what other people were thinking” (dichotomous, v2_mss_itm43)

v2_mss_recode(v2_clin$v2_mss_s3_mss43_ged_lesen_akt,v2_con$v2_mss_s3_mss43,"v2_mss_itm43")

##                N    Y  <NA>     
## [1,] No. cases 920  89 777  1786
## [2,] Percent   51.5 5  43.5 100

44. “I thought other people knew what I was thinking” (dichotomous, v2_mss_itm44)

v2_mss_recode(v2_clin$v2_mss_s3_mss44_ged_lesen_pas,v2_con$v2_mss_s3_mss44,"v2_mss_itm44")

##                N    Y   <NA>     
## [1,] No. cases 950  59  777  1786
## [2,] Percent   53.2 3.3 43.5 100

45. “I thought someone wanted to harm me” (dichotomous, v2_mss_itm45)

v2_mss_recode(v2_clin$v2_mss_s3_mss45_etw_antun,v2_con$v2_mss_s3_mss45,"v2_mss_itm45")

##                N    Y   <NA>     
## [1,] No. cases 961  49  776  1786
## [2,] Percent   53.8 2.7 43.4 100

46. “I heard voices when people weren’t there” (dichotomous, v2_mss_itm46)

v2_mss_recode(v2_clin$v2_mss_s3_mss46_stimmen,v2_con$v2_mss_s3_mss46,"v2_mss_itm46")

##                N   Y   <NA>     
## [1,] No. cases 946 62  778  1786
## [2,] Percent   53  3.5 43.6 100

47. “I had false beliefs concerning who I was” (dichotomous, v2_mss_itm47)

v2_mss_recode(v2_clin$v2_mss_s3_mss47_jmd_anders,v2_con$v2_mss_s3_mss47,"v2_mss_itm47")

##                N    Y   <NA>     
## [1,] No. cases 985  25  776  1786
## [2,] Percent   55.2 1.4 43.4 100

48. “I knew I was getting ill” (dichotomous, v2_mss_itm48)

v2_mss_recode(v2_clin$v2_mss_s3_mss48_krank_einsicht,v2_con$v2_mss_s3_mss48,"v2_mss_itm48")

##                N    Y   <NA>     
## [1,] No. cases 884  115 787  1786
## [2,] Percent   49.5 6.4 44.1 100

Create MSS sum score (continuous [0-48],v2_mss_sum)

v2_mss_sum<-ifelse(v2_mss_itm1=="Y",1,0)+
            ifelse(v2_mss_itm2=="Y",1,0)+
            ifelse(v2_mss_itm3=="Y",1,0)+
            ifelse(v2_mss_itm4=="Y",1,0)+
            ifelse(v2_mss_itm5=="Y",1,0)+
            ifelse(v2_mss_itm6=="Y",1,0)+
            ifelse(v2_mss_itm7=="Y",1,0)+
            ifelse(v2_mss_itm8=="Y",1,0)+
            ifelse(v2_mss_itm9=="Y",1,0)+
            ifelse(v2_mss_itm10=="Y",1,0)+
            ifelse(v2_mss_itm11=="Y",1,0)+
            ifelse(v2_mss_itm12=="Y",1,0)+
            ifelse(v2_mss_itm13=="Y",1,0)+
            ifelse(v2_mss_itm14=="Y",1,0)+
            ifelse(v2_mss_itm15=="Y",1,0)+
            ifelse(v2_mss_itm16=="Y",1,0)+
            ifelse(v2_mss_itm17=="Y",1,0)+
            ifelse(v2_mss_itm18=="Y",1,0)+
            ifelse(v2_mss_itm19=="Y",1,0)+
            ifelse(v2_mss_itm20=="Y",1,0)+
            ifelse(v2_mss_itm21=="Y",1,0)+
            ifelse(v2_mss_itm22=="Y",1,0)+
            ifelse(v2_mss_itm23=="Y",1,0)+
            ifelse(v2_mss_itm24=="Y",1,0)+
            ifelse(v2_mss_itm25=="Y",1,0)+
            ifelse(v2_mss_itm26=="Y",1,0)+
            ifelse(v2_mss_itm27=="Y",1,0)+
            ifelse(v2_mss_itm28=="Y",1,0)+
            ifelse(v2_mss_itm29=="Y",1,0)+
            ifelse(v2_mss_itm30=="Y",1,0)+
            ifelse(v2_mss_itm31=="Y",1,0)+
            ifelse(v2_mss_itm32=="Y",1,0)+
            ifelse(v2_mss_itm33=="Y",1,0)+
            ifelse(v2_mss_itm34=="Y",1,0)+
            ifelse(v2_mss_itm35=="Y",1,0)+
            ifelse(v2_mss_itm36=="Y",1,0)+
            ifelse(v2_mss_itm37=="Y",1,0)+
            ifelse(v2_mss_itm38=="Y",1,0)+
            ifelse(v2_mss_itm39=="Y",1,0)+
            ifelse(v2_mss_itm40=="Y",1,0)+
            ifelse(v2_mss_itm41=="Y",1,0)+
            ifelse(v2_mss_itm42=="Y",1,0)+
            ifelse(v2_mss_itm43=="Y",1,0)+
            ifelse(v2_mss_itm44=="Y",1,0)+
            ifelse(v2_mss_itm45=="Y",1,0)+
            ifelse(v2_mss_itm46=="Y",1,0)+
            ifelse(v2_mss_itm47=="Y",1,0)+
            ifelse(v2_mss_itm48=="Y",1,0)

summary(v2_mss_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   2.000   4.358   6.000  37.000     886

Create dataset

v2_mss<-data.frame(v2_mss_itm1,v2_mss_itm2,v2_mss_itm3,v2_mss_itm4,v2_mss_itm5,v2_mss_itm6,
                   v2_mss_itm7,v2_mss_itm8,v2_mss_itm9,v2_mss_itm10,v2_mss_itm11,
                   v2_mss_itm12,v2_mss_itm13,v2_mss_itm14,v2_mss_itm15,v2_mss_itm16,
                   v2_mss_itm17,v2_mss_itm18,v2_mss_itm19,v2_mss_itm20,v2_mss_itm21,
                   v2_mss_itm22,v2_mss_itm23,v2_mss_itm24,v2_mss_itm25,v2_mss_itm26,
                   v2_mss_itm27,v2_mss_itm28,v2_mss_itm29,v2_mss_itm30,v2_mss_itm31,
                   v2_mss_itm32,v2_mss_itm33,v2_mss_itm34,v2_mss_itm35,v2_mss_itm36,
                   v2_mss_itm37,v2_mss_itm38,v2_mss_itm39,v2_mss_itm40,v2_mss_itm41,
                   v2_mss_itm42,v2_mss_itm43,v2_mss_itm44,v2_mss_itm45,v2_mss_itm46,
                   v2_mss_itm47,v2_mss_itm48,v2_mss_sum)

LEQ

For explanation, please refer to the section in Visit 1

1. “Major personal illness or injury”

1A Nature (dichotomous [“good”,“bad”], v2_leq_A_1A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq1a_schw_krankh,v2_con$v2_leq_a_leq1a,"v2_leq_A_1A")

##                -999 bad  good <NA>     
## [1,] No. cases 684  253  41   808  1786
## [2,] Percent   38.3 14.2 2.3  45.2 100

1B Impact (ordinal [0,1,2,3], v2_leq_A_1B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq1e_schw_krankh,v2_con$v2_leq_a_leq1e,"v2_leq_A_1B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 682  15  39  77  165 808  1786
## [2,] Percent   38.2 0.8 2.2 4.3 9.2 45.2 100

2. “Major change in eating habits”

2A Nature (dichotomous [“good”,“bad”], v2_leq_A_2A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq2a_ernaehrung,v2_con$v2_leq_a_leq2a,"v2_leq_A_2A")

##                -999 bad good <NA>     
## [1,] No. cases 689  145 144  808  1786
## [2,] Percent   38.6 8.1 8.1  45.2 100

2B Impact (ordinal [0,1,2,3], v2_leq_A_2B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq2e_ernaehrung,v2_con$v2_leq_a_leq2e,"v2_leq_A_2B")

##                -999 0  1   2   3   <NA>     
## [1,] No. cases 683  17 68  119 91  808  1786
## [2,] Percent   38.2 1  3.8 6.7 5.1 45.2 100

3. “Major change in sleeping habits”

3A Nature (dichotomous [“good”,“bad”], v2_leq_A_3A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq3a_schlaf,v2_con$v2_leq_a_leq3a,"v2_leq_A_3A")

##                -999 bad  good <NA>     
## [1,] No. cases 694  185  99   808  1786
## [2,] Percent   38.9 10.4 5.5  45.2 100

3B Impact (ordinal [0,1,2,3], v2_leq_A_3B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq3e_schlaf,v2_con$v2_leq_a_leq3e,"v2_leq_A_3B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 690  15  61  112 100 808  1786
## [2,] Percent   38.6 0.8 3.4 6.3 5.6 45.2 100

4. “Major change in usual type and/or amount of recreation”

4A Nature (dichotomous [“good”,“bad”], v2_leq_A_4A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq4a_freizeit,v2_con$v2_leq_a_leq4a,"v2_leq_A_4A")

##                -999 bad good <NA>     
## [1,] No. cases 631  133 214  808  1786
## [2,] Percent   35.3 7.4 12   45.2 100

4B Impact (ordinal [0,1,2,3], v2_leq_A_4B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq4e_freizeit,v2_con$v2_leq_a_leq4e,"v2_leq_A_4B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 626  19  72 141 120 808  1786
## [2,] Percent   35.1 1.1 4  7.9 6.7 45.2 100

5. “Major dental work”

5A Nature (dichotomous [“good”,“bad”], v2_leq_A_5A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq5a_zahnarzt,v2_con$v2_leq_a_leq5a,"v2_leq_A_5A")

##                -999 bad good <NA>     
## [1,] No. cases 835  55  88   808  1786
## [2,] Percent   46.8 3.1 4.9  45.2 100

5B Impact (ordinal [0,1,2,3], v2_leq_A_5B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq5e_zahnarzt,v2_con$v2_leq_a_leq5e,"v2_leq_A_5B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 832  30  44  35 37  808  1786
## [2,] Percent   46.6 1.7 2.5 2  2.1 45.2 100

6. “(Female) Pregnancy”

6A Nature (dichotomous [“good”,“bad”], v2_leq_A_6A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq6a_schwanger,v2_con$v2_leq_a_leq6a,"v2_leq_A_6A")

##                -999 bad good <NA>     
## [1,] No. cases 969  1   8    808  1786
## [2,] Percent   54.3 0.1 0.4  45.2 100

6B Impact (ordinal [0,1,2,3], v2_leq_A_6B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq6e_schwanger,v2_con$v2_leq_a_leq6e,"v2_leq_A_6B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 969  1   2   6   808  1786
## [2,] Percent   54.3 0.1 0.1 0.3 45.2 100

7. “(Female) Miscarriage or abortion”

7A Nature (dichotomous [“good”,“bad”], v2_leq_A_7A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq7a_fehlg_abtr,v2_con$v2_leq_a_leq7a,"v2_leq_A_7A")

##                -999 bad <NA>     
## [1,] No. cases 974  4   808  1786
## [2,] Percent   54.5 0.2 45.2 100

7B Impact (ordinal [0,1,2,3], v2_leq_A_7B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq7e_fehlg_abtr,v2_con$v2_leq_a_leq7e,"v2_leq_A_7B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 973  1   1   3   808  1786
## [2,] Percent   54.5 0.1 0.1 0.2 45.2 100

8. “(Female) Started menopause”

8A Nature (dichotomous [“good”,“bad”], v2_leq_A_8A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq8a_wechseljahre,v2_con$v2_leq_a_leq8a,"v2_leq_A_8A")

##                -999 bad good <NA>     
## [1,] No. cases 947  26  5    808  1786
## [2,] Percent   53   1.5 0.3  45.2 100

8B Impact (ordinal [0,1,2,3], v2_leq_A_8B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq8e_wechseljahre,v2_con$v2_leq_a_leq8e,"v2_leq_A_8B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 945  1   8   17 7   808  1786
## [2,] Percent   52.9 0.1 0.4 1  0.4 45.2 100

9. “Major difficulties with birth control pills or devices”

9A Nature (dichotomous [“good”,“bad”], v2_leq_A_9A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq9a_verhuetung,v2_con$v2_leq_a_leq9a,"v2_leq_A_9A")

##                -999 bad good <NA>     
## [1,] No. cases 961  14  3    808  1786
## [2,] Percent   53.8 0.8 0.2  45.2 100

9B Impact (ordinal [0,1,2,3], v2_leq_A_9B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq9e_verhuetung,v2_con$v2_leq_a_leq9e,"v2_leq_A_9B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 960  5   5   1   7   808  1786
## [2,] Percent   53.8 0.3 0.3 0.1 0.4 45.2 100

Create dataset

v2_leq_A<-data.frame(v2_leq_A_1A,v2_leq_A_1B,v2_leq_A_2A,v2_leq_A_2B,v2_leq_A_3A,
                     v2_leq_A_3B,v2_leq_A_4A,v2_leq_A_4B,v2_leq_A_5A,v2_leq_A_5B,
                     v2_leq_A_6A,v2_leq_A_6B,v2_leq_A_7A,v2_leq_A_7B,v2_leq_A_8A,
                     v2_leq_A_8B,v2_leq_A_9A,v2_leq_A_9B)

Work

10. “Difficulty finding a job”

10A Nature (dichotomous [“good”,“bad”], v2_leq_B_10A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq10a_arbeitssuche,v2_con$v2_leq_b_leq10a,"v2_leq_B_10A")

##                -999 bad good <NA>     
## [1,] No. cases 826  122 30   808  1786
## [2,] Percent   46.2 6.8 1.7  45.2 100

10B Impact (ordinal [0,1,2,3], v2_leq_B_10B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq10e_arbeitssuche,v2_con$v2_leq_b_leq10e,"v2_leq_B_10B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 825  11  36 43  63  808  1786
## [2,] Percent   46.2 0.6 2  2.4 3.5 45.2 100

11. “Beginning work outside the home”

11A Nature (dichotomous [“good”,“bad”], v2_leq_B_11A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq11a_arbeit_aussen,v2_con$v2_leq_b_leq11a,"v2_leq_B_11A")

##                -999 bad good <NA>     
## [1,] No. cases 835  25  118  808  1786
## [2,] Percent   46.8 1.4 6.6  45.2 100

11B Impact (ordinal [0,1,2,3], v2_leq_B_11B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq11e_arbeit_aussen,v2_con$v2_leq_b_leq11e,"v2_leq_B_11B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 833  8   25  51  61  808  1786
## [2,] Percent   46.6 0.4 1.4 2.9 3.4 45.2 100

12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v2_leq_B_12A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq12a_arbeitswechs,v2_con$v2_leq_b_leq12a,"v2_leq_B_12A")

##                -999 bad good <NA>     
## [1,] No. cases 849  19  110  808  1786
## [2,] Percent   47.5 1.1 6.2  45.2 100

12B Impact (ordinal [0,1,2,3], v2_leq_B_12B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq12e_arbeitswechs,v2_con$v2_leq_b_leq12e,"v2_leq_B_12B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 847  5   22  38  66  808  1786
## [2,] Percent   47.4 0.3 1.2 2.1 3.7 45.2 100

13. “Changing your work hours or conditions”

13A Nature (dichotomous [“good”,“bad”], v2_leq_B_13A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq13a_veraend_arb,v2_con$v2_leq_b_leq13a,"v2_leq_B_13A")

##                -999 bad good <NA>     
## [1,] No. cases 795  47  136  808  1786
## [2,] Percent   44.5 2.6 7.6  45.2 100

13B Impact (ordinal [0,1,2,3], v2_leq_B_13B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq13e_veraend_arb,v2_con$v2_leq_b_leq13e,"v2_leq_B_13B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 791  7   50  74  56  808  1786
## [2,] Percent   44.3 0.4 2.8 4.1 3.1 45.2 100

14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v2_leq_B_14A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq14a_veraend_ba,v2_con$v2_leq_b_leq14a,"v2_leq_B_14A")

##                -999 bad good <NA>     
## [1,] No. cases 804  40  134  808  1786
## [2,] Percent   45   2.2 7.5  45.2 100

14B Impact (ordinal [0,1,2,3], v2_leq_B_14B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq14e_veraend_ba,v2_con$v2_leq_b_leq14e,"v2_leq_B_14B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 803  9   45  66  55  808  1786
## [2,] Percent   45   0.5 2.5 3.7 3.1 45.2 100

15. “Troubles at work with your employer or co-worker”

15A Nature (dichotomous [“good”,“bad”], v2_leq_B_15A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq15a_schw_arbeit,v2_con$v2_leq_b_leq15a,"v2_leq_B_15A")

##                -999 bad good <NA>     
## [1,] No. cases 861  99  18   808  1786
## [2,] Percent   48.2 5.5 1    45.2 100

15B Impact (ordinal [0,1,2,3], v2_leq_B_15B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq15e_schw_arbeit,v2_con$v2_leq_b_leq15e,"v2_leq_B_15B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 859  10  45  37  27  808  1786
## [2,] Percent   48.1 0.6 2.5 2.1 1.5 45.2 100

16. “Major business readjustment”

16A Nature (dichotomous [“good”,“bad”], v2_leq_B_16A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq16a_betr_reorg,v2_con$v2_leq_b_leq16a,"v2_leq_B_16A")

##                -999 bad good <NA>     
## [1,] No. cases 935  23  20   808  1786
## [2,] Percent   52.4 1.3 1.1  45.2 100

16B Impact (ordinal [0,1,2,3], v2_leq_B_16B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq16e_betr_reorg,v2_con$v2_leq_b_leq16e,"v2_leq_B_16B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 934  7   12  14  11  808  1786
## [2,] Percent   52.3 0.4 0.7 0.8 0.6 45.2 100

17. “Being fired or laid off from work”

17A Nature (dichotomous [“good”,“bad”], v2_leq_B_17A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq17a_kuendigung,v2_con$v2_leq_b_leq17a,"v2_leq_B_17A")

##                -999 bad good <NA>     
## [1,] No. cases 921  33  24   808  1786
## [2,] Percent   51.6 1.8 1.3  45.2 100

17B Impact (ordinal [0,1,2,3], v2_leq_B_17B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq17e_kuendigung,v2_con$v2_leq_b_leq17e,"v2_leq_B_17B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 919  6   9   16  28  808  1786
## [2,] Percent   51.5 0.3 0.5 0.9 1.6 45.2 100

18. “Retirement from work”

18A Nature (dichotomous [“good”,“bad”], v2_leq_B_18A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq18a_ende_beruf,v2_con$v2_leq_b_leq18a,"v2_leq_B_18A")

##                -999 bad good <NA>     
## [1,] No. cases 941  17  20   808  1786
## [2,] Percent   52.7 1   1.1  45.2 100

18B Impact (ordinal [0,1,2,3], v2_leq_B_18B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq18e_ende_beruf,v2_con$v2_leq_b_leq18e,"v2_leq_B_18B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 938  5   5   6   24  808  1786
## [2,] Percent   52.5 0.3 0.3 0.3 1.3 45.2 100

19. “Taking courses by mail or studying at home to help you in your work”

19A Nature (dichotomous [“good”,“bad”], v2_leq_B_19A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq19a_fortbildung,v2_con$v2_leq_b_leq19a,"v2_leq_B_19A")

##                -999 bad good <NA>     
## [1,] No. cases 917  12  49   808  1786
## [2,] Percent   51.3 0.7 2.7  45.2 100

19B Impact (ordinal [0,1,2,3], v2_leq_B_19B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq19e_fortbildung,v2_con$v2_leq_b_leq19e,"v2_leq_B_19B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 914  4   14  28  18 808  1786
## [2,] Percent   51.2 0.2 0.8 1.6 1  45.2 100

v2_leq_B<-data.frame(v2_leq_B_10A,v2_leq_B_10B,v2_leq_B_11A,v2_leq_B_11B,v2_leq_B_12A,
                     v2_leq_B_12B,v2_leq_B_13A,v2_leq_B_13B,v2_leq_B_14A,v2_leq_B_14B,
                     v2_leq_B_15A,v2_leq_B_15B,v2_leq_B_16A,v2_leq_B_16B,v2_leq_B_17A,
                     v2_leq_B_17B,v2_leq_B_18A,v2_leq_B_18B,v2_leq_B_19A,v2_leq_B_19B)

School

20. “Beginning or ceasing school, college, or training program”

20A Nature (dichotomous [“good”,“bad”], v2_leq_C_20A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq20a_beginn_ende,v2_con$v2_leq_c_d_leq20a,"v2_leq_C_20A")

##                -999 bad good <NA>     
## [1,] No. cases 914  8   56   808  1786
## [2,] Percent   51.2 0.4 3.1  45.2 100

20B Impact (ordinal [0,1,2,3], v2_leq_C_20B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq20e_beginn_ende,v2_con$v2_leq_c_d_leq20e,"v2_leq_C_20B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 913  4   6   25  30  808  1786
## [2,] Percent   51.1 0.2 0.3 1.4 1.7 45.2 100

21. “Change of school, college, or training program”

21A Nature (dichotomous [“good”,“bad”], v2_leq_C_21A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq21a_schulwechsel,v2_con$v2_leq_c_d_leq21a,"v2_leq_C_21A")

##                -999 bad good <NA>     
## [1,] No. cases 966  2   10   808  1786
## [2,] Percent   54.1 0.1 0.6  45.2 100

21B Impact (ordinal [0,1,2,3], v2_leq_C_21B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq21e_schulwechsel,v2_con$v2_leq_c_d_leq21e,"v2_leq_C_21B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 965  4   1   7   1   808  1786
## [2,] Percent   54   0.2 0.1 0.4 0.1 45.2 100

22. “Change in career goal or academic major”

A Nature (dichotomous [“good”,“bad”], v2_leq_C_22A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq22a_aend_karriere,v2_con$v2_leq_c_d_leq22a,"v2_leq_C_22A")

##                -999 bad good <NA>     
## [1,] No. cases 940  7   31   808  1786
## [2,] Percent   52.6 0.4 1.7  45.2 100

B Impact (ordinal [0,1,2,3], v2_leq_C_22B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq22e_aend_karriere,v2_con$v2_leq_c_d_leq22e,"v2_leq_C_22B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 939  3   7   17 12  808  1786
## [2,] Percent   52.6 0.2 0.4 1  0.7 45.2 100

23. “Problem in school, college, or training program”

23A Nature (dichotomous [“good”,“bad”], v2_leq_C_23A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq23a_schulprob,v2_con$v2_leq_c_d_leq23a,"v2_leq_C_23A")

##                -999 bad good <NA>     
## [1,] No. cases 951  25  2    808  1786
## [2,] Percent   53.2 1.4 0.1  45.2 100

23B Impact (ordinal [0,1,2,3], v2_leq_C_23B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq23e_schulprob,v2_con$v2_leq_c_d_leq23e,"v2_leq_C_23B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 950  1   9   10  8   808  1786
## [2,] Percent   53.2 0.1 0.5 0.6 0.4 45.2 100

Create dataset

v2_leq_C<-data.frame(v2_leq_C_20A,v2_leq_C_20B,v2_leq_C_21A,v2_leq_C_21B,v2_leq_C_22A,v2_leq_C_22B,v2_leq_C_23A,v2_leq_C_23B)

Residence

24. “Difficulty finding housing”

24A Nature (dichotomous [“good”,“bad”], v2_leq_D_24A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq24a_schw_wsuche,v2_con$v2_leq_c_d_leq24a,"v2_leq_D_24A")

##                -999 bad good <NA>     
## [1,] No. cases 899  61  18   808  1786
## [2,] Percent   50.3 3.4 1    45.2 100

24B Impact (ordinal [0,1,2,3], v2_leq_D_24B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq24e_schw_wsuche,v2_con$v2_leq_c_d_leq24e,"v2_leq_D_24B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 898  4   23  16  37  808  1786
## [2,] Percent   50.3 0.2 1.3 0.9 2.1 45.2 100

25. “Changing residence within the same town or city”

A Nature (dichotomous [“good”,“bad”], v2_leq_D_25A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq25a_umzug_nah,v2_con$v2_leq_c_d_leq25a,"v2_leq_D_25A")

##                -999 bad good <NA>     
## [1,] No. cases 912  15  51   808  1786
## [2,] Percent   51.1 0.8 2.9  45.2 100

B Impact (ordinal [0,1,2,3], v2_leq_D_25B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq25e_umzug_nah,v2_con$v2_leq_c_d_leq25e,"v2_leq_D_25B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 911  4   10  13  40  808  1786
## [2,] Percent   51   0.2 0.6 0.7 2.2 45.2 100

26. “Moving to a different town, city, state, or country”

26A Nature (dichotomous [“good”,“bad”], v2_leq_D_26A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq26a_umzug_fern,v2_con$v2_leq_c_d_leq26a,"v2_leq_D_26A")

##                -999 bad good <NA>     
## [1,] No. cases 933  8   37   808  1786
## [2,] Percent   52.2 0.4 2.1  45.2 100

26B Impact (ordinal [0,1,2,3], v2_leq_D_26B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq26e_umzug_fern,v2_con$v2_leq_c_d_leq26e,"v2_leq_D_26B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 932  3   4   14  25  808  1786
## [2,] Percent   52.2 0.2 0.2 0.8 1.4 45.2 100

27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”

27A Nature (dichotomous [“good”,“bad”], v2_leq_D_27A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq27a_veraend_lu,v2_con$v2_leq_c_d_leq27a,"v2_leq_D_27A")

##                -999 bad good <NA>     
## [1,] No. cases 816  52  110  808  1786
## [2,] Percent   45.7 2.9 6.2  45.2 100

27B Impact (ordinal [0,1,2,3], v2_leq_D_27B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq27e_veraend_lu,v2_con$v2_leq_c_d_leq27e,"v2_leq_D_27B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 815  9   35 55  64  808  1786
## [2,] Percent   45.6 0.5 2  3.1 3.6 45.2 100

Create dataset

v2_leq_D<-data.frame(v2_leq_D_24A,v2_leq_D_24B,v2_leq_D_25A,v2_leq_D_25B,v2_leq_D_26A,
                     v2_leq_D_26B,v2_leq_D_27A,v2_leq_D_27B)

Love and marriage

28. “Began a new, close, personal relationship”

28A Nature (dichotomous [“good”,“bad”], v2_leq_E_28A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq28a_neue_bez,v2_con$v2_leq_e_leq28a,"v2_leq_E_28A")

##                -999 bad good <NA>     
## [1,] No. cases 887  10  81   808  1786
## [2,] Percent   49.7 0.6 4.5  45.2 100

28B Impact (ordinal [0,1,2,3], v2_leq_E_28B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq28e_neue_bez,v2_con$v2_leq_e_leq28e,"v2_leq_E_28B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 886  3   8   32  49  808  1786
## [2,] Percent   49.6 0.2 0.4 1.8 2.7 45.2 100

29. “Became engaged”

29A Nature (dichotomous [“good”,“bad”], v2_leq_E_29A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq29a_verlobung,v2_con$v2_leq_e_leq29a,"v2_leq_E_29A")

##                -999 bad good <NA>     
## [1,] No. cases 962  3   13   808  1786
## [2,] Percent   53.9 0.2 0.7  45.2 100

29B Impact (ordinal [0,1,2,3], v2_leq_E_29B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq29e_verlobung,v2_con$v2_leq_e_leq29e,"v2_leq_E_29B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 961  5   5   4   3   808  1786
## [2,] Percent   53.8 0.3 0.3 0.2 0.2 45.2 100

30. “Girlfriend or boyfriend problems”

30A Nature (dichotomous [“good”,“bad”], v2_leq_E_30A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq30a_prob_partner,v2_con$v2_leq_e_leq30a,"v2_leq_E_30A")

##                -999 bad good <NA>     
## [1,] No. cases 879  87  12   808  1786
## [2,] Percent   49.2 4.9 0.7  45.2 100

30B Impact (ordinal [0,1,2,3], v2_leq_E_30B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq30e_prob_partner,v2_con$v2_leq_e_leq30e,"v2_leq_E_30B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 878  4   30  36 30  808  1786
## [2,] Percent   49.2 0.2 1.7 2  1.7 45.2 100

31. “Breaking up with a girlfriend or breaking an engagement”

31A Nature (dichotomous [“good”,“bad”], v2_leq_E_31A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq31a_trennung,v2_con$v2_leq_e_leq31a,"v2_leq_E_31A")

##                -999 bad good <NA>     
## [1,] No. cases 932  33  13   808  1786
## [2,] Percent   52.2 1.8 0.7  45.2 100

31B Impact (ordinal [0,1,2,3], v2_leq_E_31B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq31e_trennung,v2_con$v2_leq_e_leq31e,"v2_leq_E_31B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 932  2   6   14  24  808  1786
## [2,] Percent   52.2 0.1 0.3 0.8 1.3 45.2 100

32. “(Male) Wife or girlfriend’s pregnancy”

32A Nature (dichotomous [“good”,“bad”], v2_leq_E_32A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq32a_schwanger_p,v2_con$v2_leq_e_leq32a,"v2_leq_E_32A")

##                -999 bad good <NA>     
## [1,] No. cases 967  2   9    808  1786
## [2,] Percent   54.1 0.1 0.5  45.2 100

32B Impact (ordinal [0,1,2,3], v2_leq_E_32B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq32e_schwanger_p,v2_con$v2_leq_e_leq32e,"v2_leq_E_32B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 967  3   2   2   4   808  1786
## [2,] Percent   54.1 0.2 0.1 0.1 0.2 45.2 100

33. “(Male) Wife or girlfriend having a miscarriage or abortion”

33A Nature (dichotomous [“good”,“bad”], v2_leq_E_33A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq33a_fehlg_abtr_p,v2_con$v2_leq_e_leq33a,"v2_leq_E_33A")

##                -999 bad <NA>     
## [1,] No. cases 977  1   808  1786
## [2,] Percent   54.7 0.1 45.2 100

33B Impact (ordinal [0,1,2,3], v2_leq_E_33B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq33e_fehlg_abtr_p,v2_con$v2_leq_e_leq33e,"v2_leq_E_33B")

##                -999 0   <NA>     
## [1,] No. cases 977  1   808  1786
## [2,] Percent   54.7 0.1 45.2 100

34. “Getting married (or beginning to live with someone)”

34A Nature (dichotomous [“good”,“bad”], v2_leq_E_34A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq34a_heirat,v2_con$v2_leq_e_leq34a,"v2_leq_E_34A")

##                -999 bad good <NA>     
## [1,] No. cases 961  2   15   808  1786
## [2,] Percent   53.8 0.1 0.8  45.2 100

34B Impact (ordinal [0,1,2,3], v2_leq_E_34B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq34e_heirat,v2_con$v2_leq_e_leq34e,"v2_leq_E_34B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 960  2   5   2   9   808  1786
## [2,] Percent   53.8 0.1 0.3 0.1 0.5 45.2 100

35. “A change in closeness with your partner”

35A Nature (dichotomous [“good”,“bad”], v2_leq_E_35A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq35a_veraend_naehe,v2_con$v2_leq_e_leq35a,"v2_leq_E_35A")

##                -999 bad good <NA>     
## [1,] No. cases 854  50  74   808  1786
## [2,] Percent   47.8 2.8 4.1  45.2 100

35B Impact (ordinal [0,1,2,3], v2_leq_E_35B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq35e_veraend_naehe,v2_con$v2_leq_e_leq35e,"v2_leq_E_35B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 853  3   26  46  50  808  1786
## [2,] Percent   47.8 0.2 1.5 2.6 2.8 45.2 100

36. “Infidelity”

36A Nature (dichotomous [“good”,“bad”], v2_leq_E_36A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq36a_untreue,v2_con$v2_leq_e_leq36a,"v2_leq_E_36A")

##                -999 bad good <NA>     
## [1,] No. cases 949  21  8    808  1786
## [2,] Percent   53.1 1.2 0.4  45.2 100

36B Impact (ordinal [0,1,2,3], v2_leq_E_36B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq36e_untreue,v2_con$v2_leq_e_leq36e,"v2_leq_E_36B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 948  3   8   6   13  808  1786
## [2,] Percent   53.1 0.2 0.4 0.3 0.7 45.2 100

37. “Trouble with in-laws”

37A Nature (dichotomous [“good”,“bad”], v2_leq_E_37A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq37a_konf_schwiege,v2_con$v2_leq_e_leq37a,"v2_leq_E_37A")

##                -999 bad good <NA>     
## [1,] No. cases 952  24  2    808  1786
## [2,] Percent   53.3 1.3 0.1  45.2 100

37B Impact (ordinal [0,1,2,3], v2_leq_E_37B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq37e_konf_schwiege,v2_con$v2_leq_e_leq37e,"v2_leq_E_37B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 951  4   10  7   6   808  1786
## [2,] Percent   53.2 0.2 0.6 0.4 0.3 45.2 100

38. “Separation from spouse or partner due to conflict”

38A Nature (dichotomous [“good”,“bad”], v2_leq_E_38A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq38a_trennung_str,v2_con$v2_leq_e_leq38a,"v2_leq_E_38A")

##                -999 bad good <NA>     
## [1,] No. cases 953  17  8    808  1786
## [2,] Percent   53.4 1   0.4  45.2 100

38B Impact (ordinal [0,1,2,3], v2_leq_E_38B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq38e_trennung_str,v2_con$v2_leq_e_leq38e,"v2_leq_E_38B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 952  4   5   3   14  808  1786
## [2,] Percent   53.3 0.2 0.3 0.2 0.8 45.2 100

39. “Separation from spouse or partner due to work, travel, etc.”

39A Nature (dichotomous [“good”,“bad”], v2_leq_E_39A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq39a_trennung_ber,v2_con$v2_leq_e_leq39a,"v2_leq_E_39A")

##                -999 bad good <NA>     
## [1,] No. cases 968  9   1    808  1786
## [2,] Percent   54.2 0.5 0.1  45.2 100

39B Impact (ordinal [0,1,2,3], v2_leq_E_39B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq39e_trennung_ber,v2_con$v2_leq_e_leq39e,"v2_leq_E_39B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 967  2   1   3   5   808  1786
## [2,] Percent   54.1 0.1 0.1 0.2 0.3 45.2 100

40. “Reconciliation with spouse or partner”

40A Nature (dichotomous [“good”,“bad”], v2_leq_E_40A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq40a_versoehnung,v2_con$v2_leq_e_leq40a,"v2_leq_E_40A")

##                -999 bad good <NA>     
## [1,] No. cases 948  2   28   808  1786
## [2,] Percent   53.1 0.1 1.6  45.2 100

40B Impact (ordinal [0,1,2,3], v2_leq_E_40B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq40a_versoehnung,v2_con$v2_leq_e_leq40e,"v2_leq_E_40B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 948  2   26  1   1   808  1786
## [2,] Percent   53.1 0.1 1.5 0.1 0.1 45.2 100

41. “Divorce”

41A Nature (dichotomous [“good”,“bad”], v2_leq_E_41A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq41a_scheidung,v2_con$v2_leq_e_leq41a,"v2_leq_E_41A")

##                -999 bad good <NA>     
## [1,] No. cases 968  4   6    808  1786
## [2,] Percent   54.2 0.2 0.3  45.2 100

41B Impact (ordinal [0,1,2,3], v2_leq_E_41B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq41e_scheidung,v2_con$v2_leq_e_leq41e,"v2_leq_E_41B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 966  4   2   6   808  1786
## [2,] Percent   54.1 0.2 0.1 0.3 45.2 100

42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”

42A Nature (dichotomous [“good”,“bad”], v2_leq_E_42A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq42a_veraend_taet,v2_con$v2_leq_e_leq42a,"v2_leq_E_42A")

##                -999 bad good <NA>     
## [1,] No. cases 934  14  30   808  1786
## [2,] Percent   52.3 0.8 1.7  45.2 100

42B Impact (ordinal [0,1,2,3], v2_leq_E_42B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq42e_veraend_taet,v2_con$v2_leq_e_leq42e,"v2_leq_E_42B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 933  6   16  8   15  808  1786
## [2,] Percent   52.2 0.3 0.9 0.4 0.8 45.2 100

Create dataset

v2_leq_E<-data.frame(v2_leq_E_28A,v2_leq_E_28B,v2_leq_E_29A,v2_leq_E_29B,v2_leq_E_30A,
                     v2_leq_E_30B,v2_leq_E_31A,v2_leq_E_31B,v2_leq_E_32A,v2_leq_E_32B,
                     v2_leq_E_33A,v2_leq_E_33B,v2_leq_E_34A,v2_leq_E_34B,v2_leq_E_35A,
                     v2_leq_E_35B,v2_leq_E_36A,v2_leq_E_36B,v2_leq_E_37A,v2_leq_E_37B,
                     v2_leq_E_38A,v2_leq_E_38B,v2_leq_E_39A,v2_leq_E_39B,v2_leq_E_40A,
                     v2_leq_E_40B,v2_leq_E_41A,v2_leq_E_41B,v2_leq_E_42A,v2_leq_E_42B)

Family and close friends

43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”

43A Nature (dichotomous [“good”,“bad”], v2_leq_F_43A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq43a_neu_fmitglied,v2_con$v2_leq_f_g_leq43a,"v2_leq_F_43A")

##                -999 bad good <NA>     
## [1,] No. cases 908  1   69   808  1786
## [2,] Percent   50.8 0.1 3.9  45.2 100

43B Impact (ordinal [0,1,2,3], v2_leq_F_43B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq43e_neu_fmitglied,v2_con$v2_leq_f_g_leq43e,"v2_leq_F_43B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 907  11  15  21  24  808  1786
## [2,] Percent   50.8 0.6 0.8 1.2 1.3 45.2 100

44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”

44A Nature (dichotomous [“good”,“bad”], v2_leq_F_44A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq44a_auszug_fm,v2_con$v2_leq_f_g_leq44a,"v2_leq_F_44A")

##                -999 bad good <NA>     
## [1,] No. cases 942  20  16   808  1786
## [2,] Percent   52.7 1.1 0.9  45.2 100

44B Impact (ordinal [0,1,2,3], v2_leq_F_44B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq44e_auszug_fm,v2_con$v2_leq_f_g_leq44e,"v2_leq_F_44B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 941  7   8   16  6   808  1786
## [2,] Percent   52.7 0.4 0.4 0.9 0.3 45.2 100

45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”

45A Nature (dichotomous [“good”,“bad”], v2_leq_F_45A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq45a_gz_verh_fm,v2_con$v2_leq_f_g_leq45a,"v2_leq_F_45A")

##                -999 bad good <NA>     
## [1,] No. cases 829  143 6    808  1786
## [2,] Percent   46.4 8   0.3  45.2 100

45B Impact (ordinal [0,1,2,3], v2_leq_F_45B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq45e_gz_verh_fm,v2_con$v2_leq_f_g_leq45e,"v2_leq_F_45B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 829  5   30  66  48  808  1786
## [2,] Percent   46.4 0.3 1.7 3.7 2.7 45.2 100

46. “Death of spouse or partner”

46A Nature (dichotomous [“good”,“bad”], v2_leq_F_46A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq46a_tod_partner,v2_con$v2_leq_f_g_leq46a,"v2_leq_F_46A")

##                -999 bad <NA>     
## [1,] No. cases 971  7   808  1786
## [2,] Percent   54.4 0.4 45.2 100

46B Impact (ordinal [0,1,2,3], v2_leq_F_46B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq46e_tod_partner,v2_con$v2_leq_f_g_leq46e,"v2_leq_F_46B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 970  1   1   1   5   808  1786
## [2,] Percent   54.3 0.1 0.1 0.1 0.3 45.2 100

47. “Death of a child”

47A Nature (dichotomous [“good”,“bad”], v2_leq_F_47A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq47a_tod_kind,v2_con$v2_leq_f_g_leq47a,"v2_leq_F_47A")

##                -999 bad good <NA>     
## [1,] No. cases 972  5   1    808  1786
## [2,] Percent   54.4 0.3 0.1  45.2 100

47B Impact (ordinal [0,1,2,3], v2_leq_F_47B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq47e_tod_kind,v2_con$v2_leq_f_g_leq47e,"v2_leq_F_47B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 971  1   1   1   4   808  1786
## [2,] Percent   54.4 0.1 0.1 0.1 0.2 45.2 100

48. “Death of family member or close friend”

48A Nature (dichotomous [“good”,“bad”], v2_leq_F_48A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq48a_tod_fm_ef,v2_con$v2_leq_f_g_leq48a,"v2_leq_F_48A")

##                -999 bad good <NA>     
## [1,] No. cases 903  67  8    808  1786
## [2,] Percent   50.6 3.8 0.4  45.2 100

48B Impact (ordinal [0,1,2,3], v2_leq_F_48B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq48e_tod_fm_ef,v2_con$v2_leq_f_g_leq48e,"v2_leq_F_48B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 901  5   26  21  25  808  1786
## [2,] Percent   50.4 0.3 1.5 1.2 1.4 45.2 100

49. “Birth of a grandchild”

49A Nature (dichotomous [“good”,“bad”], v2_leq_F_49A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq49a_geb_enkel,v2_con$v2_leq_f_g_leq49a,"v2_leq_F_49A")

##                -999 bad good <NA>     
## [1,] No. cases 951  3   24   808  1786
## [2,] Percent   53.2 0.2 1.3  45.2 100

49B Impact (ordinal [0,1,2,3], v2_leq_F_49B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq49e_geb_enkel,v2_con$v2_leq_f_g_leq49e,"v2_leq_F_49B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 950  4   5   7   12  808  1786
## [2,] Percent   53.2 0.2 0.3 0.4 0.7 45.2 100

50. “Change in marital status of your parents”

50A Nature (dichotomous [“good”,“bad”], v2_leq_F_50A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq50a_fstand_eltern,v2_con$v2_leq_f_g_leq50a,"v2_leq_F_50A")

##                -999 bad good <NA>     
## [1,] No. cases 966  5   7    808  1786
## [2,] Percent   54.1 0.3 0.4  45.2 100

50B Impact (ordinal [0,1,2,3], v2_leq_F_50B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq50e_fstand_eltern,v2_con$v2_leq_f_g_leq50e,"v2_leq_F_50B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 965  1   6   2   4   808  1786
## [2,] Percent   54   0.1 0.3 0.1 0.2 45.2 100

Create dataset

v2_leq_F<-data.frame(v2_leq_F_43A,v2_leq_F_43B,v2_leq_F_44A,v2_leq_F_44B,v2_leq_F_45A,
                     v2_leq_F_45B,v2_leq_F_46A,v2_leq_F_46B,v2_leq_F_47A,v2_leq_F_47B,
                     v2_leq_F_48A,v2_leq_F_48B,v2_leq_F_49A,v2_leq_F_49B,v2_leq_F_50A,
                     v2_leq_F_50B)

Parenting

51. “Change in child care arrangements”

51A Nature (dichotomous [“good”,“bad”], v2_leq_G_51A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq51a_kindbetr,v2_con$v2_leq_f_g_leq51a,"v2_leq_G_51A")

##                -999 bad good <NA>     
## [1,] No. cases 950  8   20   808  1786
## [2,] Percent   53.2 0.4 1.1  45.2 100

51B Impact (ordinal [0,1,2,3], v2_leq_G_51B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq51e_kindbetr,v2_con$v2_leq_f_g_leq51e,"v2_leq_G_51B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 949  2   6   10  11  808  1786
## [2,] Percent   53.1 0.1 0.3 0.6 0.6 45.2 100

52. “Conflicts with spouse or partner about parenting”

52A Nature (dichotomous [“good”,“bad”], v2_leq_G_52A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq52a_konf_eschaft,v2_con$v2_leq_f_g_leq52a,"v2_leq_G_52A")

##                -999 bad good <NA>     
## [1,] No. cases 952  19  7    808  1786
## [2,] Percent   53.3 1.1 0.4  45.2 100

52B Impact (ordinal [0,1,2,3], v2_leq_G_52B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq52e_konf_eschaft,v2_con$v2_leq_f_g_leq52e,"v2_leq_G_52B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 951  3   5   9   10  808  1786
## [2,] Percent   53.2 0.2 0.3 0.5 0.6 45.2 100

53. “Conflicts with child’s grandparents (or other important person) about parenting”

53A Nature (dichotomous [“good”,“bad”], v2_leq_G_53A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq53a_konf_geltern,v2_con$v2_leq_f_g_leq53a,"v2_leq_G_53A")

##                -999 bad good <NA>     
## [1,] No. cases 970  6   2    808  1786
## [2,] Percent   54.3 0.3 0.1  45.2 100

53B Impact (ordinal [0,1,2,3], v2_leq_G_53B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq53e_konf_geltern,v2_con$v2_leq_f_g_leq53e,"v2_leq_G_53B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 969  3   3   2   1   808  1786
## [2,] Percent   54.3 0.2 0.2 0.1 0.1 45.2 100

54. “Taking on full responsibility for parenting as a single parent”

54A Nature (dichotomous [“good”,“bad”], v2_leq_G_54A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq54a_alleinerz,v2_con$v2_leq_f_g_leq54a,"v2_leq_G_54A")

##                -999 bad good <NA>     
## [1,] No. cases 971  4   3    808  1786
## [2,] Percent   54.4 0.2 0.2  45.2 100

54B Impact (ordinal [0,1,2,3], v2_leq_G_54B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq54e_alleinerz,v2_con$v2_leq_f_g_leq54e,"v2_leq_G_54B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 970  2   1   3   2   808  1786
## [2,] Percent   54.3 0.1 0.1 0.2 0.1 45.2 100

55. “Custody battles with former spouse or partner”

55A Nature (dichotomous [“good”,“bad”], v2_leq_G_55A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq55a_sorgerecht,v2_con$v2_leq_f_g_leq55a,"v2_leq_G_55A")

##                -999 bad good <NA>     
## [1,] No. cases 964  9   5    808  1786
## [2,] Percent   54   0.5 0.3  45.2 100

55B Impact (ordinal [0,1,2,3], v2_leq_G_55B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq55e_sorgerecht,v2_con$v2_leq_f_g_leq55e,"v2_leq_G_55B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 963  3   1   6   5   808  1786
## [2,] Percent   53.9 0.2 0.1 0.3 0.3 45.2 100

Create dataset

v2_leq_G<-data.frame(v2_leq_G_51A,v2_leq_G_51B,v2_leq_G_52A,v2_leq_G_52B,v2_leq_G_53A,
                     v2_leq_G_53B,v2_leq_G_54A,v2_leq_G_54B,v2_leq_G_55A,v2_leq_G_55B)

Personal or social

56. “Major personal achievement”

56A Nature (dichotomous [“good”,“bad”], v2_leq_H_56A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq56a_pers_leistung,v2_con$v2_leq_h_leq56a,"v2_leq_H_56A")

##                -999 bad good <NA>     
## [1,] No. cases 796  9   173  808  1786
## [2,] Percent   44.6 0.5 9.7  45.2 100

56B Impact (ordinal [0,1,2,3], v2_leq_H_56B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq56e_pers_leistung,v2_con$v2_leq_h_leq56e,"v2_leq_H_56B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 794  7   39  71 67  808  1786
## [2,] Percent   44.5 0.4 2.2 4  3.8 45.2 100

57. “Major decision regarding your immediate future”

57A Nature (dichotomous [“good”,“bad”], v2_leq_H_57A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq57a_wicht_entsch,v2_con$v2_leq_h_leq57a,"v2_leq_H_57A")

##                -999 bad good <NA>     
## [1,] No. cases 706  35  237  808  1786
## [2,] Percent   39.5 2   13.3 45.2 100

57B Impact (ordinal [0,1,2,3], v2_leq_H_57B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq57e_wicht_entsch,v2_con$v2_leq_h_leq57e,"v2_leq_H_57B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 704  12  36 94  132 808  1786
## [2,] Percent   39.4 0.7 2  5.3 7.4 45.2 100

58. “Change in your personal habits (your dress, life-style, hobbies, etc.)”

58A Nature (dichotomous [“good”,“bad”], v2_leq_H_58A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq58a_pers_gewohn,v2_con$v2_leq_h_leq58a,"v2_leq_H_58A")

##                -999 bad good <NA>     
## [1,] No. cases 769  41  168  808  1786
## [2,] Percent   43.1 2.3 9.4  45.2 100

58B Impact (ordinal [0,1,2,3], v2_leq_H_58B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq58e_pers_gewohn,v2_con$v2_leq_h_leq58e,"v2_leq_H_58B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 767  10  46  83  72 808  1786
## [2,] Percent   42.9 0.6 2.6 4.6 4  45.2 100

59. “Change in your religious beliefs”

59A Nature (dichotomous [“good”,“bad”], v2_leq_H_59A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq59a_relig_ueberz,v2_con$v2_leq_h_leq59a,"v2_leq_H_59A")

##                -999 bad good <NA>     
## [1,] No. cases 937  7   34   808  1786
## [2,] Percent   52.5 0.4 1.9  45.2 100

59B Impact (ordinal [0,1,2,3], v2_leq_H_59B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq59e_relig_ueberz,v2_con$v2_leq_h_leq59e,"v2_leq_H_59B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 935  11  16  7   9   808  1786
## [2,] Percent   52.4 0.6 0.9 0.4 0.5 45.2 100

60. “Change in your political beliefs”

60A Nature (dichotomous [“good”,“bad”], v2_leq_H_60A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq60a_pol_ansichten,v2_clin$v2_leq_h_leq60a,"v2_leq_H_60A")

## Warning in (is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con != 2) & is.na(leq_con_old_name) == : Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes

## Warning in (is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con != 2) & is.na(leq_con_old_name): Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes

##                -999 bad good <NA>     
## [1,] No. cases 927  13  38   808  1786
## [2,] Percent   51.9 0.7 2.1  45.2 100

60B Impact (ordinal [0,1,2,3], v2_leq_H_60B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq60e_pol_ansichten,v2_con$v2_leq_h_leq60e,"v2_leq_H_60B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 931  11  22  6   8   808  1786
## [2,] Percent   52.1 0.6 1.2 0.3 0.4 45.2 100

61. “Loss or damage of personal property”

61A Nature (dichotomous [“good”,“bad”], v2_leq_H_61A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq61a_pers_eigent,v2_con$v2_leq_h_leq61a,"v2_leq_H_61A")

##                -999 bad good <NA>     
## [1,] No. cases 907  64  7    808  1786
## [2,] Percent   50.8 3.6 0.4  45.2 100

61B Impact (ordinal [0,1,2,3], v2_leq_H_61B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq61e_pers_eigent,v2_con$v2_leq_h_leq61e,"v2_leq_H_61B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 906  7   34  20  11  808  1786
## [2,] Percent   50.7 0.4 1.9 1.1 0.6 45.2 100

62. “Took a vacation”

62A Nature (dichotomous [“good”,“bad”], v2_leq_H_62A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq62a_erholungsurl,v2_con$v2_leq_h_leq62a,"v2_leq_H_62A")

##                -999 bad good <NA>     
## [1,] No. cases 709  15  254  808  1786
## [2,] Percent   39.7 0.8 14.2 45.2 100

62B Impact (ordinal [0,1,2,3], v2_leq_H_62B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq62e_erholungsurl,v2_con$v2_leq_h_leq62e,"v2_leq_H_62B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 709  19  67  105 78  808  1786
## [2,] Percent   39.7 1.1 3.8 5.9 4.4 45.2 100

63. “Took a trip other than a vacation”

63A Nature (dichotomous [“good”,“bad”], v2_leq_H_63A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq63a_reise_andere,v2_con$v2_leq_h_leq63a,"v2_leq_H_63A")

##                -999 bad good <NA>     
## [1,] No. cases 840  12  126  808  1786
## [2,] Percent   47   0.7 7.1  45.2 100

63B Impact (ordinal [0,1,2,3], v2_leq_H_63B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq63e_reise_andere,v2_con$v2_leq_h_leq63e,"v2_leq_H_63B")

##                -999 0   1  2   3  <NA>     
## [1,] No. cases 838  11  35 59  35 808  1786
## [2,] Percent   46.9 0.6 2  3.3 2  45.2 100

64. “Change in family get-togethers”

64A Nature (dichotomous [“good”,“bad”], v2_leq_H_64A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq64a_familientreff,v2_con$v2_leq_h_leq64a,"v2_leq_H_64A")

##                -999 bad good <NA>     
## [1,] No. cases 874  37  67   808  1786
## [2,] Percent   48.9 2.1 3.8  45.2 100

64B Impact (ordinal [0,1,2,3], v2_leq_H_64B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq64e_familientreff,v2_con$v2_leq_h_leq64e,"v2_leq_H_64B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 873  10  29  45  21  808  1786
## [2,] Percent   48.9 0.6 1.6 2.5 1.2 45.2 100

65. “Change in your social activities (clubs, movies, visiting)”

65A Nature (dichotomous [“good”,“bad”], v2_leq_H_65A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq65a_ges_unternehm,v2_con$v2_leq_h_leq65a,"v2_leq_H_65A")

##                -999 bad good <NA>     
## [1,] No. cases 842  33  103  808  1786
## [2,] Percent   47.1 1.8 5.8  45.2 100

65B Impact (ordinal [0,1,2,3], v2_leq_H_65B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq65e_ges_unternehm,v2_con$v2_leq_h_leq65e,"v2_leq_H_65B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 840  6   43  56  33  808  1786
## [2,] Percent   47   0.3 2.4 3.1 1.8 45.2 100

66. “Made new friends”

66A Nature (dichotomous [“good”,“bad”], v2_leq_H_66A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq66a_neue_freunds,v2_con$v2_leq_h_leq66a,"v2_leq_H_66A")

##                -999 bad good <NA>     
## [1,] No. cases 704  9   265  808  1786
## [2,] Percent   39.4 0.5 14.8 45.2 100

66B Impact (ordinal [0,1,2,3], v2_leq_H_66B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq66e_neue_freunds,v2_con$v2_leq_h_leq66e,"v2_leq_H_66B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 704  12  87  109 66  808  1786
## [2,] Percent   39.4 0.7 4.9 6.1 3.7 45.2 100

67. “Broke up with a friend”

67A Nature (dichotomous [“good”,“bad”], v2_leq_H_67A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq67a_ende_freunds,v2_con$v2_leq_h_leq67a,"v2_leq_H_67A")

##                -999 bad good <NA>     
## [1,] No. cases 872  77  29   808  1786
## [2,] Percent   48.8 4.3 1.6  45.2 100

67B Impact (ordinal [0,1,2,3], v2_leq_H_67B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq67e_ende_freunds,v2_con$v2_leq_h_leq67e,"v2_leq_H_67B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 872  11  30  38  27  808  1786
## [2,] Percent   48.8 0.6 1.7 2.1 1.5 45.2 100

68. “Acquired or lost a pet”

68A Nature (dichotomous [“good”,“bad”], v2_leq_H_68A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq68a_haustier,v2_con$v2_leq_h_leq68a,"v2_leq_H_68A")

##                -999 bad good <NA>     
## [1,] No. cases 901  38  39   808  1786
## [2,] Percent   50.4 2.1 2.2  45.2 100

68B Impact (ordinal [0,1,2,3], v2_leq_H_68B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq68e_haustier,v2_con$v2_leq_h_leq68e,"v2_leq_H_68B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 898  10  16  13  41  808  1786
## [2,] Percent   50.3 0.6 0.9 0.7 2.3 45.2 100

Create dataset

v2_leq_H<-data.frame(v2_leq_H_56A,v2_leq_H_56B,v2_leq_H_57A,v2_leq_H_57B,v2_leq_H_58A,
                     v2_leq_H_58B,v2_leq_H_59A,v2_leq_H_59B,v2_leq_H_60A,v2_leq_H_60B,
                     v2_leq_H_61A,v2_leq_H_61B,v2_leq_H_62A,v2_leq_H_62B,v2_leq_H_63A,
                     v2_leq_H_63B,v2_leq_H_64A,v2_leq_H_64B,v2_leq_H_65A,v2_leq_H_65B,
                     v2_leq_H_66A,v2_leq_H_66B,v2_leq_H_67A,v2_leq_H_67B,v2_leq_H_68A,
                     v2_leq_H_68B)

Financial

69. “Major change in finances (increased or decreased income)”

69A Nature (dichotomous [“good”,“bad”], v2_leq_I_69A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq69a_finanz_sit,v2_con$v2_leq_i_j_k_leq69a,"v2_leq_I_69A")

##                -999 bad good <NA>     
## [1,] No. cases 708  131 139  808  1786
## [2,] Percent   39.6 7.3 7.8  45.2 100

69B Impact (ordinal [0,1,2,3], v2_leq_I_69B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq69e_finanz_sit,v2_con$v2_leq_i_j_k_leq69e,"v2_leq_I_69B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 705  8   73  94  98  808  1786
## [2,] Percent   39.5 0.4 4.1 5.3 5.5 45.2 100

70. “Took on a moderate purchase, such as TV, car, freezer, etc.”

70A Nature (dichotomous [“good”,“bad”], v2_leq_I_70A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq70a_finanz_verpfl,v2_con$v2_leq_i_j_k_leq70a,"v2_leq_I_70A")

##                -999 bad good <NA>     
## [1,] No. cases 894  24  60   808  1786
## [2,] Percent   50.1 1.3 3.4  45.2 100

70B Impact (ordinal [0,1,2,3], v2_leq_I_70B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq70e_finanz_verpfl,v2_con$v2_leq_i_j_k_leq70e,"v2_leq_I_70B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 892  13  33  30  10  808  1786
## [2,] Percent   49.9 0.7 1.8 1.7 0.6 45.2 100

71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”

71A Nature (dichotomous [“good”,“bad”], v2_leq_I_71A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq71a_hypothek,v2_con$v2_leq_i_j_k_leq71a,"v2_leq_I_71A")

##                -999 bad good <NA>     
## [1,] No. cases 955  12  11   808  1786
## [2,] Percent   53.5 0.7 0.6  45.2 100

71B Impact (ordinal [0,1,2,3], v2_leq_I_71B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq71e_hypothek,v2_con$v2_leq_i_j_k_leq71e,"v2_leq_I_71B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 954  3   8   7   6   808  1786
## [2,] Percent   53.4 0.2 0.4 0.4 0.3 45.2 100

72. “Experienced a foreclosure on a mortgage or loan”

72A Nature (dichotomous [“good”,“bad”], v2_leq_I_72A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq72a_hypoth_kuend,v2_con$v2_leq_i_j_k_leq72a,"v2_leq_I_72A")

##                -999 bad good <NA>     
## [1,] No. cases 967  2   9    808  1786
## [2,] Percent   54.1 0.1 0.5  45.2 100

72B Impact (ordinal [0,1,2,3], v2_leq_I_72B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq72e_hypoth_kuend,v2_con$v2_leq_i_j_k_leq72e,"v2_leq_I_72B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 966  3   6   1   2   808  1786
## [2,] Percent   54.1 0.2 0.3 0.1 0.1 45.2 100

73. “Credit rating difficulties”

73A Nature (dichotomous [“good”,“bad”], v2_leq_I_73A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq73a_kreditwuerdk,v2_con$v2_leq_i_j_k_leq73a,"v2_leq_I_73A")

##                -999 bad <NA>     
## [1,] No. cases 947  31  808  1786
## [2,] Percent   53   1.7 45.2 100

73B Impact (ordinal [0,1,2,3], v2_leq_I_73B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq73e_kreditwuerdk,v2_con$v2_leq_i_j_k_leq73e,"v2_leq_I_73B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 947  3   12  8   8   808  1786
## [2,] Percent   53   0.2 0.7 0.4 0.4 45.2 100

Create dataset

v2_leq_I<-data.frame(v2_leq_I_69A,v2_leq_I_69B,v2_leq_I_70A,v2_leq_I_70B,v2_leq_I_71A,
                     v2_leq_I_71B,v2_leq_I_72A,v2_leq_I_72B,v2_leq_I_73A,v2_leq_I_73B)

Crime and legal matters

74. “Being robbed or victim of identity theft”

74A Nature (dichotomous [“good”,“bad”], v2_leq_J_74A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq74a_opf_diebstahl,v2_con$v2_leq_i_j_k_leq74a,"v2_leq_J_74A")

##                -999 bad good <NA>     
## [1,] No. cases 945  30  3    808  1786
## [2,] Percent   52.9 1.7 0.2  45.2 100

74B Impact (ordinal [0,1,2,3], v2_leq_J_74B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq74e_opf_diebstahl,v2_con$v2_leq_i_j_k_leq74e,"v2_leq_J_74B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 944  2   13  13  6   808  1786
## [2,] Percent   52.9 0.1 0.7 0.7 0.3 45.2 100

75. “Being a victim of a violent act (rape, assault, etc.)”

75A Nature (dichotomous [“good”,“bad”], v2_leq_J_75A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq75a_opf_gewalttat,v2_con$v2_leq_i_j_k_leq75a,"v2_leq_J_75A")

##                -999 bad <NA>     
## [1,] No. cases 967  11  808  1786
## [2,] Percent   54.1 0.6 45.2 100

75B Impact (ordinal [0,1,2,3], v2_leq_J_75B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq75e_opf_gewalttat,v2_con$v2_leq_i_j_k_leq75e,"v2_leq_J_75B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 966  2   2   3   5   808  1786
## [2,] Percent   54.1 0.1 0.1 0.2 0.3 45.2 100

76. “Involved in an accident”

76A Nature (dichotomous [“good”,“bad”], v2_leq_J_76A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq76a_unfall,v2_con$v2_leq_i_j_k_leq76a,"v2_leq_J_76A")

##                -999 bad good <NA>     
## [1,] No. cases 932  43  3    808  1786
## [2,] Percent   52.2 2.4 0.2  45.2 100

76B Impact (ordinal [0,1,2,3], v2_leq_J_76B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq76e_unfall,v2_con$v2_leq_i_j_k_leq76e,"v2_leq_J_76B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 931  7   20  14  6   808  1786
## [2,] Percent   52.1 0.4 1.1 0.8 0.3 45.2 100

77. “Involved in a law suit”

77A Nature (dichotomous [“good”,“bad”], v2_leq_J_77A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq77a_rechtsstreit,v2_con$v2_leq_i_j_k_leq77a,"v2_leq_J_77A")

##                -999 bad good <NA>     
## [1,] No. cases 916  45  17   808  1786
## [2,] Percent   51.3 2.5 1    45.2 100

77B Impact (ordinal [0,1,2,3], v2_leq_J_77B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq77e_rechtsstreit,v2_con$v2_leq_i_j_k_leq77e,"v2_leq_J_77B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 915  6   19  18 20  808  1786
## [2,] Percent   51.2 0.3 1.1 1  1.1 45.2 100

78. “Involved in a minor violation of the law (traffic tickets, disturbing the peace, etc)”

78A Nature (dichotomous [“good”,“bad”], v2_leq_J_78A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq78a_owi,v2_con$v2_leq_i_j_k_leq78a,"v2_leq_J_78A")

##                -999 bad good <NA>     
## [1,] No. cases 937  38  3    808  1786
## [2,] Percent   52.5 2.1 0.2  45.2 100

78B Impact (ordinal [0,1,2,3], v2_leq_J_78B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq78e_owi,v2_con$v2_leq_i_j_k_leq78e,"v2_leq_J_78B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 935  10  20  10  3   808  1786
## [2,] Percent   52.4 0.6 1.1 0.6 0.2 45.2 100

79. “Legal troubles resulting in your being arrested or held in jail”

79A Nature (dichotomous [“good”,“bad”], v2_leq_J_79A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq79a_konf_gesetz,v2_con$v2_leq_i_j_k_leq79a,"v2_leq_J_79A")

##                -999 bad <NA>     
## [1,] No. cases 973  5   808  1786
## [2,] Percent   54.5 0.3 45.2 100

79B Impact (ordinal [0,1,2,3], v2_leq_J_79B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq79e_konf_gesetz,v2_con$v2_leq_i_j_k_leq79e,"v2_leq_J_79B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 972  1   1   2   2   808  1786
## [2,] Percent   54.4 0.1 0.1 0.1 0.1 45.2 100

Create dataset

v2_leq_J<-data.frame(v2_leq_J_74A,v2_leq_J_74B,v2_leq_J_75A,v2_leq_J_75B,v2_leq_J_76A,
                     v2_leq_J_76B,v2_leq_J_77A,v2_leq_J_77B,v2_leq_J_78A,v2_leq_J_78B,
                     v2_leq_J_79A,v2_leq_J_79B)

Create LEQ dataset

v2_leq<-data.frame(v2_leq_A,v2_leq_B,v2_leq_C,v2_leq_D,v2_leq_E,v2_leq_F,v2_leq_G,
                   v2_leq_H,v2_leq_I,v2_leq_J)

WHOQOL-BREF

For explanation, please refer to the section in Visit 1

1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v2_whoqol_itm1)

v2_quol_recode(v2_clin$v2_whoqol_bref_who1_lebensqualitaet,v2_con$v2_whoqol_bref_who1,"v2_whoqol_itm1",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 26  67  280  439  205  769  1786
## [2,] Percent   1.5 3.8 15.7 24.6 11.5 43.1 100

2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v2_whoqol_itm2)”

v2_quol_recode(v2_clin$v2_whoqol_bref_who2_gesundheit,v2_con$v2_whoqol_bref_who2,"v2_whoqol_itm2",0)

##                1   2    3    4    5   NA's     
## [1,] No. cases 49  188  218  418  144 769  1786
## [2,] Percent   2.7 10.5 12.2 23.4 8.1 43.1 100

3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v2_whoqol_itm3)

Coding reversed so that higher scores mean less impairment by pain.

v2_quol_recode(v2_clin$v2_whoqol_bref_who3_schmerzen,v2_con$v2_whoqol_bref_who3,"v2_whoqol_itm3",1)

##                1   2   3   4    5    NA's     
## [1,] No. cases 12  56  93  210  634  781  1786
## [2,] Percent   0.7 3.1 5.2 11.8 35.5 43.7 100

4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v2_whoqol_itm4)”

Coding reversed so that higher scores mean less dependence on medical treatment.

v2_quol_recode(v2_clin$v2_whoqol_bref_who4_med_behand,v2_con$v2_whoqol_bref_who4,"v2_whoqol_itm4",1)

##                1   2    3   4    5    NA's     
## [1,] No. cases 92  194  138 186  394  782  1786
## [2,] Percent   5.2 10.9 7.7 10.4 22.1 43.8 100

5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v2_whoqol_itm5)

v2_quol_recode(v2_clin$v2_whoqol_bref_who5_lebensgenuss,v2_con$v2_whoqol_bref_who5,"v2_whoqol_itm5",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 50  121 260  422  150 783  1786
## [2,] Percent   2.8 6.8 14.6 23.6 8.4 43.8 100

6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v2_whoqol_itm6)

v2_quol_recode(v2_clin$v2_whoqol_bref_who6_lebenssinn,v2_con$v2_whoqol_bref_who6,"v2_whoqol_itm6",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 62  123 178 368  262  793  1786
## [2,] Percent   3.5 6.9 10  20.6 14.7 44.4 100

7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v2_whoqol_itm7)

v2_quol_recode(v2_clin$v2_whoqol_bref_who7_konzentration,v2_con$v2_whoqol_bref_who7,"v2_whoqol_itm7",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 20  134 388  397  66  781  1786
## [2,] Percent   1.1 7.5 21.7 22.2 3.7 43.7 100

8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v2_whoqol_itm8)

v2_quol_recode(v2_clin$v2_whoqol_bref_who8_sicherheit,v2_con$v2_whoqol_bref_who8,"v2_whoqol_itm8",0)

##                1   2  3    4    5    NA's     
## [1,] No. cases 25  71 249  450  210  781  1786
## [2,] Percent   1.4 4  13.9 25.2 11.8 43.7 100

9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v2_whoqol_itm9)

v2_quol_recode(v2_clin$v2_whoqol_bref_who9_umweltbed,v2_con$v2_whoqol_bref_who9,"v2_whoqol_itm9",0)

##                1   2  3    4    5    NA's     
## [1,] No. cases 15  36 212  490  249  784  1786
## [2,] Percent   0.8 2  11.9 27.4 13.9 43.9 100

10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v2_whoqol_itm10)

v2_quol_recode(v2_clin$v2_whoqol_bref_who10_energie,v2_con$v2_whoqol_bref_who10,"v2_whoqol_itm10",0)

##                1  2   3    4    5    NA's     
## [1,] No. cases 18 100 229  432  228  779  1786
## [2,] Percent   1  5.6 12.8 24.2 12.8 43.6 100

11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v2_whoqol_itm11)

v2_quol_recode(v2_clin$v2_whoqol_bref_who11_aussehen,v2_con$v2_whoqol_bref_who11,"v2_whoqol_itm11",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 20  76  227  431  251  781  1786
## [2,] Percent   1.1 4.3 12.7 24.1 14.1 43.7 100

12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v2_whoqol_itm12)

v2_quol_recode(v2_clin$v2_whoqol_bref_who12_genug_geld,v2_con$v2_whoqol_bref_who12,"v2_whoqol_itm12",0)

##                1  2   3    4    5    NA's     
## [1,] No. cases 53 126 238  338  251  780  1786
## [2,] Percent   3  7.1 13.3 18.9 14.1 43.7 100

13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v2_whoqol_itm13)

v2_quol_recode(v2_clin$v2_whoqol_bref_who13_infozugang,v2_con$v2_whoqol_bref_who13,"v2_whoqol_itm13",0)

##                1   2   3   4   5    NA's     
## [1,] No. cases 8   20  111 358 509  780  1786
## [2,] Percent   0.4 1.1 6.2 20  28.5 43.7 100

14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v2_whoqol_itm14)

v2_quol_recode(v2_clin$v2_whoqol_bref_who14_freizeitaktiv,v2_con$v2_whoqol_bref_who14,"v2_whoqol_itm14",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 11  56  202  380  355  782  1786
## [2,] Percent   0.6 3.1 11.3 21.3 19.9 43.8 100

15. “How well are you able to get around? (ordinal [1,2,3,4,5], v2_whoqol_itm15)”

v2_quol_recode(v2_clin$v2_whoqol_bref_who15_fortbewegung,v2_con$v2_whoqol_bref_who15,"v2_whoqol_itm15",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 4   38  149 353  461  781  1786
## [2,] Percent   0.2 2.1 8.3 19.8 25.8 43.7 100

16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v2_whoqol_itm16)

v2_quol_recode(v2_clin$v2_whoqol_bref_who16_schlaf,v2_con$v2_whoqol_bref_who16,"v2_whoqol_itm16",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 46  158 177 436  201  768  1786
## [2,] Percent   2.6 8.8 9.9 24.4 11.3 43   100

17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v2_whoqol_itm17)

v2_quol_recode(v2_clin$v2_whoqol_bref_who17_alltag,v2_con$v2_whoqol_bref_who17,"v2_whoqol_itm17",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 33  115 191  443  234  770  1786
## [2,] Percent   1.8 6.4 10.7 24.8 13.1 43.1 100

18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v2_whoqol_itm18)

v2_quol_recode(v2_clin$v2_whoqol_bref_who18_arbeitsfhgk,v2_con$v2_whoqol_bref_who18,"v2_whoqol_itm18",0)

##                1  2    3    4    5    NA's     
## [1,] No. cases 71 199  181  350  206  779  1786
## [2,] Percent   4  11.1 10.1 19.6 11.5 43.6 100

19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v2_whoqol_itm19)

v2_quol_recode(v2_clin$v2_whoqol_bref_who19_selbstzufried,v2_con$v2_whoqol_bref_who19,"v2_whoqol_itm19",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 48  137 227  454  151 769  1786
## [2,] Percent   2.7 7.7 12.7 25.4 8.5 43.1 100

20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v2_whoqol_itm20)

v2_quol_recode(v2_clin$v2_whoqol_bref_who20_pers_bezieh,v2_con$v2_whoqol_bref_who20,"v2_whoqol_itm20",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 33  107 228  457  187  774  1786
## [2,] Percent   1.8 6   12.8 25.6 10.5 43.3 100

21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v2_whoqol_itm21)

v2_quol_recode(v2_clin$v2_whoqol_bref_who21_sexualleben,v2_con$v2_whoqol_bref_who21,"v2_whoqol_itm21",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 98  179 284  307  129 789  1786
## [2,] Percent   5.5 10  15.9 17.2 7.2 44.2 100

22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v2_whoqol_itm22)

v2_quol_recode(v2_clin$v2_whoqol_bref_who22_freunde,v2_con$v2_whoqol_bref_who22,"v2_whoqol_itm22",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 38  61  221  453  237  776  1786
## [2,] Percent   2.1 3.4 12.4 25.4 13.3 43.4 100

23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v2_whoqol_itm23)

v2_quol_recode(v2_clin$v2_whoqol_bref_who23_wohnbeding,v2_con$v2_whoqol_bref_who23,"v2_whoqol_itm23",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 48  63  149 450  308  768  1786
## [2,] Percent   2.7 3.5 8.3 25.2 17.2 43   100

24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v2_whoqol_itm24)

v2_quol_recode(v2_clin$v2_whoqol_bref_who24_gesundhdiens,v2_con$v2_whoqol_bref_who24,"v2_whoqol_itm24",0)

##                1  2   3   4    5    NA's     
## [1,] No. cases 17 20  117 470  391  771  1786
## [2,] Percent   1  1.1 6.6 26.3 21.9 43.2 100

25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v2_whoqol_itm25)

v2_quol_recode(v2_clin$v2_whoqol_bref_who25_transport,v2_con$v2_whoqol_bref_who25,"v2_whoqol_itm25",0)

##                1  2   3   4   5    NA's     
## [1,] No. cases 18 44  115 429 409  771  1786
## [2,] Percent   1  2.5 6.4 24  22.9 43.2 100

26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v2_whoqol_itm26)

Coding reversed so that higher scores mean symptoms less often.

v2_quol_recode(v2_clin$v2_whoqol_bref_who26_neg_gefuehle,v2_con$v2_whoqol_bref_who26,"v2_whoqol_itm26",1)

##                1   2   3    4    5   NA's     
## [1,] No. cases 28  149 241  373  215 780  1786
## [2,] Percent   1.6 8.3 13.5 20.9 12  43.7 100

WHOQOL-BREF domain scores

Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).

Global (continuous [4-20],v2_whoqol_dom_glob)

v2_whoqol_dom_glob_df<-data.frame(as.numeric(v2_whoqol_itm1),as.numeric(v2_whoqol_itm2))

v2_who_glob_no_nas<-rowSums(is.na(v2_whoqol_dom_glob_df))

v2_whoqol_dom_glob<-ifelse((v2_who_glob_no_nas==0) | (v2_who_glob_no_nas==1), 
                            rowMeans(v2_whoqol_dom_glob_df,na.rm=T)*4,NA)

v2_whoqol_dom_glob<-round(v2_whoqol_dom_glob,2)

summary(v2_whoqol_dom_glob)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.00   14.26   16.00   20.00     768

Physical Health (continuous [4-20],v2_whoqol_dom_phys)

v2_whoqol_dom_phys_df<-data.frame(as.numeric(v2_whoqol_itm3),as.numeric(v2_whoqol_itm10),as.numeric(v2_whoqol_itm16),as.numeric(v2_whoqol_itm15),as.numeric(v2_whoqol_itm17),as.numeric(v2_whoqol_itm4),as.numeric(v2_whoqol_itm18))

v2_who_phys_no_nas<-rowSums(is.na(v2_whoqol_dom_phys_df))

v2_whoqol_dom_phys<-ifelse((v2_who_phys_no_nas==0) | (v2_who_phys_no_nas==1), 
                            rowMeans(v2_whoqol_dom_phys_df,na.rm=T)*4,NA)

v2_whoqol_dom_phys<-round(v2_whoqol_dom_phys,2)

summary(v2_whoqol_dom_phys)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.14   13.14   15.43   15.24   17.71   20.00     783

Psychological (continuous [4-20],v2_whoqol_dom_psy)

v2_whoqol_dom_psy_df<-data.frame(as.numeric(v2_whoqol_itm5),as.numeric(v2_whoqol_itm7),as.numeric(v2_whoqol_itm19),as.numeric(v2_whoqol_itm11),as.numeric(v2_whoqol_itm26),as.numeric(v2_whoqol_itm6))

v2_who_psy_no_nas<-rowSums(is.na(v2_whoqol_dom_psy_df))

v2_whoqol_dom_psy<-ifelse((v2_who_psy_no_nas==0) | (v2_who_psy_no_nas==1), 
                            rowMeans(v2_whoqol_dom_psy_df,na.rm=T)*4,NA)

v2_whoqol_dom_psy<-round(v2_whoqol_dom_psy,2)

summary(v2_whoqol_dom_psy)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.29   16.67   20.00     784

Social relationships (continuous [4-20],v2_whoqol_dom_soc)

v2_whoqol_dom_soc_df<-data.frame(as.numeric(v2_whoqol_itm20),as.numeric(v2_whoqol_itm22),as.numeric(v2_whoqol_itm21))

v2_who_soc_no_nas<-rowSums(is.na(v2_whoqol_dom_soc_df))

v2_whoqol_dom_soc<-ifelse((v2_who_soc_no_nas==0) | (v2_who_soc_no_nas==1), 
                            rowMeans(v2_whoqol_dom_soc_df,na.rm=T)*4,NA)

v2_whoqol_dom_soc<-round(v2_whoqol_dom_soc,2)

summary(v2_whoqol_dom_soc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.17   16.00   20.00     769

Environment (continuous [4-20],v2_whoqol_dom_env)

v2_whoqol_dom_env_df<-data.frame(as.numeric(v2_whoqol_itm8),as.numeric(v2_whoqol_itm23),as.numeric(v2_whoqol_itm12),as.numeric(v2_whoqol_itm24),as.numeric(v2_whoqol_itm13),as.numeric(v2_whoqol_itm14),as.numeric(v2_whoqol_itm9),as.numeric(v2_whoqol_itm25))

v2_who_env_no_nas<-rowSums(is.na(v2_whoqol_dom_env_df))

v2_whoqol_dom_env<-ifelse((v2_who_env_no_nas==0) | (v2_who_env_no_nas==1), 
                            rowMeans(v2_whoqol_dom_env_df,na.rm=T)*4,NA)

v2_whoqol_dom_env<-round(v2_whoqol_dom_env,2)

summary(v2_whoqol_dom_env)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    6.00   14.50   16.00   15.92   18.00   20.00     781

Create dataset

v2_whoqol<-data.frame(v2_whoqol_itm1,v2_whoqol_itm2,v2_whoqol_itm3,v2_whoqol_itm4,
                      v2_whoqol_itm5,v2_whoqol_itm6,v2_whoqol_itm7,v2_whoqol_itm8,
                      v2_whoqol_itm9,v2_whoqol_itm10,v2_whoqol_itm11,v2_whoqol_itm12,
                      v2_whoqol_itm13,v2_whoqol_itm14,v2_whoqol_itm15,v2_whoqol_itm16,
                      v2_whoqol_itm17,v2_whoqol_itm18,v2_whoqol_itm19,v2_whoqol_itm20,
                      v2_whoqol_itm21,v2_whoqol_itm22,v2_whoqol_itm23,v2_whoqol_itm24,
                      v2_whoqol_itm25,v2_whoqol_itm26,v2_whoqol_dom_glob,
                      v2_whoqol_dom_phys,v2_whoqol_dom_psy,v2_whoqol_dom_soc,
                      v2_whoqol_dom_env)

Visit 2: Create dataframe

v2_df<-data.frame(v2_id,
                  v2_rec,
                  v2_clin_ill_ep,
                  v2_con_problems,
                  v2_dem,
                  v2_ev_prc_fst_ep,
                  v2_suic,
                  v2_leprcp,
                  v2_med,
                  v2_subst,
                  v2_symp_panss,
                  v2_symp_ids_c,
                  v2_symp_ymrs,
                  v2_ill_sev,
                  v2_nrpsy,
                  v2_sf12,
                  v2_med_adh,
                  v2_bdi2,
                  v2_asrm,
                  v2_mss,
                  v2_leq,
                  v2_whoqol)

Visit 3: Data preparation

Read in data of clinical participants

## [1] 1320

Read in data of control participants

## [1] 466

Modify column names

Only include subjects for which data for the first visit is present

v3_clin<-subset(v3_clin, as.character(v3_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v3_clin)[1]

## [1] 1320

v3_con<-subset(v3_con, as.character(v3_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v3_con)[1] 

## [1] 466

Participant identity column (categorical [id], v3_id)

v3_id<-as.factor(c(as.character(v3_clin$mnppsd),as.character(v3_con$mnppsd)))                               

##Create separation column

v3_sep<-rep(as.factor("XXXXX"),(dim(v3_clin)[1]+dim(v3_con)[1]))

Visit 3: Recruitment data

In some participants, an incorrect date of interview was entered into the original phenotype database, which I correct here.

## [1] 20151128

## [1] "20141128"

## [1] "20131123"

## [1] "20121123"

## [1] "20140129"

## [1] "20150128"

## [1] "20141009"

## [1] "20140109"

## [1] "20141229"

## [1] "20131129"

## [1] "20160302"

## [1] "20150810"

## [1] "20150115"

## [1] "20140115"

## [1] 20150222

## [1] "20160222"

## [1] "20111109"

## [1] "20171109"

Date of interview (categorical [year-month-day], v3_interv_date)

v3_interv_date<-c(as.Date(as.character(v3_clin$v3_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v3_con$v3_rekru_visit_rekr_datum), "%Y%m%d"))

Age at third interview (continuous [years], v3_age)

v3_age_years_clin<-as.numeric(substr(v3_clin$v3_ausschluss1_rekr_datum,1,4))-
  as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))

v3_age_years_con<-as.numeric(substr(v3_con$v3_rekru_visit_rekr_datu,1,4))-
  as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))

v3_age_years<-c(v3_age_years_clin,v3_age_years_con)

v3_age<-ifelse(c(as.numeric(substr(v3_clin$v3_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v3_con$v3_rekru_visit_rekr_datu,5,6)))<
                 c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
                   v3_age_years-1,v3_age_years)
summary(v3_age) 

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   19.00   30.00   45.00   43.34   54.00   87.00     845

Create dataset

v3_rec<-data.frame(v3_age,v3_interv_date)

Visit 3: Illness episodes between study visits

Please see Visit 2 for explanation.

Illness episodes since last study visit (only in clinical participants)

“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v3_clin_ill_ep_snc_lst)

v3_clin_ill_ep_snc_lst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_ill_ep_snc_lst<-ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==1,"Y",
                          ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==2,"N",
                            ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==3,"C",v3_clin_ill_ep_snc_lst)))

v3_clin_ill_ep_snc_lst<-factor(v3_clin_ill_ep_snc_lst)                         
descT(v3_clin_ill_ep_snc_lst)

##                -999 C   N    Y    <NA>     
## [1,] No. cases 466  91  386  181  662  1786
## [2,] Percent   26.1 5.1 21.6 10.1 37.1 100

“If yes, how many illness episodes? (continuous [no. illness episodes], v3_clin_no_ep)”

v3_clin_no_ep<-ifelse(v3_clin_ill_ep_snc_lst=="Y",c(v3_clin$v3_aktu_situat_anzahl_episoden,rep(-999,dim(v3_con)[1])),-999)
descT(v3_clin_no_ep)

##                -999 1   2  3   4   <NA>     
## [1,] No. cases 943  136 36 2   1   668  1786
## [2,] Percent   52.8 7.6 2  0.1 0.1 37.4 100

In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).

First illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_man)

v3_clin_fst_ill_ep_man<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_man<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", 
                          -999)

descT(v3_clin_fst_ill_ep_man)

##                -999 Y  <NA>     
## [1,] No. cases 1100 36 650  1786
## [2,] Percent   61.6 2  36.4 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_dep)

v3_clin_fst_ill_ep_dep<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_dep<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", 
                          -999)

descT(v3_clin_fst_ill_ep_dep)

##                -999 Y   <NA>     
## [1,] No. cases 1035 101 650  1786
## [2,] Percent   58   5.7 36.4 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v3_clin_fst_ill_ep_mx<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_mx<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", 
                         -999)

descT(v3_clin_fst_ill_ep_mx)

##                -999 Y   <NA>     
## [1,] No. cases 1124 12  650  1786
## [2,] Percent   62.9 0.7 36.4 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_psy)

v3_clin_fst_ill_ep_psy<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_psy<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", 
                          -999)

descT(v3_clin_fst_ill_ep_psy)

##                -999 Y   <NA>     
## [1,] No. cases 1089 47  650  1786
## [2,] Percent   61   2.6 36.4 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v3_clin_fst_ill_ep_dur)

v3_clin_fst_ill_ep_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_dur<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",
                               ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==2,"two to four weeks", 
                                      ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
                                             ifelse(v3_clin_ill_ep_snc_lst=="N" | v3_clin_ill_ep_snc_lst=="C",-999,v3_clin_fst_ill_ep_dur))))

v3_clin_fst_ill_ep_dur<-ordered(v3_clin_fst_ill_ep_dur, 
                               levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_fst_ill_ep_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 943  32                  44                100                 
## [2,] Percent   52.8 1.8                 2.5               5.6                 
##      <NA>     
## [1,] 667  1786
## [2,] 37.3 100

“During this episode, were you hospitalized?” (dichotomous, v3_clin_fst_ill_ep_hsp)

v3_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="N" | v3_clin_ill_ep_snc_lst=="C",-999,
                          ifelse(v3_clin_ill_ep_snc_lst=="Y" &
                            c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v3_con)[1]))==2,"N",
                              ifelse(v3_clin_ill_ep_snc_lst=="Y" &
                                c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", v3_clin_fst_ill_ep_hsp)))

descT(v3_clin_fst_ill_ep_hsp)

##                -999 N   Y   <NA>     
## [1,] No. cases 943  94  84  665  1786
## [2,] Percent   52.8 5.3 4.7 37.2 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v3_clin_fst_ill_ep_hsp_dur)

v3_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_hsp_dur<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",  
                              ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
                                ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",    
                                            -999)))

v3_clin_fst_ill_ep_hsp_dur<-ifelse((v3_clin_ill_ep_snc_lst=="Y" & is.na(c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1])))) | 
                                 (v3_clin_ill_ep_snc_lst=="N" | v3_clin_ill_ep_snc_lst=="C"),-999, v3_clin_fst_ill_ep_hsp_dur)

v3_clin_fst_ill_ep_hsp_dur<-ordered(v3_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v3_clin_fst_ill_ep_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1046 14                  19                44                  
## [2,] Percent   58.6 0.8                 1.1               2.5                 
##      <NA>     
## [1,] 663  1786
## [2,] 37.1 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):

Reason for hospitalization: symptom worsensing (checkbox [Y], v3_clin_fst_ill_ep_symp_wrs)

v3_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_symp_wrs<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y",-999)
  
descT(v3_clin_fst_ill_ep_symp_wrs)

##                -999 Y   <NA>     
## [1,] No. cases 1067 68  651  1786
## [2,] Percent   59.7 3.8 36.5 100

Reason for hospitalization: self-endangerment (checkbox [Y], v3_clin_fst_ill_ep_slf_end)

v3_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_slf_end<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y", 
                              -999)

descT(v3_clin_fst_ill_ep_slf_end)

##                -999 Y   <NA>     
## [1,] No. cases 1126 10  650  1786
## [2,] Percent   63   0.6 36.4 100

Reason for hospitalization: suicidality (checkbox [Y], v3_clin_fst_ill_ep_suic)

v3_clin_fst_ill_ep_suic<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_suic<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y", 
                           -999)

descT(v3_clin_fst_ill_ep_suic)

##                -999 Y   <NA>     
## [1,] No. cases 1125 11  650  1786
## [2,] Percent   63   0.6 36.4 100

Reason for hospitalization: endangerment of others (checkbox [Y], v3_clin_fst_ill_ep_oth_end)

v3_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_oth_end<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)

descT(v3_clin_fst_ill_ep_oth_end)

##                -999 Y   <NA>     
## [1,] No. cases 1133 3   650  1786
## [2,] Percent   63.4 0.2 36.4 100

Reason for hospitalization: medication change (checkbox [Y], v3_clin_fst_ill_ep_med_chg)

v3_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_med_chg<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)

descT(v3_clin_fst_ill_ep_med_chg)

##                -999 Y   <NA>     
## [1,] No. cases 1125 11  650  1786
## [2,] Percent   63   0.6 36.4 100

Reason for hospitalization: other (checkbox [Y], v3_clin_fst_ill_ep_othr)

v3_clin_fst_ill_ep_othr<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_othr<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
 
descT(v3_clin_fst_ill_ep_othr)

##                -999 Y   <NA>     
## [1,] No. cases 1126 10  650  1786
## [2,] Percent   63   0.6 36.4 100

Second illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_man)

v3_clin_sec_ill_ep_man<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_man<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",-999)

descT(v3_clin_sec_ill_ep_man)

##                -999 Y   <NA>     
## [1,] No. cases 965  6   815  1786
## [2,] Percent   54   0.3 45.6 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_dep) #frstill

v3_clin_sec_ill_ep_dep<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_dep<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",
                          -999)

descT(v3_clin_sec_ill_ep_dep)

##                -999 Y   <NA>     
## [1,] No. cases 952  19  815  1786
## [2,] Percent   53.3 1.1 45.6 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v3_clin_sec_ill_ep_mx<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_mx<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y", 
                         -999)

descT(v3_clin_sec_ill_ep_mx)

##                -999 Y   <NA>     
## [1,] No. cases 970  1   815  1786
## [2,] Percent   54.3 0.1 45.6 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_psy)

v3_clin_sec_ill_ep_psy<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_psy<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y", 
                          -999)

descT(v3_clin_sec_ill_ep_psy)

##                -999 Y   <NA>     
## [1,] No. cases 968  3   815  1786
## [2,] Percent   54.2 0.2 45.6 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v3_clin_sec_ill_ep_dur)

v3_clin_sec_ill_ep_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_dur<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==1,"less than two weeks", 
                           ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",    
                              ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",  
                                ifelse(v3_clin_ill_ep_snc_lst=="N",-999,v3_clin_sec_ill_ep_dur))))

v3_clin_sec_ill_ep_dur<-ifelse((v3_clin_ill_ep_snc_lst=="Y" & is.na(c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1])))) | 
                                 v3_clin_ill_ep_snc_lst=="N" | v3_clin_ill_ep_snc_lst=="C",-999, v3_clin_sec_ill_ep_dur)

v3_clin_sec_ill_ep_dur<-ordered(v3_clin_sec_ill_ep_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_sec_ill_ep_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1098 7                   7                 12                  
## [2,] Percent   61.5 0.4                 0.4               0.7                 
##      <NA>     
## [1,] 662  1786
## [2,] 37.1 100

“During this episode, were you hospitalized?” (dichotomous, v3_clin_sec_ill_ep_hsp)

v3_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="N" | v3_clin_ill_ep_snc_lst=="C",-999,
                          ifelse(v3_clin_ill_ep_snc_lst=="Y" &
                            c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v3_con)[1]))==2,"N",
                              ifelse(v3_clin_ill_ep_snc_lst=="Y" &
                                c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y", v3_clin_sec_ill_ep_hsp)))

descT(v3_clin_sec_ill_ep_hsp)

##                -999 N   Y   <NA>     
## [1,] No. cases 943  14  11  818  1786
## [2,] Percent   52.8 0.8 0.6 45.8 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v3_clin_sec_ill_ep_hsp_dur)

v3_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_hsp_dur<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",  
                              ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
                                ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",    
                                            -999)))

v3_clin_sec_ill_ep_hsp_dur<-ifelse((v3_clin_ill_ep_snc_lst=="Y" & is.na(c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1])))) | 
                                 (v3_clin_ill_ep_snc_lst=="N" | v3_clin_ill_ep_snc_lst=="C"),-999, v3_clin_sec_ill_ep_hsp_dur)

v3_clin_sec_ill_ep_hsp_dur<-ordered(v3_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v3_clin_sec_ill_ep_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1114 3                   4                 3                   
## [2,] Percent   62.4 0.2                 0.2               0.2                 
##      <NA>     
## [1,] 662  1786
## [2,] 37.1 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes): Reason for hospitalization: symptom worsensing (checkbox [Y], v3_clin_sec_ill_ep_symp_wrs)

v3_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_symp_wrs<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",-999)
  
descT(v3_clin_sec_ill_ep_symp_wrs)

##                -999 Y   <NA>     
## [1,] No. cases 964  7   815  1786
## [2,] Percent   54   0.4 45.6 100

Reason for hospitalization: self-endangerment (checkbox [Y], v3_clin_sec_ill_ep_slf_end)

v3_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_slf_end<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y", 
                              -999)

descT(v3_clin_sec_ill_ep_slf_end)

##                -999 Y   <NA>     
## [1,] No. cases 970  1   815  1786
## [2,] Percent   54.3 0.1 45.6 100

Reason for hospitalization: suicidality (checkbox [Y], v3_clin_sec_ill_ep_suic)

v3_clin_sec_ill_ep_suic<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_suic<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y", 
                           -999)

descT(v3_clin_sec_ill_ep_suic)

##                -999 Y   <NA>     
## [1,] No. cases 969  2   815  1786
## [2,] Percent   54.3 0.1 45.6 100

Reason for hospitalization: endangerment of others (checkbox [Y], v3_clin_sec_ill_ep_oth_end)

v3_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_oth_end<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)

descT(v3_clin_sec_ill_ep_oth_end)

##                -999 <NA>     
## [1,] No. cases 971  815  1786
## [2,] Percent   54.4 45.6 100

Reason for hospitalization: medication change (checkbox [Y], v3_clin_sec_ill_ep_med_chg)

v3_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_med_chg<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)

descT(v3_clin_sec_ill_ep_med_chg)

##                -999 Y   <NA>     
## [1,] No. cases 968  3   815  1786
## [2,] Percent   54.2 0.2 45.6 100

Reason for hospitalization: other (checkbox [Y], v3_clin_sec_ill_ep_othr)

v3_clin_sec_ill_ep_othr<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_othr<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
 
descT(v3_clin_sec_ill_ep_othr)

##                -999 <NA>     
## [1,] No. cases 971  815  1786
## [2,] Percent   54.4 45.6 100

Additional psychiatric hospitalization as in- or daypatient? (dichotomous, v3_clin_add_oth_hsp)

v3_clin_add_oth_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_add_oth_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_aufent,rep(-999,dim(v3_con)[1]))==1,"Y","N")

descT(v3_clin_add_oth_hsp)

##                N    Y  <NA>     
## [1,] No. cases 1102 18 666  1786
## [2,] Percent   61.7 1  37.3 100

If yes, how many other hospitalizations? (continous [no. of hospitalizations], v3_clin_oth_hsp_nmb)

v3_clin_oth_hsp_nmb<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_oth_hsp_nmb<-ifelse(v3_clin_add_oth_hsp=="Y",
          c(v3_clin$v3_aktu_situat_aenderung_anzahl,rep(-999,dim(v3_con)[1])),-999)

descT(v3_clin_oth_hsp_nmb)

##                -999 1   2   <NA>     
## [1,] No. cases 1102 10  2   672  1786
## [2,] Percent   61.7 0.6 0.1 37.6 100

If yes, duration of other hospitalizations? (ordinal, v3_clin_oth_hsp_dur)

v3_clin_oth_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_oth_hsp_dur<-
  ifelse(v3_clin_add_oth_hsp=="Y" & 
           c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==1,"less than two weeks", 
   ifelse(v3_clin_add_oth_hsp=="Y" & 
            c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
    ifelse(v3_clin_add_oth_hsp=="Y" & 
             c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
     ifelse(v3_clin_add_oth_hsp=="N",-999,v3_clin_add_oth_hsp))))

v3_clin_oth_hsp_dur<-ifelse((v3_clin_add_oth_hsp=="Y" & is.na(c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1])))),-999, v3_clin_oth_hsp_dur)

v3_clin_oth_hsp_dur<-ordered(v3_clin_oth_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_oth_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1104 3                   8                 5                   
## [2,] Percent   61.8 0.2                 0.4               0.3                 
##      <NA>     
## [1,] 666  1786
## [2,] 37.3 100

If yes, reason for other hospitalization(s) medication change? (checkbox [Y], v3_clin_othr_psy_med)

v3_clin_othr_psy_med<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_othr_psy_med<-ifelse(v3_clin_add_oth_hsp=="Y" & v3_clin_add_oth_hsp=="Y" & 
      c(v3_clin$v3_aktu_situat_aenderung_medikament,rep(-999,dim(v3_con)[1]))==1,"Y",
                        ifelse(v3_clin_add_oth_hsp=="N",-999,v3_clin_othr_psy_med))
  
descT(v3_clin_othr_psy_med)

##                -999 Y   <NA>     
## [1,] No. cases 1102 4   680  1786
## [2,] Percent   61.7 0.2 38.1 100

Current psychiatric treatment of both clinical and control participants (ordinal [1,2,3,4], v3_cur_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.

v3_clin_cur_psy_trm<-rep(NA,dim(v3_clin)[1])
v3_con_cur_psy_trm<-rep(NA,dim(v3_con)[1])

v3_clin_cur_psy_trm<-ifelse(v3_clin$v3_aktu_situat_psybehandlung==0,"1",
                        ifelse(v3_clin$v3_aktu_situat_psybehandlung==3,"2", 
                          ifelse(v3_clin$v3_aktu_situat_psybehandlung==2,"3",
                            ifelse(v3_clin$v3_aktu_situat_psybehandlung==1,"4",v3_clin_cur_psy_trm)))) 

v3_con_cur_psy_trm<-ifelse(v3_con$v3_bildung_beruf_psybehandlung==0,"1",
                      ifelse(v3_con$v3_bildung_beruf_psybehandlung==3,"2",
                        ifelse(v3_con$v3_bildung_beruf_psybehandlung==2,"3",
                          ifelse(v3_con$v3_bildung_beruf_psybehandlung==1,"4",v3_con_cur_psy_trm))))

v3_cur_psy_trm<-factor(c(v3_clin_cur_psy_trm,v3_con_cur_psy_trm),ordered=T)
descT(v3_cur_psy_trm)

##                1   2    3   4   <NA>     
## [1,] No. cases 304 559  7   37  879  1786
## [2,] Percent   17  31.3 0.4 2.1 49.2 100

Create dataset

v3_clin_ill_ep<-data.frame(v3_clin_ill_ep_snc_lst,
                           v3_clin_no_ep,
                           v3_clin_fst_ill_ep_man,
                           v3_clin_fst_ill_ep_dep,
                           v3_clin_fst_ill_ep_mx,
                           v3_clin_fst_ill_ep_psy,
                           v3_clin_fst_ill_ep_dur,
                           v3_clin_fst_ill_ep_hsp,
                           v3_clin_fst_ill_ep_hsp_dur,
                           v3_clin_fst_ill_ep_symp_wrs,
                           v3_clin_fst_ill_ep_slf_end,
                           v3_clin_fst_ill_ep_suic,
                           v3_clin_fst_ill_ep_oth_end,
                           v3_clin_fst_ill_ep_med_chg,
                           v3_clin_fst_ill_ep_othr,
                           v3_clin_sec_ill_ep_man,
                           v3_clin_sec_ill_ep_dep,
                           v3_clin_sec_ill_ep_mx,
                           v3_clin_sec_ill_ep_psy,
                           v3_clin_sec_ill_ep_dur,
                           v3_clin_sec_ill_ep_hsp,
                           v3_clin_sec_ill_ep_hsp_dur,
                           v3_clin_sec_ill_ep_symp_wrs,
                           v3_clin_sec_ill_ep_slf_end,
                           v3_clin_sec_ill_ep_suic,
                           v3_clin_sec_ill_ep_oth_end,
                           v3_clin_sec_ill_ep_med_chg,
                           v3_clin_sec_ill_ep_othr,
                           v3_clin_add_oth_hsp,
                           v3_clin_oth_hsp_nmb,
                           v3_clin_oth_hsp_dur,
                           v3_clin_othr_psy_med,
                           v3_cur_psy_trm)

Visit 3: Emotional, psychological, alcohol-related or similar problems between study visits (only in control participants)

“Did you experience emotional, psychological, alcohol-related or similar problems since the last study visit?” (categorical [Y,N], v3_con_problems_snc_lst)

v3_con_problems_snc_lst<-c(rep(-999,dim(v3_clin)[1]),rep(NA,dim(v3_con)[1]))

v3_con_problems_snc_lst<-ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_stand)==1,"Y",
                          ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_stand)==2,"N",v3_con_problems_snc_lst))

v3_con_problems_snc_lst<-factor(v3_con_problems_snc_lst)                        
descT(v3_con_problems_snc_lst)

##                -999 N   Y   <NA>     
## [1,] No. cases 1320 214 52  200  1786
## [2,] Percent   73.9 12  2.9 11.2 100

“Since the last study visit, were there psychiatric hospitalizations” (categorical [Y,N], v3_con_psy_snc_lst) This item is -999 in patients.

v3_con_psy_snc_lst<-c(rep(-999,dim(v3_clin)[1]),rep(NA,dim(v3_con)[1]))
v3_con_psy_snc_lst<-ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_tstbehand)==1,"Y",
                           ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_tstbehand)==2,"N",v3_con_psy_snc_lst))
v3_con_psy_snc_lst<-factor(v3_con_psy_snc_lst)

descT(v3_con_psy_snc_lst)

##                -999 N    Y   <NA>     
## [1,] No. cases 1320 262  2   202  1786
## [2,] Percent   73.9 14.7 0.1 11.3 100

“If yes, how many?” (continuous, [number of hospitalizations] ,v3_con_no_psy_hosp) This item is -999 in patients and -999 in controls that negated the previous question.

v3_con_no_psy_hosp<-ifelse(v3_con_psy_snc_lst==-999,-999,
                           ifelse(v3_con_psy_snc_lst=="N",-999,
                                  ifelse(v3_con_psy_snc_lst=="Y",c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_anzahl),NA)))
descT(v3_con_no_psy_hosp)

##                -999 1   <NA>     
## [1,] No. cases 1582 1   203  1786
## [2,] Percent   88.6 0.1 11.4 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v3_con_psy_hosp_dur)

v3_con_psy_hosp_dur<-c(ifelse((v3_con_psy_snc_lst==-999) | (v3_con_psy_snc_lst=="N"),-999,
                        ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_dauer)==1,"less than two weeks",
                          ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_dauer)==2,"two to four weeks",
                            ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_dauer)==3,"more than four weeks",NA)))))

v3_con_psy_hosp_dur<-as.factor(v3_con_psy_hosp_dur)
descT(v3_con_psy_hosp_dur)

##                -999 more than four weeks <NA>     
## [1,] No. cases 1582 1                    203  1786
## [2,] Percent   88.6 0.1                  11.4 100

“If yes, due to medication change?” (checkbox [Y], v3_con_psy_hosp_why_medchg)

v3_con_psy_hosp_why_medchg<-ifelse((v3_con_psy_snc_lst==-999) | (v3_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_medikament)==1,"Y",-999))
                          
v3_con_psy_hosp_why_medchg<-as.factor(v3_con_psy_hosp_why_medchg)
descT(v3_con_psy_hosp_why_medchg)

##                -999 <NA>     
## [1,] No. cases 1584 202  1786
## [2,] Percent   88.7 11.3 100

“If yes, due to other circumstances?” (checkbox [Y], v3_con_psy_hosp_why_medchg_why_othr)

v3_con_psy_hosp_why_othr<-ifelse((v3_con_psy_snc_lst==-999) | (v3_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_sonstiges)==1,"Y",-999))
                          
v3_con_psy_hosp_why_othr<-as.factor(v3_con_psy_hosp_why_othr)
descT(v3_con_psy_hosp_why_othr)

##                -999 Y   <NA>     
## [1,] No. cases 1583 1   202  1786
## [2,] Percent   88.6 0.1 11.3 100

“If yes and due to other circumstances, which circumstances?” (character [text], v3_con_psy_hosp_why_othr_txt)

v3_con_psy_hosp_why_othr_txt<-ifelse((v3_con_psy_snc_lst==-999) | (v3_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v3_clin)[1]),v3_con$v3_bildung_beruf_psy_grund)==1,"Y",-999))
                          
v3_con_psy_hosp_why_othr_txt<-as.factor(v3_con_psy_hosp_why_othr_txt)
descT(v3_con_psy_hosp_why_othr_txt)

##                -999 <NA>     
## [1,] No. cases 1583 203  1786
## [2,] Percent   88.6 11.4 100

Create dataset

v3_con_problems<-data.frame(v3_con_problems_snc_lst,v3_con_psy_snc_lst,v3_con_no_psy_hosp,
                            v3_con_psy_hosp_dur,v3_con_psy_hosp_why_medchg,v3_con_psy_hosp_why_othr,
                            v3_con_psy_hosp_why_othr_txt)

Visit 3: Demographic information

See Visit 1 marital status item for general explanation of the next two items.

Did your marital status change since the last study visit? (dichotomous, v3_cng_mar_stat)

v3_clin_cng_mar_stat<-rep(NA,dim(v3_clin)[1]) 
v3_clin_cng_mar_stat<-ifelse(v3_clin$v3_aktu_situat_fam_stand==1, "Y", 
                        ifelse(v3_clin$v3_aktu_situat_fam_stand==2, "N", v3_clin_cng_mar_stat))

v3_con_cng_mar_stat<-rep(NA,dim(v3_con)[1]) 
v3_con_cng_mar_stat<-ifelse(v3_con$v3_famil_wohn_fam_stand==1, "Y", 
                        ifelse(v3_con$v3_famil_wohn_fam_stand==2, "N", v3_con_cng_mar_stat))

v3_cng_mar_stat<-factor(c(v3_clin_cng_mar_stat,v3_con_cng_mar_stat))

Marital status (categorical [married, married but living separately, single, divorced, widowed], v3_marital_stat)

v3_clin_marital_stat<-rep(NA,dim(v3_clin)[1])
v3_clin_marital_stat<-ifelse(v3_clin$v3_aktu_situat_fam_familienstand==1,"Married", 
                 ifelse(v3_clin$v3_aktu_situat_fam_familienstand==2,"Married_living_sep",
                 ifelse(v3_clin$v3_aktu_situat_fam_familienstand==3,"Single",
                 ifelse(v3_clin$v3_aktu_situat_fam_familienstand==4,"Divorced",
                 ifelse(v3_clin$v3_aktu_situat_fam_familienstand==5,"Widowed",v3_clin_marital_stat)))))

v3_con_marital_stat<-rep(NA,dim(v3_con)[1])
v3_con_marital_stat<-ifelse(v3_con$v3_famil_wohn_fam_famstand==1,"Married", 
                 ifelse(v3_con$v3_famil_wohn_fam_famstand==2,"Married_living_sep",
                 ifelse(v3_con$v3_famil_wohn_fam_famstand==3,"Single",
                 ifelse(v3_con$v3_famil_wohn_fam_famstand==4,"Divorced",
                 ifelse(v3_con$v3_famil_wohn_fam_famstand==5,"Widowed",v3_con_marital_stat)))))

v3_marital_stat<-factor(c(v3_clin_marital_stat,v3_con_marital_stat))
desc(v3_marital_stat)

##                Divorced Married Married_living_sep Single Widowed NA's     
## [1,] No. cases 130      227     38                 523    12      856  1786
## [2,] Percent   7.3      12.7    2.1                29.3   0.7     47.9 100

Relationship status

“Do you currently have a partner?” (dichotomous, v3_partner)

v3_clin_partner<-rep(NA,dim(v3_clin)[1])
v3_clin_partner<-ifelse(v3_clin$v3_aktu_situat_fam_fest_partner==1,"Y",
            ifelse(v3_clin$v3_aktu_situat_fam_fest_partner==2,"N",v3_clin_partner))

v3_con_partner<-rep(NA,dim(v3_con)[1])
v3_con_partner<-ifelse(v3_con$v3_famil_wohn_fam_partner==1,"Y",
            ifelse(v3_con$v3_famil_wohn_fam_partner==2,"N",v3_con_partner))


v3_partner<-factor(c(v3_clin_partner,v3_con_partner))
descT(v3_partner)

##                N    Y    <NA>     
## [1,] No. cases 449  470  867  1786
## [2,] Percent   25.1 26.3 48.5 100

Children

Biological (continuous [number], v3_no_bio_chld)

v3_no_bio_chld<-c(v3_clin$v3_aktu_situat_fam_kind_gesamt,v3_con$v3_famil_wohn_fam_lkind)
descT(v3_no_bio_chld)

##                0    1   2   3   4   5   <NA>     
## [1,] No. cases 582  158 113 64  11  2   856  1786
## [2,] Percent   32.6 8.8 6.3 3.6 0.6 0.1 47.9 100

Non-biological

Adoptive children (continuous [number], v3_no_adpt_chld)

v3_no_adpt_chld<-c(v3_clin$v3_aktu_situat_fam_adopt_gesamt,v3_con$v3_famil_wohn_fam_adkind)
descT(v3_no_adpt_chld)  

##                0    1   2   <NA>     
## [1,] No. cases 921  2   1   862  1786
## [2,] Percent   51.6 0.1 0.1 48.3 100

Step children (continuous [number], v3_stp_chld)

v3_stp_chld<-c(v3_clin$v3_aktu_situat_fam_stift_gesamt,v3_con$v3_famil_wohn_fam_skind)
descT(v3_stp_chld)      

##                0    1   2   3   4   <NA>     
## [1,] No. cases 837  45  19  4   2   879  1786
## [2,] Percent   46.9 2.5 1.1 0.2 0.1 49.2 100

Change in housing situation since last study visit? (dichotomous, v3_chg_hsng)

v3_clin_chg_hsng<-rep(NA,dim(v3_clin)[1])
v3_clin_chg_hsng<-ifelse(v3_clin$v3_wohnsituation_wohn_aenderung==1,"Y",
                  ifelse(v3_clin$v3_wohnsituation_wohn_aenderung==2,"N",v3_clin_chg_hsng))  

v3_con_chg_hsng<-rep(NA,dim(v3_con)[1])
v3_con_chg_hsng<-ifelse(v3_con$v3_famil_wohn_wohn_stand==1,"Y",
                 ifelse(v3_con$v3_famil_wohn_wohn_stand==2,"N",v3_con_chg_hsng))

v3_chg_hsng<-factor(c(v3_clin_chg_hsng,v3_con_chg_hsng))
descT(v3_chg_hsng)

##                N   Y   <NA>     
## [1,] No. cases 839 98  849  1786
## [2,] Percent   47  5.5 47.5 100

Living alone (dichotomous, v3_liv_aln)

v3_clin_liv_aln<-rep(NA,dim(v3_clin)[1])
v3_clin_liv_aln<-ifelse(v3_clin$v3_wohnsituation_wohn_allein==1,"Y",    
                 ifelse(v3_clin$v3_wohnsituation_wohn_allein==0,"N",v3_clin_liv_aln))   

v3_con_liv_aln<-rep(NA,dim(v3_con)[1])
v3_con_liv_aln<-ifelse(v3_con$v3_famil_wohn_wohn_allein==1,"Y", 
                 ifelse(v3_con$v3_famil_wohn_wohn_allein==0,"N",v3_con_liv_aln))
                 
v3_liv_aln<-factor(c(v3_clin_liv_aln,v3_con_liv_aln))
descT(v3_liv_aln)

##                N    Y    <NA>     
## [1,] No. cases 584  365  837  1786
## [2,] Percent   32.7 20.4 46.9 100

Employment

Did your employment situation change since the last study visit?

v3_clin_chg_empl_stat<-rep(NA,dim(v3_clin)[1]) 
v3_clin_chg_empl_stat<-ifelse(v3_clin$v3_wohnsituation_erwerb_aenderung==1, "Y", 
                  ifelse(v3_clin$v3_wohnsituation_erwerb_aenderung==2, "N",v3_clin_chg_empl_stat))

v3_con_chg_empl_stat<-rep(NA,dim(v3_con)[1]) 
v3_con_chg_empl_stat<-ifelse(v3_con$v3_bildung_beruf_bild_stand==1, "Y", 
                  ifelse(v3_con$v3_bildung_beruf_bild_stand==2, "N",v3_con_chg_empl_stat))

v3_chg_empl_stat<-factor(c(v3_clin_chg_empl_stat,v3_con_chg_empl_stat))
descT(v3_chg_empl_stat)

##                N    Y   <NA>     
## [1,] No. cases 788  131 867  1786
## [2,] Percent   44.1 7.3 48.5 100

Currently paid employment (dichotomous, v3_curr_paid_empl)

Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.

v3_clin_curr_paid_empl<-rep(NA,dim(v3_clin)[1])
v3_clin_curr_paid_empl<-ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==1,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==2,"Y",   
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==3,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==4,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==5,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==6,-999,  
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==7,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==8,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==9,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==10,"Y",  
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==11,"N",v3_clin_curr_paid_empl)))))))))))

v3_con_curr_paid_empl<-rep(NA,dim(v3_con)[1])
v3_con_curr_paid_empl<-ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==1,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==2,"Y",    
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==3,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==4,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==5,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==6,-999,   
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==7,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==8,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==9,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==10,"Y",   
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==11,"N",v3_con_curr_paid_empl)))))))))))

v3_curr_paid_empl<-factor(c(v3_clin_curr_paid_empl,v3_con_curr_paid_empl))
descT(v3_curr_paid_empl)

##                -999 N    Y    <NA>     
## [1,] No. cases 19   435  473  859  1786
## [2,] Percent   1.1  24.4 26.5 48.1 100

Disability pension due to psychological/psychiatric illness (dichotomous, v3_disabl_pens)

NB: Not available (-999) in control participants

v3_clin_disabl_pens<-rep(NA,dim(v3_clin)[1])
v3_clin_disabl_pens<-ifelse(v3_clin$v3_wohnsituation_rente_psych==1,"Y",        
                     ifelse(v3_clin$v3_wohnsituation_rente_psych==2,"N",v3_clin_disabl_pens))       

v3_con_disabl_pens<-rep(-999,dim(v3_con)[1])

v3_disabl_pens<-factor(c(v3_clin_disabl_pens,v3_con_disabl_pens))
descT(v3_disabl_pens)

##                -999 N    Y    <NA>     
## [1,] No. cases 466  282  266  772  1786
## [2,] Percent   26.1 15.8 14.9 43.2 100

Employed in workshop for handicapped persons (dichotomous, v3_spec_emp)

v3_clin_spec_emp<-rep(NA,dim(v3_clin)[1])
v3_clin_spec_emp<-ifelse(v3_clin$v3_wohnsituation_erwerb_werk==1,"Y",           
                  ifelse(v3_clin$v3_wohnsituation_erwerb_werk==2,"N",v3_clin_spec_emp))         

v3_con_spec_emp<-rep(NA,dim(v3_con)[1])
v3_con_spec_emp<-ifelse(v3_con$v3_bildung_beruf_erwerb_wfbm==1,"Y",         
                 ifelse(v3_con$v3_bildung_beruf_erwerb_wfbm==2,"N",v3_con_spec_emp))            


v3_spec_emp<-factor(c(v3_clin_spec_emp,v3_con_spec_emp))
descT(v3_spec_emp)

##                N    Y   <NA>     
## [1,] No. cases 399  62  1325 1786
## [2,] Percent   22.3 3.5 74.2 100

Weeks of work absence due to psychological distress in past six months (continuous [weeks], v3_wrk_abs_pst_6_mths)

Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.

v3_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v3_clin)[1])
v3_clin_wrk_abs_pst_6_mths<-ifelse((v3_clin$v3_wohnsituation_erwerb_unbekannt==1 | v3_clin$v3_wohnsituation_erwerb_rente==1 |  
                                 v3_clin$v3_wohnsituation_erwerb_fehlen>26),-999, v3_clin$v3_wohnsituation_erwerb_fehlen)

v3_con_wrk_abs_pst_6_mths<-rep(NA,dim(v3_con)[1])
v3_con_wrk_abs_pst_6_mths<-ifelse((v3_con$v3_bildung_beruf_erwerb_ausfallu==1 | v3_con$v3_bildung_beruf_erwerb_rente==1 |  
                                 v3_con$v3_bildung_beruf_erwerb_ausfallm>26),-999, v3_con$v3_bildung_beruf_erwerb_ausfallm)

v3_wrk_abs_pst_6_mths<-c(v3_clin_wrk_abs_pst_6_mths,v3_con_wrk_abs_pst_6_mths)
descT(v3_wrk_abs_pst_6_mths)

##                -999 0    1   2   3   4   6   7   8   9   10  11  12  13  14 
## [1,] No. cases 337  265  12  15  9   6   3   2   9   1   2   1   4   1   1  
## [2,] Percent   18.9 14.8 0.7 0.8 0.5 0.3 0.2 0.1 0.5 0.1 0.1 0.1 0.2 0.1 0.1
##      15  16  17  18  24  26  <NA>     
## [1,] 1   2   1   1   15  4   1094 1786
## [2,] 0.1 0.1 0.1 0.1 0.8 0.2 61.3 100

Currently impaired by psychological/psychiatric symptoms in exercising profession (dichotomous, v3_cur_work_restr)

Important: if receiving pension, this question refers to impairments in the household

v3_clin_cur_work_restr<-rep(NA,dim(v3_clin)[1])
v3_clin_cur_work_restr<-ifelse(v3_clin$v3_wohnsituation_erwerb_psysymptom==1,"Y",   
                    ifelse(v3_clin$v3_wohnsituation_erwerb_psysymptom==2,"N",v3_clin_cur_work_restr))    

v3_con_cur_work_restr<-rep(NA,dim(v3_con)[1])
v3_con_cur_work_restr<-ifelse(v3_con$v3_bildung_beruf_erwerb_psyeinsch==1,"Y",  
                    ifelse(v3_con$v3_bildung_beruf_erwerb_psyeinsch==2,"N",v3_con_cur_work_restr))   

v3_cur_work_restr<-factor(c(v3_clin_cur_work_restr,v3_con_cur_work_restr))
descT(v3_cur_work_restr)

##                N    Y    <NA>     
## [1,] No. cases 577  284  925  1786
## [2,] Percent   32.3 15.9 51.8 100

Self-reported Weight (continuous [kilograms], v3_weight)

v3_weight<-c(v3_clin$v3_wohnsituation_erwerb_gewicht,v3_con$v3_bildung_beruf_erwerb_gewicht)
summary(v3_weight)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      41      68      80      83      95     175     862

Waist circumference (continouos [centimeters], v3_waist)

This item was only recorded in a subset of individuals, because the question was introduced while the study was running.

v3_clin_waist<-v3_clin$v3_wohnsituation_erwerb_tailumf
v3_con_waist<-v3_con$v3_bildung_beruf_erwerb_taille

v3_waist<-c(v3_clin_waist,v3_con_waist)
summary(v3_waist)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   60.00   76.00   85.00   89.04  101.00  175.00    1404

BMI (continuous [BMI], v3_bmi)

We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.

v3_bmi<-v3_weight/(v1_height/100)^2
summary(v3_bmi)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.63   22.82   26.40   27.45   30.85   50.78     866

Create dataset

v3_dem<-data.frame(v3_cng_mar_stat,v3_marital_stat,v3_partner,v3_no_bio_chld,v3_no_adpt_chld,v3_stp_chld,v3_chg_hsng,v3_liv_aln,
                    v3_chg_empl_stat,v3_curr_paid_empl,v3_disabl_pens,v3_spec_emp,v3_wrk_abs_pst_6_mths,v3_cur_work_restr,
                    v3_weight,v3_bmi,v3_waist)

Visit 3: Life events precipitating illness episode between study visits

Please see Visit 2 for explanation.

**Life events: Occurred before illness episode? (dichotomous, v3_evnt_prcp_b4_*)**

for(i in 1:length(grep("v3_ergaenz_leq_leq_zeit_31055_",names(v3_clin)))){
  b4_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_zeit_31055_",names(v3_clin))[i]],
                   paste("v3_evnt_prcp_b4_",i,sep=""))
}

**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v3_evnt_prcp_f_*)**

for(i in 1:length(grep("v3_ergaenz_leq_leq_ausloeser_31055_",names(v3_clin)))){
  prcp_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_ausloeser_31055_",names(v3_clin))[i]],
                   paste("v3_evnt_prcp_f_",i,sep=""))
}

**Life events: LEQ item number (categorical [LEQ item number], v3_evnt_prcp_it_*)**

for(i in 1:length(grep("v3_ergaenz_leq_leq_item_31055_",names(v3_clin)))){
  leq_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_item_31055_",names(v3_clin))[i]],
                   paste("v3_evnt_prcp_it_",i,sep=""))
}

Create dataset

v3_leprcp<-data.frame(v3_evnt_prcp_it_1,v3_evnt_prcp_b4_1,v3_evnt_prcp_f_1,
                      v3_evnt_prcp_it_2,v3_evnt_prcp_b4_2,v3_evnt_prcp_f_2,
                      v3_evnt_prcp_it_3,v3_evnt_prcp_b4_3,v3_evnt_prcp_f_3,
                      v3_evnt_prcp_it_4,v3_evnt_prcp_b4_4,v3_evnt_prcp_f_4,
                      v3_evnt_prcp_it_5,v3_evnt_prcp_b4_5,v3_evnt_prcp_f_5,
                      v3_evnt_prcp_it_6,v3_evnt_prcp_b4_6,v3_evnt_prcp_f_6,
                      v3_evnt_prcp_it_7,v3_evnt_prcp_b4_7,v3_evnt_prcp_f_7,
                      v3_evnt_prcp_it_8,v3_evnt_prcp_b4_8,v3_evnt_prcp_f_8,
                      v3_evnt_prcp_it_9,v3_evnt_prcp_b4_9,v3_evnt_prcp_f_9,
                      v3_evnt_prcp_it_10,v3_evnt_prcp_b4_10,v3_evnt_prcp_f_10,
                      v3_evnt_prcp_it_11,v3_evnt_prcp_b4_11,v3_evnt_prcp_f_11,
                      v3_evnt_prcp_it_12,v3_evnt_prcp_b4_12,v3_evnt_prcp_f_12,
                      v3_evnt_prcp_it_13,v3_evnt_prcp_b4_13,v3_evnt_prcp_f_13,
                      v3_evnt_prcp_it_14,v3_evnt_prcp_b4_14,v3_evnt_prcp_f_14,
                      v3_evnt_prcp_it_15,v3_evnt_prcp_b4_15,v3_evnt_prcp_f_15,
                      v3_evnt_prcp_it_16,v3_evnt_prcp_b4_16,v3_evnt_prcp_f_16,
                      v3_evnt_prcp_it_17,v3_evnt_prcp_b4_17,v3_evnt_prcp_f_17,
                      v3_evnt_prcp_it_18,v3_evnt_prcp_b4_18,v3_evnt_prcp_f_18,
                      v3_evnt_prcp_it_19,v3_evnt_prcp_b4_19,v3_evnt_prcp_f_19,
                      v3_evnt_prcp_it_20,v3_evnt_prcp_b4_20,v3_evnt_prcp_f_20,
                      v3_evnt_prcp_it_21,v3_evnt_prcp_b4_21,v3_evnt_prcp_f_21,
                      v3_evnt_prcp_it_22,v3_evnt_prcp_b4_22,v3_evnt_prcp_f_22,
                      v3_evnt_prcp_it_23,v3_evnt_prcp_b4_23,v3_evnt_prcp_f_23,
                      v3_evnt_prcp_it_24,v3_evnt_prcp_b4_24,v3_evnt_prcp_f_24,
                      v3_evnt_prcp_it_25,v3_evnt_prcp_b4_25,v3_evnt_prcp_f_25,
                      v3_evnt_prcp_it_26,v3_evnt_prcp_b4_26,v3_evnt_prcp_f_26,
                      v3_evnt_prcp_it_27,v3_evnt_prcp_b4_27,v3_evnt_prcp_f_27,
                      v3_evnt_prcp_it_28,v3_evnt_prcp_b4_28,v3_evnt_prcp_f_28,
                      v3_evnt_prcp_it_29,v3_evnt_prcp_b4_29,v3_evnt_prcp_f_29,
                      v3_evnt_prcp_it_30,v3_evnt_prcp_b4_30,v3_evnt_prcp_f_30,
                      v3_evnt_prcp_it_31,v3_evnt_prcp_b4_31,v3_evnt_prcp_f_31)

Visit 3: Suicide attempts and suicidal ideation since last study visit

Here, we used a modified version of section X of the SCID, that was assessed at visit 1 (lifetime assessment of suicidality). Specifically, we slightly changed the wording of the items, so that they covered the time window from the last study visit until the current assessment.

Suicidal ideation

Suicidal ideation since last study visit (dichotomous, v3_suic_ide_snc_lst_vst)

Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.

v3_suic_ide_snc_lst_vst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_suic_ide_snc_lst_vst<-ifelse(c(v3_clin$v3_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v3_con)[1]))==1, "N", 
                            ifelse(c(v3_clin$v3_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v3_con)[1]))==3, "Y",                                                 v3_suic_ide_snc_lst_vst))

v3_suic_ide_snc_lst_vst<-factor(v3_suic_ide_snc_lst_vst)
descT(v3_suic_ide_snc_lst_vst)

##                -999 N    Y   <NA>     
## [1,] No. cases 466  485  169 666  1786
## [2,] Percent   26.1 27.2 9.5 37.3 100

Suicidal ideation detailed (ordinal [1,2,3,4], v3_scid_suic_ide)

This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v3_scid_suic_ide<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_scid_suic_ide<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==1, "1",
                         ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==2, "2",
                                ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==3, "3",
                                       ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==4, "4",-999))))

v3_scid_suic_ide<-factor(v3_scid_suic_ide,ordered=T)                                   
descT(v3_scid_suic_ide)

##                -999 1  2   3   4   <NA>     
## [1,] No. cases 951  89 21  28  31  666  1786
## [2,] Percent   53.2 5  1.2 1.6 1.7 37.3 100

Thoughts about methods (ordinal [1,2,3], v3_scid_suic_thght_mth)

This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.

v3_scid_suic_thght_mth<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_scid_suic_thght_mth<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==1, "1",
                         ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==2, "2",
                                ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==3, "3",-999)))

v3_scid_suic_thght_mth<-factor(v3_scid_suic_thght_mth,ordered=T)                                   
descT(v3_scid_suic_thght_mth)

##                -999 1   2  3   <NA>     
## [1,] No. cases 951  69  54 39  673  1786
## [2,] Percent   53.2 3.9 3  2.2 37.7 100

Suicidal ideation: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v3_scid_suic_note_thgts)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v3_scid_suic_note_thgts<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_scid_suic_note_thgts<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==1, "1",
                         ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==2, "2",
                                ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==3, "3",
                                       ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==4, "4",-999))))

v3_scid_suic_note_thgts<-factor(v3_scid_suic_note_thgts,ordered=T)                                   
descT(v3_scid_suic_note_thgts)

##                -999 1   2   3   4   <NA>     
## [1,] No. cases 951  149 5   2   6   673  1786
## [2,] Percent   53.2 8.3 0.3 0.1 0.3 37.7 100

Suicide attemps

Suicide attempt since last study visit (ordinal [1,2,3], v3_suic_attmpt_snc_lst_vst)

This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.

v3_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_suic_attmpt_snc_lst_vst<-ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==1, "1",
                         ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==2, "2",
                                ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==3, "3",-999)))

v3_suic_attmpt_snc_lst_vst<-factor(v3_suic_attmpt_snc_lst_vst,ordered=T)                                   
descT(v3_suic_attmpt_snc_lst_vst)

##                -999 1    2   3   <NA>     
## [1,] No. cases 466  629  2   13  676  1786
## [2,] Percent   26.1 35.2 0.1 0.7 37.8 100

Number of suicide attempts (ordinal [1,2,3,4,5,6], v3_no_suic_attmpt)

This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.

v3_no_suic_attmpt<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_no_suic_attmpt<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999, ifelse(v3_suic_attmpt_snc_lst_vst>1, c(v3_clin$v3_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v3_con)[1])),v3_no_suic_attmpt))

v3_no_suic_attmpt<-factor(v3_no_suic_attmpt,ordered=T)
descT(v3_no_suic_attmpt)

##                -999 1   2   <NA>     
## [1,] No. cases 1095 13  2   676  1786
## [2,] Percent   61.3 0.7 0.1 37.8 100

Preparation of suicide attempt (ordinal [1,2,3,4], v3_prep_suic_attp_ord)

This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.

v3_prep_suic_attp_ord<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_prep_suic_attp_ord<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999, 
                              ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==1, "1",
                              ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==2, "2",             
                              ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==3, "3",
                              ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==4, "4",
                              v3_prep_suic_attp_ord))))) 

v3_prep_suic_attp_ord<-factor(v3_prep_suic_attp_ord,ordered=T)
descT(v3_prep_suic_attp_ord)

##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1095 7   3   2   3   676  1786
## [2,] Percent   61.3 0.4 0.2 0.1 0.2 37.8 100

Suicidal attempt: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v3_suic_note_attmpt)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v3_suic_note_attmpt<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_suic_note_attmpt<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999, 
                            ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==1, "1",
                            ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==2, "2",
                            ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==3, "3",
                            ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==4, "4",
                            v3_suic_note_attmpt))))) 

v3_suic_note_attmpt<-factor(v3_suic_note_attmpt,ordered=T)
descT(v3_suic_note_attmpt)

##                -999 1   3   4   <NA>     
## [1,] No. cases 1095 8   1   3   679  1786
## [2,] Percent   61.3 0.4 0.1 0.2 38   100

Create dataset

v3_suic<-data.frame(v3_suic_ide_snc_lst_vst,v3_scid_suic_ide,v3_scid_suic_thght_mth,v3_scid_suic_note_thgts,
                    v3_suic_attmpt_snc_lst_vst,v3_no_suic_attmpt,v3_prep_suic_attp_ord,
                    v3_suic_note_attmpt)

Visit 3: Medication

As in the fist visit, the code below creates the following variables that summarize the medication of each individual:

Number of antidepressants prescribed (continuous [number], v3_Antidepressants)
Number of antipsychotics prescribed (continuous [number], v3_Antipsychotics)
Number of mood stabilizers prescribed (continuous [number], v3_Mood_stabilizers)
Number of tranquilizers prescribed (continuous [number], v3_Tranquilizers)
Number of other psychiatric medications (continuous [number], v3_Other_psychiatric)

Clinical participants

#get the following variables from v3_clin
#1. Medication name     ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_1"]
#6. Bedarf category     ["_bedarf_kategorie_1"]

v3_clin_medication_variables_1<-as.data.frame(v3_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v3_clin))])
dim(v3_clin_medication_variables_1) 

## [1] 1320   61

#recode the variables that are coded as characters/logicals in the "v3_clin_medication_variables_1" as factors
v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_15<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_15)

v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_15<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_15)

v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_16<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_16)

v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_16<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_16)

v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_17<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_17)

v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_17<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_17)

v3_clin_medication_variables_1$v3_medikabehand3_depot_medi_200170_3<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_depot_medi_200170_3)

v3_clin_medication_variables_1$v3_medikabehand3_depot_kategorie_200170_3<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_depot_kategorie_200170_3)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_8<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_8)
v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_8<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_8)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_9<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_9)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_9)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_10<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_10)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_10)

#make the duplicated data frame
v3_clin_medications_duplicated_1<-as.data.frame(t(apply(v3_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v3_clin_medications_duplicated_1)

## [1] 1320   30

#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_clin, not as character
v3_clin_medication_variables_1[,!c(TRUE, FALSE)][v3_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v3_clin_medication_variables_1) 

## [1] 1320   61

#bind columns id and medication names, but not categories together 
v3_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v3_clin_medication_variables_1[,1], v3_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v3_clin_medication_name_1) 

## [1] 1320   31

#get the medication categories from the "_medication_variables_1" dataframe
v3_clin_medication_categories_1<-as.data.frame(v3_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v3_clin_medication_categories_1) 

## [1] 1320   31

#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_clin, not as character 
#Important: v3_clin_medication_name_1=="NA" replaced with is.na(v3_clin_medication_name_1)
v3_clin_medication_categories_1[is.na(v3_clin_medication_name_1)] <- NA
#write.csv(v3_clin_medication_categories_1, file="v3_clin_medication_group_1.csv") 

#Make a count table of medications
v3_clin_med_table<-data.frame("mnppsd"=v3_clin$mnppsd)
v3_clin_med_table$v3_Antidepressants<-rowSums(v3_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v3_clin_med_table$v3_Antipsychotics<-rowSums(v3_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v3_clin_med_table$v3_Mood_stabilizers<-rowSums(v3_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v3_clin_med_table$v3_Tranquilizers<-rowSums(v3_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v3_clin_med_table$v3_Other_psychiatric<-rowSums(v3_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Control participants

#get the following variables from v3_con
#1. Medication name     ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_2"]
#6. Bedarf category     ["_bedarf_kategorie_2"]

v3_con_medication_variables_1<-as.data.frame(v3_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v3_con))])
dim(v3_con_medication_variables_1) #[1] 320 29 

## [1] 466  29

#recode the variables that are coded as characters/logicals in the "v3_con_medication_variables_1" as factors
v3_con_medication_variables_1$v3_medikabehand3_med_medi_200705_8<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_med_medi_200705_8)

v3_con_medication_variables_1$v3_medikabehand3_med_kategorie_200705_8<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_med_kategorie_200705_8)

v3_con_medication_variables_1$v3_medikabehand3_bedarf_medi_201187_4<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_bedarf_medi_201187_4)

v3_con_medication_variables_1$v3_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_bedarf_kategorie_201187_4)

#make the duplicated data frame
v3_con_medications_duplicated_1<-as.data.frame(t(apply(v3_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v3_con_medications_duplicated_1) 

## [1] 466  14

#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_con, not as character 
v3_con_medication_variables_1[,!c(TRUE, FALSE)][v3_con_medications_duplicated_1=="TRUE"] <- NA
dim(v3_con_medication_variables_1) 

## [1] 466  29

#bind columns id and medication names, but not categories together 
v3_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v3_con_medication_variables_1[,1], v3_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v3_con_medication_name_1) 

## [1] 466  15

#get the medication categories from the "_medication_variables_1" dataframe
v3_con_medication_categories_1<-as.data.frame(v3_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v3_con_medication_categories_1) 

## [1] 466  15

#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_con, not as character 
#Important: v3_con_medication_name_1=="NA" replaced with is.na(v3_con_medication_name_1)
v3_con_medication_categories_1[is.na(v3_con_medication_name_1)] <- NA
#write.csv(v3_con_medication_categories_1, file="v3_con_medication_group_1.csv")

#Make a count table of medications
v3_con_med_table<-data.frame("mnppsd"=v3_con$mnppsd)
v3_con_med_table$v3_Antidepressants<-rowSums(v3_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v3_con_med_table$v3_Antipsychotics<-rowSums(v3_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v3_con_med_table$v3_Mood_stabilizers<-rowSums(v3_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v3_con_med_table$v3_Tranquilizers<-rowSums(v3_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v3_con_med_table$v3_Other_psychiatric<-rowSums(v3_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Bind v3_clin and v3_con together by rows

v3_drugs<-rbind(v3_clin_med_table,v3_con_med_table)
dim(v3_drugs) 

## [1] 1786    6

#check if the id column of v3_drugs and v1_id match
table(v3_drugs[,1]==v1_id)

## 
## TRUE 
## 1786

Adverse events under current medication (dichotomous, v3_adv)

v3_clin_adv<-ifelse(v3_clin$v3_medikabehand_medi2_nebenwirk==1,"Y","N")
v3_con_adv<-rep("-999",dim(v3_con)[1])
v3_adv<-factor(c(v3_clin_adv,v3_con_adv))
descT(v3_adv)

##                -999 N   Y    <NA>     
## [1,] No. cases 466  160 273  887  1786
## [2,] Percent   26.1 9   15.3 49.7 100

Psychiatric medication change during the past six months (dichotomous, v3_medchange)

v3_clin_medchange<-rep(NA,dim(v3_clin)[1])
v3_clin_medchange<-ifelse(v3_clin$v3_medikabehand_medi3_mediaenderung==1,"Y","N")
v3_con_medchange<-rep("-999",dim(v3_con)[1])

v3_medchange<-as.factor(c(v3_clin_medchange,v3_con_medchange))
descT(v3_medchange)

##                -999 N    Y   <NA>     
## [1,] No. cases 466  187  250 883  1786
## [2,] Percent   26.1 10.5 14  49.4 100

Lithium

Please see the section in Visit 1 for explanation.

“Did you ever take lithium?” (dichotomous, v3_lith)

v3_clin_lith<-rep(NA,dim(v3_clin)[1])
v3_clin_lith<-ifelse(v3_clin$v3_medikabehand_med_zusatz_lithium==1,"Y","N")
v3_con_lith<-rep("-999",dim(v3_con)[1])

v3_lith<-as.factor(c(v3_clin_lith,v3_con_lith))
v3_lith<-as.factor(v3_lith)

descT(v3_lith)

##                -999 N   Y   <NA>     
## [1,] No. cases 466  150 104 1066 1786
## [2,] Percent   26.1 8.4 5.8 59.7 100

“If yes, for how long?” (dichotomous, v3_lith_prd)

Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.

v3_clin_lith_prd<-rep(NA,dim(v3_clin)[1])
v3_con_lith_prd<-rep(-999,dim(v3_con)[1])

v3_clin_lith_prd<-ifelse(v3_clin_lith=="N", -999, ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==2,1,
                  ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==1,2,    
                  ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==0,3,NA))))
                                                     
v3_lith_prd<-factor(c(v3_clin_lith_prd,v3_con_lith_prd))
descT(v3_lith_prd)

##                -999 1   2   3   <NA>     
## [1,] No. cases 616  31  24  49  1066 1786
## [2,] Percent   34.5 1.7 1.3 2.7 59.7 100

Create dataset

v3_med<-data.frame(v3_drugs[,2:6],v3_adv,v3_medchange,v3_lith,v3_lith_prd)

Create datasets with raw medication information

Here, separate datasets for clinical and control participants are created that contain the raw medication information at visit 3, as specified in the phenotype database.

For each medication that the individual took at visit 3 (including non-psychiatric drugs), the information given below is assessed.

The last character of each variable name always refers to the medication in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.

The medications were not assessed in any specific order, i.e. the order was determined by the individual participant (whatever she or he mentioned first). To classify medications, we used a catalogue, from which the categories and subcategories a medication belongs to were selected (see below).

Below, the variable names of clinical/control participants, respectively, are given in quotes, and the coding is explained in the parentheses.

1.Was the individual treated with any medication? (-1-not assessed, 1-yes, 2-no, 99-unknown)
“v3_medikabehand3_keine_med”/“v3_medikabehand3_keine_med”

2. Regular medication: Name of the medication (character)
   “v3_medikabehand3_med_medi_199998”/“v3_medikabehand3_med_medi_200705”

3. Regular medication: Category to which the medication belongs (character)
   “v3_medikabehand3_med_kategorie_199998”/“v3_medikabehand3_med_kategorie_200705”

4. Regular medication: Subcategory to which the medication belongs (character)
   “v3_medikabehand3_med_kategorie_sub_199998”/“v3_medikabehand3_med_kategorie_sub_200705”

5. Regular medication: Psychiatric medication? (0-no, 1-yes) “v3_medikabehand3_med_zusatz_199998”/“v3_medikabehand3_med_zusatz_200705”

6. Regular medication: Dose in the morning (unitless)
   “v3_medikabehand3_s_medi1_morgens_199998”/“v3_medikabehand3_s_medi1_morgens_200705”

7. Regular medication: Dose at midday (unitless)
   “v3_medikabehand3_smedi1_mittags_199998”/“v3_medikabehand3_smedi1_mittags_200705”

8. Regular medication: Dose in the evening (unitless)
   “v3_medikabehand3_smedi1_abends_199998”/“v3_medikabehand3_smedi1_abends_200705”

9. Regular medication: Dose at night (unitless)
   “v3_medikabehand3_smedi1_nachts_199998”/“v3_medikabehand3_smedi1_nachts_200705”

10. Regular medication: Unit of the medication asked in the last four questions (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
    “v3_medikabehand3_smedi1_einheit_199998”/“v3_medikabehand3_smedi1_einheit_200705”

11. Regular medication: Total dose of the medication per day (unitless)
    “v3_medikabehand3_smedi1_gesamtdosis_199998”/“v3_medikabehand3_smedi1_gesamtdosis_200705”

12. Regular medication: Unit of the medication asked in the last question (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
    “v3_medikabehand3_smedi1_einheit1_199998”/“v3_medikabehand3_smedi1_einheit1_200705”

13. Regular medication: Medication name, if not contained in our catalog (character)
    “v3_medikabehand3_medikament_text_199998”/“v3_medikabehand3_medikament_text_200705”

14. Depot medication: Name of the medication (character) “v3_medikabehand3_depot_medi_200170”/“v3_medikabehand3_depot_medi_201224

15. Depot medication: Category to which the medication belongs (character) “v3_medikabehand3_depot_kategorie_200170”/“v3_medikabehand3_depot_kategorie_201224

16. Depot medication: Subcategory to which the medication belongs (character)
    “v3_medikabehand3_depot_kategorie_sub_200170”/“v3_medikabehand3_depot_kategorie_sub_201224

17. Depot medication: Psychiatric medication? (0-no, 1-yes) “v3_medikabehand3_depot_zusatz_200170”/“v3_medikabehand3_depot_zusatz_201224”

18. Depot medication: Total Dose (unitless) “v3_medikabehand3_s_depot_gesamtdosis_200170”/“v3_medikabehand3_s_depot_gesamtdosis_201224”

19. Depot medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v3_medikabehand3_s_depot_einheit_200170”/ “v3_medikabehand3_s_depot_einheit_201224”

20. Interval, at which the depot medication is given (days) “v3_medikabehand3_s_depot_tage_200170”/“v3_medikabehand3_s_depot_tage_201224”

21. Medication name, if not contained in our catalog (character) “v3_medikabehand3_medikament_text_200170”/“v3_medikabehand3_medikament_text_201224”

22. Pro re nata (PRN) medication: Name of the medication (character) “v3_medikabehand3_bedarf_medi_199584”/“v3_medikabehand3_bedarf_medi_201187”

23. Pro re nata (PRN) medication: Category to which the medication belongs (character)
    “v3_medikabehand3_bedarf_kategorie_199584”/“v3_medikabehand3_bedarf_kategorie_201187”

24. Pro re nata (PRN) medication: Subcategory to which the medication belongs (character) “v3_medikabehand3_bedarf_kategorie_sub_199584”/“v3_medikabehand3_bedarf_kategorie_sub_201187”

25. Pro re nata (PRN) medication: Psychiatric medication? (0-no, 1-yes) “v3_medikabehand3_bedarf_zusatz_199584”/“v3_medikabehand3_bedarf_zusatz_201187”

26. Pro re nata (PRN) medication: Total dose up to (unitless) “v3_medikabehand3_s_bedarf_gesamtdosis_199584”/“v3_medikabehand3_s_bedarf_kommentar_201187

27. Pro re nata (PRN) medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v3_medikabehand3_s_bedarf_einheit1_199584”/“v3_medikabehand3_s_bedarf_einheit1_201187”

28. Pro re nata (PRN) medication: Comment (character) “v3_medikabehand3_s_bedarf_kommentar_199584”/“v3_medikabehand3_s_bedarf_kommentar_201187”

29. Pro re nata (PRN) medication: Medication name, if not contained in our catalog (character) “v3_medikabehand3_medikament_text_199584”/“v3_medikabehand3_medikament_text_201187”

Make datasets containing only information on medication

v3_med_clin_orig<-data.frame(v3_clin$mnppsd,v3_clin[,147:455])
names(v3_med_clin_orig)[1]<-"v1_id"

v3_med_con_orig<-data.frame(v3_con$mnppsd,v3_con[,75:219])
names(v3_med_con_orig)[1]<-"v1_id"

Save raw medication datasets of visit 3

save(v3_med_clin_orig, file="230614_v6.0_psycourse_clin_raw_med_visit3.RData")
save(v3_med_con_orig, file="230614_v6.0_psycourse_con_raw_med_visit3.RData")

Write .csv file

write.table(v3_med_clin_orig,file="230614_v6.0_psycourse_clin_raw_med_visit3.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v3_med_con_orig,file="230614_v6.0_psycourse_con_raw_med_visit3.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Visit 3: Substance abuse

Tobacco

For more explanation, see Visit 1

“Did you start or stop smoking during the past six months?” (categorical [NS,NN,YSP,YST], v3_smk_strt_stp)

This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.

v3_clin_smk_strt_stp<-rep(NA,dim(v3_clin)[1])
v3_clin_smk_strt_stp<-ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==1,"NS",
                        ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==2,"NN",
                          ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==3,"YSP",
                            ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==4,"YST",v3_clin_smk_strt_stp))))
       
#ATTENTION: answering alternative: e-cigarette only in controls
v3_con_smk_strt_stp<-rep(NA,dim(v3_clin)[1])
v3_con_smk_strt_stp<-ifelse(v3_con$v3_tabalk_folge_tabak1==1 | v3_con$v3_tabalk_folge_tabak1==2,"NS",
                        ifelse(v3_con$v3_tabalk_folge_tabak1==3,"NN",
                          ifelse(v3_con$v3_tabalk_folge_tabak1==4,"YSP",
                            ifelse(v3_con$v3_tabalk_folge_tabak1==5,"YST",v3_con_smk_strt_stp))))
                        
v3_smk_strt_stp<-c(v3_clin_smk_strt_stp,v3_con_smk_strt_stp)
descT(v3_smk_strt_stp)

##                NN   NS   YSP YST <NA>     
## [1,] No. cases 318  577  30  10  851  1786
## [2,] Percent   17.8 32.3 1.7 0.6 47.6 100

“How many cigarettes do you presently smoke on average?” (continuous [number cigarettes], v3_no_cig)

In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.

Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.

v3_no_cig<-c(rep(NA,dim(v3_clin)[1]),rep(NA,dim(v3_con)[1]))

v3_no_cig<-ifelse((v3_smk_strt_stp=="NN" | v3_smk_strt_stp=="YSP"), -999, 
            ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") & 
                     c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==1,                                
                     c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*365,
            ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") & 
                     c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==2,                                
                     c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*52,
            ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") & 
                     c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==3,                                
                     c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*12,
                      v3_no_cig))))

summary(v3_no_cig[v3_no_cig>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0    3650    5475    5990    7300   73000    1085

Alcohol

“How often did you consume alcoholic beverages during the past six months?” (ordinal [1,2,3,4,5,6,7], v3_alc_pst6_mths)

This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.

v3_alc_pst6_mths<-c(v3_clin$v3_tabalk1_ta9_alkkonsum,v3_con$v3_tabalk_folge_alkohol4)
v3_alc_pst6_mths<-factor(v3_alc_pst6_mths, ordered=T)

descT(v3_alc_pst6_mths)

##                1    2    3   4    5   6   7   <NA>     
## [1,] No. cases 210  183  100 223  130 47  43  850  1786
## [2,] Percent   11.8 10.2 5.6 12.5 7.3 2.6 2.4 47.6 100

“On how many occasions during the past six months did you drink FIVE (men)/FOUR (women) or more alcoholic beverages?”” (ordinal [1,2,3,4,5,6,7,8,9], v3_alc_5orm)

This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.

v3_alc_5orm<-ifelse(v3_alc_pst6_mths<4,-999,
                    ifelse(is.na(c(v3_clin$v3_tabalk1_ta10_alk_haeufigk_m1,v3_con$v3_tabalk_folge_alkohol5))==T,   
                            c(v3_clin$v3_tabalk1_ta11_alk_haeufigk_f1,v3_con$v3_tabalk_folge_alkohol6),
                            c(v3_clin$v3_tabalk1_ta10_alk_haeufigk_m1,v3_con$v3_tabalk_folge_alkohol5)))

v3_alc_5orm<-factor(v3_alc_5orm, ordered=T)

descT(v3_alc_5orm)

##                -999 1   2   3   4   5   6   7  8   9   <NA>     
## [1,] No. cases 493  175 76  62  34  20  40  18 4   9   855  1786
## [2,] Percent   27.6 9.8 4.3 3.5 1.9 1.1 2.2 1  0.2 0.5 47.9 100

Illicit drugs

For more information see visit 2.

“During the past six months, did you take ANY illicit drugs?” (dichotomous, v3_pst6_ill_drg)

v3_pst6_ill_drg<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_pst6_ill_drg<-ifelse(c(v3_clin$v3_drogen1_dg1_konsum,v3_con$v3_drogen_folge_drogenkonsum)==2, "Y", "N")

descT(v3_pst6_ill_drg)

##                N   Y   <NA>     
## [1,] No. cases 857 77  852  1786
## [2,] Percent   48  4.3 47.7 100

Create dataset

v3_subst<-data.frame(v3_smk_strt_stp,
                     v3_no_cig,
                     v3_alc_pst6_mths,
                     v3_alc_5orm,
                     v3_pst6_ill_drg)

Create dataset with raw illicit drug information

Here, separate datasets for clinical and control participants are created that contain the raw information on illicit drugs at visit 3, exactly as specified in the phenotype database.

For each illicit drug ever taken, the information given below is assessed.

The last character of each variable name always refers to the drug in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.

The drugs are not assessed in any specific order, i.e. the order is determined by the individual participant (whatever she or he mentions first).

Below, the variable names of clinical/control participants are given in quotes, and the coding is explained in the parentheses.

1. Whether the individual consumed illicit drugs since the last visit. (Coding: 1-no, 2-yes) “v3_drogen1_dg1_konsum”/“v3_drogen_folge_drogenkonsum”

2. The name of the drug: (character) “v3_drogen1_s_dg_droge_28483”/“v3_drogen_folge_droge_117794”

The category to which the drug belongs (each item below is a checkbox: 0-not checked, 1-checked):
3. Stimulants: “v3_drogen1_s_dg_drogekt1_28483”/“v3_drogen_folge_droge1_117794”
4. Cannabis: “v3_drogen2_s_dg_drogekt1_28483”/“v3_drogen_folge_droge2_117794” 5. Opiates and pain reliefers: “v3_drogen3_s_dg_drogekt1_28483”/“v3_drogen_folge_droge3_117794”
6. Cocaine: “v3_drogen4_s_dg_drogekt1_28483”/“v3_drogen_folge_droge4_117794”
7. Hallucinogens: “v3_drogen5_s_dg_drogekt1_28483”/“v3_drogen_folge_droge5_117794”
8. Inhalants: “v3_drogen6_s_dg_drogekt1_28483”/“v3_drogen_folge_droge6_117794”
9. Tranquilizers: “v3_drogen7_s_dg_drogekt1_28483”/“v3_drogen_folge_droge7_117794”
10. Other: “v3_drogen8_s_dg_drogekt1_28483”/“v3_drogen_folge_droge8_117794”

11. “Referring to the time since the last study visit, how often did you consume it?” “v3_drogen1_s_dga_haeufigk_28483”/“v3_drogen_folge_droge_haeufig_117794”

The coding is given below:
1 - tried 1 time
2 - less than once a month
3 - about once a month
4 - at least 2 times but less than 10 times a month
5 - at least 10 times a month

12. “Referring to the period of time since the last study visit, did you have to take more of the drug to achieve the same effect?” (Coding: 1-no, 2-yes) “v3_drogen1_s_dgf_l6m_dosis_28483”/“v3_drogen_folge_droge_dosis_117794”

Important: There is an error in the original phenotype database, that affects the coding of item 10 (above). In all drugs the exports of the phenotype database do not reflect the input into the graphical user interface. Below, the incorrect variable is replaced with the corrected one

Make datasets containing only information on illicit drugs

v3_drg_clin<-v3_clin[,725:780]
v3_drg_con<-v3_con[,315:392]

Clinical participants

v3_clin_ill_drugs_orig<-data.frame(v3_clin$mnppsd,v3_drg_clin)
names(v3_clin_ill_drugs_orig)[1]<-"v1_id"

#recode wrongly coded item 10
for(i in c(0:4)){

v3_clin_ill_drugs_orig[,12+i*11]<-ifelse(v3_clin_ill_drugs_orig[,12+i*11]==5,1,
                               ifelse(v3_clin_ill_drugs_orig[,12+i*11]==4,5,
                                ifelse(v3_clin_ill_drugs_orig[,12+i*11]==3,4,
                                 ifelse(v3_clin_ill_drugs_orig[,12+i*11]==2,3,
                                  ifelse(v3_clin_ill_drugs_orig[,12+i*11]==1,2,NA)))))}

Control participants

v3_con_ill_drugs_orig<-data.frame(v3_con$mnppsd,v3_drg_con)
names(v3_con_ill_drugs_orig)[1]<-"v1_id"

#recode wrongly coded item 10
for(i in c(0:6)){

v3_con_ill_drugs_orig[,12+i*11]<-ifelse(v3_con_ill_drugs_orig[,12+i*11]==5,1,
                               ifelse(v3_con_ill_drugs_orig[,12+i*11]==4,5,
                                ifelse(v3_con_ill_drugs_orig[,12+i*11]==3,4,
                                 ifelse(v3_con_ill_drugs_orig[,12+i*11]==2,3,
                                  ifelse(v3_con_ill_drugs_orig[,12+i*11]==1,2,NA)))))}

Save raw illicit drug dataset from visit 3

save(v3_clin_ill_drugs_orig, file="230614_v6.0_psycourse_clin_raw_ill_drg_visit3.RData")
save(v3_con_ill_drugs_orig, file="230614_v6.0_psycourse_con_raw_ill_drg_visit3.RData")

Write .csv file

write.table(v3_clin_ill_drugs_orig,file="230614_v6.0_psycourse_clin_raw_ill_drg_visit3.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v3_con_ill_drugs_orig,file="230614_v6.0_psycourse_con_raw_ill_drg_visit3.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Visit 3: Symptom rating scales (interviewer rates patient)

PANSS

For more information on the scale, please see Visit 1

Positive subscale

P1 Delusions (ordinal [1,2,3,4,5,6,7], v3_panss_p1)

v3_panss_p1<-c(v3_clin$v3_panss_p_p1_wahnideen,v3_con$v3_panss_p_p1_wahnideen)
v3_panss_p1<-factor(v3_panss_p1, ordered=T)

descT(v3_panss_p1)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 718  39  64  30  14  6   1   914  1786
## [2,] Percent   40.2 2.2 3.6 1.7 0.8 0.3 0.1 51.2 100

P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v3_panss_p2)

v3_panss_p2<-c(v3_clin$v3_panss_p_p2_form_denkst,v3_con$v3_panss_p_p2_form_denkst)
v3_panss_p2<-factor(v3_panss_p2, ordered=T)

descT(v3_panss_p2)

##                1   2   3  4   5   7   <NA>     
## [1,] No. cases 661 66  89 45  10  1   914  1786
## [2,] Percent   37  3.7 5  2.5 0.6 0.1 51.2 100

P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v3_panss_p3)

v3_panss_p3<-c(v3_clin$v3_panss_p_p3_halluz,v3_con$v3_panss_p_p3_halluz)
v3_panss_p3<-factor(v3_panss_p3, ordered=T)

descT(v3_panss_p3)

##                1   2   3   4   5   6   <NA>     
## [1,] No. cases 785 25  28  20  11  3   914  1786
## [2,] Percent   44  1.4 1.6 1.1 0.6 0.2 51.2 100

P4 Excitement (ordinal [1,2,3,4,5,6,7], v3_panss_p4)

v3_panss_p4<-c(v3_clin$v3_panss_p_p4_erregung,v3_con$v3_panss_p_p4_erregung)
v3_panss_p4<-factor(v3_panss_p4, ordered=T)

descT(v3_panss_p4)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 676  57  112 24  3   914  1786
## [2,] Percent   37.8 3.2 6.3 1.3 0.2 51.2 100

P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v3_panss_p5)

v3_panss_p5<-c(v3_clin$v3_panss_p_p5_groessenideen,v3_con$v3_panss_p_p5_groessenideen)
v3_panss_p5<-factor(v3_panss_p5, ordered=T)

descT(v3_panss_p5)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 808  21  29  11  3   914  1786
## [2,] Percent   45.2 1.2 1.6 0.6 0.2 51.2 100

P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v3_panss_p6)

v3_panss_p6<-c(v3_clin$v3_panss_p_p6_misstr_verfolg,v3_con$v3_panss_p_p6_misstr_verfolg)
v3_panss_p6<-factor(v3_panss_p6, ordered=T)

descT(v3_panss_p6)

##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 711  48  72 25  13  3   914  1786
## [2,] Percent   39.8 2.7 4  1.4 0.7 0.2 51.2 100

P7 Hostility (ordinal [1,2,3,4,5,6,7], v3_panss_p7)

v3_panss_p7<-c(v3_clin$v3_panss_p_p7_feindseligkeit,v3_con$v3_panss_p_p7_feindseligkeit)
v3_panss_p7<-factor(v3_panss_p7, ordered=T)

descT(v3_panss_p7)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 802  34  28  6   1   1   914  1786
## [2,] Percent   44.9 1.9 1.6 0.3 0.1 0.1 51.2 100

PANSS Positive sum score (continuous [7-49], v3_panss_sum_pos)

v3_panss_sum_pos<-as.numeric.factor(v3_panss_p1)+
                  as.numeric.factor(v3_panss_p2)+
                  as.numeric.factor(v3_panss_p3)+
                  as.numeric.factor(v3_panss_p4)+
                  as.numeric.factor(v3_panss_p5)+
                  as.numeric.factor(v3_panss_p6)+
                  as.numeric.factor(v3_panss_p7)

summary(v3_panss_sum_pos)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   7.000   7.000   7.000   9.195  10.000  30.000     914

Negative subscale

N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v3_panss_n1)

v3_panss_n1<-c(v3_clin$v3_panss_n_n1_affektverflachung,v3_con$v3_panss_n_n1_affektverflachung)
v3_panss_n1<-factor(v3_panss_n1, ordered=T)

descT(v3_panss_n1)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 568  83  109 64  41  3   918  1786
## [2,] Percent   31.8 4.6 6.1 3.6 2.3 0.2 51.4 100

N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v3_panss_n2)

v3_panss_n2<-c(v3_clin$v3_panss_n_n2_emot_rueckzug,v3_con$v3_panss_n_n2_emot_rueckzug)
v3_panss_n2<-factor(v3_panss_n2, ordered=T)

descT(v3_panss_n2)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 626  74  87  68  15  2   914  1786
## [2,] Percent   35.1 4.1 4.9 3.8 0.8 0.1 51.2 100

N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v3_panss_n3)

v3_panss_n3<-c(v3_clin$v3_panss_n_n3_mang_aff_rapp,v3_con$v3_panss_n_n3_mang_aff_rapp)
v3_panss_n3<-factor(v3_panss_n3, ordered=T)

descT(v3_panss_n3)

##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 673  71 87  33  7   1   914  1786
## [2,] Percent   37.7 4  4.9 1.8 0.4 0.1 51.2 100

N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v3_panss_n4)

v3_panss_n4<-c(v3_clin$v3_panss_n_n4_soz_pass_apath,v3_con$v3_panss_n_n4_soz_pass_apath)
v3_panss_n4<-factor(v3_panss_n4, ordered=T)

descT(v3_panss_n4)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 628  67  115 39  16  7   914  1786
## [2,] Percent   35.2 3.8 6.4 2.2 0.9 0.4 51.2 100

N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v3_panss_n5)

v3_panss_n5<-c(v3_clin$v3_panss_n_n5_abstr_denken,v3_con$v3_panss_n_n5_abstr_denken)
v3_panss_n5<-factor(v3_panss_n5, ordered=T)

descT(v3_panss_n5)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 583  85  128 56  10  4   920  1786
## [2,] Percent   32.6 4.8 7.2 3.1 0.6 0.2 51.5 100

N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v3_panss_n6)

v3_panss_n6<-c(v3_clin$v3_panss_n_n6_spon_fl_sprache,v3_con$v3_panss_n_n6_spon_fl_sprache)
v3_panss_n6<-factor(v3_panss_n6, ordered=T)

descT(v3_panss_n6)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 720  45  67  28  10  2   914  1786
## [2,] Percent   40.3 2.5 3.8 1.6 0.6 0.1 51.2 100

N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v3_panss_n7)

v3_panss_n7<-c(v3_clin$v3_panss_n_n7_stereotyp_ged,v3_con$v3_panss_n_n7_stereotyp_ged)
v3_panss_n7<-factor(v3_panss_n7, ordered=T)

descT(v3_panss_n7)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 736  51  66  13  3   1   916  1786
## [2,] Percent   41.2 2.9 3.7 0.7 0.2 0.1 51.3 100

PANSS Negative sum score (continuous [7-49], v3_panss_sum_neg)

v3_panss_sum_neg<-as.numeric.factor(v3_panss_n1)+
                  as.numeric.factor(v3_panss_n2)+
                  as.numeric.factor(v3_panss_n3)+
                  as.numeric.factor(v3_panss_n4)+
                  as.numeric.factor(v3_panss_n5)+
                  as.numeric.factor(v3_panss_n6)+
                  as.numeric.factor(v3_panss_n7)

summary(v3_panss_sum_neg)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    9.00   10.65   12.00   34.00     926

General psychopathology subscale

G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v3_panss_g1)

v3_panss_g1<-c(v3_clin$v3_panss_g_g1_sorge_gesundh,v3_con$v3_panss_g_g1_sorge_gesundh)
v3_panss_g1<-factor(v3_panss_g1, ordered=T)

descT(v3_panss_g1)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 650  91  88  32  7   2   916  1786
## [2,] Percent   36.4 5.1 4.9 1.8 0.4 0.1 51.3 100

G2 Anxiety (ordinal [1,2,3,4,5,6,7], v3_panss_g2)

v3_panss_g2<-c(v3_clin$v3_panss_g_g2_angst,v3_con$v3_panss_g_g2_angst)
v3_panss_g2<-factor(v3_panss_g2, ordered=T)

descT(v3_panss_g2)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 600  55  155 41  19  1   1   914  1786
## [2,] Percent   33.6 3.1 8.7 2.3 1.1 0.1 0.1 51.2 100

G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v3_panss_g3)

v3_panss_g3<-c(v3_clin$v3_panss_g_g3_schuldgefuehle,v3_con$v3_panss_g_g3_schuldgefuehle)
v3_panss_g3<-factor(v3_panss_g3, ordered=T)

descT(v3_panss_g3)

##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 693  36 88  39  14  1   915  1786
## [2,] Percent   38.8 2  4.9 2.2 0.8 0.1 51.2 100

G4 Tension (ordinal [1,2,3,4,5,6,7], v3_panss_g4)

v3_panss_g4<-c(v3_clin$v3_panss_g_g4_anspannung,v3_con$v3_panss_g_g4_anspannung)
v3_panss_g4<-factor(v3_panss_g4, ordered=T)

descT(v3_panss_g4)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 611  84  113 51  10  2   915  1786
## [2,] Percent   34.2 4.7 6.3 2.9 0.6 0.1 51.2 100

G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v3_panss_g5)

v3_panss_g5<-c(v3_clin$v3_panss_g_g5_manier_koerperh,v3_con$v3_panss_g_g5_manier_koerperh)
v3_panss_g5<-factor(v3_panss_g5, ordered=T)

descT(v3_panss_g5)

##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 805  35 21  5   3   2   915  1786
## [2,] Percent   45.1 2  1.2 0.3 0.2 0.1 51.2 100

G6 Depression (ordinal [1,2,3,4,5,6,7], v3_panss_g6)

v3_panss_g6<-c(v3_clin$v3_panss_g_g6_depression,v3_con$v3_panss_g_g6_depression)
v3_panss_g6<-factor(v3_panss_g6, ordered=T)

descT(v3_panss_g6)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 540  59  144 84  38  5   1   915  1786
## [2,] Percent   30.2 3.3 8.1 4.7 2.1 0.3 0.1 51.2 100

G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v3_panss_g7)

v3_panss_g7<-c(v3_clin$v3_panss_g_g7_mot_verlangs,v3_con$v3_panss_g_g7_mot_verlangs)
v3_panss_g7<-factor(v3_panss_g7, ordered=T)

descT(v3_panss_g7)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 651  63  104 49  4   1   914  1786
## [2,] Percent   36.5 3.5 5.8 2.7 0.2 0.1 51.2 100

G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v3_panss_g8)

v3_panss_g8<-c(v3_clin$v3_panss_g_g8_unkoop_verh,v3_con$v3_panss_g_g8_unkoop_verh)
v3_panss_g8<-factor(v3_panss_g8, ordered=T)

descT(v3_panss_g8)

##                1    2   3   4   6   <NA>     
## [1,] No. cases 820  22  27  2   1   914  1786
## [2,] Percent   45.9 1.2 1.5 0.1 0.1 51.2 100

G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v3_panss_g9)

v3_panss_g9<-c(v3_clin$v3_panss_g_g9_ungew_denkinh,v3_con$v3_panss_g_g9_ungew_denkinh)
v3_panss_g9<-factor(v3_panss_g9, ordered=T)

descT(v3_panss_g9)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 719  50  73  21  6   2   1   914  1786
## [2,] Percent   40.3 2.8 4.1 1.2 0.3 0.1 0.1 51.2 100

G10 Disorientation (ordinal [1,2,3,4,5,6,7], v3_panss_g10)

v3_panss_g10<-c(v3_clin$v3_panss_g_g10_desorient,v3_con$v3_panss_g_g10_desorient)
v3_panss_g10<-factor(v3_panss_g10, ordered=T)

descT(v3_panss_g10)

##                1    2   3   4   <NA>     
## [1,] No. cases 823  26  19  1   917  1786
## [2,] Percent   46.1 1.5 1.1 0.1 51.3 100

G11 Poor attention (ordinal [1,2,3,4,5,6,7], v3_panss_g11)

v3_panss_g11<-c(v3_clin$v3_panss_g_g11_mang_aufmerks,v3_con$v3_panss_g_g11_mang_aufmerks)
v3_panss_g11<-factor(v3_panss_g11, ordered=T)

descT(v3_panss_g11)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 558  79  162 66  4   1   916  1786
## [2,] Percent   31.2 4.4 9.1 3.7 0.2 0.1 51.3 100

G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v3_panss_g12)

v3_panss_g12<-c(v3_clin$v3_panss_g_g12_mang_urt_einsi,v3_con$v3_panss_g_g12_mang_urt_einsi)

v3_panss_g12<-factor(v3_panss_g12, ordered=T)
descT(v3_panss_g12)

##                1   2   3   4   5   6   7   <NA>     
## [1,] No. cases 751 40  50  22  4   3   2   914  1786
## [2,] Percent   42  2.2 2.8 1.2 0.2 0.2 0.1 51.2 100

G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v3_panss_g13)

v3_panss_g13<-c(v3_clin$v3_panss_g_g13_willensschwae,v3_con$v3_panss_g_g13_willensschwae)
v3_panss_g13<-factor(v3_panss_g13, ordered=T)

descT(v3_panss_g13)

##                1    2   3   4   <NA>     
## [1,] No. cases 774  23  51  24  914  1786
## [2,] Percent   43.3 1.3 2.9 1.3 51.2 100

G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v3_panss_g14)

v3_panss_g14<-c(v3_clin$v3_panss_g_g14_mang_impulsk,v3_con$v3_panss_g_g14_mang_impulsk)
v3_panss_g14<-factor(v3_panss_g14, ordered=T)

descT(v3_panss_g14)

##                1    2   3  4   <NA>     
## [1,] No. cases 760  29  72 11  914  1786
## [2,] Percent   42.6 1.6 4  0.6 51.2 100

G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v3_panss_g15)

v3_panss_g15<-c(v3_clin$v3_panss_g_g15_selbstbezog,v3_con$v3_panss_g_g15_selbstbezog)
v3_panss_g15<-factor(v3_panss_g15, ordered=T)

descT(v3_panss_g15)

##                1   2   3   4   5   <NA>     
## [1,] No. cases 785 45  29  10  3   914  1786
## [2,] Percent   44  2.5 1.6 0.6 0.2 51.2 100

G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v3_panss_g16)

v3_panss_g16<-c(v3_clin$v3_panss_g_g16_aktsoz_vermeid,v3_con$v3_panss_g_g16_aktsoz_vermeid)
v3_panss_g16<-factor(v3_panss_g16, ordered=T)

descT(v3_panss_g16)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 718  45  74  19  15  1   914  1786
## [2,] Percent   40.2 2.5 4.1 1.1 0.8 0.1 51.2 100

PANSS General Psychopathology sum score (continuous [16-112], v3_panss_sum_gen)

v3_panss_sum_gen<-as.numeric.factor(v3_panss_g1)+
                  as.numeric.factor(v3_panss_g2)+
                  as.numeric.factor(v3_panss_g3)+
                  as.numeric.factor(v3_panss_g4)+
                  as.numeric.factor(v3_panss_g5)+
                  as.numeric.factor(v3_panss_g6)+
                  as.numeric.factor(v3_panss_g7)+
                  as.numeric.factor(v3_panss_g8)+
                  as.numeric.factor(v3_panss_g9)+
                  as.numeric.factor(v3_panss_g10)+
                  as.numeric.factor(v3_panss_g11)+
                  as.numeric.factor(v3_panss_g12)+
                  as.numeric.factor(v3_panss_g13)+
                  as.numeric.factor(v3_panss_g14)+
                  as.numeric.factor(v3_panss_g15)+
                  as.numeric.factor(v3_panss_g16)

summary(v3_panss_sum_gen)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.00   16.00   20.00   22.21   26.00   56.00     925

Create PANSS Total score (continuous [30-210], v3_panss_sum_tot)

v3_panss_sum_tot<-v3_panss_sum_pos+v3_panss_sum_neg+v3_panss_sum_gen
summary(v3_panss_sum_tot)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   30.00   31.00   37.00   41.98   49.00  112.00     936

Create dataset

v3_symp_panss<-data.frame(v3_panss_p1,v3_panss_p2,v3_panss_p3,v3_panss_p4,v3_panss_p5,v3_panss_p6,v3_panss_p7,
                          v3_panss_n1,v3_panss_n2,v3_panss_n3,v3_panss_n4,v3_panss_n5,v3_panss_n6,v3_panss_n7,
                          v3_panss_g1,v3_panss_g2,v3_panss_g3,v3_panss_g4,v3_panss_g5,v3_panss_g6,v3_panss_g7,
                          v3_panss_g8,v3_panss_g9,v3_panss_g10,v3_panss_g11,v3_panss_g12,v3_panss_g13,v3_panss_g14,
                          v3_panss_g15,v3_panss_g16,v3_panss_sum_pos,v3_panss_sum_neg,v3_panss_sum_gen,
                          v3_panss_sum_tot)

IDS-C₃₀

For more information on the scale, please see Visit 1

Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v3_idsc_itm1)

v3_idsc_itm1<-c(v3_clin$v3_ids_c_s1_ids1_einschlafschw,v3_con$v3_ids_c_s1_ids1_einschlafschw)
v3_idsc_itm1<-factor(v3_idsc_itm1, ordered=T)

descT(v3_idsc_itm1)

##                0    1   2   3   <NA>     
## [1,] No. cases 618  112 75  60  921  1786
## [2,] Percent   34.6 6.3 4.2 3.4 51.6 100

Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v3_idsc_itm2)

v3_idsc_itm2<-c(v3_clin$v3_ids_c_s1_ids2_naechtl_aufw,v3_con$v3_ids_c_s1_ids2_naechtl_aufw)
v3_idsc_itm2<-factor(v3_idsc_itm2, ordered=T)

descT(v3_idsc_itm2)

##                0    1   2   3   <NA>     
## [1,] No. cases 552  143 98  74  919  1786
## [2,] Percent   30.9 8   5.5 4.1 51.5 100

Item 3 Early morning insomnia (ordinal [0,1,2,3], v3_idsc_itm3)

v3_idsc_itm3<-c(v3_clin$v3_ids_c_s1_ids3_frueh_aufw,v3_con$v3_ids_c_s1_ids3_frueh_aufw)
v3_idsc_itm3<-factor(v3_idsc_itm3, ordered=T)

descT(v3_idsc_itm3)

##                0    1   2   3   <NA>     
## [1,] No. cases 725  62  40  41  918  1786
## [2,] Percent   40.6 3.5 2.2 2.3 51.4 100

Item 4 Hypersomnia (ordinal [0,1,2,3], v3_idsc_itm4)

v3_idsc_itm4<-c(v3_clin$v3_ids_c_s1_ids4_hypersomnie,v3_con$v3_ids_c_s1_ids4_hypersomnie)
v3_idsc_itm4<-factor(v3_idsc_itm4, ordered=T)

descT(v3_idsc_itm4)

##                0    1    2   3  <NA>     
## [1,] No. cases 592  192  66  18 918  1786
## [2,] Percent   33.1 10.8 3.7 1  51.4 100

Item 5 Mood (sad) (ordinal [0,1,2,3], v3_idsc_itm5)

v3_idsc_itm5<-c(v3_clin$v3_ids_c_s1_ids5_stimmung_trgk,v3_con$v3_ids_c_s1_ids5_stimmung_trgk)
v3_idsc_itm5<-factor(v3_idsc_itm5, ordered=T)

descT(v3_idsc_itm5)

##                0    1    2   3   <NA>     
## [1,] No. cases 559  192  76  40  919  1786
## [2,] Percent   31.3 10.8 4.3 2.2 51.5 100

Item 6 Mood (irritable) (ordinal [0,1,2,3], v3_idsc_itm6)

v3_idsc_itm6<-c(v3_clin$v3_ids_c_s1_ids6_stimmung_grzt,v3_con$v3_ids_c_s1_ids6_stimmung_grzt)
v3_idsc_itm6<-factor(v3_idsc_itm6, ordered=T)

descT(v3_idsc_itm6)

##                0    1    2   3  <NA>     
## [1,] No. cases 578  217  52  18 921  1786
## [2,] Percent   32.4 12.2 2.9 1  51.6 100

Item 7 Mood (anxious) (ordinal [0,1,2,3], v3_idsc_itm7)

v3_idsc_itm7<-c(v3_clin$v3_ids_c_s1_ids7_stimmung_agst,v3_con$v3_ids_c_s1_ids7_stimmung_agst)
v3_idsc_itm7<-factor(v3_idsc_itm7, ordered=T)

descT(v3_idsc_itm7)

##                0    1   2  3   <NA>     
## [1,] No. cases 602  162 71 33  918  1786
## [2,] Percent   33.7 9.1 4  1.8 51.4 100

Item 8 Reactivity of mood (ordinal [0,1,2,3], v3_idsc_itm8)

v3_idsc_itm8<-c(v3_clin$v3_ids_c_s1_ids8_reakt_stimmung,v3_con$v3_ids_c_s1_ids8_reakt_stimmung)
v3_idsc_itm8<-factor(v3_idsc_itm8, ordered=T)

descT(v3_idsc_itm8)

##                0    1   2   3   <NA>     
## [1,] No. cases 711  102 29  25  919  1786
## [2,] Percent   39.8 5.7 1.6 1.4 51.5 100

Item 9 Mood Variation (ordinal [0,1,2,3], v3_idsc_itm9)

v3_idsc_itm9<-c(v3_clin$v3_ids_c_s1_ids9_stimmungsschw,v3_con$v3_ids_c_s1_ids9_stimmungsschw)
v3_idsc_itm9<-factor(v3_idsc_itm9, ordered=T)

descT(v3_idsc_itm9)

##                0    1   2   3   <NA>     
## [1,] No. cases 689  77  28  74  918  1786
## [2,] Percent   38.6 4.3 1.6 4.1 51.4 100

Item 9A (categorical [M, A, N], v3_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).

v3_idsc_itm9a_pre<-c(v3_clin$v3_ids_c_s1_ids9a_stimmungsschw,v3_con$v3_ids_c_s1_ids9a_stimmungsschw)

v3_idsc_itm9a<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==1, "M", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==2, "A", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==3, "N", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-factor(v3_idsc_itm9a, ordered=F)

descT(v3_idsc_itm9a)

##                -999 A  M   N   <NA>     
## [1,] No. cases 689  18 92  38  949  1786
## [2,] Percent   38.6 1  5.2 2.1 53.1 100

Item 9B (dichotomous, v3_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.

v3_idsc_itm9b_pre<-c(v3_clin$v3_ids_c_s1_ids9b_stimmungsschw,v3_con$v3_ids_c_s1_ids9b_stimmungsschw)

v3_idsc_itm9b<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm9b<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9b_pre==0, "N", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9b))
v3_idsc_itm9b<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9b_pre==1, "Y", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9b))
v3_idsc_itm9b<-factor(v3_idsc_itm9b, ordered=F)

descT(v3_idsc_itm9b)

##                -999 N  Y   <NA>     
## [1,] No. cases 689  53 74  970  1786
## [2,] Percent   38.6 3  4.1 54.3 100

Item 10 Quality of mood (ordinal [0,1,2,3], v3_idsc_itm10)

v3_idsc_itm10<-c(v3_clin$v3_ids_c_s1_ids10_quali_stimmung,v3_con$v3_ids_c_s1_ids10_quali_stimmung)
v3_idsc_itm10<-factor(v3_idsc_itm10, ordered=T)

descT(v3_idsc_itm10)

##                0    1  2   3   <NA>     
## [1,] No. cases 731  54 25  48  928  1786
## [2,] Percent   40.9 3  1.4 2.7 52   100

Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.

The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.

Item 11 (ordinal [0,1,2,3], v3_idsc_itm11)

v3_idsc_app_verm<-c(v3_clin$v3_ids_c_s2_ids11_appetit_verm,v3_con$v3_ids_c_s2_ids11_appetit_verm)
v3_idsc_app_gest<-c(v3_clin$v3_ids_c_s2_ids12_appetit_steig,v3_con$v3_ids_c_s2_ids12_appetit_steig)

v3_idsc_itm11<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm11<-ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==F, -999,                
                  ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==T,          
                         v3_idsc_app_verm, 
                      ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &    
                     (v3_idsc_app_verm>v3_idsc_app_gest), v3_idsc_app_verm,                                            ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &                                                         (v3_idsc_app_gest>=v3_idsc_app_verm),-999,v3_idsc_itm11)))))

#Important: do not code as factor, see after calculation of sum score!
descT(v3_idsc_itm11)

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 263  503  74  24  5   917  1786
## [2,] Percent   14.7 28.2 4.1 1.3 0.3 51.3 100

Item 12 (ordinal [0,1,2,3], v3_idsc_itm12)

v3_idsc_itm12<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm12<-ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==F,    
                         v3_idsc_app_gest,                
                  ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==T,          
                         -999, 
                      ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &    
                     (v3_idsc_app_verm>v3_idsc_app_gest), -999,                                            
                     ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &                                                         (v3_idsc_app_gest>=v3_idsc_app_verm),
                            v3_idsc_app_gest,v3_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v3_idsc_itm12)

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 606  137 82  25  19  917  1786
## [2,] Percent   33.9 7.7 4.6 1.4 1.1 51.3 100

Item 13 (ordinal [0,1,2,3], v3_idsc_itm13)

v3_idsc_gew_abn<-c(v3_clin$v3_ids_c_s2_ids13_gewichtsabn,v3_con$v3_ids_c_s2_ids13_gewichtsabn)
v3_idsc_gew_zun<-c(v3_clin$v3_ids_c_s2_ids14_gewichtszun,v3_con$v3_ids_c_s2_ids14_gewichtszun)

v3_idsc_itm13<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm13<-ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==F, -999,                
                  ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==T,          
                         v3_idsc_gew_abn, 
                      ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F &    
                     (v3_idsc_gew_abn>v3_idsc_gew_zun), v3_idsc_gew_abn,                                            ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F & (v3_idsc_gew_zun >= v3_idsc_gew_abn),-999,v3_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v3_idsc_itm13)

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 292  465 48  41  22  918  1786
## [2,] Percent   16.3 26  2.7 2.3 1.2 51.4 100

Item 14 (ordinal [0,1,2,3], v3_idsc_itm14)

v3_idsc_itm14<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm14<-ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==F,    
                         v3_idsc_gew_zun,                
                  ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==T,          
                         -999, 
                      ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F &    
                     (v3_idsc_gew_abn>v3_idsc_gew_zun), -999,                                            
                     ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F &                                                         (v3_idsc_gew_zun>=v3_idsc_gew_abn),
                            v3_idsc_gew_zun,v3_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v3_idsc_itm14)

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 576  171 66  32  23  918  1786
## [2,] Percent   32.3 9.6 3.7 1.8 1.3 51.4 100

Item 15 Concentration/decision making (ordinal [0,1,2,3], v3_idsc_itm15)

v3_idsc_itm15<-c(v3_clin$v3_ids_c_s2_ids15_konz_entscheid,v3_con$v3_ids_c_s2_ids15_konz_entscheid)
v3_idsc_itm15<-factor(v3_idsc_itm15, ordered=T)

descT(v3_idsc_itm15)

##                0    1    2   3  <NA>     
## [1,] No. cases 511  224  118 17 916  1786
## [2,] Percent   28.6 12.5 6.6 1  51.3 100

Item 16 Outlook (self) (ordinal [0,1,2,3], v3_idsc_itm16)

v3_idsc_itm16<-c(v3_clin$v3_ids_c_s2_ids16_selbstbild,v3_con$v3_ids_c_s2_ids16_selbstbild)
v3_idsc_itm16<-factor(v3_idsc_itm16, ordered=T)

descT(v3_idsc_itm16)

##                0    1   2   3   <NA>     
## [1,] No. cases 654  137 38  41  916  1786
## [2,] Percent   36.6 7.7 2.1 2.3 51.3 100

Item 17 Outlook (future) (ordinal [0,1,2,3], v3_idsc_itm17)

v3_idsc_itm17<-c(v3_clin$v3_ids_c_s2_ids17_zukunftssicht,v3_con$v3_ids_c_s2_ids17_zukunftssicht)
v3_idsc_itm17<-factor(v3_idsc_itm17, ordered=T)

descT(v3_idsc_itm17)

##                0   1    2   3   <NA>     
## [1,] No. cases 590 202  63  14  917  1786
## [2,] Percent   33  11.3 3.5 0.8 51.3 100

Item 18 Suicidal ideation (ordinal [0,1,2,3], v3_idsc_itm18)

v3_idsc_itm18<-c(v3_clin$v3_ids_c_s2_ids18_selbstmordged,v3_con$v3_ids_c_s2_ids18_selbstmordged)
v3_idsc_itm18<-factor(v3_idsc_itm18, ordered=T)

descT(v3_idsc_itm18)

##                0    1   2   3   <NA>     
## [1,] No. cases 789  42  37  2   916  1786
## [2,] Percent   44.2 2.4 2.1 0.1 51.3 100

Item 19 Involvement (ordinal [0,1,2,3], v3_idsc_itm19)

v3_idsc_itm19<-c(v3_clin$v3_ids_c_s2_ids19_interess_aktiv,v3_con$v3_ids_c_s2_ids19_interess_aktiv)
v3_idsc_itm19<-factor(v3_idsc_itm19, ordered=T)

descT(v3_idsc_itm19)

##                0    1   2   3   <NA>     
## [1,] No. cases 722  104 26  16  918  1786
## [2,] Percent   40.4 5.8 1.5 0.9 51.4 100

Item 20 Energy/fatigability (ordinal [0,1,2,3], v3_idsc_itm20)

v3_idsc_itm20<-c(v3_clin$v3_ids_c_s2_ids20_energ_ermued,v3_con$v3_ids_c_s2_ids20_energ_ermued)
v3_idsc_itm20<-factor(v3_idsc_itm20, ordered=T)

descT(v3_idsc_itm20)

##                0    1   2   3   <NA>     
## [1,] No. cases 587  174 96  13  916  1786
## [2,] Percent   32.9 9.7 5.4 0.7 51.3 100

Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v3_idsc_itm21)

v3_idsc_itm21<-c(v3_clin$v3_ids_c_s3_ids21_vergn_genuss,v3_con$v3_ids_c_s3_ids21_vergn_genuss)
v3_idsc_itm21<-factor(v3_idsc_itm21, ordered=T)

descT(v3_idsc_itm21)

##                0   1   2   3   <NA>     
## [1,] No. cases 733 94  30  11  918  1786
## [2,] Percent   41  5.3 1.7 0.6 51.4 100

Item 22 Sexual interest (ordinal [0,1,2,3], v3_idsc_itm22)

v3_idsc_itm22<-c(v3_clin$v3_ids_c_s3_ids22_sex_interesse,v3_con$v3_ids_c_s3_ids22_sex_interesse)
v3_idsc_itm22<-factor(v3_idsc_itm22, ordered=T)

descT(v3_idsc_itm22)

##                0    1   2   3   <NA>     
## [1,] No. cases 638  64  86  75  923  1786
## [2,] Percent   35.7 3.6 4.8 4.2 51.7 100

Item 23 Psychomotor slowing (ordinal [0,1,2,3], v3_idsc_itm23)

v3_idsc_itm23<-c(v3_clin$v3_ids_c_s3_ids23_psymo_hemm,v3_con$v3_ids_c_s3_ids23_psymo_hemm)
v3_idsc_itm23<-factor(v3_idsc_itm23, ordered=T)

descT(v3_idsc_itm23)

##                0    1   2   3   <NA>     
## [1,] No. cases 711  128 27  3   917  1786
## [2,] Percent   39.8 7.2 1.5 0.2 51.3 100

Item 24 Psychomotor agitation (ordinal [0,1,2,3], v3_idsc_itm24)

v3_idsc_itm24<-c(v3_clin$v3_ids_c_s3_ids24_psymo_agitht,v3_con$v3_ids_c_s3_ids24_psymo_agitht)
v3_idsc_itm24<-factor(v3_idsc_itm24, ordered=T)

descT(v3_idsc_itm24)

##                0    1   2   3   <NA>     
## [1,] No. cases 701  110 50  5   920  1786
## [2,] Percent   39.2 6.2 2.8 0.3 51.5 100

Item 25 Somatic complaints (ordinal [0,1,2,3], v3_idsc_itm25)

v3_idsc_itm25<-c(v3_clin$v3_ids_c_s3_ids25_som_beschw,v3_con$v3_ids_c_s3_ids25_som_beschw)
v3_idsc_itm25<-factor(v3_idsc_itm25, ordered=T)

descT(v3_idsc_itm25)

##                0    1    2   3   <NA>     
## [1,] No. cases 583  216  52  19  916  1786
## [2,] Percent   32.6 12.1 2.9 1.1 51.3 100

Item 26 Sympathetic arousal (ordinal [0,1,2,3], v3_idsc_itm26)

v3_idsc_itm26<-c(v3_clin$v3_ids_c_s3_ids26_veg_erreg,v3_con$v3_ids_c_s3_ids26_veg_erreg)
v3_idsc_itm26<-factor(v3_idsc_itm26, ordered=T)

descT(v3_idsc_itm26)

##                0    1   2   3   <NA>     
## [1,] No. cases 640  169 44  15  918  1786
## [2,] Percent   35.8 9.5 2.5 0.8 51.4 100

Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v3_idsc_itm27)

v3_idsc_itm27<-c(v3_clin$v3_ids_c_s3_ids27_panik_phob,v3_con$v3_ids_c_s3_ids27_panik_phob)
v3_idsc_itm27<-factor(v3_idsc_itm27, ordered=T)

descT(v3_idsc_itm27)

##                0    1   2   3   <NA>     
## [1,] No. cases 777  48  28  14  919  1786
## [2,] Percent   43.5 2.7 1.6 0.8 51.5 100

Item 28 Gastrointestinal (ordinal [0,1,2,3], v3_idsc_itm28)

v3_idsc_itm28<-c(v3_clin$v3_ids_c_s3_ids28_verdauung,v3_con$v3_ids_c_s3_ids28_verdauung)
v3_idsc_itm28<-factor(v3_idsc_itm28, ordered=T)

descT(v3_idsc_itm28)

##                0   1   2   3   <NA>     
## [1,] No. cases 733 84  40  12  917  1786
## [2,] Percent   41  4.7 2.2 0.7 51.3 100

Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v3_idsc_itm29)

v3_idsc_itm29<-c(v3_clin$v3_ids_c_s3_ids29_pers_bezieh,v3_con$v3_ids_c_s3_ids29_pers_bezieh)
v3_idsc_itm29<-factor(v3_idsc_itm29, ordered=T)

descT(v3_idsc_itm29)

##                0    1   2   3   <NA>     
## [1,] No. cases 700  108 43  15  920  1786
## [2,] Percent   39.2 6   2.4 0.8 51.5 100

Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v3_idsc_itm30)

v3_idsc_itm30<-c(v3_clin$v3_ids_c_s3_ids30_schwgf_k_energ,v3_con$v3_ids_c_s3_ids30_schwgf_k_energ)
v3_idsc_itm30<-factor(v3_idsc_itm30, ordered=T)

descT(v3_idsc_itm30)

##                0    1   2   3   <NA>     
## [1,] No. cases 716  102 38  12  918  1786
## [2,] Percent   40.1 5.7 2.1 0.7 51.4 100

Create IDS-C₃₀ total score (continuous [0-84], v3_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.

v3_idsc_sum<-as.numeric.factor(v3_idsc_itm1)+
             as.numeric.factor(v3_idsc_itm2)+
             as.numeric.factor(v3_idsc_itm3)+
             as.numeric.factor(v3_idsc_itm4)+
             as.numeric.factor(v3_idsc_itm5)+
             as.numeric.factor(v3_idsc_itm6)+
             as.numeric.factor(v3_idsc_itm7)+
             as.numeric.factor(v3_idsc_itm8)+
             as.numeric.factor(v3_idsc_itm9)+
             as.numeric.factor(v3_idsc_itm10)+
  
 ifelse(is.na(v3_idsc_itm11)==T & is.na(v3_idsc_itm12)==T, NA, 
        ifelse((v3_idsc_itm11==-999 & v3_idsc_itm12!=-999), v3_idsc_itm12,                
              ifelse((v3_idsc_itm11!=-999 & v3_idsc_itm12==-999),v3_idsc_itm11, NA)))+
  
   ifelse(is.na(v3_idsc_itm13)==T & is.na(v3_idsc_itm14)==T, NA, 
        ifelse((v3_idsc_itm13==-999 & v3_idsc_itm14!=-999), v3_idsc_itm14,                
              ifelse((v3_idsc_itm13!=-999 & v3_idsc_itm14==-999),v3_idsc_itm13, NA)))+
                                                  
             as.numeric.factor(v3_idsc_itm15)+
             as.numeric.factor(v3_idsc_itm16)+
             as.numeric.factor(v3_idsc_itm17)+
             as.numeric.factor(v3_idsc_itm18)+
             as.numeric.factor(v3_idsc_itm19)+
             as.numeric.factor(v3_idsc_itm20)+
             as.numeric.factor(v3_idsc_itm21)+
             as.numeric.factor(v3_idsc_itm22)+
             as.numeric.factor(v3_idsc_itm23)+
             as.numeric.factor(v3_idsc_itm24)+
             as.numeric.factor(v3_idsc_itm25)+
             as.numeric.factor(v3_idsc_itm26)+
             as.numeric.factor(v3_idsc_itm27)+
             as.numeric.factor(v3_idsc_itm28)+
             as.numeric.factor(v3_idsc_itm29)+
             as.numeric.factor(v3_idsc_itm30)

summary(v3_idsc_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    3.00    7.00   10.34   15.00   71.00     966

Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)

v3_idsc_itm11<-factor(v3_idsc_itm11,ordered=T)
v3_idsc_itm12<-factor(v3_idsc_itm12,ordered=T)
v3_idsc_itm13<-factor(v3_idsc_itm13,ordered=T)
v3_idsc_itm14<-factor(v3_idsc_itm14,ordered=T)

Create dataset

v3_symp_ids_c<-data.frame(v3_idsc_itm1,v3_idsc_itm2,v3_idsc_itm3,v3_idsc_itm4,v3_idsc_itm5,v3_idsc_itm6,v3_idsc_itm7,
                          v3_idsc_itm8,v3_idsc_itm9,v3_idsc_itm9a,v3_idsc_itm9b,v3_idsc_itm10,v3_idsc_itm11,v3_idsc_itm12,
                          v3_idsc_itm13,v3_idsc_itm14,v3_idsc_itm15,v3_idsc_itm16,v3_idsc_itm17,v3_idsc_itm18,v3_idsc_itm19,
                          v3_idsc_itm20,v3_idsc_itm21,v3_idsc_itm22,v3_idsc_itm23,v3_idsc_itm24,v3_idsc_itm25,v3_idsc_itm26,
                          v3_idsc_itm27,v3_idsc_itm28,v3_idsc_itm29,v3_idsc_itm30,v3_idsc_sum)

YMRS

For more information on the scale, please see Visit 1

Item 1 Elevated mood (ordinal [0,1,2,3,4], v3_ymrs_itm1)

v3_ymrs_itm1<-c(v3_clin$v3_ymrs_ymrs1_gehob_stimm,v3_con$v3_ymrs_ymrs1_gehob_stimm)
v3_ymrs_itm1<-factor(v3_ymrs_itm1, ordered=T)

descT(v3_ymrs_itm1)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 721  101 44  2   1   917  1786
## [2,] Percent   40.4 5.7 2.5 0.1 0.1 51.3 100

Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v3_ymrs_itm2)

v3_ymrs_itm2<-c(v3_clin$v3_ymrs_ymrs2_gest_aktiv,v3_con$v3_ymrs_ymrs2_gest_aktiv)
v3_ymrs_itm2<-factor(v3_ymrs_itm2, ordered=T)

descT(v3_ymrs_itm2)

##                0    1   2   3   <NA>     
## [1,] No. cases 749  82  30  9   916  1786
## [2,] Percent   41.9 4.6 1.7 0.5 51.3 100

Item 3 Sexual interest (ordinal [0,1,2,3,4], v3_ymrs_itm3)

v3_ymrs_itm3<-c(v3_clin$v3_ymrs_ymrs3_sex_interesse,v3_con$v3_ymrs_ymrs3_sex_interesse)
v3_ymrs_itm3<-factor(v3_ymrs_itm3, ordered=T)

descT(v3_ymrs_itm3)

##                0   1   2   3   <NA>     
## [1,] No. cases 821 21  25  2   917  1786
## [2,] Percent   46  1.2 1.4 0.1 51.3 100

Item 4 Sleep (ordinal [0,1,2,3,4], v3_ymrs_itm4)

v3_ymrs_itm4<-c(v3_clin$v3_ymrs_ymrs4_schlaf,v3_con$v3_ymrs_ymrs4_schlaf)
v3_ymrs_itm4<-factor(v3_ymrs_itm4, ordered=T)

descT(v3_ymrs_itm4)

##                0    1   2   3   <NA>     
## [1,] No. cases 793  42  21  12  918  1786
## [2,] Percent   44.4 2.4 1.2 0.7 51.4 100

Item 5 Irritability (ordinal [0,2,4,6,8], v3_ymrs_itm5)

v3_ymrs_itm5<-c(v3_clin$v3_ymrs_ymrs5_reizbarkeit,v3_con$v3_ymrs_ymrs5_reizbarkeit)
v3_ymrs_itm5<-factor(v3_ymrs_itm5, ordered=T)

descT(v3_ymrs_itm5)

##                0    2   4   6   <NA>     
## [1,] No. cases 721  127 21  1   916  1786
## [2,] Percent   40.4 7.1 1.2 0.1 51.3 100

Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v3_ymrs_itm6)

v3_ymrs_itm6<-c(v3_clin$v3_ymrs_ymrs6_sprechweise,v3_con$v3_ymrs_ymrs6_sprechweise)
v3_ymrs_itm6<-factor(v3_ymrs_itm6, ordered=T)

descT(v3_ymrs_itm6)

##                0    2   4   6   8   <NA>     
## [1,] No. cases 741  56  57  14  1   917  1786
## [2,] Percent   41.5 3.1 3.2 0.8 0.1 51.3 100

Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v3_ymrs_itm7)

v3_ymrs_itm7<-c(v3_clin$v3_ymrs_ymrs7_sprachstoer,v3_con$v3_ymrs_ymrs7_sprachstoer)
v3_ymrs_itm7<-factor(v3_ymrs_itm7, ordered=T)

descT(v3_ymrs_itm7)

##                0    1   2   3   <NA>     
## [1,] No. cases 772  70  22  4   918  1786
## [2,] Percent   43.2 3.9 1.2 0.2 51.4 100

Item 8 Content (ordinal [0,2,4,6,8], v3_ymrs_itm8)

v3_ymrs_itm8<-c(v3_clin$v3_ymrs_ymrs8_inhalte,v3_con$v3_ymrs_ymrs8_inhalte)
v3_ymrs_itm8<-factor(v3_ymrs_itm8, ordered=T)

descT(v3_ymrs_itm8)

##                0    2   4   6   8   <NA>     
## [1,] No. cases 819  23  4   9   12  919  1786
## [2,] Percent   45.9 1.3 0.2 0.5 0.7 51.5 100

Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v3_ymrs_itm9)

v3_ymrs_itm9<-c(v3_clin$v3_ymrs_ymrs9_exp_aggr_verh,v3_con$v3_ymrs_ymrs9_exp_aggr_verh)
v3_ymrs_itm9<-factor(v3_ymrs_itm9, ordered=T)

descT(v3_ymrs_itm9)

##                0    2   4   <NA>     
## [1,] No. cases 837  29  2   918  1786
## [2,] Percent   46.9 1.6 0.1 51.4 100

Item 10 Appearance (ordinal [0,1,2,3,4], v3_ymrs_itm10)

v3_ymrs_itm10<-c(v3_clin$v3_ymrs_ymrs10_erscheinung,v3_con$v3_ymrs_ymrs10_erscheinung)
v3_ymrs_itm10<-factor(v3_ymrs_itm10, ordered=T)

descT(v3_ymrs_itm10)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 783  66  15  3   1   918  1786
## [2,] Percent   43.8 3.7 0.8 0.2 0.1 51.4 100

Item 11 Insight (ordinal [0,1,2,3,4], v3_ymrs_itm11)

v3_ymrs_itm11<-c(v3_clin$v3_ymrs_ymrs11_krkh_einsicht,v3_con$v3_ymrs_ymrs11_krkh_einsicht)
v3_ymrs_itm11<-factor(v3_ymrs_itm11, ordered=T)

descT(v3_ymrs_itm11)

##                0    1   2  3   4   <NA>     
## [1,] No. cases 827  15  17 4   2   921  1786
## [2,] Percent   46.3 0.8 1  0.2 0.1 51.6 100

Create YMRS total score (continuous [0-60], v3_ymrs_sum)

v3_ymrs_sum<-(as.numeric.factor(v3_ymrs_itm1)+
        as.numeric.factor(v3_ymrs_itm2)+
        as.numeric.factor(v3_ymrs_itm3)+
        as.numeric.factor(v3_ymrs_itm4)+
        as.numeric.factor(v3_ymrs_itm5)+
        as.numeric.factor(v3_ymrs_itm6)+
        as.numeric.factor(v3_ymrs_itm7)+
        as.numeric.factor(v3_ymrs_itm8)+
        as.numeric.factor(v3_ymrs_itm9)+
        as.numeric.factor(v3_ymrs_itm10)+
        as.numeric.factor(v3_ymrs_itm11))

summary(v3_ymrs_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   2.212   3.000  30.000     933

Create dataset

v3_symp_ymrs<-data.frame(v3_ymrs_itm1,
                         v3_ymrs_itm2,
                         v3_ymrs_itm3,
                         v3_ymrs_itm4,
                         v3_ymrs_itm5,
                         v3_ymrs_itm6,
                         v3_ymrs_itm7,
                         v3_ymrs_itm8,
                         v3_ymrs_itm9,
                         v3_ymrs_itm10,
                         v3_ymrs_itm11,
                         v3_ymrs_sum)

CGI

Please see Visit 1 for more details and explicit rating instructions.

Illness severity (ordinal [1,2,3,4,5,6,7], v3_cgi_s)

v3_cgi_s<-c(v3_clin$v3_cgi1_cgi1_schweregrad,rep(-999,dim(v3_con)[1]))

v3_cgi_s[v3_cgi_s==0]<- -999
v3_cgi_s<-factor(v3_cgi_s, ordered=T)

descT(v3_cgi_s)

##                -999 1   2   3   4    5   6   7   <NA>     
## [1,] No. cases 466  16  58  215 193  134 32  1   671  1786
## [2,] Percent   26.1 0.9 3.2 12  10.8 7.5 1.8 0.1 37.6 100

Change since last study visit (ordinal [1,2,3,4,5,6,7], v3_cgi_c)

Here, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “very much improved”-1 to “very much worse”-7.

v3_cgi_c<-c(v3_clin$v3_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v3_con)[1]))

v3_cgi_c[v3_cgi_c==0]<- -999
v3_cgi_c<-factor(v3_cgi_c, ordered=T)

descT(v3_cgi_c)

##                -999 1   2  3   4    5   6   7   <NA>     
## [1,] No. cases 482  12  89 129 256  107 16  2   693  1786
## [2,] Percent   27   0.7 5  7.2 14.3 6   0.9 0.1 38.8 100

GAF (continuous [1-100], v3_gaf)

Please see Visit 1 for more details and explicit rating instructions.

v3_gaf<-c(v3_clin$v3_gaf_gaf_code,v3_con$v3_gaf_gaf_code)
v3_gaf[v3_gaf==0]<- -999

summary(v3_gaf[v3_gaf>0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   25.00   55.00   68.00   67.69   81.00  100.00     913

boxplot(v3_gaf[v3_gaf>0 & v1_stat=="CLINICAL"], v3_gaf[v3_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))

Create dataset

v3_ill_sev<-data.frame(v3_cgi_s,v3_cgi_c,v3_gaf)

Visit 3: Neuropsychology (cognitive tests)

There are no differences compared to the test battery assessed in Visit 2.

General comments on the testing (character, v3_nrpsy_com) If there were no comments, this item was coded -999.

Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v3_nrpsy_lng)

v3_nrpsy_lng<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_nrpsy_lng<-ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==0, "mother tongue", 
                     ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==1, "good", 
                            ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==2, "sufficient", 
                              ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==3, "not sufficient",v3_nrpsy_lng))))
                            
v3_nrpsy_lng<-factor(v3_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))

descT(v3_nrpsy_lng)

##                mother tongue good sufficient not sufficient <NA>     
## [1,] No. cases 865           45   4          1              871  1786
## [2,] Percent   48.4          2.5  0.2        0.1            48.8 100

Motivation of the participant (ordinal [“poor”,“average”,“good”], v3_nrpsy_mtv)

v3_nrpsy_mtv_pre<-c(v3_clin$v3_npu1_np_mot,v3_con$v3_npu_folge_np_mot)

v3_nrpsy_mtv<-ifelse(v3_nrpsy_mtv_pre==0, "poor", 
                  ifelse(v3_nrpsy_mtv_pre==1, "average", 
                      ifelse(v3_nrpsy_mtv_pre==2, "good", NA)))

v3_nrpsy_mtv<-factor(v3_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))

descT(v3_nrpsy_mtv)

##                poor average good <NA>     
## [1,] No. cases 14   67      820  885  1786
## [2,] Percent   0.8  3.8     45.9 49.6 100

VLMT

For a description of the test and the variables, see Visit 2.

Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.

VLMT_introcheck (categorical [0, 1, 9], v3_nrpsy_vlmt_check)

v3_nrpsy_vlmt_check<-c(v3_clin$v3_vlmt_vlmt_introcheck1,v3_con$v3_npu_folge_np_vlmt)
descT(v3_nrpsy_vlmt_check)

##                0   1    9   <NA>     
## [1,] No. cases 67  834  29  856  1786
## [2,] Percent   3.8 46.7 1.6 47.9 100

Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v3_nrpsy_vlmt_corr)

v3_nrpsy_vlmt_corr<-c(v3_clin$v3_vlmt_vlmt3_sw_a5d,v3_con$v3_npu_folge_np_vlmt_gl)
summary(v3_nrpsy_vlmt_corr)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   42.00   52.00   51.08   61.00   75.00     915

Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v3_nrpsy_vlmt_lss_d)

v3_nrpsy_vlmt_lss_d<-c(v3_clin$v3_vlmt_vlmt5_aw_ilsd6,v3_con$v3_npu_folge_np_vlmt_vni)
summary(v3_nrpsy_vlmt_lss_d)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -5.000   0.000   1.000   1.706   3.000  14.000     925

Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v3_nrpsy_vlmt_lss_t)

v3_nrpsy_vlmt_lss_t<-c(v3_clin$v3_vlmt_vlmt6_aw_vwd7,v3_con$v3_npu_folge_np_vlmt_vnzv)
summary(v3_nrpsy_vlmt_lss_t)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -5.000   0.000   2.000   1.945   3.000  15.000     935

Recognition performance (corrected for falsely recognized words) (continuous [number of words], v3_nrpsy_vlmt_rec)

v3_nrpsy_vlmt_rec<-c(v3_clin$v3_vlmt_vlmt8_kwl,v3_con$v3_npu_folge_np_vlmt_kw)
summary(v3_nrpsy_vlmt_rec)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   -19.0    10.0    13.0    11.6    15.0    15.0     939

TMT

For a description of the test, see Visit 1.

TMT Part A, time (continuous [seconds], v3_nrpsy_tmt_A_rt)

v3_nrpsy_tmt_A_rt<-c(v3_clin$v3_npu1_tmt_001,v3_con$v3_npu_folge_np_tmt_001)
summary(v3_nrpsy_tmt_A_rt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    9.00   20.00   27.00   30.81   37.00  179.00     872

TMT Part A, errors (continuous [number of errors], v3_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).

v3_nrpsy_tmt_A_err<-c(v3_clin$v3_npu1_tmt_af_001,v3_con$v3_npu_folge_np_tmtfehler_001)
summary(v3_nrpsy_tmt_A_err)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.0903  0.0000  6.0000     878

TMT Part B, time (continuous [seconds], v3_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.

v3_nrpsy_tmt_B_rt<-c(v3_clin$v3_npu1_tmt_002,v3_con$v3_npu_folge_tmt_002) 
v3_nrpsy_tmt_B_rt[v3_nrpsy_tmt_B_rt>300]<-300

summary(v3_nrpsy_tmt_B_rt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    23.0    47.0    63.0    73.3    85.0   300.0     891

TMT Part B, errors (continuous [number of errors], v3_nrpsy_tmt_B_err)

v3_nrpsy_tmt_B_err<-c(v3_clin$v3_npu1_tmt_af_002,v3_con$v3_npu_folge_tmt_af_002)
summary(v3_nrpsy_tmt_B_err)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.5112  1.0000 18.0000     896

Verbal digit span

For a description of the test, see Visit 1.

Forward (continuous [number of items], v3_nrpsy_dgt_sp_frw)

v3_nrpsy_dgt_sp_frw<-c(v3_clin$v3_npu1_zns_001,v3_con$v3_npu_folge_np_wie_001)
summary(v3_nrpsy_dgt_sp_frw)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.000   8.000  10.000   9.765  11.000  16.000     883

Backward (continuous [number of items], v3_nrpsy_dgt_sp_bck)

v3_nrpsy_dgt_sp_bck<-c(v3_clin$v3_npu1_zns_002,v3_con$v3_npu_folge_np_wie_002)
summary(v3_nrpsy_dgt_sp_bck)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00    5.00    6.00    6.63    8.00   14.00     885

DST (continuous [number of correct symbols], v3_nrpsy_dg_sym)

For a description of the test, see Visit 1.

v3_introcheck3<-c(v3_clin$v3_npu1_np_introcheck3,v3_con$v3_npu_folge_np_hawier)
v3_nrpsy_dg_sym_pre<-c(v3_clin$v3_npu1_zst_001,v3_con$v3_npu_folge_np_hawier_001)

v3_nrpsy_dg_sym<-ifelse(v3_introcheck3==1, v3_nrpsy_dg_sym_pre, 
                           ifelse(v3_introcheck3==9,-999,
                                  ifelse(v3_introcheck3==0,NA,NA)))

summary(subset(v3_nrpsy_dg_sym,v3_nrpsy_dg_sym>=0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   15.00   54.00   70.00   69.91   86.00  133.00

Create dataset

v3_nrpsy<-data.frame(v3_nrpsy_com,
                     v3_nrpsy_lng,
                     v3_nrpsy_mtv,
                     v3_nrpsy_vlmt_check,
                     v3_nrpsy_vlmt_corr,
                     v3_nrpsy_vlmt_lss_d,
                     v3_nrpsy_vlmt_lss_t,
                     v3_nrpsy_vlmt_rec,
                     v3_nrpsy_tmt_A_rt,
                     v3_nrpsy_tmt_A_err,
                     v3_nrpsy_tmt_B_rt,
                     v3_nrpsy_tmt_B_err,
                     v3_nrpsy_dgt_sp_frw,
                     v3_nrpsy_dgt_sp_bck,
                     v3_nrpsy_dg_sym)

Visit 3: Questionnaires (patient rates her/himself)

Participants were asked to fill out questionnaires on the following topics: childhood trauma/early life stress (CTS), current medication adherence (compliance), current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 2 and 3) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1 and 2, questionnaires that were not filled out correctly were excluded from the dataset.

SF-12

For explanation, please refer to the section in Visit 1

“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v3_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.

v3_sf12_recode(v3_con$v3_sf12_sf_allgemein,"v3_sf12_itm0")

##                -999 2   3   4   5   6   7   8   9   10  <NA>     
## [1,] No. cases 1320 2   6   7   7   10  32  78  80  37  207  1786
## [2,] Percent   73.9 0.1 0.3 0.4 0.4 0.6 1.8 4.4 4.5 2.1 11.6 100

“In general, would you say your health is…” (ordinal [1,2,3,4,5], v3_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.

v3_sf12_recode(v3_con$v3_sf12_sf1,"v3_sf12_itm1")

##                -999 1   2   3  4   5   <NA>     
## [1,] No. cases 1320 60  128 72 11  3   192  1786
## [2,] Percent   73.9 3.4 7.2 4  0.6 0.2 10.8 100

“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”

“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v3_sf12_itm2) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v3_sf12_recode(v3_con$v3_sf12_sf2,"v3_sf12_itm2")

##                -999 1   2   3    <NA>     
## [1,] No. cases 1320 2   20  252  192  1786
## [2,] Percent   73.9 0.1 1.1 14.1 10.8 100

“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v3_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v3_sf12_recode(v3_con$v3_sf12_sf3,"v3_sf12_itm3")

##                -999 1   2   3    <NA>     
## [1,] No. cases 1320 1   30  243  192  1786
## [2,] Percent   73.9 0.1 1.7 13.6 10.8 100

During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?

“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v3_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.

v3_sf12_recode(v3_con$v3_sf12_sf4,"v3_sf12_itm4")

##                -999 1  2    <NA>     
## [1,] No. cases 1320 36 238  192  1786
## [2,] Percent   73.9 2  13.3 10.8 100

“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v3_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.

v3_sf12_recode(v3_con$v3_sf12_sf5,"v3_sf12_itm5")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 24  248  194  1786
## [2,] Percent   73.9 1.3 13.9 10.9 100

During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?

“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v3_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.

v3_sf12_recode(v3_con$v3_sf12_sf6,"v3_sf12_itm6")

##                -999 1   2   <NA>     
## [1,] No. cases 1320 24  250 192  1786
## [2,] Percent   73.9 1.3 14  10.8 100

“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v3_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.

v3_sf12_recode(v3_con$v3_sf12_sf7,"v3_sf12_itm7")

##                -999 1  2    <NA>     
## [1,] No. cases 1320 18 256  192  1786
## [2,] Percent   73.9 1  14.3 10.8 100

“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v3_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.

v3_sf12_recode(v3_con$v3_sf12_st8,"v3_sf12_itm8")

##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1320 157 61  28  20  7   1   192  1786
## [2,] Percent   73.9 8.8 3.4 1.6 1.1 0.4 0.1 10.8 100

The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS

Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.

“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v3_sf12_itm9)

v3_sf12_recode(v3_con$v3_sf12_st9,"v3_sf12_itm9")

##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1320 28  156 63  24  2   1   192  1786
## [2,] Percent   73.9 1.6 8.7 3.5 1.3 0.1 0.1 10.8 100

“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v3_sf12_itm10)

v3_sf12_recode(v3_con$v3_sf12_st10,"v3_sf12_itm10")

##                -999 1   2   3   4   5   <NA>     
## [1,] No. cases 1320 20  103 75  61  15  192  1786
## [2,] Percent   73.9 1.1 5.8 4.2 3.4 0.8 10.8 100

“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v3_sf12_itm11)

v3_sf12_recode(v3_con$v3_sf12_st11,"v3_sf12_itm11")

##                -999 2   3   4   5   6   <NA>     
## [1,] No. cases 1320 5   12  34  113 110 192  1786
## [2,] Percent   73.9 0.3 0.7 1.9 6.3 6.2 10.8 100

“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v3_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.

There is an error in the phenotype database regarding this item. The answering alternatives 3, 4, and 5 appear as 4, 5, and 6 in the database exports. These errors are corrected below.

v3_sf12_recode(v3_con$v3_sf12_st12,"v3_sf12_itm12")

##                -999 2   4   5  6    <NA>     
## [1,] No. cases 1320 3   15  53 194  201  1786
## [2,] Percent   73.9 0.2 0.8 3  10.9 11.3 100

#recode error in phenotype database
v3_sf12_itm12[v3_sf12_itm12==4]<-3
v3_sf12_itm12[v3_sf12_itm12==5]<-4
v3_sf12_itm12[v3_sf12_itm12==6]<-5

descT(v3_sf12_itm12)

##                -999 2   3   4  5    <NA>     
## [1,] No. cases 1320 3   15  53 194  201  1786
## [2,] Percent   73.9 0.2 0.8 3  10.9 11.3 100

Create dataset

v3_sf12<-data.frame(v3_sf12_itm0,
                    v3_sf12_itm1,
                    v3_sf12_itm2,
                    v3_sf12_itm3,
                    v3_sf12_itm4,
                    v3_sf12_itm5,
                    v3_sf12_itm6,
                    v3_sf12_itm7,
                    v3_sf12_itm8,
                    v3_sf12_itm9,
                    v3_sf12_itm10,
                    v3_sf12_itm11,
                    v3_sf12_itm12)

Childhood Trauma Screener (CTS)

The CTS (David P. Bernstein et al., 2003) used here is a German short version (Grabe et al., 2012) of the CTQ (D. P. Bernstein et al., 1994). It is used as a screening instrument to assess childhood trauma/early life stress. Validated threshold values are available (Glaesmer et al., 2013) to transform these values into a dichotomous scale (childhood trauma/early life stress: yes/no; see below). Each of the five questions is on a five-point scale.
Important: analogous to other questionnaires, we have, as specified in the test manual, reversed the encoding so that, in the present dataset, higher scores on every item indicate a higher level of childhood trauma/early life stress. Do not reverse encoding. Each questions starts with “When I grew up”

CTS single items (ordinal)

1. “…I had the feeling to be loved” (ordinal [1,2,3,4,5], v3_cts_1)
Encoding reversed so that higher scores on each item indicate a higher level of childhood trauma/early life stress. This item measures emotional neglect.

cts_recode(v3_clin$v3_chidlhood_childhood_1,v3_con$v3_chidlhood_childhood_1,"v3_cts_1",1)

##                1    2    3   4   5  NA's     
## [1,] No. cases 273  332  136 114 36 895  1786
## [2,] Percent   15.3 18.6 7.6 6.4 2  50.1 100

2. “…persons in my family hit me so hard that I bruised” (ordinal [1,2,3,4,5], v3_cts_2)
This item measures physical abuse.

cts_recode(v3_clin$v3_chidlhood_childhood_2,v3_con$v3_chidlhood_childhood_2,"v3_cts_2",0)

##                1    2   3   4   5   NA's     
## [1,] No. cases 637  111 73  40  22  903  1786
## [2,] Percent   35.7 6.2 4.1 2.2 1.2 50.6 100

3. “…I had the feeling someone in my family hated me” (ordinal [1,2,3,4,5], v3_cts_3)
This item measures emotional abuse.

cts_recode(v3_clin$v3_chidlhood_childhood_3,v3_con$v3_chidlhood_childhood_3,"v3_cts_3",0)

##                1   2   3   4   5   NA's     
## [1,] No. cases 571 128 82  63  42  900  1786
## [2,] Percent   32  7.2 4.6 3.5 2.4 50.4 100

4. “…someone harassed me sexually” (ordinal [1,2,3,4,5], v3_cts_4)
This items measures sexual abuse.

cts_recode(v3_clin$v3_chidlhood_childhood_4,v3_con$v3_chidlhood_childhood_4,"v3_cts_4",0)

##                1    2   3   4   5   NA's     
## [1,] No. cases 740  65  44  21  10  906  1786
## [2,] Percent   41.4 3.6 2.5 1.2 0.6 50.7 100

5. “…there was someone who took me to the doctor when I needed it” (ordinal [1,2,3,4,5], v3_cts_5) Encoding reversed so that higher scores on each item indicate a higher level of childhood trauma/early life stress. This item measures physical neglect.

cts_recode(v3_clin$v3_chidlhood_childhood_5,v3_con$v3_chidlhood_childhood_5,"v3_cts_5",1)

##                1    2   3   4  5   NA's     
## [1,] No. cases 451  214 128 54 43  896  1786
## [2,] Percent   25.3 12  7.2 3  2.4 50.2 100

Overall assessment whether childhood trauma/early life stress was present or not (dichotomous, v3_cts_els_dic)

This assessment indicates whether a participant suffered from childhood trauma/early life stress or not (see description above). More specifically, if any of the five items exceeded the threshold given by (Glaesmer et al., 2013), an individual was determined to have experienced childhood trauma/early life stress. If individuals filled out the questionnaire incompletely but one item nevertheless passed the threshold, these individuals are included in the present dataset. Only those individuals in which all items are completet and below the threshold are set to “N”.

v3_cts_els_dic<-c(rep(NA,dim(v3_clin)[1]),rep(NA,dim(v3_clin)[1]))
v3_cts_els_dic<-ifelse(v3_cts_1>3 | v3_cts_2>2 | v3_cts_3>2 | v3_cts_4>1 | v3_cts_5>3, "Y",
                      ifelse((is.na(v3_cts_1)==F & is.na(v3_cts_2)==F & is.na(v3_cts_3)==F & 
                                is.na(v3_cts_4)==F & is.na(v3_cts_5)==F),"N", v3_cts_els_dic))

descT(v3_cts_els_dic)

##                N    Y    <NA>     
## [1,] No. cases 511  370  905  1786
## [2,] Percent   28.6 20.7 50.7 100

Assessments of each item whether it exceeds the given theshold for childhood trauma/early life stress (ordinal)

1. “…I had the feeling to be loved” (dichotomous, v3_cts_1_dic)

v3_cts_1_dic<-ifelse(v3_cts_1>3, "Y","N")
descT(v3_cts_1_dic)

##                N    Y   <NA>     
## [1,] No. cases 741  150 895  1786
## [2,] Percent   41.5 8.4 50.1 100

2. “…persons in my family hit me so hard that I bruised” (dichotomous, v3_cts_2_dic)

v3_cts_2_dic<-ifelse(v3_cts_2>2, "Y","N")
descT(v3_cts_2_dic)

##                N    Y   <NA>     
## [1,] No. cases 748  135 903  1786
## [2,] Percent   41.9 7.6 50.6 100

3. “…I had the feeling someone in my family hated me” (dichotomous, v3_cts_3_dic)

v3_cts_3_dic<-ifelse(v3_cts_3>2, "Y","N")
descT(v3_cts_3_dic)

##                N    Y    <NA>     
## [1,] No. cases 699  187  900  1786
## [2,] Percent   39.1 10.5 50.4 100

4. “…someone harassed me sexually” (dichotomous, v3_cts_4_dic)

v3_cts_4_dic<-ifelse(v3_cts_4>1, "Y","N")
descT(v3_cts_4_dic)

##                N    Y   <NA>     
## [1,] No. cases 740  140 906  1786
## [2,] Percent   41.4 7.8 50.7 100

5. “…there was someone who took me to the doctor when I needed it” (dichotomous, v3_cts_5_dic)

v3_cts_5_dic<-ifelse(v3_cts_5>3, "Y","N")
descT(v3_cts_5_dic)

##                N    Y   <NA>     
## [1,] No. cases 793  97  896  1786
## [2,] Percent   44.4 5.4 50.2 100

Create dataset

v3_cts<-data.frame(v3_cts_1,v3_cts_2,v3_cts_3,v3_cts_4,v3_cts_5,v3_cts_els_dic,
                   v3_cts_1_dic,v3_cts_2_dic,v3_cts_3_dic,v3_cts_4_dic,v3_cts_5_dic)

Medication adherence (compliance)

For a description of the questionnaire, see Visit 1.

Past seven days (ordinal [1,2,3,4,5,6], v3_med_pst_wk)

v3_med_chk<-c(v3_clin$v3_compl_verwer_fragebogen,rep(1,dim(v3_con)[1]))
v3_med_pst_wk_pre<-c(v3_clin$v3_compl_psychopharm_7_tag,rep(-999,dim(v3_con)[1]))
  
v3_med_pst_wk<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_med_pst_wk<-ifelse((is.na(v3_med_chk) | v3_med_chk!=2), 
                      v3_med_pst_wk_pre, v3_med_pst_wk)

descT(v3_med_pst_wk)

##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 466  509  62  19  4   3   13  710  1786
## [2,] Percent   26.1 28.5 3.5 1.1 0.2 0.2 0.7 39.8 100

Past six months (ordinal [1,2,3,4,5,6], v3_med_pst_sx_mths)

v3_med_pre<-c(v3_clin$v3_compl_psychopharm_6_mon,rep(-999,dim(v3_con)[1]))

v3_med_pst_sx_mths<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_med_pst_sx_mths<-ifelse((is.na(v3_med_chk) | v3_med_chk!=2),
                           v3_med_pre, v3_med_pst_sx_mths)

descT(v3_med_pst_sx_mths)

##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 466  460  88  40  12  4   10  706  1786
## [2,] Percent   26.1 25.8 4.9 2.2 0.7 0.2 0.6 39.5 100

Create dataset

v3_med_adh<-data.frame(v3_med_pst_wk,v3_med_pst_sx_mths)

BDI-II

For explanation, please refer to the section in Visit 1

1. Sadness (ordinal [0,1,2,3], v3_bdi2_itm1)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi1_traurigkeit,v3_con$v3_bdi2_s1_bdi1,"v3_bdi2_itm1")

##                0    1    2   3   <NA>     
## [1,] No. cases 668  212  21  4   881  1786
## [2,] Percent   37.4 11.9 1.2 0.2 49.3 100

2. Pessimism (ordinal [0,1,2,3], v3_bdi2_itm2)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi2_pessimismus,v3_con$v3_bdi2_s1_bdi2,"v3_bdi2_itm2")

##                0    1   2   3   <NA>     
## [1,] No. cases 701  144 48  10  883  1786
## [2,] Percent   39.2 8.1 2.7 0.6 49.4 100

3. Past failure (ordinal [0,1,2,3], v3_bdi2_itm3)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi3_versagensgef,v3_con$v3_bdi2_s1_bdi3,"v3_bdi2_itm3")

##                0    1   2   3   <NA>     
## [1,] No. cases 646  139 106 14  881  1786
## [2,] Percent   36.2 7.8 5.9 0.8 49.3 100

4. Loss of pleasure (ordinal [0,1,2,3], v3_bdi2_itm4)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi4_verlust_freude,v3_con$v3_bdi2_s1_bdi4,"v3_bdi2_itm4")

##                0    1    2   3   <NA>     
## [1,] No. cases 592  247  45  19  883  1786
## [2,] Percent   33.1 13.8 2.5 1.1 49.4 100

5. Guilty feelings (ordinal [0,1,2,3], v3_bdi2_itm5)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi5_schuldgef,v3_con$v3_bdi2_s1_bdi5,"v3_bdi2_itm5")

##                0    1   2   3   <NA>     
## [1,] No. cases 677  196 20  10  883  1786
## [2,] Percent   37.9 11  1.1 0.6 49.4 100

6. Punishment feelings (ordinal [0,1,2,3], v3_bdi2_itm6)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi6_bestrafungsgef,v3_con$v3_bdi2_s1_bdi6,"v3_bdi2_itm6")

##                0    1   2   3   <NA>     
## [1,] No. cases 740  113 11  37  885  1786
## [2,] Percent   41.4 6.3 0.6 2.1 49.6 100

7. Self-dislike (ordinal [0,1,2,3], v3_bdi2_itm7)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi7_selbstablehnung,v3_con$v3_bdi2_s1_bdi7,"v3_bdi2_itm7")

##                0    1   2   3   <NA>     
## [1,] No. cases 721  112 56  13  884  1786
## [2,] Percent   40.4 6.3 3.1 0.7 49.5 100

8. Self-criticalness (ordinal [0,1,2,3], v3_bdi2_itm8)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi8_selbstvorwuerfe,v3_con$v3_bdi2_s1_bdi8,"v3_bdi2_itm8")

##                0    1    2  3   <NA>     
## [1,] No. cases 617  217  54 16  882  1786
## [2,] Percent   34.5 12.2 3  0.9 49.4 100

9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v3_bdi2_itm9)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi9_selbstmordged,v3_con$v3_bdi2_s1_bdi9,"v3_bdi2_itm9")

##                0    1   2   3   <NA>     
## [1,] No. cases 779  114 7   6   880  1786
## [2,] Percent   43.6 6.4 0.4 0.3 49.3 100

10. Crying (ordinal [0,1,2,3], v3_bdi2_itm10)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi10_weinen,v3_con$v3_bdi2_s1_bdi10,"v3_bdi2_itm10")

##                0    1   2   3   <NA>     
## [1,] No. cases 745  75  20  63  883  1786
## [2,] Percent   41.7 4.2 1.1 3.5 49.4 100

11. Agitation (ordinal [0,1,2,3], v3_bdi2_itm11)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi11_unruhe,v3_con$v3_bdi2_s2_bdi11,"v3_bdi2_itm11")

##                0    1    2   3   <NA>     
## [1,] No. cases 672  188  26  10  890  1786
## [2,] Percent   37.6 10.5 1.5 0.6 49.8 100

12. Loss of interest (ordinal [0,1,2,3], v3_bdi2_itm12)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi12_interessverl,v3_con$v3_bdi2_s2_bdi12,"v3_bdi2_itm12")

##                0    1   2   3   <NA>     
## [1,] No. cases 683  154 34  23  892  1786
## [2,] Percent   38.2 8.6 1.9 1.3 49.9 100

13. Indecisiveness (ordinal [0,1,2,3], v3_bdi2_itm13)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi13_entschlussunf,v3_con$v3_bdi2_s2_bdi13,"v3_bdi2_itm13")

##                0    1    2   3   <NA>     
## [1,] No. cases 619  198  43  34  892  1786
## [2,] Percent   34.7 11.1 2.4 1.9 49.9 100

14. Worthlessness (ordinal [0,1,2,3], v3_bdi2_itm14)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi14_wertlosigkeit,v3_con$v3_bdi2_s2_bdi14,"v3_bdi2_itm14")

##                0    1   2   3   <NA>     
## [1,] No. cases 693  119 68  13  893  1786
## [2,] Percent   38.8 6.7 3.8 0.7 50   100

15. Loss of energy (ordinal [0,1,2,3], v3_bdi2_itm15)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi15_energieverlust,v3_con$v3_bdi2_s2_bdi15,"v3_bdi2_itm15")

##                0    1    2   3   <NA>     
## [1,] No. cases 527  291  66  6   896  1786
## [2,] Percent   29.5 16.3 3.7 0.3 50.2 100

16. Changes in sleeping pattern (ordinal [0,1,2,3], v3_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep”. There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.

v3_itm_bdi2_chk<-c(v3_clin$v3_bdi2_s1_verwer_fragebogen,v3_con$v3_bdi2_s1_bdi_korrekt)
v3_itm_bdi2_itm16_clin_con<-c(v3_clin$v3_bdi2_s2_bdi16_schlafgewohn,v3_con$v3_bdi2_s2_bdi16)

v3_bdi2_itm16<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])

v3_bdi2_itm16<-ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & v3_itm_bdi2_itm16_clin_con==0, 0,
                ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                           (v3_itm_bdi2_itm16_clin_con==1 | v3_itm_bdi2_itm16_clin_con==100), 1, 
                 ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                               (v3_itm_bdi2_itm16_clin_con==2 | v3_itm_bdi2_itm16_clin_con==200), 2, 
                  ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                               (v3_itm_bdi2_itm16_clin_con==3 | v3_itm_bdi2_itm16_clin_con==300), 3, v3_bdi2_itm16))))  

v3_bdi2_itm16<-factor(v3_bdi2_itm16,ordered=T)
descT(v3_bdi2_itm16)

##                0    1    2   3  <NA>     
## [1,] No. cases 506  287  67  35 891  1786
## [2,] Percent   28.3 16.1 3.8 2  49.9 100

17. Irritability (ordinal [0,1,2,3], v3_bdi2_itm17)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi17_reizbarkeit,v3_con$v3_bdi2_s2_bdi17,"v3_bdi2_itm17")

##                0    1   2   3   <NA>     
## [1,] No. cases 686  171 27  9   893  1786
## [2,] Percent   38.4 9.6 1.5 0.5 50   100

18. Change in appetite (ordinal [0,1,2,3], v3_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.

v3_itm_bdi2_itm18_clin_con<-c(v3_clin$v3_bdi2_s2_bdi18_appetit,v3_con$v3_bdi2_s2_bdi18)
v3_bdi2_itm18<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])

v3_bdi2_itm18<-ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & v3_itm_bdi2_itm18_clin_con==0, 0,
                ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                           (v3_itm_bdi2_itm18_clin_con==1 | v3_itm_bdi2_itm18_clin_con==100), 1, 
                 ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                               (v3_itm_bdi2_itm18_clin_con==2 | v3_itm_bdi2_itm18_clin_con==200), 2, 
                  ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                               (v3_itm_bdi2_itm18_clin_con==3 | v3_itm_bdi2_itm18_clin_con==300), 3, v3_bdi2_itm18))))  

v3_bdi2_itm18<-factor(v3_bdi2_itm18,ordered=T)
descT(v3_bdi2_itm18)

##                0   1    2   3  <NA>     
## [1,] No. cases 625 211  40  17 893  1786
## [2,] Percent   35  11.8 2.2 1  50   100

19. Concentration difficulty (ordinal [0,1,2,3], v3_bdi2_itm19)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi19_konzschw,v3_con$v3_bdi2_s2_bdi19,"v3_bdi2_itm19")

##                0    1    2   3   <NA>     
## [1,] No. cases 547  231  111 7   890  1786
## [2,] Percent   30.6 12.9 6.2 0.4 49.8 100

20. Tiredness or fatigue (ordinal [0,1,2,3], v3_bdi2_itm20)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi20_ermued_ersch,v3_con$v3_bdi2_s2_bdi20,"v3_bdi2_itm20")

##                0    1    2  3  <NA>     
## [1,] No. cases 541  283  54 17 891  1786
## [2,] Percent   30.3 15.8 3  1  49.9 100

21. Loss of interest in sex (ordinal [0,1,2,3], v3_bdi2_itm21)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi21_sex_interess,v3_con$v3_bdi2_s2_bdi21,"v3_bdi2_itm21")

##                0    1   2   3   <NA>     
## [1,] No. cases 651  127 41  73  894  1786
## [2,] Percent   36.5 7.1 2.3 4.1 50.1 100

BDI-II sum score calculation (continuous [0-63], v3_bdi2_sum)

v3_bdi2_sum<-as.numeric.factor(v3_bdi2_itm1)+
              as.numeric.factor(v3_bdi2_itm2)+
              as.numeric.factor(v3_bdi2_itm3)+
              as.numeric.factor(v3_bdi2_itm4)+
              as.numeric.factor(v3_bdi2_itm5)+
              as.numeric.factor(v3_bdi2_itm6)+
              as.numeric.factor(v3_bdi2_itm7)+
              as.numeric.factor(v3_bdi2_itm8)+
              as.numeric.factor(v3_bdi2_itm9)+
              as.numeric.factor(v3_bdi2_itm10)+
              as.numeric.factor(v3_bdi2_itm11)+
              as.numeric.factor(v3_bdi2_itm12)+
              as.numeric.factor(v3_bdi2_itm13)+
              as.numeric.factor(v3_bdi2_itm14)+
              as.numeric.factor(v3_bdi2_itm15)+
              as.numeric.factor(v3_bdi2_itm16)+
              as.numeric.factor(v3_bdi2_itm17)+
              as.numeric.factor(v3_bdi2_itm18)+
              as.numeric.factor(v3_bdi2_itm19)+
              as.numeric.factor(v3_bdi2_itm20)+
              as.numeric.factor(v3_bdi2_itm21)

summary(v3_bdi2_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   4.000   7.707  11.000  53.000     925

Create dataset

v3_bdi2<-data.frame(v3_bdi2_itm1,v3_bdi2_itm2,v3_bdi2_itm3,v3_bdi2_itm4,v3_bdi2_itm5,
                    v3_bdi2_itm6,v3_bdi2_itm7,v3_bdi2_itm8,v3_bdi2_itm9,v3_bdi2_itm10,
                    v3_bdi2_itm11,v3_bdi2_itm12,v3_bdi2_itm13,v3_bdi2_itm14,
                    v3_bdi2_itm15,v3_bdi2_itm16,v3_bdi2_itm17,v3_bdi2_itm18,
                    v3_bdi2_itm19,v3_bdi2_itm20,v3_bdi2_itm21, v3_bdi2_sum)

ASRM

For explanation, please refer to the section in Visit 1

1. Positive Mood (ordinal [0,1,2,3,4], v3_asrm_itm1)

v3_asrm_recode(v3_clin$v3_asrm_asrm1_gluecklich,v3_con$v3_asrm_asrm1,"v3_asrm_itm1")

##                0    1    2   3   4   <NA>     
## [1,] No. cases 611  201  56  24  5   889  1786
## [2,] Percent   34.2 11.3 3.1 1.3 0.3 49.8 100

2 Self-Confidence (ordinal [0,1,2,3,4], v3_asrm_itm2)

v3_asrm_recode(v3_clin$v3_asrm_asrm2_selbstbewusst,v3_con$v3_asrm_asrm2,"v3_asrm_itm2")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 659  171 42  21  3   890  1786
## [2,] Percent   36.9 9.6 2.4 1.2 0.2 49.8 100

3. Sleep (ordinal [0,1,2,3,4], v3_asrm_itm3)

v3_asrm_recode(v3_clin$v3_asrm_asrm3_schlaf,v3_con$v3_asrm_asrm3,"v3_asrm_itm3")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 748  96  29  16  8   889  1786
## [2,] Percent   41.9 5.4 1.6 0.9 0.4 49.8 100

4. Speech (ordinal [0,1,2,3,4], v3_asrm_itm4)

v3_asrm_recode(v3_clin$v3_asrm_asrm4_reden,v3_con$v3_asrm_asrm4,"v3_asrm_itm4")

##                0    1   2  3   4   <NA>     
## [1,] No. cases 703  159 18 13  3   890  1786
## [2,] Percent   39.4 8.9 1  0.7 0.2 49.8 100

5. Activity Level (ordinal [0,1,2,3,4], v3_asrm_itm5)

v3_asrm_recode(v3_clin$v3_asrm_asrm5_aktiv,v3_con$v3_asrm_asrm5,"v3_asrm_itm5")

##                0    1    2   3   4   <NA>     
## [1,] No. cases 653  181  41  9   11  891  1786
## [2,] Percent   36.6 10.1 2.3 0.5 0.6 49.9 100

Create ASRM sum score (continuous [0-20],v3_asrm_sum)

v3_asrm_sum<-as.numeric.factor(v3_asrm_itm1)+
            as.numeric.factor(v3_asrm_itm2)+
            as.numeric.factor(v3_asrm_itm3)+
            as.numeric.factor(v3_asrm_itm4)+
            as.numeric.factor(v3_asrm_itm5)

summary(v3_asrm_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   1.000   1.712   2.000  18.000     892

Create dataset

v3_asrm<-data.frame(v3_asrm_itm1,v3_asrm_itm2,v3_asrm_itm3,v3_asrm_itm4,v3_asrm_itm5,v3_asrm_sum)

MSS

For explanation, please refer to the section in Visit 1

1. “I had more energy” (dichotomous, v3_mss_itm1)

v3_mss_recode(v3_clin$v3_mss_s1_mss1_energie,v3_con$v3_mss_s1_mss1,"v3_mss_itm1")

##                N    Y   <NA>     
## [1,] No. cases 735  162 889  1786
## [2,] Percent   41.2 9.1 49.8 100

2. “I had trouble sitting still” (dichotomous, v3_mss_itm2)

v3_mss_recode(v3_clin$v3_mss_s1_mss2_ruhig_sitzen,v3_con$v3_mss_s1_mss2,"v3_mss_itm2")

##                N    Y   <NA>     
## [1,] No. cases 783  113 890  1786
## [2,] Percent   43.8 6.3 49.8 100

3. “I drove faster” (dichotomous, v3_mss_itm3)

v3_mss_recode(v3_clin$v3_mss_s1_mss3_auto_fahren,v3_con$v3_mss_s1_mss3,"v3_mss_itm3")

##                N    Y   <NA>     
## [1,] No. cases 837  30  919  1786
## [2,] Percent   46.9 1.7 51.5 100

4. “I drank more alcoholic beverages” (dichotomous, v3_mss_itm4)

v3_mss_recode(v3_clin$v3_mss_s1_mss4_alkohol,v3_con$v3_mss_s1_mss4,"v3_mss_itm4")

##                N    Y   <NA>     
## [1,] No. cases 813  76  897  1786
## [2,] Percent   45.5 4.3 50.2 100

5. “I changed clothes several times a day” (dichotomous, v3_mss_itm5)

v3_mss_recode(v3_clin$v3_mss_s1_mss5_umziehen, v3_con$v3_mss_s1_mss5,"v3_mss_itm5")

##                N   Y   <NA>     
## [1,] No. cases 821 70  895  1786
## [2,] Percent   46  3.9 50.1 100

6. “I wore brighter clothes/make-up” (dichotomous, v3_mss_itm6)

v3_mss_recode(v3_clin$v3_mss_s1_mss6_bunter,v3_con$v3_mss_s1_mss6,"v3_mss_itm6")

##                N    Y   <NA>     
## [1,] No. cases 841  55  890  1786
## [2,] Percent   47.1 3.1 49.8 100

7. “I played music louder” (dichotomous, v3_mss_itm7)

v3_mss_recode(v3_clin$v3_mss_s1_mss7_musik_lauter,v3_con$v3_mss_s1_mss7,"v3_mss_itm7")

##                N    Y   <NA>     
## [1,] No. cases 778  118 890  1786
## [2,] Percent   43.6 6.6 49.8 100

8. “I ate faster than usual” (dichotomous, v3_mss_itm8)

v3_mss_recode(v3_clin$v3_mss_s1_mss8_hastiger_essen,v3_con$v3_mss_s1_mss8,"v3_mss_itm8")

##                N    Y  <NA>     
## [1,] No. cases 806  89 891  1786
## [2,] Percent   45.1 5  49.9 100

9. “I ate more than usual” (dichotomous, v3_mss_itm9)

v3_mss_recode(v3_clin$v3_mss_s1_mss9_mehr_essen,v3_con$v3_mss_s1_mss9,"v3_mss_itm9")

##                N    Y   <NA>     
## [1,] No. cases 742  152 892  1786
## [2,] Percent   41.5 8.5 49.9 100

10. “I slept fewer hours than usual” (dichotomous, v3_mss_itm10)

v3_mss_recode(v3_clin$v3_mss_s1_mss10_weniger_schlaf,v3_con$v3_mss_s1_mss10,"v3_mss_itm10")

##                N    Y   <NA>     
## [1,] No. cases 788  107 891  1786
## [2,] Percent   44.1 6   49.9 100

11. “I started things that I didn’t finish” (dichotomous, v3_mss_itm11)

v3_mss_recode(v3_clin$v3_mss_s1_mss11_unbeendet,v3_con$v3_mss_s1_mss11,"v3_mss_itm11")

##                N    Y   <NA>     
## [1,] No. cases 727  169 890  1786
## [2,] Percent   40.7 9.5 49.8 100

12. “I gave away my own possessions” (dichotomous, v3_mss_itm12)

v3_mss_recode(v3_clin$v3_mss_s1_mss12_weggeben,v3_con$v3_mss_s1_mss12,"v3_mss_itm12")

##                N    Y   <NA>     
## [1,] No. cases 817  77  892  1786
## [2,] Percent   45.7 4.3 49.9 100

13. “I bought gifts for people” (dichotomous, v3_mss_itm13)

v3_mss_recode(v3_clin$v3_mss_s1_mss13_geschenke,v3_con$v3_mss_s1_mss13,"v3_mss_itm13")

##                N    Y   <NA>     
## [1,] No. cases 810  85  891  1786
## [2,] Percent   45.4 4.8 49.9 100

14. “I spent money more freely” (dichotomous, v3_mss_itm14)

v3_mss_recode(v3_clin$v3_mss_s1_mss14_mehr_geld,v3_con$v3_mss_s1_mss14,"v3_mss_itm14")

##                N    Y    <NA>     
## [1,] No. cases 688  208  890  1786
## [2,] Percent   38.5 11.6 49.8 100

15. “I accumulated debts” (dichotomous, v3_mss_itm15)

v3_mss_recode(v3_clin$v3_mss_s1_mss15_schulden,v3_con$v3_mss_s1_mss15,"v3_mss_itm15")

##                N    Y   <NA>     
## [1,] No. cases 844  52  890  1786
## [2,] Percent   47.3 2.9 49.8 100

16. “I made unwise business decisions” (dichotomous, v3_mss_itm16)

v3_mss_recode(v3_clin$v3_mss_s1_mss16_unkluge_entsch,v3_con$v3_mss_s1_mss16,"v3_mss_itm16")

##                N    Y   <NA>     
## [1,] No. cases 866  30  890  1786
## [2,] Percent   48.5 1.7 49.8 100

17. “I partied more” (dichotomous, v3_mss_itm17)

v3_mss_recode(v3_clin$v3_mss_s1_mss17_parties,v3_con$v3_mss_s1_mss17,"v3_mss_itm17")

##                N    Y   <NA>     
## [1,] No. cases 853  44  889  1786
## [2,] Percent   47.8 2.5 49.8 100

18. “I enjoyed flirting” (dichotomous, v3_mss_itm18)

v3_mss_recode(v3_clin$v3_mss_s1_mss18_flirten,v3_con$v3_mss_s1_mss18,"v3_mss_itm18")

##                N    Y   <NA>     
## [1,] No. cases 828  65  893  1786
## [2,] Percent   46.4 3.6 50   100

19. “I masturbated more often” (dichotomous, v3_mss_itm19)

v3_mss_recode(v3_clin$v3_mss_s2_mss19_selbstbefried,v3_con$v3_mss_s2_mss19,"v3_mss_itm19")

##                N    Y   <NA>     
## [1,] No. cases 841  42  903  1786
## [2,] Percent   47.1 2.4 50.6 100

20. “I was more interested in sex than usual” (dichotomous, v3_mss_itm20)

v3_mss_recode(v3_clin$v3_mss_s2_mss20_sex_interess,v3_con$v3_mss_s2_mss20,"v3_mss_itm20")

##                N    Y  <NA>     
## [1,] No. cases 797  89 900  1786
## [2,] Percent   44.6 5  50.4 100

21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v3_mss_itm21)

v3_mss_recode(v3_clin$v3_mss_s2_mss21_sexpartner,v3_con$v3_mss_s2_mss21,"v3_mss_itm21")

##                N    Y   <NA>     
## [1,] No. cases 870  15  901  1786
## [2,] Percent   48.7 0.8 50.4 100

22. “I spent more time on the phone” (dichotomous, v3_mss_itm22)

v3_mss_recode(v3_clin$v3_mss_s2_mss22_mehr_telefon,v3_con$v3_mss_s2_mss22,"v3_mss_itm22")

##                N    Y   <NA>     
## [1,] No. cases 782  107 897  1786
## [2,] Percent   43.8 6   50.2 100

23. “I spoke louder than usual” (dichotomous, v3_mss_itm23)

v3_mss_recode(v3_clin$v3_mss_s2_mss23_sprache_lauter,v3_con$v3_mss_s2_mss23,"v3_mss_itm23")

##                N    Y   <NA>     
## [1,] No. cases 828  59  899  1786
## [2,] Percent   46.4 3.3 50.3 100

24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v3_mss_itm24)

v3_mss_recode(v3_clin$v3_mss_s2_mss24_spr_schneller,v3_con$v3_mss_s2_mss24,"v3_mss_itm24")

##                N    Y   <NA>     
## [1,] No. cases 837  49  900  1786
## [2,] Percent   46.9 2.7 50.4 100

25. “1 enjoyed punning or rhyming” (dichotomous, v3_mss_itm25)

v3_mss_recode(v3_clin$v3_mss_s2_mss25_witze,v3_con$v3_mss_s2_mss25,"v3_mss_itm25")

##                N   Y   <NA>     
## [1,] No. cases 804 84  898  1786
## [2,] Percent   45  4.7 50.3 100

26. “I butted into conversations” (dichotomous, v3_mss_itm26)

v3_mss_recode(v3_clin$v3_mss_s2_mss26_einmischen,v3_con$v3_mss_s2_mss26,"v3_mss_itm26")

##                N    Y   <NA>     
## [1,] No. cases 838  52  896  1786
## [2,] Percent   46.9 2.9 50.2 100

27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v3_mss_itm27)

v3_mss_recode(v3_clin$v3_mss_s2_mss27_red_pausenlos,v3_con$v3_mss_s2_mss27,"v3_mss_itm27")

##                N    Y   <NA>     
## [1,] No. cases 864  26  896  1786
## [2,] Percent   48.4 1.5 50.2 100

28. “I enjoyed being the centre of attention” (dichotomous, v3_mss_itm28)

v3_mss_recode(v3_clin$v3_mss_s2_mss28_mittelpunkt,v3_con$v3_mss_s2_mss28,"v3_mss_itm28")

##                N    Y   <NA>     
## [1,] No. cases 831  57  898  1786
## [2,] Percent   46.5 3.2 50.3 100

29. “I liked to joke and laugh” (dichotomous, v3_mss_itm29)

v3_mss_recode(v3_clin$v3_mss_s2_mss29_herumalbern,v3_con$v3_mss_s2_mss29,"v3_mss_itm29")

##                N    Y   <NA>     
## [1,] No. cases 766  122 898  1786
## [2,] Percent   42.9 6.8 50.3 100

30. “People found me entertaining” (dichotomous, v3_mss_itm30)

v3_mss_recode(v3_clin$v3_mss_s2_mss30_unterhaltsamer,v3_con$v3_mss_s2_mss30,"v3_mss_itm30")

##                N    Y   <NA>     
## [1,] No. cases 807  82  897  1786
## [2,] Percent   45.2 4.6 50.2 100

31. “I felt as if I was on top of the world” (dichotomous, v3_mss_itm31)

v3_mss_recode(v3_clin$v3_mss_s2_mss31_obenauf,v3_con$v3_mss_s2_mss31,"v3_mss_itm31")

##                N    Y   <NA>     
## [1,] No. cases 802  86  898  1786
## [2,] Percent   44.9 4.8 50.3 100

32. “I was more cheerful than my usual self” (dichotomous, v3_mss_itm32)

v3_mss_recode(v3_clin$v3_mss_s2_mss32_froehlicher,v3_con$v3_mss_s2_mss32,"v3_mss_itm32")

##                N   Y   <NA>     
## [1,] No. cases 733 155 898  1786
## [2,] Percent   41  8.7 50.3 100

33. “Other people got on my nerves” (dichotomous, v3_mss_itm33)

v3_mss_recode(v3_clin$v3_mss_s2_mss33_ungeduldiger,v3_con$v3_mss_s2_mss33,"v3_mss_itm33")

##                N    Y    <NA>     
## [1,] No. cases 693  195  898  1786
## [2,] Percent   38.8 10.9 50.3 100

34. “I was getting into arguments” (dichotomous, v3_mss_itm34)

v3_mss_recode(v3_clin$v3_mss_s2_mss34_streiten,v3_con$v3_mss_s2_mss34,"v3_mss_itm34")

##                N    Y   <NA>     
## [1,] No. cases 823  64  899  1786
## [2,] Percent   46.1 3.6 50.3 100

35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v3_mss_itm35)

v3_mss_recode(v3_clin$v3_mss_s2_mss35_ideen,v3_con$v3_mss_s2_mss35,"v3_mss_itm35")

##                N    Y   <NA>     
## [1,] No. cases 758  131 897  1786
## [2,] Percent   42.4 7.3 50.2 100

36. “My thoughts raced through my mind” (dichotomous, v3_mss_itm36)

v3_mss_recode(v3_clin$v3_mss_s2_mss36_gedanken,v3_con$v3_mss_s2_mss36,"v3_mss_itm36")

##                N    Y    <NA>     
## [1,] No. cases 681  207  898  1786
## [2,] Percent   38.1 11.6 50.3 100

37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v3_mss_itm37)

v3_mss_recode(v3_clin$v3_mss_s2_mss37_konzentration,v3_con$v3_mss_s2_mss37,"v3_mss_itm37")

##                N    Y   <NA>     
## [1,] No. cases 764  124 898  1786
## [2,] Percent   42.8 6.9 50.3 100

38. “I thought I was an especially important person” (dichotomous, v3_mss_itm38)

v3_mss_recode(v3_clin$v3_mss_s2_mss38_etw_besonderes,v3_con$v3_mss_s2_mss38,"v3_mss_itm38")

##                N    Y   <NA>     
## [1,] No. cases 832  55  899  1786
## [2,] Percent   46.6 3.1 50.3 100

39. “I thought I could change the world” (dichotomous, v3_mss_itm39)

v3_mss_recode(v3_clin$v3_mss_s2_mss39_welt_veraender,v3_con$v3_mss_s2_mss39,"v3_mss_itm39")

##                N    Y   <NA>     
## [1,] No. cases 833  55  898  1786
## [2,] Percent   46.6 3.1 50.3 100

40. “I thought I was right most of the time” (dichotomous, v3_mss_itm40)

v3_mss_recode(v3_clin$v3_mss_s2_mss40_recht_haben,v3_con$v3_mss_s2_mss40,"v3_mss_itm40")

##                N    Y   <NA>     
## [1,] No. cases 845  42  899  1786
## [2,] Percent   47.3 2.4 50.3 100

41. “I thought I was superior to others” (dichotomous, v3_mss_itm41)

v3_mss_recode(v3_clin$v3_mss_s3_mss41_ueberlegen,v3_con$v3_mss_s3_mss41,"v3_mss_itm41")

##                N    Y   <NA>     
## [1,] No. cases 864  29  893  1786
## [2,] Percent   48.4 1.6 50   100

42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v3_mss_itm42)

v3_mss_recode(v3_clin$v3_mss_s3_mss42_uebermut,v3_con$v3_mss_s3_mss42,"v3_mss_itm42")

##                N    Y   <NA>     
## [1,] No. cases 830  63  893  1786
## [2,] Percent   46.5 3.5 50   100

43. “I thought I knew what other people were thinking” (dichotomous, v3_mss_itm43)

v3_mss_recode(v3_clin$v3_mss_s3_mss43_ged_lesen_akt,v3_con$v3_mss_s3_mss43,"v3_mss_itm43")

##                N    Y   <NA>     
## [1,] No. cases 828  66  892  1786
## [2,] Percent   46.4 3.7 49.9 100

44. “I thought other people knew what I was thinking” (dichotomous, v3_mss_itm44)

v3_mss_recode(v3_clin$v3_mss_s3_mss44_ged_lesen_pas,v3_con$v3_mss_s3_mss44,"v3_mss_itm44")

##                N    Y   <NA>     
## [1,] No. cases 848  42  896  1786
## [2,] Percent   47.5 2.4 50.2 100

45. “I thought someone wanted to harm me” (dichotomous, v3_mss_itm45)

v3_mss_recode(v3_clin$v3_mss_s3_mss45_etw_antun,v3_con$v3_mss_s3_mss45,"v3_mss_itm45")

##                N    Y   <NA>     
## [1,] No. cases 855  38  893  1786
## [2,] Percent   47.9 2.1 50   100

46. “I heard voices when people weren’t there” (dichotomous, v3_mss_itm46)

v3_mss_recode(v3_clin$v3_mss_s3_mss46_stimmen,v3_con$v3_mss_s3_mss46,"v3_mss_itm46")

##                N    Y   <NA>     
## [1,] No. cases 842  52  892  1786
## [2,] Percent   47.1 2.9 49.9 100

47. “I had false beliefs concerning who I was” (dichotomous, v3_mss_itm47)

v3_mss_recode(v3_clin$v3_mss_s3_mss47_jmd_anders,v3_con$v3_mss_s3_mss47,"v3_mss_itm47")

##                N    Y   <NA>     
## [1,] No. cases 869  24  893  1786
## [2,] Percent   48.7 1.3 50   100

48. “I knew I was getting ill” (dichotomous, v3_mss_itm48)

v3_mss_recode(v3_clin$v3_mss_s3_mss48_krank_einsicht,v3_con$v3_mss_s3_mss48,"v3_mss_itm48")

##                N   Y   <NA>     
## [1,] No. cases 803 83  900  1786
## [2,] Percent   45  4.6 50.4 100

Create MSS sum score (continuous [0-48],v3_mss_sum)

v3_mss_sum<-ifelse(v3_mss_itm1=="Y",1,0)+
            ifelse(v3_mss_itm2=="Y",1,0)+
            ifelse(v3_mss_itm3=="Y",1,0)+
            ifelse(v3_mss_itm4=="Y",1,0)+
            ifelse(v3_mss_itm5=="Y",1,0)+
            ifelse(v3_mss_itm6=="Y",1,0)+
            ifelse(v3_mss_itm7=="Y",1,0)+
            ifelse(v3_mss_itm8=="Y",1,0)+
            ifelse(v3_mss_itm9=="Y",1,0)+
            ifelse(v3_mss_itm10=="Y",1,0)+
            ifelse(v3_mss_itm11=="Y",1,0)+
            ifelse(v3_mss_itm12=="Y",1,0)+
            ifelse(v3_mss_itm13=="Y",1,0)+
            ifelse(v3_mss_itm14=="Y",1,0)+
            ifelse(v3_mss_itm15=="Y",1,0)+
            ifelse(v3_mss_itm16=="Y",1,0)+
            ifelse(v3_mss_itm17=="Y",1,0)+
            ifelse(v3_mss_itm18=="Y",1,0)+
            ifelse(v3_mss_itm19=="Y",1,0)+
            ifelse(v3_mss_itm20=="Y",1,0)+
            ifelse(v3_mss_itm21=="Y",1,0)+
            ifelse(v3_mss_itm22=="Y",1,0)+
            ifelse(v3_mss_itm23=="Y",1,0)+
            ifelse(v3_mss_itm24=="Y",1,0)+
            ifelse(v3_mss_itm25=="Y",1,0)+
            ifelse(v3_mss_itm26=="Y",1,0)+
            ifelse(v3_mss_itm27=="Y",1,0)+
            ifelse(v3_mss_itm28=="Y",1,0)+
            ifelse(v3_mss_itm29=="Y",1,0)+
            ifelse(v3_mss_itm30=="Y",1,0)+
            ifelse(v3_mss_itm31=="Y",1,0)+
            ifelse(v3_mss_itm32=="Y",1,0)+
            ifelse(v3_mss_itm33=="Y",1,0)+
            ifelse(v3_mss_itm34=="Y",1,0)+
            ifelse(v3_mss_itm35=="Y",1,0)+
            ifelse(v3_mss_itm36=="Y",1,0)+
            ifelse(v3_mss_itm37=="Y",1,0)+
            ifelse(v3_mss_itm38=="Y",1,0)+
            ifelse(v3_mss_itm39=="Y",1,0)+
            ifelse(v3_mss_itm40=="Y",1,0)+
            ifelse(v3_mss_itm41=="Y",1,0)+
            ifelse(v3_mss_itm42=="Y",1,0)+
            ifelse(v3_mss_itm43=="Y",1,0)+
            ifelse(v3_mss_itm44=="Y",1,0)+
            ifelse(v3_mss_itm45=="Y",1,0)+
            ifelse(v3_mss_itm46=="Y",1,0)+
            ifelse(v3_mss_itm47=="Y",1,0)+
            ifelse(v3_mss_itm48=="Y",1,0)

summary(v3_mss_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   2.000   4.175   6.000  48.000     981

Create dataset

v3_mss<-data.frame(v3_mss_itm1,v3_mss_itm2,v3_mss_itm3,v3_mss_itm4,v3_mss_itm5,v3_mss_itm6,
                   v3_mss_itm7,v3_mss_itm8,v3_mss_itm9,v3_mss_itm10,v3_mss_itm11,
                   v3_mss_itm12,v3_mss_itm13,v3_mss_itm14,v3_mss_itm15,v3_mss_itm16,
                   v3_mss_itm17,v3_mss_itm18,v3_mss_itm19,v3_mss_itm20,v3_mss_itm21,
                   v3_mss_itm22,v3_mss_itm23,v3_mss_itm24,v3_mss_itm25,v3_mss_itm26,
                   v3_mss_itm27,v3_mss_itm28,v3_mss_itm29,v3_mss_itm30,v3_mss_itm31,
                   v3_mss_itm32,v3_mss_itm33,v3_mss_itm34,v3_mss_itm35,v3_mss_itm36,
                   v3_mss_itm37,v3_mss_itm38,v3_mss_itm39,v3_mss_itm40,v3_mss_itm41,
                   v3_mss_itm42,v3_mss_itm43,v3_mss_itm44,v3_mss_itm45,v3_mss_itm46,
                   v3_mss_itm47,v3_mss_itm48, v3_mss_sum)

LEQ

For explanation, please refer to the section in Visit 1

Health

1. “Major personal illness or injury”

1A Nature (dichotomous [“good”,“bad”], v3_leq_A_1A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq1a_schw_krankh,v3_con$v3_leq_a_leq1a,"v3_leq_A_1A")

##                -999 bad  good <NA>     
## [1,] No. cases 631  193  29   933  1786
## [2,] Percent   35.3 10.8 1.6  52.2 100

1B Impact (ordinal [0,1,2,3], v3_leq_A_1B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq1e_schw_krankh,v3_con$v3_leq_a_leq1e,"v3_leq_A_1B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 627  14  33  61  118 933  1786
## [2,] Percent   35.1 0.8 1.8 3.4 6.6 52.2 100

2. “Major change in eating habits”

2A Nature (dichotomous [“good”,“bad”], v3_leq_A_2A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq2a_ernaehrung,v3_con$v3_leq_a_leq2a,"v3_leq_A_2A")

##                -999 bad good <NA>     
## [1,] No. cases 650  88  115  933  1786
## [2,] Percent   36.4 4.9 6.4  52.2 100

2B Impact (ordinal [0,1,2,3], v3_leq_A_2B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq2e_ernaehrung,v3_con$v3_leq_a_leq2e,"v3_leq_A_2B")

##                -999 0  1  2   3   <NA>     
## [1,] No. cases 644  17 53 66  73  933  1786
## [2,] Percent   36.1 1  3  3.7 4.1 52.2 100

3. “Major change in sleeping habits”

3A Nature (dichotomous [“good”,“bad”], v3_leq_A_3A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq3a_schlaf,v3_con$v3_leq_a_leq3a,"v3_leq_A_3A")

##                -999 bad good <NA>     
## [1,] No. cases 647  137 69   933  1786
## [2,] Percent   36.2 7.7 3.9  52.2 100

3B Impact (ordinal [0,1,2,3], v3_leq_A_3B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq3e_schlaf,v3_con$v3_leq_a_leq3e,"v3_leq_A_3B")

##                -999 0  1  2   3   <NA>     
## [1,] No. cases 645  18 35 73  82  933  1786
## [2,] Percent   36.1 1  2  4.1 4.6 52.2 100

4. “Major change in usual type and/or amount of recreation”

4A Nature (dichotomous [“good”,“bad”], v3_leq_A_4A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq4a_freizeit,v3_con$v3_leq_a_leq4a,"v3_leq_A_4A")

##                -999 bad good <NA>     
## [1,] No. cases 591  87  175  933  1786
## [2,] Percent   33.1 4.9 9.8  52.2 100

4B Impact (ordinal [0,1,2,3], v3_leq_A_4B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq4e_freizeit,v3_con$v3_leq_a_leq4e,"v3_leq_A_4B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 584  8   59  109 93  933  1786
## [2,] Percent   32.7 0.4 3.3 6.1 5.2 52.2 100

5. “Major dental work”

5A Nature (dichotomous [“good”,“bad”], v3_leq_A_5A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq5a_zahnarzt,v3_con$v3_leq_a_leq5a,"v3_leq_A_5A")

##                -999 bad good <NA>     
## [1,] No. cases 743  45  65   933  1786
## [2,] Percent   41.6 2.5 3.6  52.2 100

5B Impact (ordinal [0,1,2,3], v3_leq_A_5B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq5e_zahnarzt,v3_con$v3_leq_a_leq5e,"v3_leq_A_5B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 739  24  29  31  30  933  1786
## [2,] Percent   41.4 1.3 1.6 1.7 1.7 52.2 100

6. “(Female) Pregnancy”

6A Nature (dichotomous [“good”,“bad”], v3_leq_A_6A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq6a_schwanger,v3_con$v3_leq_a_leq6a,"v3_leq_A_6A")

##                -999 bad good <NA>     
## [1,] No. cases 846  1   6    933  1786
## [2,] Percent   47.4 0.1 0.3  52.2 100

6B Impact (ordinal [0,1,2,3], v3_leq_A_6B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq6e_schwanger,v3_con$v3_leq_a_leq6e,"v3_leq_A_6B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 846  1   1   5   933  1786
## [2,] Percent   47.4 0.1 0.1 0.3 52.2 100

7. “(Female) Miscarriage or abortion”

7A Nature (dichotomous [“good”,“bad”], v3_leq_A_7A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq7a_fehlg_abtr,v3_con$v3_leq_a_leq7a,"v3_leq_A_7A")

##                -999 bad <NA>     
## [1,] No. cases 852  1   933  1786
## [2,] Percent   47.7 0.1 52.2 100

7B Impact (ordinal [0,1,2,3], v3_leq_A_7B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq7e_fehlg_abtr,v3_con$v3_leq_a_leq7e,"v3_leq_A_7B")

##                -999 1   <NA>     
## [1,] No. cases 852  1   933  1786
## [2,] Percent   47.7 0.1 52.2 100

8. “(Female) Started menopause”

8A Nature (dichotomous [“good”,“bad”], v3_leq_A_8A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq8a_wechseljahre,v3_con$v3_leq_a_leq8a,"v3_leq_A_8A")

##                -999 bad good <NA>     
## [1,] No. cases 823  23  7    933  1786
## [2,] Percent   46.1 1.3 0.4  52.2 100

8B Impact (ordinal [0,1,2,3], v3_leq_A_8B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq8e_wechseljahre,v3_con$v3_leq_a_leq8e,"v3_leq_A_8B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 822  4   6   14  7   933  1786
## [2,] Percent   46   0.2 0.3 0.8 0.4 52.2 100

9. “Major difficulties with birth control pills or devices”

9A Nature (dichotomous [“good”,“bad”], v3_leq_A_9A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq9a_verhuetung,v3_con$v3_leq_a_leq9a,"v3_leq_A_9A")

##                -999 bad good <NA>     
## [1,] No. cases 836  14  3    933  1786
## [2,] Percent   46.8 0.8 0.2  52.2 100

9B Impact (ordinal [0,1,2,3], v3_leq_A_9B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq9e_verhuetung,v3_con$v3_leq_a_leq9e,"v3_leq_A_9B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 836  2   4   6   5   933  1786
## [2,] Percent   46.8 0.1 0.2 0.3 0.3 52.2 100

Create dataset

v3_leq_A<-data.frame(v3_leq_A_1A,v3_leq_A_1B,v3_leq_A_2A,v3_leq_A_2B,v3_leq_A_3A,
                     v3_leq_A_3B,v3_leq_A_4A,v3_leq_A_4B,v3_leq_A_5A,v3_leq_A_5B,
                     v3_leq_A_6A,v3_leq_A_6B,v3_leq_A_7A,v3_leq_A_7B,v3_leq_A_8A,
                     v3_leq_A_8B,v3_leq_A_9A,v3_leq_A_9B)

Work

10. “Difficulty finding a job”

10A Nature (dichotomous [“good”,“bad”], v3_leq_B_10A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq10a_arbeitssuche,v3_con$v3_leq_b_leq10a,"v3_leq_B_10A")

##                -999 bad good <NA>     
## [1,] No. cases 740  88  25   933  1786
## [2,] Percent   41.4 4.9 1.4  52.2 100

10B Impact (ordinal [0,1,2,3], v3_leq_B_10B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq10e_arbeitssuche,v3_con$v3_leq_b_leq10e,"v3_leq_B_10B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 739  7   29  43  35 933  1786
## [2,] Percent   41.4 0.4 1.6 2.4 2  52.2 100

11. “Beginning work outside the home”

11A Nature (dichotomous [“good”,“bad”], v3_leq_B_11A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq11a_arbeit_aussen,v3_con$v3_leq_b_leq11a,"v3_leq_B_11A")

##                -999 bad good <NA>     
## [1,] No. cases 749  16  88   933  1786
## [2,] Percent   41.9 0.9 4.9  52.2 100

11B Impact (ordinal [0,1,2,3], v3_leq_B_11B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq11e_arbeit_aussen,v3_con$v3_leq_b_leq11e,"v3_leq_B_11B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 746  6   17 38  46  933  1786
## [2,] Percent   41.8 0.3 1  2.1 2.6 52.2 100

12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v3_leq_B_12A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq12a_arbeitswechs,v3_con$v3_leq_b_leq12a,"v3_leq_B_12A")

##                -999 bad good <NA>     
## [1,] No. cases 743  12  98   933  1786
## [2,] Percent   41.6 0.7 5.5  52.2 100

12B Impact (ordinal [0,1,2,3], v3_leq_B_12B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq12e_arbeitswechs,v3_con$v3_leq_b_leq12e,"v3_leq_B_12B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 741  7   16  34  55  933  1786
## [2,] Percent   41.5 0.4 0.9 1.9 3.1 52.2 100

13. “Changing your work hours or conditions”

13A Nature (dichotomous [“good”,“bad”], v3_leq_B_13A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq13a_veraend_arb,v3_con$v3_leq_b_leq13a,"v3_leq_B_13A")

##                -999 bad good <NA>     
## [1,] No. cases 689  36  128  933  1786
## [2,] Percent   38.6 2   7.2  52.2 100

13B Impact (ordinal [0,1,2,3], v3_leq_B_13B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq13e_veraend_arb,v3_con$v3_leq_b_leq13e,"v3_leq_B_13B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 688  7   47  61  50  933  1786
## [2,] Percent   38.5 0.4 2.6 3.4 2.8 52.2 100

14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v3_leq_B_14A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq14a_veraend_ba,v3_con$v3_leq_b_leq14a,"v3_leq_B_14A")

##                -999 bad good <NA>     
## [1,] No. cases 689  27  137  933  1786
## [2,] Percent   38.6 1.5 7.7  52.2 100

14B Impact (ordinal [0,1,2,3], v3_leq_B_14B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq14e_veraend_ba,v3_con$v3_leq_b_leq14e,"v3_leq_B_14B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 686  8   39  65  55  933  1786
## [2,] Percent   38.4 0.4 2.2 3.6 3.1 52.2 100

15. “Troubles at work with your employer or co-worker”

15A Nature (dichotomous [“good”,“bad”], v3_leq_B_15A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq15a_schw_arbeit,v3_con$v3_leq_b_leq15a,"v3_leq_B_15A")

##                -999 bad good <NA>     
## [1,] No. cases 752  81  20   933  1786
## [2,] Percent   42.1 4.5 1.1  52.2 100

15B Impact (ordinal [0,1,2,3], v3_leq_B_15B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq15e_schw_arbeit,v3_con$v3_leq_b_leq15e,"v3_leq_B_15B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 751  13  39  28  22  933  1786
## [2,] Percent   42   0.7 2.2 1.6 1.2 52.2 100

16. “Major business readjustment”

16A Nature (dichotomous [“good”,“bad”], v3_leq_B_16A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq16a_betr_reorg,v3_con$v3_leq_b_leq16a,"v3_leq_B_16A")

##                -999 bad good <NA>     
## [1,] No. cases 814  20  19   933  1786
## [2,] Percent   45.6 1.1 1.1  52.2 100

16B Impact (ordinal [0,1,2,3], v3_leq_B_16B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq16e_betr_reorg,v3_con$v3_leq_b_leq16e,"v3_leq_B_16B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 813  8   9   8   15  933  1786
## [2,] Percent   45.5 0.4 0.5 0.4 0.8 52.2 100

17. “Being fired or laid off from work”

17A Nature (dichotomous [“good”,“bad”], v3_leq_B_17A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq17a_kuendigung,v3_con$v3_leq_b_leq17a,"v3_leq_B_17A")

##                -999 bad good <NA>     
## [1,] No. cases 802  28  23   933  1786
## [2,] Percent   44.9 1.6 1.3  52.2 100

17B Impact (ordinal [0,1,2,3], v3_leq_B_17B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq17e_kuendigung,v3_con$v3_leq_b_leq17e,"v3_leq_B_17B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 800  4   10  16  23  933  1786
## [2,] Percent   44.8 0.2 0.6 0.9 1.3 52.2 100

18. “Retirement from work”

18A Nature (dichotomous [“good”,“bad”], v3_leq_B_18A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq18a_ende_beruf,v3_con$v3_leq_b_leq18a,"v3_leq_B_18A")

##                -999 bad good <NA>     
## [1,] No. cases 829  8   16   933  1786
## [2,] Percent   46.4 0.4 0.9  52.2 100

18B Impact (ordinal [0,1,2,3], v3_leq_B_18B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq18e_ende_beruf,v3_con$v3_leq_b_leq18e,"v3_leq_B_18B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 829  1   5   5   13  933  1786
## [2,] Percent   46.4 0.1 0.3 0.3 0.7 52.2 100

19. “Taking courses by mail or studying at home to help you in your work”

19A Nature (dichotomous [“good”,“bad”], v3_leq_B_19A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq19a_fortbildung,v3_con$v3_leq_b_leq19a,"v3_leq_B_19A")

##                -999 bad good <NA>     
## [1,] No. cases 808  5   40   933  1786
## [2,] Percent   45.2 0.3 2.2  52.2 100

19B Impact (ordinal [0,1,2,3], v3_leq_B_19B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq19e_fortbildung,v3_con$v3_leq_b_leq19e,"v3_leq_B_19B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 808  5   12  14  14  933  1786
## [2,] Percent   45.2 0.3 0.7 0.8 0.8 52.2 100

v3_leq_B<-data.frame(v3_leq_B_10A,v3_leq_B_10B,v3_leq_B_11A,v3_leq_B_11B,v3_leq_B_12A,
                     v3_leq_B_12B,v3_leq_B_13A,v3_leq_B_13B,v3_leq_B_14A,v3_leq_B_14B,
                     v3_leq_B_15A,v3_leq_B_15B,v3_leq_B_16A,v3_leq_B_16B,v3_leq_B_17A,
                     v3_leq_B_17B,v3_leq_B_18A,v3_leq_B_18B,v3_leq_B_19A,v3_leq_B_19B)

School

20. “Beginning or ceasing school, college, or training program”

20A Nature (dichotomous [“good”,“bad”], v3_leq_C_20A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq20a_beginn_ende,v3_con$v3_leq_c_d_leq20a,"v3_leq_C_20A")

##                -999 bad good <NA>     
## [1,] No. cases 797  7   49   933  1786
## [2,] Percent   44.6 0.4 2.7  52.2 100

20B Impact (ordinal [0,1,2,3], v3_leq_C_20B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq20e_beginn_ende,v3_con$v3_leq_c_d_leq20e,"v3_leq_C_20B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 795  4   10  14  30  933  1786
## [2,] Percent   44.5 0.2 0.6 0.8 1.7 52.2 100

21. “Change of school, college, or training program”

21A Nature (dichotomous [“good”,“bad”], v3_leq_C_21A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq21a_schulwechsel,v3_con$v3_leq_c_d_leq21a,"v3_leq_C_21A")

##                -999 bad good <NA>     
## [1,] No. cases 847  3   3    933  1786
## [2,] Percent   47.4 0.2 0.2  52.2 100

21B Impact (ordinal [0,1,2,3], v3_leq_C_21B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq21e_schulwechsel,v3_con$v3_leq_c_d_leq21e,"v3_leq_C_21B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 846  1   1   2   3   933  1786
## [2,] Percent   47.4 0.1 0.1 0.1 0.2 52.2 100

22. “Change in career goal or academic major”

A Nature (dichotomous [“good”,“bad”], v3_leq_C_22A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq22a_aend_karriere,v3_con$v3_leq_c_d_leq22a,"v3_leq_C_22A")

##                -999 bad good <NA>     
## [1,] No. cases 813  6   34   933  1786
## [2,] Percent   45.5 0.3 1.9  52.2 100

B Impact (ordinal [0,1,2,3], v3_leq_C_22B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq22e_aend_karriere,v3_con$v3_leq_c_d_leq22e,"v3_leq_C_22B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 812  2   8   12  19  933  1786
## [2,] Percent   45.5 0.1 0.4 0.7 1.1 52.2 100

23. “Problem in school, college, or training program”

23A Nature (dichotomous [“good”,“bad”], v3_leq_C_23A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq23a_schulprob,v3_con$v3_leq_c_d_leq23a,"v3_leq_C_23A")

##                -999 bad <NA>     
## [1,] No. cases 836  17  933  1786
## [2,] Percent   46.8 1   52.2 100

23B Impact (ordinal [0,1,2,3], v3_leq_C_23B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq23e_schulprob,v3_con$v3_leq_c_d_leq23e,"v3_leq_C_23B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 835  2   3   8   5   933  1786
## [2,] Percent   46.8 0.1 0.2 0.4 0.3 52.2 100

Create dataset

v3_leq_C<-data.frame(v3_leq_C_20A,v3_leq_C_20B,v3_leq_C_21A,v3_leq_C_21B,v3_leq_C_22A,v3_leq_C_22B,v3_leq_C_23A,v3_leq_C_23B)

Residence

24. “Difficulty finding housing”

24A Nature (dichotomous [“good”,“bad”], v3_leq_D_24A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq24a_schw_wsuche,v3_con$v3_leq_c_d_leq24a,"v3_leq_D_24A")

##                -999 bad good <NA>     
## [1,] No. cases 801  43  9    933  1786
## [2,] Percent   44.8 2.4 0.5  52.2 100

24B Impact (ordinal [0,1,2,3], v3_leq_D_24B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq24e_schw_wsuche,v3_con$v3_leq_c_d_leq24e,"v3_leq_D_24B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 801  2   11  18 21  933  1786
## [2,] Percent   44.8 0.1 0.6 1  1.2 52.2 100

25. “Changing residence within the same town or city”

A Nature (dichotomous [“good”,“bad”], v3_leq_D_25A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq25a_umzug_nah,v3_con$v3_leq_c_d_leq25a,"v3_leq_D_25A")

##                -999 bad good <NA>     
## [1,] No. cases 809  6   38   933  1786
## [2,] Percent   45.3 0.3 2.1  52.2 100

B Impact (ordinal [0,1,2,3], v3_leq_D_25B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq25e_umzug_nah,v3_con$v3_leq_c_d_leq25e,"v3_leq_D_25B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 809  4   3   12  25  933  1786
## [2,] Percent   45.3 0.2 0.2 0.7 1.4 52.2 100

26. “Moving to a different town, city, state, or country”

26A Nature (dichotomous [“good”,“bad”], v3_leq_D_26A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq26a_umzug_fern,v3_con$v3_leq_c_d_leq26a,"v3_leq_D_26A")

##                -999 bad good <NA>     
## [1,] No. cases 821  4   28   933  1786
## [2,] Percent   46   0.2 1.6  52.2 100

26B Impact (ordinal [0,1,2,3], v3_leq_D_26B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq26e_umzug_fern,v3_con$v3_leq_c_d_leq26e,"v3_leq_D_26B")

##                -999 1   2   3   <NA>     
## [1,] No. cases 821  3   5   24  933  1786
## [2,] Percent   46   0.2 0.3 1.3 52.2 100

27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”

27A Nature (dichotomous [“good”,“bad”], v3_leq_D_27A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq27a_veraend_lu,v3_con$v3_leq_c_d_leq27a,"v3_leq_D_27A")

##                -999 bad good <NA>     
## [1,] No. cases 735  44  74   933  1786
## [2,] Percent   41.2 2.5 4.1  52.2 100

27B Impact (ordinal [0,1,2,3], v3_leq_D_27B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq27e_veraend_lu,v3_con$v3_leq_c_d_leq27e,"v3_leq_D_27B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 734  4   23  51  41  933  1786
## [2,] Percent   41.1 0.2 1.3 2.9 2.3 52.2 100

Create dataset

v3_leq_D<-data.frame(v3_leq_D_24A,v3_leq_D_24B,v3_leq_D_25A,v3_leq_D_25B,v3_leq_D_26A,
                     v3_leq_D_26B,v3_leq_D_27A,v3_leq_D_27B)

Love and marriage

28. “Began a new, close, personal relationship”

28A Nature (dichotomous [“good”,“bad”], v3_leq_E_28A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq28a_neue_bez,v3_con$v3_leq_e_leq28a,"v3_leq_E_28A")

##                -999 bad good <NA>     
## [1,] No. cases 772  4   77   933  1786
## [2,] Percent   43.2 0.2 4.3  52.2 100

28B Impact (ordinal [0,1,2,3], v3_leq_E_28B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq28e_neue_bez,v3_con$v3_leq_e_leq28e,"v3_leq_E_28B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 771  4   7   31  40  933  1786
## [2,] Percent   43.2 0.2 0.4 1.7 2.2 52.2 100

29. “Became engaged”

29A Nature (dichotomous [“good”,“bad”], v3_leq_E_29A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq29a_verlobung,v3_con$v3_leq_e_leq29a,"v3_leq_E_29A")

##                -999 good <NA>     
## [1,] No. cases 845  8    933  1786
## [2,] Percent   47.3 0.4  52.2 100

29B Impact (ordinal [0,1,2,3], v3_leq_E_29B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq29e_verlobung,v3_con$v3_leq_e_leq29e,"v3_leq_E_29B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 844  1   1   2   5   933  1786
## [2,] Percent   47.3 0.1 0.1 0.1 0.3 52.2 100

30. “Girlfriend or boyfriend problems”

30A Nature (dichotomous [“good”,“bad”], v3_leq_E_30A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq30a_prob_partner,v3_con$v3_leq_e_leq30a,"v3_leq_E_30A")

##                -999 bad good <NA>     
## [1,] No. cases 741  106 6    933  1786
## [2,] Percent   41.5 5.9 0.3  52.2 100

30B Impact (ordinal [0,1,2,3], v3_leq_E_30B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq30e_prob_partner,v3_con$v3_leq_e_leq30e,"v3_leq_E_30B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 740  5   28  43  37  933  1786
## [2,] Percent   41.4 0.3 1.6 2.4 2.1 52.2 100

31. “Breaking up with a girlfriend or breaking an engagement”

31A Nature (dichotomous [“good”,“bad”], v3_leq_E_31A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq31a_trennung,v3_con$v3_leq_e_leq31a,"v3_leq_E_31A")

##                -999 bad good <NA>     
## [1,] No. cases 794  36  23   933  1786
## [2,] Percent   44.5 2   1.3  52.2 100

31B Impact (ordinal [0,1,2,3], v3_leq_E_31B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq31e_trennung,v3_con$v3_leq_e_leq31e,"v3_leq_E_31B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 794  2   8   18 31  933  1786
## [2,] Percent   44.5 0.1 0.4 1  1.7 52.2 100

32. “(Male) Wife or girlfriend’s pregnancy”

32A Nature (dichotomous [“good”,“bad”], v3_leq_E_32A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq32a_schwanger_p,v3_con$v3_leq_e_leq32a,"v3_leq_E_32A")

##                -999 good <NA>     
## [1,] No. cases 849  4    933  1786
## [2,] Percent   47.5 0.2  52.2 100

32B Impact (ordinal [0,1,2,3], v3_leq_E_32B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq32e_schwanger_p,v3_con$v3_leq_e_leq32e,"v3_leq_E_32B")

##                -999 0   3   <NA>     
## [1,] No. cases 849  1   3   933  1786
## [2,] Percent   47.5 0.1 0.2 52.2 100

33. “(Male) Wife or girlfriend having a miscarriage or abortion”

33A Nature (dichotomous [“good”,“bad”], v3_leq_E_33A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq33a_fehlg_abtr_p,v3_con$v3_leq_e_leq33a,"v3_leq_E_33A")

##                -999 bad <NA>     
## [1,] No. cases 851  2   933  1786
## [2,] Percent   47.6 0.1 52.2 100

33B Impact (ordinal [0,1,2,3], v3_leq_E_33B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq33e_fehlg_abtr_p,v3_con$v3_leq_e_leq33e,"v3_leq_E_33B")

##                -999 2   3   <NA>     
## [1,] No. cases 851  1   1   933  1786
## [2,] Percent   47.6 0.1 0.1 52.2 100

34. “Getting married (or beginning to live with someone)”

34A Nature (dichotomous [“good”,“bad”], v3_leq_E_34A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq34a_heirat,v3_con$v3_leq_e_leq34a,"v3_leq_E_34A")

##                -999 bad good <NA>     
## [1,] No. cases 839  2   12   933  1786
## [2,] Percent   47   0.1 0.7  52.2 100

34B Impact (ordinal [0,1,2,3], v3_leq_E_34B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq34e_heirat,v3_con$v3_leq_e_leq34e,"v3_leq_E_34B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 839  2   4   5   3   933  1786
## [2,] Percent   47   0.1 0.2 0.3 0.2 52.2 100

35. “A change in closeness with your partner”

35A Nature (dichotomous [“good”,“bad”], v3_leq_E_35A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq35a_veraend_naehe,v3_con$v3_leq_e_leq35a,"v3_leq_E_35A")

##                -999 bad good <NA>     
## [1,] No. cases 758  43  52   933  1786
## [2,] Percent   42.4 2.4 2.9  52.2 100

35B Impact (ordinal [0,1,2,3], v3_leq_E_35B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq35e_veraend_naehe,v3_con$v3_leq_e_leq35e,"v3_leq_E_35B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 757  3   15  35 43  933  1786
## [2,] Percent   42.4 0.2 0.8 2  2.4 52.2 100

36. “Infidelity”

36A Nature (dichotomous [“good”,“bad”], v3_leq_E_36A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq36a_untreue,v3_con$v3_leq_e_leq36a,"v3_leq_E_36A")

##                -999 bad good <NA>     
## [1,] No. cases 834  17  2    933  1786
## [2,] Percent   46.7 1   0.1  52.2 100

36B Impact (ordinal [0,1,2,3], v3_leq_E_36B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq36e_untreue,v3_con$v3_leq_e_leq36e,"v3_leq_E_36B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 833  1   7   4   8   933  1786
## [2,] Percent   46.6 0.1 0.4 0.2 0.4 52.2 100

37. “Trouble with in-laws”

37A Nature (dichotomous [“good”,“bad”], v3_leq_E_37A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq37a_konf_schwiege,v3_con$v3_leq_e_leq37a,"v3_leq_E_37A")

##                -999 bad good <NA>     
## [1,] No. cases 823  27  3    933  1786
## [2,] Percent   46.1 1.5 0.2  52.2 100

37B Impact (ordinal [0,1,2,3], v3_leq_E_37B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq37e_konf_schwiege,v3_con$v3_leq_e_leq37e,"v3_leq_E_37B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 822  2   13  8   8   933  1786
## [2,] Percent   46   0.1 0.7 0.4 0.4 52.2 100

38. “Separation from spouse or partner due to conflict”

38A Nature (dichotomous [“good”,“bad”], v3_leq_E_38A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq38a_trennung_str,v3_con$v3_leq_e_leq38a,"v3_leq_E_38A")

##                -999 bad good <NA>     
## [1,] No. cases 838  10  5    933  1786
## [2,] Percent   46.9 0.6 0.3  52.2 100

38B Impact (ordinal [0,1,2,3], v3_leq_E_38B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq38e_trennung_str,v3_con$v3_leq_e_leq38e,"v3_leq_E_38B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 836  2   2   4   9   933  1786
## [2,] Percent   46.8 0.1 0.1 0.2 0.5 52.2 100

39. “Separation from spouse or partner due to work, travel, etc.”

39A Nature (dichotomous [“good”,“bad”], v3_leq_E_39A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq39a_trennung_ber,v3_con$v3_leq_e_leq39a,"v3_leq_E_39A")

##                -999 bad good <NA>     
## [1,] No. cases 848  4   1    933  1786
## [2,] Percent   47.5 0.2 0.1  52.2 100

39B Impact (ordinal [0,1,2,3], v3_leq_E_39B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq39e_trennung_ber,v3_con$v3_leq_e_leq39e,"v3_leq_E_39B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 847  2   2   2   933  1786
## [2,] Percent   47.4 0.1 0.1 0.1 52.2 100

40. “Reconciliation with spouse or partner”

40A Nature (dichotomous [“good”,“bad”], v3_leq_E_40A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq40a_versoehnung,v3_con$v3_leq_e_leq40a,"v3_leq_E_40A")

##                -999 good <NA>     
## [1,] No. cases 831  22   933  1786
## [2,] Percent   46.5 1.2  52.2 100

40B Impact (ordinal [0,1,2,3], v3_leq_E_40B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq40a_versoehnung,v3_con$v3_leq_e_leq40e,"v3_leq_E_40B")

##                -999 1  2   3   <NA>     
## [1,] No. cases 831  18 2   2   933  1786
## [2,] Percent   46.5 1  0.1 0.1 52.2 100

41. “Divorce”

41A Nature (dichotomous [“good”,“bad”], v3_leq_E_41A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq41a_scheidung,v3_con$v3_leq_e_leq41a,"v3_leq_E_41A")

##                -999 bad good <NA>     
## [1,] No. cases 844  5   4    933  1786
## [2,] Percent   47.3 0.3 0.2  52.2 100

41B Impact (ordinal [0,1,2,3], v3_leq_E_41B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq41e_scheidung,v3_con$v3_leq_e_leq41e,"v3_leq_E_41B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 843  3   2   5   933  1786
## [2,] Percent   47.2 0.2 0.1 0.3 52.2 100

42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”

42A Nature (dichotomous [“good”,“bad”], v3_leq_E_42A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq42a_veraend_taet,v3_con$v3_leq_e_leq42a,"v3_leq_E_42A")

##                -999 bad good <NA>     
## [1,] No. cases 818  10  25   933  1786
## [2,] Percent   45.8 0.6 1.4  52.2 100

42B Impact (ordinal [0,1,2,3], v3_leq_E_42B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq42e_veraend_taet,v3_con$v3_leq_e_leq42e,"v3_leq_E_42B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 817  2   11  12  11  933  1786
## [2,] Percent   45.7 0.1 0.6 0.7 0.6 52.2 100

Create dataset

v3_leq_E<-data.frame(v3_leq_E_28A,v3_leq_E_28B,v3_leq_E_29A,v3_leq_E_29B,v3_leq_E_30A,
                     v3_leq_E_30B,v3_leq_E_31A,v3_leq_E_31B,v3_leq_E_32A,v3_leq_E_32B,
                     v3_leq_E_33A,v3_leq_E_33B,v3_leq_E_34A,v3_leq_E_34B,v3_leq_E_35A,
                     v3_leq_E_35B,v3_leq_E_36A,v3_leq_E_36B,v3_leq_E_37A,v3_leq_E_37B,
                     v3_leq_E_38A,v3_leq_E_38B,v3_leq_E_39A,v3_leq_E_39B,v3_leq_E_40A,
                     v3_leq_E_40B,v3_leq_E_41A,v3_leq_E_41B,v3_leq_E_42A,v3_leq_E_42B)

Family and close friends

43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”

43A Nature (dichotomous [“good”,“bad”], v3_leq_F_43A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq43a_neu_fmitglied,v3_con$v3_leq_f_g_leq43a,"v3_leq_F_43A")

##                -999 bad good <NA>     
## [1,] No. cases 805  4   44   933  1786
## [2,] Percent   45.1 0.2 2.5  52.2 100

43B Impact (ordinal [0,1,2,3], v3_leq_F_43B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq43e_neu_fmitglied,v3_con$v3_leq_f_g_leq43e,"v3_leq_F_43B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 804  6   12  15  16  933  1786
## [2,] Percent   45   0.3 0.7 0.8 0.9 52.2 100

44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”

44A Nature (dichotomous [“good”,“bad”], v3_leq_F_44A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq44a_auszug_fm,v3_con$v3_leq_f_g_leq44a,"v3_leq_F_44A")

##                -999 bad good <NA>     
## [1,] No. cases 830  9   14   933  1786
## [2,] Percent   46.5 0.5 0.8  52.2 100

44B Impact (ordinal [0,1,2,3], v3_leq_F_44B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq44e_auszug_fm,v3_con$v3_leq_f_g_leq44e,"v3_leq_F_44B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 829  3   5   5   11  933  1786
## [2,] Percent   46.4 0.2 0.3 0.3 0.6 52.2 100

45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”

45A Nature (dichotomous [“good”,“bad”], v3_leq_F_45A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq45a_gz_verh_fm,v3_con$v3_leq_f_g_leq45a,"v3_leq_F_45A")

##                -999 bad good <NA>     
## [1,] No. cases 714  126 13   933  1786
## [2,] Percent   40   7.1 0.7  52.2 100

45B Impact (ordinal [0,1,2,3], v3_leq_F_45B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq45e_gz_verh_fm,v3_con$v3_leq_f_g_leq45e,"v3_leq_F_45B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 713  8   31  54 47  933  1786
## [2,] Percent   39.9 0.4 1.7 3  2.6 52.2 100

46. “Death of spouse or partner”

46A Nature (dichotomous [“good”,“bad”], v3_leq_F_46A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq46a_tod_partner,v3_con$v3_leq_f_g_leq46a,"v3_leq_F_46A")

##                -999 bad <NA>     
## [1,] No. cases 851  2   933  1786
## [2,] Percent   47.6 0.1 52.2 100

46B Impact (ordinal [0,1,2,3], v3_leq_F_46B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq46e_tod_partner,v3_con$v3_leq_f_g_leq46e,"v3_leq_F_46B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 849  2   1   1   933  1786
## [2,] Percent   47.5 0.1 0.1 0.1 52.2 100

47. “Death of a child”

47A Nature (dichotomous [“good”,“bad”], v3_leq_F_47A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq47a_tod_kind,v3_con$v3_leq_f_g_leq47a,"v3_leq_F_47A")

##                -999 bad <NA>     
## [1,] No. cases 850  3   933  1786
## [2,] Percent   47.6 0.2 52.2 100

47B Impact (ordinal [0,1,2,3], v3_leq_F_47B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq47e_tod_kind,v3_con$v3_leq_f_g_leq47e,"v3_leq_F_47B")

##                -999 0   3   <NA>     
## [1,] No. cases 848  2   3   933  1786
## [2,] Percent   47.5 0.1 0.2 52.2 100

48. “Death of family member or close friend”

48A Nature (dichotomous [“good”,“bad”], v3_leq_F_48A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq48a_tod_fm_ef,v3_con$v3_leq_f_g_leq48a,"v3_leq_F_48A")

##                -999 bad good <NA>     
## [1,] No. cases 779  69  5    933  1786
## [2,] Percent   43.6 3.9 0.3  52.2 100

48B Impact (ordinal [0,1,2,3], v3_leq_F_48B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq48e_tod_fm_ef,v3_con$v3_leq_f_g_leq48e,"v3_leq_F_48B")

##                -999 0   1  2  3   <NA>     
## [1,] No. cases 777  13  18 17 28  933  1786
## [2,] Percent   43.5 0.7 1  1  1.6 52.2 100

49. “Birth of a grandchild”

49A Nature (dichotomous [“good”,“bad”], v3_leq_F_49A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq49a_geb_enkel,v3_con$v3_leq_f_g_leq49a,"v3_leq_F_49A")

##                -999 good <NA>     
## [1,] No. cases 835  18   933  1786
## [2,] Percent   46.8 1    52.2 100

49B Impact (ordinal [0,1,2,3], v3_leq_F_49B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq49e_geb_enkel,v3_con$v3_leq_f_g_leq49e,"v3_leq_F_49B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 834  1   4   2   12  933  1786
## [2,] Percent   46.7 0.1 0.2 0.1 0.7 52.2 100

50. “Change in marital status of your parents”

50A Nature (dichotomous [“good”,“bad”], v3_leq_F_50A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq50a_fstand_eltern,v3_con$v3_leq_f_g_leq50a,"v3_leq_F_50A")

##                -999 bad good <NA>     
## [1,] No. cases 848  3   2    933  1786
## [2,] Percent   47.5 0.2 0.1  52.2 100

50B Impact (ordinal [0,1,2,3], v3_leq_F_50B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq50e_fstand_eltern,v3_con$v3_leq_f_g_leq50e,"v3_leq_F_50B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 847  1   2   3   933  1786
## [2,] Percent   47.4 0.1 0.1 0.2 52.2 100

Create dataset

v3_leq_F<-data.frame(v3_leq_F_43A,v3_leq_F_43B,v3_leq_F_44A,v3_leq_F_44B,v3_leq_F_45A,
                     v3_leq_F_45B,v3_leq_F_46A,v3_leq_F_46B,v3_leq_F_47A,v3_leq_F_47B,
                     v3_leq_F_48A,v3_leq_F_48B,v3_leq_F_49A,v3_leq_F_49B,v3_leq_F_50A,
                     v3_leq_F_50B)

Parenting

51. “Change in child care arrangements”

51A Nature (dichotomous [“good”,“bad”], v3_leq_G_51A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq51a_kindbetr,v3_con$v3_leq_f_g_leq51a,"v3_leq_G_51A")

##                -999 bad good <NA>     
## [1,] No. cases 834  4   15   933  1786
## [2,] Percent   46.7 0.2 0.8  52.2 100

51B Impact (ordinal [0,1,2,3], v3_leq_G_51B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq51e_kindbetr,v3_con$v3_leq_f_g_leq51e,"v3_leq_G_51B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 833  1   5   5   9   933  1786
## [2,] Percent   46.6 0.1 0.3 0.3 0.5 52.2 100

52. “Conflicts with spouse or partner about parenting”

52A Nature (dichotomous [“good”,“bad”], v3_leq_G_52A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq52a_konf_eschaft,v3_con$v3_leq_f_g_leq52a,"v3_leq_G_52A")

##                -999 bad good <NA>     
## [1,] No. cases 840  11  2    933  1786
## [2,] Percent   47   0.6 0.1  52.2 100

52B Impact (ordinal [0,1,2,3], v3_leq_G_52B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq52e_konf_eschaft,v3_con$v3_leq_f_g_leq52e,"v3_leq_G_52B")

##                -999 1   2   3   <NA>     
## [1,] No. cases 839  6   6   2   933  1786
## [2,] Percent   47   0.3 0.3 0.1 52.2 100

53. “Conflicts with child’s grandparents (or other important person) about parenting”

53A Nature (dichotomous [“good”,“bad”], v3_leq_G_53A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq53a_konf_geltern,v3_con$v3_leq_f_g_leq53a,"v3_leq_G_53A")

##                -999 bad good <NA>     
## [1,] No. cases 844  8   1    933  1786
## [2,] Percent   47.3 0.4 0.1  52.2 100

53B Impact (ordinal [0,1,2,3], v3_leq_G_53B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq53e_konf_geltern,v3_con$v3_leq_f_g_leq53e,"v3_leq_G_53B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 843  1   1   4   4   933  1786
## [2,] Percent   47.2 0.1 0.1 0.2 0.2 52.2 100

54. “Taking on full responsibility for parenting as a single parent”

54A Nature (dichotomous [“good”,“bad”], v3_leq_G_54A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq54a_alleinerz,v3_con$v3_leq_f_g_leq54a,"v3_leq_G_54A")

##                -999 bad good <NA>     
## [1,] No. cases 845  1   7    933  1786
## [2,] Percent   47.3 0.1 0.4  52.2 100

54B Impact (ordinal [0,1,2,3], v3_leq_G_54B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq54e_alleinerz,v3_con$v3_leq_f_g_leq54e,"v3_leq_G_54B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 844  1   1   2   5   933  1786
## [2,] Percent   47.3 0.1 0.1 0.1 0.3 52.2 100

55. “Custody battles with former spouse or partner”

55A Nature (dichotomous [“good”,“bad”], v3_leq_G_55A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq55a_sorgerecht,v3_con$v3_leq_f_g_leq55a,"v3_leq_G_55A")

##                -999 bad good <NA>     
## [1,] No. cases 845  7   1    933  1786
## [2,] Percent   47.3 0.4 0.1  52.2 100

55B Impact (ordinal [0,1,2,3], v3_leq_G_55B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq55e_sorgerecht,v3_con$v3_leq_f_g_leq55e,"v3_leq_G_55B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 844  2   2   1   4   933  1786
## [2,] Percent   47.3 0.1 0.1 0.1 0.2 52.2 100

Create dataset

v3_leq_G<-data.frame(v3_leq_G_51A,v3_leq_G_51B,v3_leq_G_52A,v3_leq_G_52B,v3_leq_G_53A,
                     v3_leq_G_53B,v3_leq_G_54A,v3_leq_G_54B,v3_leq_G_55A,v3_leq_G_55B)

Personal or social

56. “Major personal achievement”

56A Nature (dichotomous [“good”,“bad”], v3_leq_H_56A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq56a_pers_leistung,v3_con$v3_leq_h_leq56a,"v3_leq_H_56A")

##                -999 bad good <NA>     
## [1,] No. cases 699  6   148  933  1786
## [2,] Percent   39.1 0.3 8.3  52.2 100

56B Impact (ordinal [0,1,2,3], v3_leq_H_56B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq56e_pers_leistung,v3_con$v3_leq_h_leq56e,"v3_leq_H_56B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 696  5   28  62  62  933  1786
## [2,] Percent   39   0.3 1.6 3.5 3.5 52.2 100

57. “Major decision regarding your immediate future”

57A Nature (dichotomous [“good”,“bad”], v3_leq_H_57A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq57a_wicht_entsch,v3_con$v3_leq_h_leq57a,"v3_leq_H_57A")

##                -999 bad good <NA>     
## [1,] No. cases 614  29  210  933  1786
## [2,] Percent   34.4 1.6 11.8 52.2 100

57B Impact (ordinal [0,1,2,3], v3_leq_H_57B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq57e_wicht_entsch,v3_con$v3_leq_h_leq57e,"v3_leq_H_57B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 611  1   41  87  113 933  1786
## [2,] Percent   34.2 0.1 2.3 4.9 6.3 52.2 100

58. “Change in your personal habits (your dress, life-style, hobbies, etc.)”

58A Nature (dichotomous [“good”,“bad”], v3_leq_H_58A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq58a_pers_gewohn,v3_con$v3_leq_h_leq58a,"v3_leq_H_58A")

##                -999 bad good <NA>     
## [1,] No. cases 697  17  139  933  1786
## [2,] Percent   39   1   7.8  52.2 100

58B Impact (ordinal [0,1,2,3], v3_leq_H_58B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq58e_pers_gewohn,v3_con$v3_leq_h_leq58e,"v3_leq_H_58B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 695  7   42  61  48  933  1786
## [2,] Percent   38.9 0.4 2.4 3.4 2.7 52.2 100

59. “Change in your religious beliefs”

59A Nature (dichotomous [“good”,“bad”], v3_leq_H_59A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq59a_relig_ueberz,v3_con$v3_leq_h_leq59a,"v3_leq_H_59A")

##                -999 bad good <NA>     
## [1,] No. cases 818  1   34   933  1786
## [2,] Percent   45.8 0.1 1.9  52.2 100

59B Impact (ordinal [0,1,2,3], v3_leq_H_59B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq59e_relig_ueberz,v3_con$v3_leq_h_leq59e,"v3_leq_H_59B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 815  6   8   14  10  933  1786
## [2,] Percent   45.6 0.3 0.4 0.8 0.6 52.2 100

60. “Change in your political beliefs”

60A Nature (dichotomous [“good”,“bad”], v3_leq_H_60A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq60a_pol_ansichten,v3_clin$v3_leq_h_leq60a,"v3_leq_H_60A")

## Warning in (is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con != 2) & is.na(leq_con_old_name) == : Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes

## Warning in (is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con != 2) & is.na(leq_con_old_name): Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes

##                -999 bad good <NA>     
## [1,] No. cases 816  7   30   933  1786
## [2,] Percent   45.7 0.4 1.7  52.2 100

60B Impact (ordinal [0,1,2,3], v3_leq_H_60B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq60e_pol_ansichten,v3_con$v3_leq_h_leq60e,"v3_leq_H_60B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 809  5   19  14  6   933  1786
## [2,] Percent   45.3 0.3 1.1 0.8 0.3 52.2 100

61. “Loss or damage of personal property”

61A Nature (dichotomous [“good”,“bad”], v3_leq_H_61A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq61a_pers_eigent,v3_con$v3_leq_h_leq61a,"v3_leq_H_61A")

##                -999 bad good <NA>     
## [1,] No. cases 810  39  4    933  1786
## [2,] Percent   45.4 2.2 0.2  52.2 100

61B Impact (ordinal [0,1,2,3], v3_leq_H_61B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq61e_pers_eigent,v3_con$v3_leq_h_leq61e,"v3_leq_H_61B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 809  3   16  13  12  933  1786
## [2,] Percent   45.3 0.2 0.9 0.7 0.7 52.2 100

62. “Took a vacation”

62A Nature (dichotomous [“good”,“bad”], v3_leq_H_62A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq62a_erholungsurl,v3_con$v3_leq_h_leq62a,"v3_leq_H_62A")

##                -999 bad good <NA>     
## [1,] No. cases 580  9   264  933  1786
## [2,] Percent   32.5 0.5 14.8 52.2 100

62B Impact (ordinal [0,1,2,3], v3_leq_H_62B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq62e_erholungsurl,v3_con$v3_leq_h_leq62e,"v3_leq_H_62B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 578  16  48  122 89 933  1786
## [2,] Percent   32.4 0.9 2.7 6.8 5  52.2 100

63. “Took a trip other than a vacation”

63A Nature (dichotomous [“good”,“bad”], v3_leq_H_63A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq63a_reise_andere,v3_con$v3_leq_h_leq63a,"v3_leq_H_63A")

##                -999 bad good <NA>     
## [1,] No. cases 740  6   107  933  1786
## [2,] Percent   41.4 0.3 6    52.2 100

63B Impact (ordinal [0,1,2,3], v3_leq_H_63B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq63e_reise_andere,v3_con$v3_leq_h_leq63e,"v3_leq_H_63B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 739  5   32  48  29  933  1786
## [2,] Percent   41.4 0.3 1.8 2.7 1.6 52.2 100

64. “Change in family get-togethers”

64A Nature (dichotomous [“good”,“bad”], v3_leq_H_64A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq64a_familientreff,v3_con$v3_leq_h_leq64a,"v3_leq_H_64A")

##                -999 bad good <NA>     
## [1,] No. cases 782  19  52   933  1786
## [2,] Percent   43.8 1.1 2.9  52.2 100

64B Impact (ordinal [0,1,2,3], v3_leq_H_64B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq64e_familientreff,v3_con$v3_leq_h_leq64e,"v3_leq_H_64B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 781  5   18 28  21  933  1786
## [2,] Percent   43.7 0.3 1  1.6 1.2 52.2 100

65. “Change in your social activities (clubs, movies, visiting)”

65A Nature (dichotomous [“good”,“bad”], v3_leq_H_65A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq65a_ges_unternehm,v3_con$v3_leq_h_leq65a,"v3_leq_H_65A")

##                -999 bad good <NA>     
## [1,] No. cases 758  15  80   933  1786
## [2,] Percent   42.4 0.8 4.5  52.2 100

65B Impact (ordinal [0,1,2,3], v3_leq_H_65B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq65e_ges_unternehm,v3_con$v3_leq_h_leq65e,"v3_leq_H_65B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 753  4   35 34  27  933  1786
## [2,] Percent   42.2 0.2 2  1.9 1.5 52.2 100

66. “Made new friends”

66A Nature (dichotomous [“good”,“bad”], v3_leq_H_66A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq66a_neue_freunds,v3_con$v3_leq_h_leq66a,"v3_leq_H_66A")

##                -999 bad good <NA>     
## [1,] No. cases 653  3   197  933  1786
## [2,] Percent   36.6 0.2 11   52.2 100

66B Impact (ordinal [0,1,2,3], v3_leq_H_66B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq66e_neue_freunds,v3_con$v3_leq_h_leq66e,"v3_leq_H_66B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 650  7   57  87  52  933  1786
## [2,] Percent   36.4 0.4 3.2 4.9 2.9 52.2 100

67. “Broke up with a friend”

67A Nature (dichotomous [“good”,“bad”], v3_leq_H_67A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq67a_ende_freunds,v3_con$v3_leq_h_leq67a,"v3_leq_H_67A")

##                -999 bad good <NA>     
## [1,] No. cases 781  50  22   933  1786
## [2,] Percent   43.7 2.8 1.2  52.2 100

67B Impact (ordinal [0,1,2,3], v3_leq_H_67B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq67e_ende_freunds,v3_con$v3_leq_h_leq67e,"v3_leq_H_67B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 780  7   28  19  19  933  1786
## [2,] Percent   43.7 0.4 1.6 1.1 1.1 52.2 100

68. “Acquired or lost a pet”

68A Nature (dichotomous [“good”,“bad”], v3_leq_H_68A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq68a_haustier,v3_con$v3_leq_h_leq68a,"v3_leq_H_68A")

##                -999 bad good <NA>     
## [1,] No. cases 804  20  29   933  1786
## [2,] Percent   45   1.1 1.6  52.2 100

68B Impact (ordinal [0,1,2,3], v3_leq_H_68B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq68e_haustier,v3_con$v3_leq_h_leq68e,"v3_leq_H_68B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 803  2   4   17 27  933  1786
## [2,] Percent   45   0.1 0.2 1  1.5 52.2 100

Create dataset

v3_leq_H<-data.frame(v3_leq_H_56A,v3_leq_H_56B,v3_leq_H_57A,v3_leq_H_57B,v3_leq_H_58A,
                     v3_leq_H_58B,v3_leq_H_59A,v3_leq_H_59B,v3_leq_H_60A,v3_leq_H_60B,
                     v3_leq_H_61A,v3_leq_H_61B,v3_leq_H_62A,v3_leq_H_62B,v3_leq_H_63A,
                     v3_leq_H_63B,v3_leq_H_64A,v3_leq_H_64B,v3_leq_H_65A,v3_leq_H_65B,
                     v3_leq_H_66A,v3_leq_H_66B,v3_leq_H_67A,v3_leq_H_67B,v3_leq_H_68A,
                     v3_leq_H_68B)

Financial

69. “Major change in finances (increased or decreased income)”

69A Nature (dichotomous [“good”,“bad”], v3_leq_I_69A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq69a_finanz_sit,v3_con$v3_leq_i_j_k_leq69a,"v3_leq_I_69A")

##                -999 bad good <NA>     
## [1,] No. cases 661  77  115  933  1786
## [2,] Percent   37   4.3 6.4  52.2 100

69B Impact (ordinal [0,1,2,3], v3_leq_I_69B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq69e_finanz_sit,v3_con$v3_leq_i_j_k_leq69e,"v3_leq_I_69B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 656  8   45  73  71 933  1786
## [2,] Percent   36.7 0.4 2.5 4.1 4  52.2 100

70. “Took on a moderate purchase, such as TV, car, freezer, etc.”

70A Nature (dichotomous [“good”,“bad”], v3_leq_I_70A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq70a_finanz_verpfl,v3_con$v3_leq_i_j_k_leq70a,"v3_leq_I_70A")

##                -999 bad good <NA>     
## [1,] No. cases 798  18  37   933  1786
## [2,] Percent   44.7 1   2.1  52.2 100

70B Impact (ordinal [0,1,2,3], v3_leq_I_70B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq70e_finanz_verpfl,v3_con$v3_leq_i_j_k_leq70e,"v3_leq_I_70B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 797  4   22  20  10  933  1786
## [2,] Percent   44.6 0.2 1.2 1.1 0.6 52.2 100

71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”

71A Nature (dichotomous [“good”,“bad”], v3_leq_I_71A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq71a_hypothek,v3_con$v3_leq_i_j_k_leq71a,"v3_leq_I_71A")

##                -999 bad good <NA>     
## [1,] No. cases 841  5   7    933  1786
## [2,] Percent   47.1 0.3 0.4  52.2 100

71B Impact (ordinal [0,1,2,3], v3_leq_I_71B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq71e_hypothek,v3_con$v3_leq_i_j_k_leq71e,"v3_leq_I_71B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 840  3   1   4   5   933  1786
## [2,] Percent   47   0.2 0.1 0.2 0.3 52.2 100

72. “Experienced a foreclosure on a mortgage or loan”

72A Nature (dichotomous [“good”,“bad”], v3_leq_I_72A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq72a_hypoth_kuend,v3_con$v3_leq_i_j_k_leq72a,"v3_leq_I_72A")

##                -999 bad good <NA>     
## [1,] No. cases 843  1   9    933  1786
## [2,] Percent   47.2 0.1 0.5  52.2 100

72B Impact (ordinal [0,1,2,3], v3_leq_I_72B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq72e_hypoth_kuend,v3_con$v3_leq_i_j_k_leq72e,"v3_leq_I_72B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 842  2   1   4   4   933  1786
## [2,] Percent   47.1 0.1 0.1 0.2 0.2 52.2 100

73. “Credit rating difficulties”

73A Nature (dichotomous [“good”,“bad”], v3_leq_I_73A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq73a_kreditwuerdk,v3_con$v3_leq_i_j_k_leq73a,"v3_leq_I_73A")

##                -999 bad good <NA>     
## [1,] No. cases 829  23  1    933  1786
## [2,] Percent   46.4 1.3 0.1  52.2 100

73B Impact (ordinal [0,1,2,3], v3_leq_I_73B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq73e_kreditwuerdk,v3_con$v3_leq_i_j_k_leq73e,"v3_leq_I_73B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 827  5   3   6   12  933  1786
## [2,] Percent   46.3 0.3 0.2 0.3 0.7 52.2 100

Create dataset

v3_leq_I<-data.frame(v3_leq_I_69A,v3_leq_I_69B,v3_leq_I_70A,v3_leq_I_70B,v3_leq_I_71A,
                     v3_leq_I_71B,v3_leq_I_72A,v3_leq_I_72B,v3_leq_I_73A,v3_leq_I_73B)

Crime and legal matters

74. “Being robbed or victim of identity theft”

74A Nature (dichotomous [“good”,“bad”], v3_leq_J_74A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq74a_opf_diebstahl,v3_con$v3_leq_i_j_k_leq74a,"v3_leq_J_74A")

##                -999 bad good <NA>     
## [1,] No. cases 821  31  1    933  1786
## [2,] Percent   46   1.7 0.1  52.2 100

74B Impact (ordinal [0,1,2,3], v3_leq_J_74B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq74e_opf_diebstahl,v3_con$v3_leq_i_j_k_leq74e,"v3_leq_J_74B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 819  6   13  9   6   933  1786
## [2,] Percent   45.9 0.3 0.7 0.5 0.3 52.2 100

75. “Being a victim of a violent act (rape, assault, etc.)”

75A Nature (dichotomous [“good”,“bad”], v3_leq_J_75A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq75a_opf_gewalttat,v3_con$v3_leq_i_j_k_leq75a,"v3_leq_J_75A")

##                -999 bad <NA>     
## [1,] No. cases 845  8   933  1786
## [2,] Percent   47.3 0.4 52.2 100

75B Impact (ordinal [0,1,2,3], v3_leq_J_75B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq75e_opf_gewalttat,v3_con$v3_leq_i_j_k_leq75e,"v3_leq_J_75B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 843  3   3   4   933  1786
## [2,] Percent   47.2 0.2 0.2 0.2 52.2 100

76. “Involved in an accident”

76A Nature (dichotomous [“good”,“bad”], v3_leq_J_76A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq76a_unfall,v3_con$v3_leq_i_j_k_leq76a,"v3_leq_J_76A")

##                -999 bad <NA>     
## [1,] No. cases 832  21  933  1786
## [2,] Percent   46.6 1.2 52.2 100

76B Impact (ordinal [0,1,2,3], v3_leq_J_76B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq76e_unfall,v3_con$v3_leq_i_j_k_leq76e,"v3_leq_J_76B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 830  7   9   3   4   933  1786
## [2,] Percent   46.5 0.4 0.5 0.2 0.2 52.2 100

77. “Involved in a law suit”

77A Nature (dichotomous [“good”,“bad”], v3_leq_J_77A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq77a_rechtsstreit,v3_con$v3_leq_i_j_k_leq77a,"v3_leq_J_77A")

##                -999 bad good <NA>     
## [1,] No. cases 809  32  12   933  1786
## [2,] Percent   45.3 1.8 0.7  52.2 100

77B Impact (ordinal [0,1,2,3], v3_leq_J_77B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq77e_rechtsstreit,v3_con$v3_leq_i_j_k_leq77e,"v3_leq_J_77B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 807  3   16  11  16  933  1786
## [2,] Percent   45.2 0.2 0.9 0.6 0.9 52.2 100

78. “Involved in a minor violation of the law (traffic tickets, disturbing the peace, etc)”

78A Nature (dichotomous [“good”,“bad”], v3_leq_J_78A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq78a_owi,v3_con$v3_leq_i_j_k_leq78a,"v3_leq_J_78A")

##                -999 bad good <NA>     
## [1,] No. cases 812  40  1    933  1786
## [2,] Percent   45.5 2.2 0.1  52.2 100

78B Impact (ordinal [0,1,2,3], v3_leq_J_78B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq78e_owi,v3_con$v3_leq_i_j_k_leq78e,"v3_leq_J_78B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 810  16  15  8   4   933  1786
## [2,] Percent   45.4 0.9 0.8 0.4 0.2 52.2 100

79. “Legal troubles resulting in your being arrested or held in jail”

79A Nature (dichotomous [“good”,“bad”], v3_leq_J_79A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq79a_konf_gesetz,v3_con$v3_leq_i_j_k_leq79a,"v3_leq_J_79A")

##                -999 bad <NA>     
## [1,] No. cases 849  4   933  1786
## [2,] Percent   47.5 0.2 52.2 100

79B Impact (ordinal [0,1,2,3], v3_leq_J_79B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq79e_konf_gesetz,v3_con$v3_leq_i_j_k_leq79e,"v3_leq_J_79B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 848  2   1   2   933  1786
## [2,] Percent   47.5 0.1 0.1 0.1 52.2 100

Create dataset

v3_leq_J<-data.frame(v3_leq_J_74A,v3_leq_J_74B,v3_leq_J_75A,v3_leq_J_75B,v3_leq_J_76A,
                     v3_leq_J_76B,v3_leq_J_77A,v3_leq_J_77B,v3_leq_J_78A,v3_leq_J_78B,
                     v3_leq_J_79A,v3_leq_J_79B)

Create LEQ dataset

v3_leq<-data.frame(v3_leq_A,v3_leq_B,v3_leq_C,v3_leq_D,v3_leq_E,v3_leq_F,v3_leq_G,
                   v3_leq_H,v3_leq_I,v3_leq_J)

WHOQOL-BREF

For explanation, please refer to the section in Visit 1

1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v3_whoqol_itm1)

v3_quol_recode(v3_clin$v3_whoqol_bref_who1_lebensqualitaet,v3_con$v3_whoqol_bref_who1,"v3_whoqol_itm1",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 15  56  216  403  201  895  1786
## [2,] Percent   0.8 3.1 12.1 22.6 11.3 50.1 100

2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v3_whoqol_itm2)”

v3_quol_recode(v3_clin$v3_whoqol_bref_who2_gesundheit,v3_con$v3_whoqol_bref_who2,"v3_whoqol_itm2",0)

##                1  2   3    4    5   NA's     
## [1,] No. cases 36 149 182  365  160 894  1786
## [2,] Percent   2  8.3 10.2 20.4 9   50.1 100

3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v3_whoqol_itm3)

Coding reversed so that higher scores mean less impairment by pain.

v3_quol_recode(v3_clin$v3_whoqol_bref_who3_schmerzen,v3_con$v3_whoqol_bref_who3,"v3_whoqol_itm3",1)

##                1   2   3   4   5    NA's     
## [1,] No. cases 7   43  80  173 583  900  1786
## [2,] Percent   0.4 2.4 4.5 9.7 32.6 50.4 100

4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v3_whoqol_itm4)”

Coding reversed so that higher scores mean less dependence on medical treatment.

v3_quol_recode(v3_clin$v3_whoqol_bref_who4_med_behand,v3_con$v3_whoqol_bref_who4,"v3_whoqol_itm4",1)

##                1  2   3   4   5    NA's     
## [1,] No. cases 90 171 107 160 359  899  1786
## [2,] Percent   5  9.6 6   9   20.1 50.3 100

5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v3_whoqol_itm5)

v3_quol_recode(v3_clin$v3_whoqol_bref_who5_lebensgenuss,v3_con$v3_whoqol_bref_who5,"v3_whoqol_itm5",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 25  88  234  389  155 895  1786
## [2,] Percent   1.4 4.9 13.1 21.8 8.7 50.1 100

6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v3_whoqol_itm6)

v3_quol_recode(v3_clin$v3_whoqol_bref_who6_lebenssinn,v3_con$v3_whoqol_bref_who6,"v3_whoqol_itm6",0)

##                1   2  3   4    5    NA's     
## [1,] No. cases 46  89 169 320  264  898  1786
## [2,] Percent   2.6 5  9.5 17.9 14.8 50.3 100

7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v3_whoqol_itm7)

v3_quol_recode(v3_clin$v3_whoqol_bref_who7_konzentration,v3_con$v3_whoqol_bref_who7,"v3_whoqol_itm7",0)

##                1   2   3    4    5  NA's     
## [1,] No. cases 14  126 313  369  72 892  1786
## [2,] Percent   0.8 7.1 17.5 20.7 4  49.9 100

8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v3_whoqol_itm8)

v3_quol_recode(v3_clin$v3_whoqol_bref_who8_sicherheit,v3_con$v3_whoqol_bref_who8,"v3_whoqol_itm8",0)

##                1   2  3    4    5   NA's     
## [1,] No. cases 14  54 194  417  214 893  1786
## [2,] Percent   0.8 3  10.9 23.3 12  50   100

9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v3_whoqol_itm9)

v3_quol_recode(v3_clin$v3_whoqol_bref_who9_umweltbed,v3_con$v3_whoqol_bref_who9,"v3_whoqol_itm9",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 12  32  180  425  242  895  1786
## [2,] Percent   0.7 1.8 10.1 23.8 13.5 50.1 100

10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v3_whoqol_itm10)

v3_quol_recode(v3_clin$v3_whoqol_bref_who10_energie,v3_con$v3_whoqol_bref_who10,"v3_whoqol_itm10",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 13  66  203  362  250 892  1786
## [2,] Percent   0.7 3.7 11.4 20.3 14  49.9 100

11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v3_whoqol_itm11)

v3_quol_recode(v3_clin$v3_whoqol_bref_who11_aussehen,v3_con$v3_whoqol_bref_who11,"v3_whoqol_itm11",0)

##                1   2  3   4    5    NA's     
## [1,] No. cases 21  53 171 400  247  894  1786
## [2,] Percent   1.2 3  9.6 22.4 13.8 50.1 100

12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v3_whoqol_itm12)

v3_quol_recode(v3_clin$v3_whoqol_bref_who12_genug_geld,v3_con$v3_whoqol_bref_who12,"v3_whoqol_itm12",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 34  117 194  297  248  896  1786
## [2,] Percent   1.9 6.6 10.9 16.6 13.9 50.2 100

13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v3_whoqol_itm13)

v3_quol_recode(v3_clin$v3_whoqol_bref_who13_infozugang,v3_con$v3_whoqol_bref_who13,"v3_whoqol_itm13",0)

##                1   2  3   4   5    NA's     
## [1,] No. cases 3   17 78  321 472  895  1786
## [2,] Percent   0.2 1  4.4 18  26.4 50.1 100

14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v3_whoqol_itm14)

v3_quol_recode(v3_clin$v3_whoqol_bref_who14_freizeitaktiv,v3_con$v3_whoqol_bref_who14,"v3_whoqol_itm14",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 9   56  178 302  348  893  1786
## [2,] Percent   0.5 3.1 10  16.9 19.5 50   100

15. “How well are you able to get around? (ordinal [1,2,3,4,5], v3_whoqol_itm15)”

v3_quol_recode(v3_clin$v3_whoqol_bref_who15_fortbewegung,v3_con$v3_whoqol_bref_who15,"v3_whoqol_itm15",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 7   31  109 323  418  898  1786
## [2,] Percent   0.4 1.7 6.1 18.1 23.4 50.3 100

16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v3_whoqol_itm16)

v3_quol_recode(v3_clin$v3_whoqol_bref_who16_schlaf,v3_con$v3_whoqol_bref_who16,"v3_whoqol_itm16",0)

##                1   2   3   4   5   NA's     
## [1,] No. cases 29  126 152 411 178 890  1786
## [2,] Percent   1.6 7.1 8.5 23  10  49.8 100

17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v3_whoqol_itm17)

v3_quol_recode(v3_clin$v3_whoqol_bref_who17_alltag,v3_con$v3_whoqol_bref_who17,"v3_whoqol_itm17",0)

##                1  2   3   4    5    NA's     
## [1,] No. cases 18 93  145 414  227  889  1786
## [2,] Percent   1  5.2 8.1 23.2 12.7 49.8 100

18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v3_whoqol_itm18)

v3_quol_recode(v3_clin$v3_whoqol_bref_who18_arbeitsfhgk,v3_con$v3_whoqol_bref_who18,"v3_whoqol_itm18",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 51  146 173 305  208  903  1786
## [2,] Percent   2.9 8.2 9.7 17.1 11.6 50.6 100

19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v3_whoqol_itm19)

v3_quol_recode(v3_clin$v3_whoqol_bref_who19_selbstzufried,v3_con$v3_whoqol_bref_who19,"v3_whoqol_itm19",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 27  98  202  418  147 894  1786
## [2,] Percent   1.5 5.5 11.3 23.4 8.2 50.1 100

20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v3_whoqol_itm20)

v3_quol_recode(v3_clin$v3_whoqol_bref_who20_pers_bezieh,v3_con$v3_whoqol_bref_who20,"v3_whoqol_itm20",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 20  80  181  405  202  898  1786
## [2,] Percent   1.1 4.5 10.1 22.7 11.3 50.3 100

21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v3_whoqol_itm21)

v3_quol_recode(v3_clin$v3_whoqol_bref_who21_sexualleben,v3_con$v3_whoqol_bref_who21,"v3_whoqol_itm21",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 86  135 252  252  148 913  1786
## [2,] Percent   4.8 7.6 14.1 14.1 8.3 51.1 100

22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v3_whoqol_itm22)

v3_quol_recode(v3_clin$v3_whoqol_bref_who22_freunde,v3_con$v3_whoqol_bref_who22,"v3_whoqol_itm22",0)

##                1   2  3   4    5    NA's     
## [1,] No. cases 20  53 175 420  225  893  1786
## [2,] Percent   1.1 3  9.8 23.5 12.6 50   100

23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v3_whoqol_itm23)

v3_quol_recode(v3_clin$v3_whoqol_bref_who23_wohnbeding,v3_con$v3_whoqol_bref_who23,"v3_whoqol_itm23",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 24  68  126 387  290  891  1786
## [2,] Percent   1.3 3.8 7.1 21.7 16.2 49.9 100

24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v3_whoqol_itm24)

v3_quol_recode(v3_clin$v3_whoqol_bref_who24_gesundhdiens,v3_con$v3_whoqol_bref_who24,"v3_whoqol_itm24",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 6   15  91  405  377  892  1786
## [2,] Percent   0.3 0.8 5.1 22.7 21.1 49.9 100

25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v3_whoqol_itm25)

v3_quol_recode(v3_clin$v3_whoqol_bref_who25_transport,v3_con$v3_whoqol_bref_who25,"v3_whoqol_itm25",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 9   30  104 355  396  892  1786
## [2,] Percent   0.5 1.7 5.8 19.9 22.2 49.9 100

26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v3_whoqol_itm26)

Coding reversed so that higher scores mean symptoms less often.

v3_quol_recode(v3_clin$v3_whoqol_bref_who26_neg_gefuehle,v3_con$v3_whoqol_bref_who26,"v3_whoqol_itm26",1)

##                1   2   3    4    5    NA's     
## [1,] No. cases 21  103 217  341  211  893  1786
## [2,] Percent   1.2 5.8 12.2 19.1 11.8 50   100

WHOQOL-BREF domain scores

Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).

Global (continuous [4-20],v3_whoqol_dom_glob)

v3_whoqol_dom_glob_df<-data.frame(as.numeric(v3_whoqol_itm1),as.numeric(v3_whoqol_itm2))

v3_who_glob_no_nas<-rowSums(is.na(v3_whoqol_dom_glob_df))

v3_whoqol_dom_glob<-ifelse((v3_who_glob_no_nas==0) | (v3_who_glob_no_nas==1), 
                            rowMeans(v3_whoqol_dom_glob_df,na.rm=T)*4,NA)

v3_whoqol_dom_glob<-round(v3_whoqol_dom_glob,2)

summary(v3_whoqol_dom_glob)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   16.00   14.65   18.00   20.00     892

Physical Health (continuous [4-20],v3_whoqol_dom_phys)

v3_whoqol_dom_phys_df<-data.frame(as.numeric(v3_whoqol_itm3),as.numeric(v3_whoqol_itm10),as.numeric(v3_whoqol_itm16),as.numeric(v3_whoqol_itm15),as.numeric(v3_whoqol_itm17),as.numeric(v3_whoqol_itm4),as.numeric(v3_whoqol_itm18))

v3_who_phys_no_nas<-rowSums(is.na(v3_whoqol_dom_phys_df))

v3_whoqol_dom_phys<-ifelse((v3_who_phys_no_nas==0) | (v3_who_phys_no_nas==1), 
                            rowMeans(v3_whoqol_dom_phys_df,na.rm=T)*4,NA)

v3_whoqol_dom_phys<-round(v3_whoqol_dom_phys,2)

summary(v3_whoqol_dom_phys)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   13.71   16.00   15.52   17.71   20.00     900

Psychological (continuous [4-20],v3_whoqol_dom_psy)

v3_whoqol_dom_psy_df<-data.frame(as.numeric(v3_whoqol_itm5),as.numeric(v3_whoqol_itm7),as.numeric(v3_whoqol_itm19),as.numeric(v3_whoqol_itm11),as.numeric(v3_whoqol_itm26),as.numeric(v3_whoqol_itm6))

v3_who_psy_no_nas<-rowSums(is.na(v3_whoqol_dom_psy_df))

v3_whoqol_dom_psy<-ifelse((v3_who_psy_no_nas==0) | (v3_who_psy_no_nas==1), 
                            rowMeans(v3_whoqol_dom_psy_df,na.rm=T)*4,NA)

v3_whoqol_dom_psy<-round(v3_whoqol_dom_psy,2)

summary(v3_whoqol_dom_psy)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.67   12.67   15.33   14.67   16.67   20.00     895

Social relationships (continuous [4-20],v3_whoqol_dom_soc)

v3_whoqol_dom_soc_df<-data.frame(as.numeric(v3_whoqol_itm20),as.numeric(v3_whoqol_itm22),as.numeric(v3_whoqol_itm21))

v3_who_soc_no_nas<-rowSums(is.na(v3_whoqol_dom_soc_df))

v3_whoqol_dom_soc<-ifelse((v3_who_soc_no_nas==0) | (v3_who_soc_no_nas==1), 
                            rowMeans(v3_whoqol_dom_soc_df,na.rm=T)*4,NA)

v3_whoqol_dom_soc<-round(v3_whoqol_dom_soc,2)

summary(v3_whoqol_dom_soc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.57   17.33   20.00     894

Environment (continuous [4-20],v3_whoqol_dom_env)

v3_whoqol_dom_env_df<-data.frame(as.numeric(v3_whoqol_itm8),as.numeric(v3_whoqol_itm23),as.numeric(v3_whoqol_itm12),as.numeric(v3_whoqol_itm24),as.numeric(v3_whoqol_itm13),as.numeric(v3_whoqol_itm14),as.numeric(v3_whoqol_itm9),as.numeric(v3_whoqol_itm25))

v3_who_env_no_nas<-rowSums(is.na(v3_whoqol_dom_env_df))

v3_whoqol_dom_env<-ifelse((v3_who_env_no_nas==0) | (v3_who_env_no_nas==1), 
                            rowMeans(v3_whoqol_dom_env_df,na.rm=T)*4,NA)

v3_whoqol_dom_env<-round(v3_whoqol_dom_env,2)

summary(v3_whoqol_dom_env)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    6.00   14.50   16.50   16.19   18.00   20.00     896

Create dataset

v3_whoqol<-data.frame(v3_whoqol_itm1,v3_whoqol_itm2,v3_whoqol_itm3,v3_whoqol_itm4,
                      v3_whoqol_itm5,v3_whoqol_itm6,v3_whoqol_itm7,v3_whoqol_itm8,
                      v3_whoqol_itm9,v3_whoqol_itm10,v3_whoqol_itm11,v3_whoqol_itm12,
                      v3_whoqol_itm13,v3_whoqol_itm14,v3_whoqol_itm15,v3_whoqol_itm16,
                      v3_whoqol_itm17,v3_whoqol_itm18,v3_whoqol_itm19,v3_whoqol_itm20,
                      v3_whoqol_itm21,v3_whoqol_itm22,v3_whoqol_itm23,v3_whoqol_itm24,
                      v3_whoqol_itm25,v3_whoqol_itm26,v3_whoqol_dom_glob,
                      v3_whoqol_dom_phys,v3_whoqol_dom_psy,v3_whoqol_dom_soc,
                      v3_whoqol_dom_env)

Visit 3: Create dataframe

v3_df<-data.frame(v3_id,
                  v3_rec,
                  v3_clin_ill_ep,
                  v3_con_problems,
                  v3_dem,
                  v3_leprcp,
                  v3_suic,
                  v3_med,
                  v3_subst,
                  v3_symp_panss,
                  v3_symp_ids_c,
                  v3_symp_ymrs,
                  v3_ill_sev,
                  v3_nrpsy,
                  v3_sf12,
                  v3_cts,
                  v3_med_adh,
                  v3_bdi2,
                  v3_asrm,
                  v3_mss,
                  v3_leq,
                  v3_whoqol)

Visit 4: Data preparation

Read in data of clinical participants

## [1] 1320

Read in data of control participants

## [1] 466

Modify column names

Only include subjects for which data for the first visit is present

v4_clin<-subset(v4_clin, as.character(v4_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v4_clin)[1] 

## [1] 1320

v4_con<-subset(v4_con, as.character(v4_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v4_con)[1] 

## [1] 466

Participant identity column (categorical [id], v4_id)

v4_id<-as.factor(c(as.character(v4_clin$mnppsd),as.character(v4_con$mnppsd)))                               

Visit 4: Recruitment data

In some participants, an incorrect date of interview was entered into the original phenotype database, which I correct here.

## [1] 20120803

## [1] "20170803"

## [1] "20151114"

## [1] "20141114"

## [1] "20140128"

## [1] "20150128"

Date of interview (categorical [year-month-day], v4_interv_date)

v4_interv_date<-c(as.Date(as.character(v4_clin$v4_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v4_con$v4_rekru_visit_rekr_datum), "%Y%m%d"))

Age at fourth interview (continuous [years], v4_age)

v4_age_years_clin<-as.numeric(substr(v4_clin$v4_ausschluss1_rekr_datum,1,4))-
  as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))

v4_age_years_con<-as.numeric(substr(v4_con$v4_rekru_visit_rekr_datu,1,4))-
  as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))

v4_age_years<-c(v4_age_years_clin,v4_age_years_con)

v4_age<-ifelse(c(as.numeric(substr(v4_clin$v4_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v4_con$v4_rekru_visit_rekr_datu,5,6)))<
                 c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
                   v4_age_years-1,v4_age_years)
summary(v4_age) 

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   19.00   31.00   45.00   43.86   54.00   79.00     946

Create dataset

v4_rec<-data.frame(v4_age,v4_interv_date)

Visit 4: Illness episodes between study visits

Study participant are asked whether an acute illness episode occurred since the last study visit. Possible answers are “Y”-yes, “N”-no and “C”-chronic symptomatology. The latter category is for people which continually experience symptoms. If the answer was yes, additional questions were asked about the episodes, if not these are omitted. For participants with chronic symptomatology, the participant is asked about the nature of the chronic symptomatology (manic/depressive/mixed/psychotic) and answers are coded in the questions “Did you experience … symptoms during this illness episode?” (first illness episode).
Importantly, if the participant experienced multiple illness episodes since the last study visit, a set of questions (see below) is supposed to be answered for each illness episode. As most interviewers answered these questions only for a maximum of two illness episodes and few participants experienced more than two illness episodes, data are included only for the first two illness episodes.

Illness episodes since last study visit (categorical [Y, N, C], v4_ill_ep_snc_lst)

Illness episodes since last study visit (only in clinical participants)

“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v4_clin_ill_ep_snc_lst)

v4_clin_ill_ep_snc_lst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_ill_ep_snc_lst<-ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==1,"Y",
                          ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==2,"N",
                            ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==3,"C",v4_clin_ill_ep_snc_lst)))

v4_clin_ill_ep_snc_lst<-factor(v4_clin_ill_ep_snc_lst)                         
descT(v4_clin_ill_ep_snc_lst)

##                -999 C   N    Y   <NA>     
## [1,] No. cases 466  77  342  164 737  1786
## [2,] Percent   26.1 4.3 19.1 9.2 41.3 100

“If yes, how many illness episodes? (continuous [no. illness episodes], v4_clin_no_ep)”

v4_clin_no_ep<-ifelse(v4_clin_ill_ep_snc_lst=="Y",c(v4_clin$v4_aktu_situat_anzahl_episoden,rep(-999,dim(v4_con)[1])),-999)
descT(v4_clin_no_ep)

##                -999 1   2   3   5   <NA>     
## [1,] No. cases 885  122 27  9   2   741  1786
## [2,] Percent   49.6 6.8 1.5 0.5 0.1 41.5 100

In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).

First illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_man)

v4_clin_fst_ill_ep_man<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_man<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", 
                          -999)

descT(v4_clin_fst_ill_ep_man)

##                -999 Y   <NA>     
## [1,] No. cases 1032 26  728  1786
## [2,] Percent   57.8 1.5 40.8 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_dep)

v4_clin_fst_ill_ep_dep<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_dep<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", 
                          -999)

descT(v4_clin_fst_ill_ep_dep)

##                -999 Y   <NA>     
## [1,] No. cases 972  86  728  1786
## [2,] Percent   54.4 4.8 40.8 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v4_clin_fst_ill_ep_mx<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_mx<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", 
                         -999)

descT(v4_clin_fst_ill_ep_mx)

##                -999 Y   <NA>     
## [1,] No. cases 1048 10  728  1786
## [2,] Percent   58.7 0.6 40.8 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_psy)

v4_clin_fst_ill_ep_psy<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_psy<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", 
                          -999)

descT(v4_clin_fst_ill_ep_psy)

##                -999 Y  <NA>     
## [1,] No. cases 1004 54 728  1786
## [2,] Percent   56.2 3  40.8 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v4_clin_fst_ill_ep_dur)

v4_clin_fst_ill_ep_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_dur<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",
                               ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==2,"two to four weeks", 
                                      ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
                                             ifelse(v4_clin_ill_ep_snc_lst=="N" | v4_clin_ill_ep_snc_lst=="C",-999,v4_clin_fst_ill_ep_dur))))

v4_clin_fst_ill_ep_dur<-ordered(v4_clin_fst_ill_ep_dur, 
                               levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_fst_ill_ep_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 885  34                  35                90                  
## [2,] Percent   49.6 1.9                 2                 5                   
##      <NA>     
## [1,] 742  1786
## [2,] 41.5 100

“During this episode, were you hospitalized?” (dichotomous, v4_clin_fst_ill_ep_hsp)

v4_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="N" | v4_clin_ill_ep_snc_lst=="C",-999,
                          ifelse(v4_clin_ill_ep_snc_lst=="Y" &
                            c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v4_con)[1]))==2,"N",
                              ifelse(v4_clin_ill_ep_snc_lst=="Y" &
                                c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", v4_clin_fst_ill_ep_hsp)))

v4_clin_fst_ill_ep_hsp<-factor(v4_clin_fst_ill_ep_hsp)                         
descT(v4_clin_fst_ill_ep_hsp)

##                -999 N   Y   <NA>     
## [1,] No. cases 885  96  64  741  1786
## [2,] Percent   49.6 5.4 3.6 41.5 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v4_clin_fst_ill_ep_hsp_dur)

v4_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_hsp_dur<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",  
                              ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
                                ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",    
                                            -999)))

v4_clin_fst_ill_ep_hsp_dur<-ifelse((v4_clin_ill_ep_snc_lst=="Y" & is.na(c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1])))) | 
                                 (v4_clin_ill_ep_snc_lst=="N" | v4_clin_ill_ep_snc_lst=="C"),-999, v4_clin_fst_ill_ep_hsp_dur)

v4_clin_fst_ill_ep_hsp_dur<-ordered(v4_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v4_clin_fst_ill_ep_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 987  6                   20                34                  
## [2,] Percent   55.3 0.3                 1.1               1.9                 
##      <NA>     
## [1,] 739  1786
## [2,] 41.4 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):

Reason for hospitalization: symptom worsensing (checkbox [Y], v4_clin_fst_ill_ep_symp_wrs)

v4_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_symp_wrs<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y",-999)
  
descT(v4_clin_fst_ill_ep_symp_wrs)

##                -999 Y   <NA>     
## [1,] No. cases 1001 56  729  1786
## [2,] Percent   56   3.1 40.8 100

Reason for hospitalization: self-endangerment (checkbox [Y], v4_clin_fst_ill_ep_slf_end)

v4_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_slf_end<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y", 
                              -999)

descT(v4_clin_fst_ill_ep_slf_end)

##                -999 Y   <NA>     
## [1,] No. cases 1046 12  728  1786
## [2,] Percent   58.6 0.7 40.8 100

Reason for hospitalization: suicidality (checkbox [Y], v4_clin_fst_ill_ep_suic)

v4_clin_fst_ill_ep_suic<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_suic<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y", 
                           -999)

descT(v4_clin_fst_ill_ep_suic)

##                -999 Y   <NA>     
## [1,] No. cases 1052 6   728  1786
## [2,] Percent   58.9 0.3 40.8 100

Reason for hospitalization: endangerment of others (checkbox [Y], v4_clin_fst_ill_ep_oth_end)

v4_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_oth_end<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)

descT(v4_clin_fst_ill_ep_oth_end)

##                -999 Y   <NA>     
## [1,] No. cases 1057 1   728  1786
## [2,] Percent   59.2 0.1 40.8 100

Reason for hospitalization: medication change (checkbox [Y], v4_clin_fst_ill_ep_med_chg)

v4_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_med_chg<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)

descT(v4_clin_fst_ill_ep_med_chg)

##                -999 Y   <NA>     
## [1,] No. cases 1051 7   728  1786
## [2,] Percent   58.8 0.4 40.8 100

Reason for hospitalization: other (checkbox [Y], v4_clin_fst_ill_ep_othr)

v4_clin_fst_ill_ep_othr<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_othr<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
 
descT(v4_clin_fst_ill_ep_othr)

##                -999 Y   <NA>     
## [1,] No. cases 1045 13  728  1786
## [2,] Percent   58.5 0.7 40.8 100

Second illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_man)

v4_clin_sec_ill_ep_man<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_man<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",-999)

descT(v4_clin_sec_ill_ep_man)

##                -999 Y   <NA>     
## [1,] No. cases 917  3   866  1786
## [2,] Percent   51.3 0.2 48.5 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_dep) #frstill

v4_clin_sec_ill_ep_dep<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_dep<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",
                          -999)

descT(v4_clin_sec_ill_ep_dep)

##                -999 Y   <NA>     
## [1,] No. cases 896  24  866  1786
## [2,] Percent   50.2 1.3 48.5 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v4_clin_sec_ill_ep_mx<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_mx<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y", 
                         -999)

descT(v4_clin_sec_ill_ep_mx)

##                -999 Y   <NA>     
## [1,] No. cases 917  3   866  1786
## [2,] Percent   51.3 0.2 48.5 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_psy)

v4_clin_sec_ill_ep_psy<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_psy<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y", 
                          -999)

descT(v4_clin_sec_ill_ep_psy)

##                -999 Y   <NA>     
## [1,] No. cases 914  6   866  1786
## [2,] Percent   51.2 0.3 48.5 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v4_clin_sec_ill_ep_dur)

v4_clin_sec_ill_ep_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_dur<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==1,"less than two weeks", 
                           ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",    
                              ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",  
                                ifelse(v4_clin_ill_ep_snc_lst=="N",-999,v4_clin_sec_ill_ep_dur))))

v4_clin_sec_ill_ep_dur<-ifelse((v4_clin_ill_ep_snc_lst=="Y" & is.na(c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1])))) | 
                                 v4_clin_ill_ep_snc_lst=="N" | v4_clin_ill_ep_snc_lst=="C",-999, v4_clin_sec_ill_ep_dur)
 
v4_clin_sec_ill_ep_dur<-ordered(v4_clin_sec_ill_ep_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_sec_ill_ep_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1015 9                   7                 18                  
## [2,] Percent   56.8 0.5                 0.4               1                   
##      <NA>     
## [1,] 737  1786
## [2,] 41.3 100

“During this episode, were you hospitalized?” (dichotomous, v4_clin_sec_ill_ep_hsp)

v4_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="N" | v4_clin_ill_ep_snc_lst=="C",-999,
                          ifelse(v4_clin_ill_ep_snc_lst=="Y" &
                            c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v4_con)[1]))==2,"N",
                              ifelse(v4_clin_ill_ep_snc_lst=="Y" &
                                c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y", v4_clin_sec_ill_ep_hsp)))

v4_clin_sec_ill_ep_hsp<-factor(v4_clin_sec_ill_ep_hsp)                         
descT(v4_clin_sec_ill_ep_hsp)

##                -999 N   Y   <NA>     
## [1,] No. cases 885  24  11  866  1786
## [2,] Percent   49.6 1.3 0.6 48.5 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v4_clin_sec_ill_ep_hsp_dur)

v4_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_hsp_dur<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",  
                              ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
                                ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",    
                                            -999)))

v4_clin_sec_ill_ep_hsp_dur<-ifelse((v4_clin_ill_ep_snc_lst=="Y" & is.na(c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1])))) | 
                                 (v4_clin_ill_ep_snc_lst=="N" | v4_clin_ill_ep_snc_lst=="C"),-999, v4_clin_sec_ill_ep_hsp_dur)

v4_clin_sec_ill_ep_hsp_dur<-ordered(v4_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v4_clin_sec_ill_ep_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1038 1                   6                 4                   
## [2,] Percent   58.1 0.1                 0.3               0.2                 
##      <NA>     
## [1,] 737  1786
## [2,] 41.3 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes): Reason for hospitalization: symptom worsensing (checkbox [Y], v4_clin_sec_ill_ep_symp_wrs)

v4_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_symp_wrs<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",-999)
  
descT(v4_clin_sec_ill_ep_symp_wrs)

##                -999 Y   <NA>     
## [1,] No. cases 912  8   866  1786
## [2,] Percent   51.1 0.4 48.5 100

Reason for hospitalization: self-endangerment (checkbox [Y], v4_clin_sec_ill_ep_slf_end)

v4_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_slf_end<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y", 
                              -999)

descT(v4_clin_sec_ill_ep_slf_end)

##                -999 Y   <NA>     
## [1,] No. cases 919  1   866  1786
## [2,] Percent   51.5 0.1 48.5 100

Reason for hospitalization: suicidality (checkbox [Y], v4_clin_sec_ill_ep_suic)

v4_clin_sec_ill_ep_suic<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_suic<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y", 
                           -999)

descT(v4_clin_sec_ill_ep_suic)

##                -999 Y   <NA>     
## [1,] No. cases 919  1   866  1786
## [2,] Percent   51.5 0.1 48.5 100

Reason for hospitalization: endangerment of others (checkbox [Y], v4_clin_sec_ill_ep_oth_end)

v4_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_oth_end<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)

descT(v4_clin_sec_ill_ep_oth_end)

##                -999 <NA>     
## [1,] No. cases 920  866  1786
## [2,] Percent   51.5 48.5 100

Reason for hospitalization: medication change (checkbox [Y], v4_clin_sec_ill_ep_med_chg)

v4_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_med_chg<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)

descT(v4_clin_sec_ill_ep_med_chg)

##                -999 <NA>     
## [1,] No. cases 920  866  1786
## [2,] Percent   51.5 48.5 100

Reason for hospitalization: other (checkbox [Y], v4_clin_sec_ill_ep_othr)

v4_clin_sec_ill_ep_othr<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_othr<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
 
descT(v4_clin_sec_ill_ep_othr)

##                -999 Y   <NA>     
## [1,] No. cases 916  4   866  1786
## [2,] Percent   51.3 0.2 48.5 100

Additional psychiatric hospitalization as in- or daypatient? (dichotomous, v4_clin_add_oth_hsp)

v4_clin_add_oth_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_add_oth_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_aufent,rep(-999,dim(v4_con)[1]))==1,"Y","N")

descT(v4_clin_add_oth_hsp)

##                N    Y   <NA>     
## [1,] No. cases 1029 15  742  1786
## [2,] Percent   57.6 0.8 41.5 100

If yes, how many other hospitalizations? (continous [no. of hospitalizations], v4_clin_oth_hsp_nmb)

v4_clin_oth_hsp_nmb<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_oth_hsp_nmb<-ifelse(v4_clin_add_oth_hsp=="Y",
          c(v4_clin$v4_aktu_situat_aenderung_anzahl,rep(-999,dim(v4_con)[1])),-999)

descT(v4_clin_oth_hsp_nmb)

##                -999 1   2   3   <NA>     
## [1,] No. cases 1029 8   1   1   747  1786
## [2,] Percent   57.6 0.4 0.1 0.1 41.8 100

If yes, duration of other hospitalizations? (ordinal, v4_clin_oth_hsp_dur)

v4_clin_oth_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_oth_hsp_dur<-
  ifelse(v4_clin_add_oth_hsp=="Y" & 
           c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==1,"less than two weeks", 
   ifelse(v4_clin_add_oth_hsp=="Y" & 
            c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
    ifelse(v4_clin_add_oth_hsp=="Y" & 
             c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
     ifelse(v4_clin_add_oth_hsp=="N",-999,v4_clin_add_oth_hsp))))

v4_clin_oth_hsp_dur<-ifelse((v4_clin_add_oth_hsp=="Y" & is.na(c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1])))),-999, v4_clin_oth_hsp_dur)

v4_clin_oth_hsp_dur<-ordered(v4_clin_oth_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_oth_hsp_dur)

##                -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1031 2                   3                 8                   
## [2,] Percent   57.7 0.1                 0.2               0.4                 
##      <NA>     
## [1,] 742  1786
## [2,] 41.5 100

If yes, reason for other hospitalization(s) medication change? (checkbox [Y], v4_clin_othr_psy_med)

v4_clin_othr_psy_med<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_othr_psy_med<-ifelse(v4_clin_add_oth_hsp=="Y" & v4_clin_add_oth_hsp=="Y" & 
      c(v4_clin$v4_aktu_situat_aenderung_medikament,rep(-999,dim(v4_con)[1]))==1,"Y",
                        ifelse(v4_clin_add_oth_hsp=="N",-999,v4_clin_othr_psy_med))
  
descT(v4_clin_othr_psy_med)

##                -999 Y   <NA>     
## [1,] No. cases 1029 2   755  1786
## [2,] Percent   57.6 0.1 42.3 100

Current psychiatric treatment of both clinical and control participants (ordinal [1,2,3,4], v4_cur_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.

v4_clin_cur_psy_trm<-rep(NA,dim(v4_clin)[1])
v4_con_cur_psy_trm<-rep(NA,dim(v4_con)[1])

v4_clin_cur_psy_trm<-ifelse(v4_clin$v4_aktu_situat_psybehandlung==0,"1",
                        ifelse(v4_clin$v4_aktu_situat_psybehandlung==3,"2", 
                          ifelse(v4_clin$v4_aktu_situat_psybehandlung==2,"3",
                            ifelse(v4_clin$v4_aktu_situat_psybehandlung==1,"4",v4_clin_cur_psy_trm)))) 

v4_con_cur_psy_trm<-ifelse(v4_con$v4_bildung_beruf_psybehandlung==0,"1",
                      ifelse(v4_con$v4_bildung_beruf_psybehandlung==3,"2",
                        ifelse(v4_con$v4_bildung_beruf_psybehandlung==2,"3",
                          ifelse(v4_con$v4_bildung_beruf_psybehandlung==1,"4",v4_con_cur_psy_trm))))

v4_cur_psy_trm<-factor(c(v4_clin_cur_psy_trm,v4_con_cur_psy_trm),ordered=T)
descT(v4_cur_psy_trm)

##                1    2    3   4   <NA>     
## [1,] No. cases 283  517  6   23  957  1786
## [2,] Percent   15.8 28.9 0.3 1.3 53.6 100

Create dataset

v4_clin_ill_ep<-data.frame(v4_clin_ill_ep_snc_lst,
                           v4_clin_no_ep,
                           v4_clin_fst_ill_ep_man,
                           v4_clin_fst_ill_ep_dep,
                           v4_clin_fst_ill_ep_mx,
                           v4_clin_fst_ill_ep_psy,
                           v4_clin_fst_ill_ep_dur,
                           v4_clin_fst_ill_ep_hsp,
                           v4_clin_fst_ill_ep_hsp_dur,
                           v4_clin_fst_ill_ep_symp_wrs,
                           v4_clin_fst_ill_ep_slf_end,
                           v4_clin_fst_ill_ep_suic,
                           v4_clin_fst_ill_ep_oth_end,
                           v4_clin_fst_ill_ep_med_chg,
                           v4_clin_fst_ill_ep_othr,
                           v4_clin_sec_ill_ep_man,
                           v4_clin_sec_ill_ep_dep,
                           v4_clin_sec_ill_ep_mx,
                           v4_clin_sec_ill_ep_psy,
                           v4_clin_sec_ill_ep_dur,
                           v4_clin_sec_ill_ep_hsp,
                           v4_clin_sec_ill_ep_hsp_dur,
                           v4_clin_sec_ill_ep_symp_wrs,
                           v4_clin_sec_ill_ep_slf_end,
                           v4_clin_sec_ill_ep_suic,
                           v4_clin_sec_ill_ep_oth_end,
                           v4_clin_sec_ill_ep_med_chg,
                           v4_clin_sec_ill_ep_othr,
                           v4_clin_add_oth_hsp,
                           v4_clin_oth_hsp_nmb,
                           v4_clin_oth_hsp_dur,
                           v4_clin_othr_psy_med,
                           v4_cur_psy_trm)

Visit 4: Emotional, psychological, alcohol-related or similar problems between study visits (only in control participants)

“Did you experience emotional, psychological, alcohol-related or similar problems since the last study visit?” (categorical [Y,N], v4_con_problems_snc_lst)

v4_con_problems_snc_lst<-c(rep(-999,dim(v4_clin)[1]),rep(NA,dim(v4_con)[1]))

v4_con_problems_snc_lst<-ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_stand)==1,"Y",
                          ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_stand)==2,"N",v4_con_problems_snc_lst))

v4_con_problems_snc_lst<-factor(v4_con_problems_snc_lst)                        
descT(v4_con_problems_snc_lst)

##                -999 N    Y   <NA>     
## [1,] No. cases 1320 205  42  219  1786
## [2,] Percent   73.9 11.5 2.4 12.3 100

“Since the last study visit, were there psychiatric hospitalizations” (categorical [Y,N], v4_con_psy_snc_lst) This item is -999 in patients.

v4_con_psy_snc_lst<-c(rep(-999,dim(v4_clin)[1]),rep(NA,dim(v4_con)[1]))
v4_con_psy_snc_lst<-ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_tstbehand)==1,"Y",
                           ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_tstbehand)==2,"N",v4_con_psy_snc_lst))
v4_con_psy_snc_lst<-factor(v4_con_psy_snc_lst)

descT(v4_con_psy_snc_lst)

##                -999 N    Y   <NA>     
## [1,] No. cases 1320 240  1   225  1786
## [2,] Percent   73.9 13.4 0.1 12.6 100

“If yes, how many?” (continuous, [number of hospitalizations] ,v4_con_no_psy_hosp) This item is -999 in patients and -999 in controls that negated the previous question.

v4_con_no_psy_hosp<-ifelse(v4_con_psy_snc_lst==-999,-999,
                           ifelse(v4_con_psy_snc_lst=="N",-999,
                                  ifelse(v4_con_psy_snc_lst=="Y",c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_anzahl),NA)))
descT(v4_con_no_psy_hosp)

##                -999 1   <NA>     
## [1,] No. cases 1560 1   225  1786
## [2,] Percent   87.3 0.1 12.6 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v4_con_psy_hosp_dur)

v4_con_psy_hosp_dur<-c(ifelse((v4_con_psy_snc_lst==-999) | (v4_con_psy_snc_lst=="N"),-999,
                        ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_dauer)==1,"less than two weeks",
                          ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_dauer)==2,"two to four weeks",
                            ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_dauer)==3,"more than four weeks",NA)))))

v4_con_psy_hosp_dur<-as.factor(v4_con_psy_hosp_dur)
descT(v4_con_psy_hosp_dur)

##                -999 more than four weeks <NA>     
## [1,] No. cases 1560 1                    225  1786
## [2,] Percent   87.3 0.1                  12.6 100

“If yes, due to medication change?” (checkbox [Y], v4_con_psy_hosp_why_medchg)

v4_con_psy_hosp_why_medchg<-ifelse((v4_con_psy_snc_lst==-999) | (v4_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_medikament)==1,"Y",-999))
                          
v4_con_psy_hosp_why_medchg<-as.factor(v4_con_psy_hosp_why_medchg)
descT(v4_con_psy_hosp_why_medchg)

##                -999 <NA>     
## [1,] No. cases 1561 225  1786
## [2,] Percent   87.4 12.6 100

“If yes, due to other circumstances?” (checkbox [Y], v4_con_psy_hosp_why_othr)

v4_con_psy_hosp_why_othr<-ifelse((v4_con_psy_snc_lst==-999) | (v4_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_sonstiges)==1,"Y",-999))
                          
v4_con_psy_hosp_why_othr<-as.factor(v4_con_psy_hosp_why_othr)
descT(v4_con_psy_hosp_why_othr)

##                -999 Y   <NA>     
## [1,] No. cases 1560 1   225  1786
## [2,] Percent   87.3 0.1 12.6 100

“If yes and due to other circumstances, which circumstances?” (character [text], v4_con_psy_hosp_why_othr_txt)

v4_con_psy_hosp_why_othr_txt<-ifelse((v4_con_psy_snc_lst==-999) | (v4_con_psy_snc_lst=="N"),-999,
                                  ifelse(c(rep(-999,dim(v4_clin)[1]),v4_con$v4_bildung_beruf_psy_grund)==1,"Y",-999))
                          
v4_con_psy_hosp_why_othr_txt<-as.factor(v4_con_psy_hosp_why_othr_txt)
descT(v4_con_psy_hosp_why_othr_txt)

##                -999 <NA>     
## [1,] No. cases 1561 225  1786
## [2,] Percent   87.4 12.6 100

Create dataset

v4_con_problems<-data.frame(v4_con_problems_snc_lst,v4_con_psy_snc_lst,v4_con_no_psy_hosp,
                            v4_con_psy_hosp_dur,v4_con_psy_hosp_why_medchg,v4_con_psy_hosp_why_othr,
                            v4_con_psy_hosp_why_othr_txt)

Visit 4: Demographic information

See Visit 1 marital status item for general explanation of the next two items.

Did your marital status change since the last study visit? (dichotomous, v4_cng_mar_stat)

v4_clin_cng_mar_stat<-rep(NA,dim(v4_clin)[1]) 
v4_clin_cng_mar_stat<-ifelse(v4_clin$v4_aktu_situat_fam_stand==1, "Y", 
                        ifelse(v4_clin$v4_aktu_situat_fam_stand==2, "N", v4_clin_cng_mar_stat))

v4_con_cng_mar_stat<-rep(NA,dim(v4_con)[1]) 
v4_con_cng_mar_stat<-ifelse(v4_con$v4_famil_wohn_fam_stand==1, "Y", 
                        ifelse(v4_con$v4_famil_wohn_fam_stand==2, "N", v4_con_cng_mar_stat))

v4_cng_mar_stat<-factor(c(v4_clin_cng_mar_stat,v4_con_cng_mar_stat))

Marital status (categorical [married, married but living separately, single, divorced, widowed], v4_marital_stat)

v4_clin_marital_stat<-rep(NA,dim(v4_clin)[1])
v4_clin_marital_stat<-ifelse(v4_clin$v4_aktu_situat_fam_familienstand==1,"Married", 
                 ifelse(v4_clin$v4_aktu_situat_fam_familienstand==2,"Married_living_sep",
                 ifelse(v4_clin$v4_aktu_situat_fam_familienstand==3,"Single",
                 ifelse(v4_clin$v4_aktu_situat_fam_familienstand==4,"Divorced",
                 ifelse(v4_clin$v4_aktu_situat_fam_familienstand==5,"Widowed",v4_clin_marital_stat)))))

v4_con_marital_stat<-rep(NA,dim(v4_con)[1])
v4_con_marital_stat<-ifelse(v4_con$v4_famil_wohn_fam_famstand==1,"Married", 
                 ifelse(v4_con$v4_famil_wohn_fam_famstand==2,"Married_living_sep",
                 ifelse(v4_con$v4_famil_wohn_fam_famstand==3,"Single",
                 ifelse(v4_con$v4_famil_wohn_fam_famstand==4,"Divorced",
                 ifelse(v4_con$v4_famil_wohn_fam_famstand==5,"Widowed",v4_con_marital_stat)))))

v4_marital_stat<-factor(c(v4_clin_marital_stat,v4_con_marital_stat))
desc(v4_marital_stat)

##                Divorced Married Married_living_sep Single Widowed NA's     
## [1,] No. cases 110      207     21                 480    13      955  1786
## [2,] Percent   6.2      11.6    1.2                26.9   0.7     53.5 100

Relationship status

“Do you currently have a partner?” (dichotomous, v4_partner)

v4_clin_partner<-rep(NA,dim(v4_clin)[1])
v4_clin_partner<-ifelse(v4_clin$v4_aktu_situat_fam_fest_partner==1,"Y",
            ifelse(v4_clin$v4_aktu_situat_fam_fest_partner==2,"N",v4_clin_partner))

v4_con_partner<-rep(NA,dim(v4_con)[1])
v4_con_partner<-ifelse(v4_con$v4_famil_wohn_fam_partner==1,"Y",
            ifelse(v4_con$v4_famil_wohn_fam_partner==2,"N",v4_con_partner))


v4_partner<-factor(c(v4_clin_partner,v4_con_partner))
descT(v4_partner)

##                N    Y   <NA>     
## [1,] No. cases 388  429 969  1786
## [2,] Percent   21.7 24  54.3 100

Children

Biological (continuous [number], v4_no_bio_chld)

v4_no_bio_chld<-c(v4_clin$v4_aktu_situat_fam_kind_gesamt,v4_con$v4_famil_wohn_fam_lkind)
descT(v4_no_bio_chld)

##                0    1   2   3   4   5   <NA>     
## [1,] No. cases 521  146 100 56  6   4   953  1786
## [2,] Percent   29.2 8.2 5.6 3.1 0.3 0.2 53.4 100

Non-biological

Adoptive children (continuous [number], v4_no_adpt_chld)

v4_no_adpt_chld<-c(v4_clin$v4_aktu_situat_fam_adopt_gesamt,v4_con$v4_famil_wohn_fam_adkind)
descT(v4_no_adpt_chld)  

##                0   1   2   <NA>     
## [1,] No. cases 822 2   2   960  1786
## [2,] Percent   46  0.1 0.1 53.8 100

Step children (continuous [number], v4_stp_chld)

v4_stp_chld<-c(v4_clin$v4_aktu_situat_fam_stift_gesamt,v4_con$v4_famil_wohn_fam_skind)
descT(v4_stp_chld)      

##                0    1   2   3   4   5   <NA>     
## [1,] No. cases 764  40  19  4   1   1   957  1786
## [2,] Percent   42.8 2.2 1.1 0.2 0.1 0.1 53.6 100

Change in housing situation since last study visit? (dichotomous, v4_chg_hsng)

v4_clin_chg_hsng<-rep(NA,dim(v4_clin)[1])
v4_clin_chg_hsng<-ifelse(v4_clin$v4_wohnsituation_wohn_aenderung==1,"Y",
                  ifelse(v4_clin$v4_wohnsituation_wohn_aenderung==2,"N",v4_clin_chg_hsng))  

v4_con_chg_hsng<-rep(NA,dim(v4_con)[1])
v4_con_chg_hsng<-ifelse(v4_con$v4_famil_wohn_wohn_stand==1,"Y",
                 ifelse(v4_con$v4_famil_wohn_wohn_stand==2,"N",v4_con_chg_hsng))

v4_chg_hsng<-factor(c(v4_clin_chg_hsng,v4_con_chg_hsng))
descT(v4_chg_hsng)

##                N    Y   <NA>     
## [1,] No. cases 731  104 951  1786
## [2,] Percent   40.9 5.8 53.2 100

Living alone (dichotomous, v4_liv_aln)

v4_clin_liv_aln<-rep(NA,dim(v4_clin)[1])
v4_clin_liv_aln<-ifelse(v4_clin$v4_wohnsituation_wohn_allein==1,"Y",    
                 ifelse(v4_clin$v4_wohnsituation_wohn_allein==0,"N",v4_clin_liv_aln))   

v4_con_liv_aln<-rep(NA,dim(v4_con)[1])
v4_con_liv_aln<-ifelse(v4_con$v4_famil_wohn_wohn_allein==1,"Y", 
                 ifelse(v4_con$v4_famil_wohn_wohn_allein==0,"N",v4_con_liv_aln))
                 
v4_liv_aln<-factor(c(v4_clin_liv_aln,v4_con_liv_aln))
descT(v4_liv_aln)

##                N    Y    <NA>     
## [1,] No. cases 510  333  943  1786
## [2,] Percent   28.6 18.6 52.8 100

Employment

Did your employment situation change since the last study visit?

v4_clin_chg_empl_stat<-rep(NA,dim(v4_clin)[1]) 
v4_clin_chg_empl_stat<-ifelse(v4_clin$v4_wohnsituation_erwerb_aenderung==1, "Y", 
                  ifelse(v4_clin$v4_wohnsituation_erwerb_aenderung==2, "N",v4_clin_chg_empl_stat))

v4_con_chg_empl_stat<-rep(NA,dim(v4_con)[1]) 
v4_con_chg_empl_stat<-ifelse(v4_con$v4_bildung_beruf_bild_stand==1, "Y", 
                  ifelse(v4_con$v4_bildung_beruf_bild_stand==2, "N",v4_con_chg_empl_stat))

v4_chg_empl_stat<-factor(c(v4_clin_chg_empl_stat,v4_con_chg_empl_stat))
descT(v4_chg_empl_stat)

##                N    Y   <NA>     
## [1,] No. cases 727  100 959  1786
## [2,] Percent   40.7 5.6 53.7 100

Currently paid employment (dichotomous, v4_curr_paid_empl)

Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.

v4_clin_curr_paid_empl<-rep(NA,dim(v4_clin)[1])
v4_clin_curr_paid_empl<-ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==1,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==2,"Y",   
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==3,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==4,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==5,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==6,-999,  
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==7,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==8,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==9,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==10,"Y",  
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==11,"N",v4_clin_curr_paid_empl)))))))))))

v4_con_curr_paid_empl<-rep(NA,dim(v4_con)[1])
v4_con_curr_paid_empl<-ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==1,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==2,"Y",    
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==3,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==4,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==5,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==6,-999,   
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==7,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==8,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==9,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==10,"Y",   
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==11,"N",v4_con_curr_paid_empl)))))))))))

v4_curr_paid_empl<-factor(c(v4_clin_curr_paid_empl,v4_con_curr_paid_empl))
descT(v4_curr_paid_empl)

##                -999 N    Y    <NA>     
## [1,] No. cases 18   383  427  958  1786
## [2,] Percent   1    21.4 23.9 53.6 100

Disability pension due to psychological/psychiatric illness (dichotomous, v4_disabl_pens)

NB: Not available (-999) in control participants

v4_clin_disabl_pens<-rep(NA,dim(v4_clin)[1])
v4_clin_disabl_pens<-ifelse(v4_clin$v4_wohnsituation_rente_psych==1,"Y",        
                     ifelse(v4_clin$v4_wohnsituation_rente_psych==2,"N",v4_clin_disabl_pens))       

v4_con_disabl_pens<-rep(-999,dim(v4_con)[1])

v4_disabl_pens<-factor(c(v4_clin_disabl_pens,v4_con_disabl_pens))
descT(v4_disabl_pens)

##                -999 N    Y   <NA>     
## [1,] No. cases 466  225  250 845  1786
## [2,] Percent   26.1 12.6 14  47.3 100

Employed in workshop for handicapped persons (dichotomous, v4_spec_emp)

v4_clin_spec_emp<-rep(NA,dim(v4_clin)[1])
v4_clin_spec_emp<-ifelse(v4_clin$v4_wohnsituation_erwerb_werk==1,"Y",           
                  ifelse(v4_clin$v4_wohnsituation_erwerb_werk==2,"N",v4_clin_spec_emp))         

v4_con_spec_emp<-rep(NA,dim(v4_con)[1])
v4_con_spec_emp<-ifelse(v4_con$v4_bildung_beruf_erwerb_wfbm==1,"Y",         
                 ifelse(v4_con$v4_bildung_beruf_erwerb_wfbm==2,"N",v4_con_spec_emp))            


v4_spec_emp<-factor(c(v4_clin_spec_emp,v4_con_spec_emp))
descT(v4_spec_emp)

##                N    Y   <NA>     
## [1,] No. cases 372  65  1349 1786
## [2,] Percent   20.8 3.6 75.5 100

Weeks of work absence due to psychological distress in past six months (continuous [weeks], v4_wrk_abs_pst_6_mths)

Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.

v4_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v4_clin)[1])
v4_clin_wrk_abs_pst_6_mths<-ifelse((v4_clin$v4_wohnsituation_erwerb_unbekannt==1 | v4_clin$v4_wohnsituation_erwerb_rente==1 |  
                                 v4_clin$v4_wohnsituation_erwerb_fehlen>26),-999, v4_clin$v4_wohnsituation_erwerb_fehlen)

v4_con_wrk_abs_pst_6_mths<-rep(NA,dim(v4_con)[1])
v4_con_wrk_abs_pst_6_mths<-ifelse((v4_con$v4_bildung_beruf_erwerb_ausfallu==1 | v4_con$v4_bildung_beruf_erwerb_rente==1 |  
                                 v4_con$v4_bildung_beruf_erwerb_ausfallm>26),-999, v4_con$v4_bildung_beruf_erwerb_ausfallm)

v4_wrk_abs_pst_6_mths<-c(v4_clin_wrk_abs_pst_6_mths,v4_con_wrk_abs_pst_6_mths)
descT(v4_wrk_abs_pst_6_mths)

##                -999 0    1  2   3   4   5   6   8   10  12  13  15  16  20  22 
## [1,] No. cases 309  259  17 9   6   7   2   6   3   3   6   1   1   1   2   1  
## [2,] Percent   17.3 14.5 1  0.5 0.3 0.4 0.1 0.3 0.2 0.2 0.3 0.1 0.1 0.1 0.1 0.1
##      24  26  <NA>     
## [1,] 11  7   1135 1786
## [2,] 0.6 0.4 63.5 100

Currently impaired by psychological/psychiatric symptoms in exercising profession (dichotomous, v4_cur_work_restr)

Important: if receiving pension, this question refers to impairments in the household

v4_clin_cur_work_restr<-rep(NA,dim(v4_clin)[1])
v4_clin_cur_work_restr<-ifelse(v4_clin$v4_wohnsituation_erwerb_psysymptom==1,"Y",   
                    ifelse(v4_clin$v4_wohnsituation_erwerb_psysymptom==2,"N",v4_clin_cur_work_restr))    

v4_con_cur_work_restr<-rep(NA,dim(v4_con)[1])
v4_con_cur_work_restr<-ifelse(v4_con$v4_bildung_beruf_erwerb_psyeinsch==1,"Y",  
                    ifelse(v4_con$v4_bildung_beruf_erwerb_psyeinsch==2,"N",v4_con_cur_work_restr))   

v4_cur_work_restr<-factor(c(v4_clin_cur_work_restr,v4_con_cur_work_restr))
descT(v4_cur_work_restr)

##                N    Y    <NA>     
## [1,] No. cases 512  242  1032 1786
## [2,] Percent   28.7 13.5 57.8 100

Self-reported Weight (continuous [kilograms], v4_weight)

v4_weight<-c(v4_clin$v4_wohnsituation_erwerb_gewicht,v4_con$v4_bildung_beruf_erwerb_gewicht)
summary(v4_weight)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   25.00   69.00   81.00   83.87   96.00  193.00     971

Waist circumference (continouos [centimeters], v4_waist)**

This item was only recorded in a subset of individuals, because the question was introduced while the study was running.

v4_clin_waist<-v4_clin$v4_wohnsituation_erwerb_tailumf
v4_con_waist<-v4_con$v4_bildung_beruf_erwerb_taille

v4_waist<-c(v4_clin_waist,v4_con_waist)
summary(v4_waist)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   62.00   77.00   87.00   90.29  101.50  150.00    1435

BMI (continuous [BMI], v4_bmi)

We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.

v4_bmi<-v4_weight/(v1_height/100)^2
summary(v4_bmi)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    8.65   23.05   26.59   27.81   31.25   70.02     976

Create dataset

v4_dem<-data.frame(v4_cng_mar_stat,v4_marital_stat,v4_partner,v4_no_bio_chld,v4_no_adpt_chld,v4_stp_chld,v4_chg_hsng,v4_liv_aln,
                    v4_chg_empl_stat,v4_curr_paid_empl,v4_disabl_pens,v4_spec_emp,v4_wrk_abs_pst_6_mths,v4_cur_work_restr,
                    v4_weight,v4_bmi,v4_waist)

Visit 4: Assessment of disease course (OPCRIT Item 90) (ordinal [1,2,3,4,5], v4_opcrit)

The OPCRIT is an operational criteria checklist (and computer program) for psychotic illness (McGuffin, Farmer, & Harvey, 1991). We use item 90 of the OPCRIT to broadly assess the course of disorder from onset to the current state. All available information is to be used to answer the item (interview, medical records etc.).

IMPORTANT: this item was assessed in CLINICAL participants only, all CONTROL participants are assigned -999.

In clinical participants, this item has the following gradation: “single episode wirh good remission”-1, “multiple episodes with good remission between episodes”-2,“multiple episodes with partial remission between episodes”-3, “ongoing chronic disease”-4, “ongoing chronic disease with deterioration”-5 and “not estimable”-99. In the current dataset, 99 is replaced with -999. Note: this item is to be rated hierarchically, meaning if the past course of disease is to be rated with 2 but the present course of disease would require a 4, 4 is the right assessment.

v4_opcrit<-c(v4_clin$v4_opcrit_opcrit_verlauf,rep(-999,dim(v4_con)[1]))
v4_opcrit[v4_opcrit==99]<--999
descT(v4_opcrit)

##                -999 1   2    3    4   5   <NA>     
## [1,] No. cases 471  21  201  201  127 10  755  1786
## [2,] Percent   26.4 1.2 11.3 11.3 7.1 0.6 42.3 100

Visit 4: Life events precipitating illness episode between study visits

Please see Visit 2 for explanation.

**Life events: Occurred before illness episode? (dichotomous, v4_evnt_prcp_b4_*)**

for(i in 1:length(grep("v4_ergaenz_leq_leq_zeit_31055_",names(v4_clin)))){
  b4_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_zeit_31055_",names(v4_clin))[i]],
                   paste("v4_evnt_prcp_b4_",i,sep=""))
}

**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v4_evnt_prcp_f_*)**

for(i in 1:length(grep("v4_ergaenz_leq_leq_ausloeser_31055_",names(v4_clin)))){
  prcp_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_ausloeser_31055_",names(v4_clin))[i]],
                   paste("v4_evnt_prcp_f_",i,sep=""))
}

**Life events: LEQ item number (categorical [LEQ item number], v4_evnt_prcp_it_*)**

for(i in 1:length(grep("v4_ergaenz_leq_leq_item_31055_",names(v4_clin)))){
  leq_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_item_31055_",names(v4_clin))[i]],
                   paste("v4_evnt_prcp_it_",i,sep=""))
}

Create dataset

v4_leprcp<-data.frame(v4_evnt_prcp_it_1,v4_evnt_prcp_b4_1,v4_evnt_prcp_f_1,
                      v4_evnt_prcp_it_2,v4_evnt_prcp_b4_2,v4_evnt_prcp_f_2,
                      v4_evnt_prcp_it_3,v4_evnt_prcp_b4_3,v4_evnt_prcp_f_3,
                      v4_evnt_prcp_it_4,v4_evnt_prcp_b4_4,v4_evnt_prcp_f_4,
                      v4_evnt_prcp_it_5,v4_evnt_prcp_b4_5,v4_evnt_prcp_f_5,
                      v4_evnt_prcp_it_6,v4_evnt_prcp_b4_6,v4_evnt_prcp_f_6,
                      v4_evnt_prcp_it_7,v4_evnt_prcp_b4_7,v4_evnt_prcp_f_7,
                      v4_evnt_prcp_it_8,v4_evnt_prcp_b4_8,v4_evnt_prcp_f_8,
                      v4_evnt_prcp_it_9,v4_evnt_prcp_b4_9,v4_evnt_prcp_f_9,
                      v4_evnt_prcp_it_10,v4_evnt_prcp_b4_10,v4_evnt_prcp_f_10,
                      v4_evnt_prcp_it_11,v4_evnt_prcp_b4_11,v4_evnt_prcp_f_11,
                      v4_evnt_prcp_it_12,v4_evnt_prcp_b4_12,v4_evnt_prcp_f_12,
                      v4_evnt_prcp_it_13,v4_evnt_prcp_b4_13,v4_evnt_prcp_f_13,
                      v4_evnt_prcp_it_14,v4_evnt_prcp_b4_14,v4_evnt_prcp_f_14,
                      v4_evnt_prcp_it_15,v4_evnt_prcp_b4_15,v4_evnt_prcp_f_15,
                      v4_evnt_prcp_it_16,v4_evnt_prcp_b4_16,v4_evnt_prcp_f_16,
                      v4_evnt_prcp_it_17,v4_evnt_prcp_b4_17,v4_evnt_prcp_f_17,
                      v4_evnt_prcp_it_18,v4_evnt_prcp_b4_18,v4_evnt_prcp_f_18,
                      v4_evnt_prcp_it_19,v4_evnt_prcp_b4_19,v4_evnt_prcp_f_19,
                      v4_evnt_prcp_it_20,v4_evnt_prcp_b4_20,v4_evnt_prcp_f_20,
                      v4_evnt_prcp_it_21,v4_evnt_prcp_b4_21,v4_evnt_prcp_f_21,
                      v4_evnt_prcp_it_22,v4_evnt_prcp_b4_22,v4_evnt_prcp_f_22,
                      v4_evnt_prcp_it_23,v4_evnt_prcp_b4_23,v4_evnt_prcp_f_23,
                      v4_evnt_prcp_it_24,v4_evnt_prcp_b4_24,v4_evnt_prcp_f_24,
                      v4_evnt_prcp_it_25,v4_evnt_prcp_b4_25,v4_evnt_prcp_f_25,
                      v4_evnt_prcp_it_26,v4_evnt_prcp_b4_26,v4_evnt_prcp_f_26,
                      v4_evnt_prcp_it_27,v4_evnt_prcp_b4_27,v4_evnt_prcp_f_27,
                      v4_evnt_prcp_it_28,v4_evnt_prcp_b4_28,v4_evnt_prcp_f_28,
                      v4_evnt_prcp_it_29,v4_evnt_prcp_b4_29,v4_evnt_prcp_f_29,
                      v4_evnt_prcp_it_30,v4_evnt_prcp_b4_30,v4_evnt_prcp_f_30,
                      v4_evnt_prcp_it_31,v4_evnt_prcp_b4_31,v4_evnt_prcp_f_31)

Visit 4: Suicide attempts and suicidal ideation since last study visit

Here, we used a modified version of section X of the SCID, that was assessed at visit 1 (lifetime assessment of suicidality). Specifically, we slightly changed the wording of the items, so that they covered the time window from the last study visit until the current assessment.

Suicidal ideation

Suicidal ideation since last study visit (dichotomous, v4_suic_ide_snc_lst_vst)

Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.

v4_suic_ide_snc_lst_vst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_suic_ide_snc_lst_vst<-ifelse(c(v4_clin$v4_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v4_con)[1]))==1, "N", 
                            ifelse(c(v4_clin$v4_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v4_con)[1]))==3, "Y",                                                 v4_suic_ide_snc_lst_vst))

v4_suic_ide_snc_lst_vst<-factor(v4_suic_ide_snc_lst_vst)
descT(v4_suic_ide_snc_lst_vst)

##                -999 N    Y   <NA>     
## [1,] No. cases 466  434  144 742  1786
## [2,] Percent   26.1 24.3 8.1 41.5 100

Suicidal ideation detailed (ordinal [1,2,3,4], v4_scid_suic_ide)

This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v4_scid_suic_ide<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_scid_suic_ide<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==1, "1",
                         ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==2, "2",
                                ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==3, "3",
                                       ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==4, "4",-999))))

v4_scid_suic_ide<-factor(v4_scid_suic_ide,ordered=T)                                   
descT(v4_scid_suic_ide)

##                -999 1  2   3   4   <NA>     
## [1,] No. cases 900  89 23  13  19  742  1786
## [2,] Percent   50.4 5  1.3 0.7 1.1 41.5 100

Thoughts about methods (ordinal [1,2,3], v4_scid_suic_thght_mth)

This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.

v4_scid_suic_thght_mth<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_scid_suic_thght_mth<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==1, "1",
                         ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==2, "2",
                                ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==3, "3",-999)))

v4_scid_suic_thght_mth<-factor(v4_scid_suic_thght_mth,ordered=T)                                   
descT(v4_scid_suic_thght_mth)

##                -999 1   2   3   <NA>     
## [1,] No. cases 900  83  45  15  743  1786
## [2,] Percent   50.4 4.6 2.5 0.8 41.6 100

Suicidal ideation: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v4_scid_suic_note_thgts)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v4_scid_suic_note_thgts<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_scid_suic_note_thgts<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==1, "1",
                         ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==2, "2",
                                ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==3, "3",
                                       ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==4, "4",-999))))

v4_scid_suic_note_thgts<-factor(v4_scid_suic_note_thgts,ordered=T)                                   
descT(v4_scid_suic_note_thgts)

##                -999 1   2   4   <NA>     
## [1,] No. cases 900  132 5   3   746  1786
## [2,] Percent   50.4 7.4 0.3 0.2 41.8 100

Suicide attemps

Suicide attempt since last study visit (ordinal [1,2,3], v4_suic_attmpt_snc_lst_vst)

This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.

v4_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_suic_attmpt_snc_lst_vst<-ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==1, "1",
                         ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==2, "2",
                                ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==3, "3",-999)))

v4_suic_attmpt_snc_lst_vst<-factor(v4_suic_attmpt_snc_lst_vst,ordered=T)                                   
descT(v4_suic_attmpt_snc_lst_vst)

##                -999 1    2   3   <NA>     
## [1,] No. cases 466  564  4   4   748  1786
## [2,] Percent   26.1 31.6 0.2 0.2 41.9 100

Number of suicide attempts (ordinal [1,2,3,4,5,6], v4_no_suic_attmpt)

This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.

v4_no_suic_attmpt<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_no_suic_attmpt<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999, ifelse(v4_suic_attmpt_snc_lst_vst>1, c(v4_clin$v4_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v4_con)[1])),v4_no_suic_attmpt))

v4_no_suic_attmpt<-factor(v4_no_suic_attmpt,ordered=T)
descT(v4_no_suic_attmpt)

##                -999 1   3   <NA>     
## [1,] No. cases 1030 7   1   748  1786
## [2,] Percent   57.7 0.4 0.1 41.9 100

Preparation of suicide attempt (ordinal [1,2,3,4], v4_prep_suic_attp_ord)

This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.

v4_prep_suic_attp_ord<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_prep_suic_attp_ord<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999, 
                              ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==1, "1",
                              ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==2, "2",             
                              ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==3, "3",
                              ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==4, "4",
                              v4_prep_suic_attp_ord))))) 

v4_prep_suic_attp_ord<-factor(v4_prep_suic_attp_ord,ordered=T)
descT(v4_prep_suic_attp_ord)

##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1030 3   1   1   2   749  1786
## [2,] Percent   57.7 0.2 0.1 0.1 0.1 41.9 100

Suicidal attempt: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v4_suic_note_attmpt)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v4_suic_note_attmpt<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_suic_note_attmpt<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999, 
                            ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==1, "1",
                            ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==2, "2",
                            ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==3, "3",
                            ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==4, "4",
                            v4_suic_note_attmpt))))) 

v4_suic_note_attmpt<-factor(v4_suic_note_attmpt,ordered=T)
descT(v4_suic_note_attmpt)

##                -999 1   2   4   <NA>     
## [1,] No. cases 1030 5   1   1   749  1786
## [2,] Percent   57.7 0.3 0.1 0.1 41.9 100

Create dataset

v4_suic<-data.frame(v4_suic_ide_snc_lst_vst,v4_scid_suic_ide,v4_scid_suic_thght_mth,v4_scid_suic_note_thgts,
                    v4_suic_attmpt_snc_lst_vst,v4_no_suic_attmpt,v4_prep_suic_attp_ord,
                    v4_suic_note_attmpt)

Visit 4: Medication

As in the fist visit, the code below creates the following variables that summarize the medication of each individual:

Number of antidepressants prescribed (continuous [number], v4_Antidepressants)
Number of antipsychotics prescribed (continuous [number], v4_Antipsychotics)
Number of mood stabilizers prescribed (continuous [number], v4_Mood_stabilizers)
Number of tranquilizers prescribed (continuous [number], v4_Tranquilizers)
Number of other psychiatric medications (continuous [number], v4_Other_psychiatric)

Clinical participants

#get the following variables from v4_clin
#1. Medication name     ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_1"]
#6. Bedarf category     ["_bedarf_kategorie_1"]

v4_clin_medication_variables_1<-as.data.frame(v4_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v4_clin))])
dim(v4_clin_medication_variables_1) 

## [1] 1320   61

#recode the variables that are coded as characters/logicals in the "v4_clin_medication_variables_1" as factors
v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_15<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_15)

v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_15<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_15)

v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_16<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_16)

v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_16<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_16)

v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_17<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_17)

v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_17<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_17)

v4_clin_medication_variables_1$v4_medikabehand3_depot_medi_200170_3<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_depot_medi_200170_3)

v4_clin_medication_variables_1$v4_medikabehand3_depot_kategorie_200170_3<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_depot_kategorie_200170_3)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_8<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_8)
v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_8<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_8)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_9<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_9)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_9)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_10<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_10)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_10)

#make the duplicated data frame
v4_clin_medications_duplicated_1<-as.data.frame(t(apply(v4_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v4_clin_medications_duplicated_1) 

## [1] 1320   30

#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_clin, not as character
v4_clin_medication_variables_1[,!c(TRUE, FALSE)][v4_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v4_clin_medication_variables_1) 

## [1] 1320   61

#bind columns id and medication names, but not categories together 
v4_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v4_clin_medication_variables_1[,1], v4_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v4_clin_medication_name_1)

## [1] 1320   31

#get the medication categories from the "_medication_variables_1" dataframe
v4_clin_medication_categories_1<-as.data.frame(v4_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v4_clin_medication_categories_1) 

## [1] 1320   31

#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_clin, not as character 
#Important: v4_clin_medication_name_1=="NA" replaced with is.na(v4_clin_medication_name_1)
v4_clin_medication_categories_1[is.na(v4_clin_medication_name_1)] <- NA
#write.csv(v4_clin_medication_categories_1, file="v4_clin_medication_group_1.csv") 

#Make a count table of medications
v4_clin_med_table<-data.frame("mnppsd"=v4_clin$mnppsd)
v4_clin_med_table$v4_Antidepressants<-rowSums(v4_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v4_clin_med_table$v4_Antipsychotics<-rowSums(v4_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v4_clin_med_table$v4_Mood_stabilizers<-rowSums(v4_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v4_clin_med_table$v4_Tranquilizers<-rowSums(v4_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v4_clin_med_table$v4_Other_psychiatric<-rowSums(v4_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Control participants

#get the following variables from v4_con
#1. Medication name     ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_2"]
#6. Bedarf category     ["_bedarf_kategorie_2"]

v4_con_medication_variables_1<-as.data.frame(v4_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v4_con))])
dim(v4_con_medication_variables_1) #[1] 320 29 

## [1] 466  29

#recode the variables that are coded as characters/logicals in the "v4_con_medication_variables_1" as factors
v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_7<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_7)

v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_7<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_7)

v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_8<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_8)

v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_8<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_8)

v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_2<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_2)

v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_2<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_2)

v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_4<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_4)

v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_4)

#make the duplicated data frame
v4_con_medications_duplicated_1<-as.data.frame(t(apply(v4_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v4_con_medications_duplicated_1) 

## [1] 466  14

#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_con, not as character 
v4_con_medication_variables_1[,!c(TRUE, FALSE)][v4_con_medications_duplicated_1=="TRUE"] <- NA
dim(v4_con_medication_variables_1) 

## [1] 466  29

#bind columns id and medication names, but not categories together 
v4_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v4_con_medication_variables_1[,1], v4_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v4_con_medication_name_1) 

## [1] 466  15

#get the medication categories from the "_medication_variables_1" dataframe
v4_con_medication_categories_1<-as.data.frame(v4_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v4_con_medication_categories_1) 

## [1] 466  15

#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_con, not as character 
#Important: v4_con_medication_name_1=="NA" replaced with is.na(v4_con_medication_name_1)
v4_con_medication_categories_1[is.na(v4_con_medication_name_1)] <- NA
#write.csv(v4_con_medication_categories_1, file="v4_con_medication_group_1.csv")

#Make a count table of medications
v4_con_med_table<-data.frame("mnppsd"=v4_con$mnppsd)
v4_con_med_table$v4_Antidepressants<-rowSums(v4_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v4_con_med_table$v4_Antipsychotics<-rowSums(v4_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v4_con_med_table$v4_Mood_stabilizers<-rowSums(v4_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v4_con_med_table$v4_Tranquilizers<-rowSums(v4_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v4_con_med_table$v4_Other_psychiatric<-rowSums(v4_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Bind v4_clin and v4_con together by rows

v4_drugs<-rbind(v4_clin_med_table,v4_con_med_table)
dim(v4_drugs) 

## [1] 1786    6

#check if the id column of v4_drugs and v1_id match
table(v4_drugs[,1]==v1_id)

## 
## TRUE 
## 1786

Adverse events under current medication (dichotomous, v4_adv)

v4_clin_adv<-ifelse(v4_clin$v4_medikabehand_medi2_nebenwirk==1,"Y","N")
v4_con_adv<-rep("-999",dim(v4_con)[1])
v4_adv<-factor(c(v4_clin_adv,v4_con_adv))
descT(v4_adv)

##                -999 N   Y    <NA>     
## [1,] No. cases 466  159 236  925  1786
## [2,] Percent   26.1 8.9 13.2 51.8 100

Psychiatric medication change during the past six months (dichotomous, v4_medchange)

v4_clin_medchange<-rep(NA,dim(v4_clin)[1])
v4_clin_medchange<-ifelse(v4_clin$v4_medikabehand_medi3_mediaenderung==1,"Y","N")
v4_con_medchange<-rep("-999",dim(v4_con)[1])

v4_medchange<-as.factor(c(v4_clin_medchange,v4_con_medchange))
descT(v4_medchange)

##                -999 N   Y    <NA>     
## [1,] No. cases 466  177 217  926  1786
## [2,] Percent   26.1 9.9 12.2 51.8 100

Lithium

Please see the section in Visit 1 for explanation.

“Did you ever take lithium?” (Dichotomous, v4_lith)

v4_clin_lith<-rep(NA,dim(v4_clin)[1])
v4_clin_lith<-ifelse(v4_clin$v4_medikabehand_med_zusatz_lithium==1,"Y","N")
v4_con_lith<-rep("-999",dim(v4_con)[1])

v4_lith<-as.factor(c(v4_clin_lith,v4_con_lith))
v4_lith<-as.factor(v4_lith)

descT(v4_lith)

##                -999 N    Y   <NA>     
## [1,] No. cases 466  256  146 918  1786
## [2,] Percent   26.1 14.3 8.2 51.4 100

“If yes, for how long?” (Dichotomous, v4_lith_prd)

Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.

v4_clin_lith_prd<-rep(NA,dim(v4_clin)[1])
v4_con_lith_prd<-rep(-999,dim(v4_con)[1])

v4_clin_lith_prd<-ifelse(v4_clin_lith=="N", -999, ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==2,1,
                  ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==1,2,    
                  ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==0,3,NA))))
                                                     
v4_lith_prd<-factor(c(v4_clin_lith_prd,v4_con_lith_prd))
descT(v4_lith_prd)

##                -999 1   2   3   <NA>     
## [1,] No. cases 722  42  26  78  918  1786
## [2,] Percent   40.4 2.4 1.5 4.4 51.4 100

Visit 4: ALDA scale

The ALDA scale (Grof et al., 2002) measures reponse to lithium and was thus only used in clinical participants (see below). Control subjects, and clinical participants without a bipolar disorder (see below) have a -999 in this item. The scale is formally called “Retrospective criteria of long-term treatment response in research subjects with bipolar disorder”. The ALDA scale quantifies symptom improvement in the course of treatment (A score, range 0–10), which is then weighted against five criteria (B score) that assess confounding factors, each scored 0,1, or 2. The total score is then derived by subtracting the total B score from the A score. Negative scores are set to 0 by default so that the total score ranges from 0 to 10 (Hou et al., 2016).

This questionnaire was only assessed if…

…the participant had a DSM-IV diagnosis of bipolar I oder bipolar II disorder, and …the patent had ever been treated continuously with lithium for at least one year.

The scale was also assessed in some clinical participants with other diagnoses, because the bipolar diagnosis criterion had not been formalized at the start of the study.

Now, the ALDA items are coded so that all individuals with values in these item are included in the dataset. If no value is given (NA), and the fourth visit took place, all diagnoses other that BP-I and BP-II (this includes controls), including BP-I and BP-II individuals that never (or not long enough) received lithium, are coded -999.

All scale levels are described as being continuous, but the true level of the scales are probably ordinal.

The ALDA Total Score is given as in the paper case report form, please check yourself if it was correctly calculated

A score (continuous [0,1,2,3,4,5,6,7,8,9,10], v4_alda_A)

v4_clin_alda_A<-rep(NA,dim(v4_clin)[1])
v4_con_alda_A<-rep(-999,dim(v4_con)[1])

v4_clin_alda_A<-ifelse(is.na(v4_clin$v4_lithium_lithium_crit_a_score)==F, v4_clin$v4_lithium_lithium_crit_a_score, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_A<-c(v4_clin_alda_A,v4_con_alda_A)
summary(v4_alda_A[v4_alda_A>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.000   7.000   6.176   8.000  10.000     751

B1 score (continuous [0,1,2], v4_alda_B1)

v4_clin_alda_B1<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B1<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B1<-ifelse(is.na(v4_clin$v4_lithium_b1_score)==F, v4_clin$v4_lithium_b1_score, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
v4_alda_B1<-c(v4_clin_alda_B1,v4_con_alda_B1)
summary(v4_alda_B1[v4_alda_B1>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.3145  0.2500  2.0000     752

B2 score (continuous [0,1,2], v4_alda_B2)

v4_clin_alda_B2<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B2<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B2<-ifelse(is.na(v4_clin$v4_lithium_b2_score)==F, v4_clin$v4_lithium_b2_score, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_B2<-c(v4_clin_alda_B2,v4_con_alda_B2)
summary(v4_alda_B2[v4_alda_B2>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.4516  1.0000  2.0000     752

B3 score (continuous [0,1,2], v4_alda_B3)

v4_clin_alda_B3<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B3<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B3<-ifelse(is.na(v4_clin$v4_lithium_b3_score)==F, v4_clin$v4_lithium_b3_score, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_B3<-c(v4_clin_alda_B3,v4_con_alda_B3)
summary(v4_alda_B3[v4_alda_B3>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   0.264   1.000   1.000     751

B4 score (continuous [0,1,2], v4_alda_B4)

v4_clin_alda_B4<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B4<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B4<-ifelse(is.na(v4_clin$v4_lithium_b4_score)==F, v4_clin$v4_lithium_b4_score, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_B4<-c(v4_clin_alda_B4,v4_con_alda_B4)
summary(v4_alda_B4[v4_alda_B4>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.2857  0.0000  2.0000     750

B5 score (continuous [0,1,2], v4_alda_B5)

v4_clin_alda_B5<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B5<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B5<-ifelse(is.na(v4_clin$v4_lithium_b5_score)==F, v4_clin$v4_lithium_b5_score, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_B5<-c(v4_clin_alda_B5,v4_con_alda_B5)
summary(v4_alda_B5[v4_alda_B5>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   2.000   1.352   2.000   2.000     751

Create dataset

v4_med<-data.frame(v4_drugs[,2:6],v4_adv,v4_medchange,v4_lith,v4_lith_prd,v4_alda_A,v4_alda_B1,v4_alda_B2,v4_alda_B3,v4_alda_B4,v4_alda_B5)

Create datasets with raw medication information

Here, separate datasets for clinical and control participants are created that contain the raw medication information at visit 4, as specified in the phenotype database.

Please note: The ALDA scale is not contained in the dataset of clinical participants.

For each medication that the individual took at visit 4 (including non-psychiatric drugs), the information given below is assessed.

The last character of each variable name always refers to the medication in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.

The medications were not assessed in any specific order, i.e. the order was determined by the individual participant (whatever she or he mentioned first). To classify medications, we used a catalogue, from which the categories and subcategories a medication belongs to were selected (see below).

Below, the variable names of clinical/control participants, respectively, are given in quotes, and the coding is explained in the parentheses.

1.Was the individual treated with any medication? (-1-not assessed, 1-yes, 2-no)
“v4_medikabehand3_keine_med”/“v4_medikabehand3_keine_med”

2. Regular medication: Name of the medication (character)
   “v4_medikabehand3_med_medi_199998”/“v4_medikabehand3_med_medi_200705”

3. Regular medication: Category to which the medication belongs (character)
   “v4_medikabehand3_med_kategorie_199998”/“v4_medikabehand3_med_kategorie_200705”

4. Regular medication: Subcategory to which the medication belongs (character)
   “v4_medikabehand3_med_kategorie_sub_199998”/“v4_medikabehand3_med_kategorie_sub_200705”

5. Regular medication: Psychiatric medication? (0-no, 1-yes) “v4_medikabehand3_med_zusatz_199998”/“v4_medikabehand3_med_zusatz_200705”

6. Regular medication: Dose in the morning (unitless)
   “v4_medikabehand3_s_medi1_morgens_199998”/“v4_medikabehand3_s_medi1_morgens_200705”

7. Regular medication: Dose at midday (unitless)
   “v4_medikabehand3_smedi1_mittags_199998”/“v4_medikabehand3_smedi1_mittags_200705”

8. Regular medication: Dose in the evening (unitless)
   “v4_medikabehand3_smedi1_abends_199998”/“v4_medikabehand3_smedi1_abends_200705”

9. Regular medication: Dose at night (unitless)
   “v4_medikabehand3_smedi1_nachts_199998”/“v4_medikabehand3_smedi1_nachts_200705”

10. Regular medication: Unit of the medication asked in the last four questions (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
    “v4_medikabehand3_smedi1_einheit_199998”/“v4_medikabehand3_smedi1_einheit_200705”

11. Regular medication: Total dose of the medication per day (unitless)
    “v4_medikabehand3_smedi1_gesamtdosis_199998”/“v4_medikabehand3_smedi1_gesamtdosis_200705”

12. Regular medication: Unit of the medication asked in the last question (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
    “v4_medikabehand3_smedi1_einheit1_199998”/“v4_medikabehand3_smedi1_einheit1_200705”

13. Regular medication: Medication name, if not contained in our catalog (character)
    “v4_medikabehand3_medikament_text_199998”/“v4_medikabehand3_medikament_text_200705”

14. Depot medication: Name of the medication (character) “v4_medikabehand3_depot_medi_200170”/“v4_medikabehand3_depot_medi_201224

15. Depot medication: Category to which the medication belongs (character) “v4_medikabehand3_depot_kategorie_200170”/“v4_medikabehand3_depot_kategorie_201224

16. Depot medication: Subcategory to which the medication belongs (character)
    “v4_medikabehand3_depot_kategorie_sub_200170”/“v4_medikabehand3_depot_kategorie_sub_201224

17. Depot medication: Psychiatric medication? (0-no, 1-yes) “v4_medikabehand3_depot_zusatz_200170”/“v4_medikabehand3_depot_zusatz_201224”

18. Depot medication: Total Dose (unitless) “v4_medikabehand3_s_depot_gesamtdosis_200170”/“v4_medikabehand3_s_depot_gesamtdosis_201224”

19. Depot medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v4_medikabehand3_s_depot_einheit_200170”/ “v4_medikabehand3_s_depot_einheit_201224”

20. Interval, at which the depot medication is given (days) “v4_medikabehand3_s_depot_tage_200170”/“v4_medikabehand3_s_depot_tage_201224”

21. Medication name, if not contained in our catalog (character) “v4_medikabehand3_medikament_text_200170”/“v4_medikabehand3_medikament_text_201224”

22. Pro re nata (PRN) medication: Name of the medication (character) “v4_medikabehand3_bedarf_medi_199584”/“v4_medikabehand3_bedarf_medi_201187”

23. Pro re nata (PRN) medication: Category to which the medication belongs (character)
    “v4_medikabehand3_bedarf_kategorie_199584”/“v4_medikabehand3_bedarf_kategorie_201187”

24. Pro re nata (PRN) medication: Subcategory to which the medication belongs (character) “v4_medikabehand3_bedarf_kategorie_sub_199584”/“v4_medikabehand3_bedarf_kategorie_sub_201187”

25. Pro re nata (PRN) medication: Psychiatric medication? (0-no, 1-yes) “v4_medikabehand3_bedarf_zusatz_199584”/“v4_medikabehand3_bedarf_zusatz_201187”

26. Pro re nata (PRN) medication: Total dose up to (unitless) “v4_medikabehand3_s_bedarf_gesamtdosis_199584”/“v4_medikabehand3_s_bedarf_kommentar_201187

27. Pro re nata (PRN) medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v4_medikabehand3_s_bedarf_einheit1_199584”/“v4_medikabehand3_s_bedarf_einheit1_201187”

28. Pro re nata (PRN) medication: Comment (character) “v4_medikabehand3_s_bedarf_kommentar_199584”/“v4_medikabehand3_s_bedarf_kommentar_201187”

29. Pro re nata (PRN) medication: Medication name, if not contained in our catalog (character) “v4_medikabehand3_medikament_text_199584”/“v4_medikabehand3_medikament_text_201187”

Make datasets containing only information on medication

v4_med_clin_orig<-data.frame(v4_clin$mnppsd,v4_clin[,147:455])
names(v4_med_clin_orig)[1]<-"v1_id"

v4_med_con_orig<-data.frame(v4_con$mnppsd,v4_con[,75:219])
names(v4_med_con_orig)[1]<-"v1_id"

Save raw medication datasets of visit 4

save(v4_med_clin_orig, file="230614_v6.0_psycourse_clin_raw_med_visit4.RData")
save(v4_med_con_orig, file="230614_v6.0_psycourse_con_raw_med_visit4.RData")

Write .csv file

write.table(v4_med_clin_orig,file="230614_v6.0_psycourse_clin_raw_med_visit4.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v4_med_con_orig,file="230614_v6.0_psycourse_con_raw_med_visit4.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Visit 4: Substance abuse

Tobacco

For more explanation, see Visit 1

“Did you start or stop smoking during the past six months?” (categorical [NS,NN,YSP,YST], v4_smk_strt_stp)

This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.

v4_clin_smk_strt_stp<-rep(NA,dim(v4_clin)[1])
v4_clin_smk_strt_stp<-ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==1,"NS",
                        ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==2,"NN",
                          ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==3,"YSP",
                            ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==4,"YST",v4_clin_smk_strt_stp))))
       
#ATTENTION: answering alternative: e-cigarette only in controls
v4_con_smk_strt_stp<-rep(NA,dim(v4_con)[1])
v4_con_smk_strt_stp<-ifelse(v4_con$v4_tabalk_folge_tabak1==1 | v4_con$v4_tabalk_folge_tabak1==2,"NS",
                        ifelse(v4_con$v4_tabalk_folge_tabak1==3,"NN",
                          ifelse(v4_con$v4_tabalk_folge_tabak1==4,"YSP",
                            ifelse(v4_con$v4_tabalk_folge_tabak1==5,"YST",v4_con_smk_strt_stp))))
                        
v4_smk_strt_stp<-c(v4_clin_smk_strt_stp,v4_con_smk_strt_stp)
descT(v4_smk_strt_stp)

##                NN   NS   YSP YST <NA>     
## [1,] No. cases 277  525  20  8   956  1786
## [2,] Percent   15.5 29.4 1.1 0.4 53.5 100

“How many cigarettes do you presently smoke on average?” (continuous [number cigarettes], v4_no_cig)

In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.

Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.

v4_no_cig<-c(rep(NA,dim(v4_clin)[1]),rep(NA,dim(v4_con)[1]))

v4_no_cig<-ifelse((v4_smk_strt_stp=="NN" | v4_smk_strt_stp=="YSP"), -999, 
            ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") & 
                     c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==1,                                
                     c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*365,
            ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") & 
                     c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==2,                                
                     c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*52,
            ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") & 
                     c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==3,                                
                     c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*12,
                      v4_no_cig))))

summary(v4_no_cig[v4_no_cig>=0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0    3650    5475    6320    9125   21900    1181

Alcohol

“How often did you consume alcoholic beverages during the past six months?” (ordinal [1,2,3,4,5,6,7], v4_alc_pst6_mths)

This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.

v4_alc_pst6_mths<-c(v4_clin$v4_tabalk1_ta9_alkkonsum,v4_con$v4_tabalk_folge_alkohol4)
v4_alc_pst6_mths<-factor(v4_alc_pst6_mths, ordered=T)

descT(v4_alc_pst6_mths)

##                1   2   3  4   5   6   7   <NA>     
## [1,] No. cases 214 160 90 154 130 49  34  955  1786
## [2,] Percent   12  9   5  8.6 7.3 2.7 1.9 53.5 100

“On how many occasions during the past six months did you drink FIVE (men)/FOUR (women) or more alcoholic beverages?”” (ordinal [1,2,3,4,5,6,7,8,9], v4_alc_5orm)

This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.

v4_alc_5orm<-ifelse(v4_alc_pst6_mths<4,-999,
                    ifelse(is.na(c(v4_clin$v4_tabalk1_ta10_alk_haeufigk_m1,v4_con$v4_tabalk_folge_alkohol5))==T,   
                            c(v4_clin$v4_tabalk1_ta11_alk_haeufigk_f1,v4_con$v4_tabalk_folge_alkohol6),
                            c(v4_clin$v4_tabalk1_ta10_alk_haeufigk_m1,v4_con$v4_tabalk_folge_alkohol5)))

v4_alc_5orm<-factor(v4_alc_5orm, ordered=T)

descT(v4_alc_5orm)

##                -999 1    2   3   4   5   6   7  8   9   <NA>     
## [1,] No. cases 464  182  56  40  13  24  23  18 4   7   955  1786
## [2,] Percent   26   10.2 3.1 2.2 0.7 1.3 1.3 1  0.2 0.4 53.5 100

Illicit drugs

For more information see visit 2.

“During the past six months, did you take ANY illicit drugs?” (dichotomous, v4_pst6_ill_drg)

v4_pst6_ill_drg<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_pst6_ill_drg<-ifelse(c(v4_clin$v4_drogen1_dg1_konsum,v4_con$v4_drogen_folge_drogenkonsum)==2, "Y", "N")

descT(v4_pst6_ill_drg)

##                N   Y   <NA>     
## [1,] No. cases 768 63  955  1786
## [2,] Percent   43  3.5 53.5 100

Create dataset

v4_subst<-data.frame(v4_smk_strt_stp,
                     v4_no_cig,
                     v4_alc_pst6_mths,
                     v4_alc_5orm,
                     v4_pst6_ill_drg)

Create dataset with raw illicit drug information

Here, separate datasets for clinical and control participants are created that contain the raw information on illicit drugs at visit 4, exactly as specified in the phenotype database.

For each illicit drug ever taken, the information given below is assessed.

The last character of each variable name always refers to the drug in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.

The drugs are not assessed in any specific order, i.e. the order is determined by the individual participant (whatever she or he mentions first).

Below, the variable names of clinical/control participants are given in quotes, and the coding is explained in the parentheses.

1. Whether the individual consumed illicit drugs since the last visit. (Coding: 1-no, 2-yes) “v4_drogen1_dg1_konsum”/“v4_drogen_folge_drogenkonsum”

2. The name of the drug: (character)
“v4_drogen1_s_dg_droge_28483”/“v4_drogen_folge_droge_117794”

The category to which the drug belongs (each item below is a checkbox: 0-not checked, 1-checked):
3. Stimulants: “v4_drogen1_s_dg_drogekt1_28483”/“v4_drogen_folge_droge1_117794”
4. Cannabis: “v4_drogen2_s_dg_drogekt1_28483”/“v4_drogen_folge_droge2_117794”
5. Opiates and pain reliefers: “v4_drogen3_s_dg_drogekt1_28483”/“v4_drogen_folge_droge3_117794”
6. Cocaine: “v4_drogen1_s_dg_drogekt1_28483”/“v4_drogen_folge_droge4_117794”
7. Hallucinogens: “v4_drogen1_s_dg_drogekt5_28483”/“v4_drogen_folge_droge5_117794”
8. Inhalants: “v4_drogen6_s_dg_drogekt1_28483”/“v4_drogen_folge_droge6_117794”
9. Tranquilizers: “v4_drogen7_s_dg_drogekt1_28483”/“v4_drogen_folge_droge7_117794”
10. Other: “v4_drogen8_s_dg_drogekt1_28483”/“v4_drogen_folge_droge8_117794”

11. “Referring to the time since the last study visit, how often did you consume it?”
“v4_drogen1_s_dga_haeufigk_28483”/“v4_drogen_folge_droge_haeufig_117794”

The coding is given below:
1 - tried 1 time
2 - less than once a month
3 - about once a month
4 - at least 2 times but less than 10 times a month
5 - at least 10 times a month

12. “Referring to the period of time since the last study visit, did you have to take more of the drug to achieve the same effect?” (Coding: 1-no, 2-yes). “v4_drogen1_s_dgf_l6m_dosis_28483”/“v4_drogen_folge_droge_dosis_117794”

Important: There is an error in the original phenotype database, that affects the coding of item 10 (above). In all drugs the exports of the phenotype database do not reflect the input into the graphical user interface. Below, the incorrect variable is replaced with the corrected one

Make datasets containing only information on illicit drugs

v4_drg_clin<-v4_clin[,733:788]
v4_drg_con<-v4_con[,315:392]

Clinical participants

v4_clin_ill_drugs_orig<-data.frame(v4_clin$mnppsd,v4_drg_clin)
names(v4_clin_ill_drugs_orig)[1]<-"v1_id"

#recode wrongly coded item 10
for(i in c(0:4)){

v4_clin_ill_drugs_orig[,12+i*11]<-ifelse(v4_clin_ill_drugs_orig[,12+i*11]==5,1,
                               ifelse(v4_clin_ill_drugs_orig[,12+i*11]==4,5,
                                ifelse(v4_clin_ill_drugs_orig[,12+i*11]==3,4,
                                 ifelse(v4_clin_ill_drugs_orig[,12+i*11]==2,3,
                                  ifelse(v4_clin_ill_drugs_orig[,12+i*11]==1,2,NA)))))}

Control participants

v4_con_ill_drugs_orig<-data.frame(v4_con$mnppsd,v4_drg_con)
names(v4_con_ill_drugs_orig)[1]<-"v1_id"

#recode wrongly coded item 10
for(i in c(0:6)){

v4_con_ill_drugs_orig[,12+i*11]<-ifelse(v4_con_ill_drugs_orig[,12+i*11]==5,1,
                               ifelse(v4_con_ill_drugs_orig[,12+i*11]==4,5,
                                ifelse(v4_con_ill_drugs_orig[,12+i*11]==3,4,
                                 ifelse(v4_con_ill_drugs_orig[,12+i*11]==2,3,
                                  ifelse(v4_con_ill_drugs_orig[,12+i*11]==1,2,NA)))))}

Save raw illicit drug dataset from visit 4

save(v4_clin_ill_drugs_orig, file="230614_v6.0_psycourse_clin_raw_ill_drg_visit4.RData")
save(v4_con_ill_drugs_orig, file="230614_v6.0_psycourse_con_raw_ill_drg_visit4.RData")

Write .csv file

write.table(v4_clin_ill_drugs_orig,file="230614_v6.0_psycourse_clin_raw_ill_drg_visit4.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v4_con_ill_drugs_orig,file="230614_v6.0_psycourse_con_raw_ill_drg_visit4.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Visit 4: Symptom rating scales (interviewer rates patient)

PANSS

For more information on the scale, please see Visit 1

Positive subscale

P1 Delusions (ordinal [1,2,3,4,5,6,7], v4_panss_p1)

v4_panss_p1<-c(v4_clin$v4_panss_p_p1_wahnideen,v4_con$v4_panss_p_p1_wahnideen)
v4_panss_p1<-factor(v4_panss_p1, ordered=T)

descT(v4_panss_p1)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 654  31  50  28  7   9   2   1005 1786
## [2,] Percent   36.6 1.7 2.8 1.6 0.4 0.5 0.1 56.3 100

P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v4_panss_p2)

v4_panss_p2<-c(v4_clin$v4_panss_p_p2_form_denkst,v4_con$v4_panss_p_p2_form_denkst)
v4_panss_p2<-factor(v4_panss_p2, ordered=T)

descT(v4_panss_p2)

##                1    2   3  4  5   <NA>     
## [1,] No. cases 615  47  72 35 12  1005 1786
## [2,] Percent   34.4 2.6 4  2  0.7 56.3 100

P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v4_panss_p3)

v4_panss_p3<-c(v4_clin$v4_panss_p_p3_halluz,v4_con$v4_panss_p_p3_halluz)
v4_panss_p3<-factor(v4_panss_p3, ordered=T)

descT(v4_panss_p3)

##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 695  17 32  19  15  3   1005 1786
## [2,] Percent   38.9 1  1.8 1.1 0.8 0.2 56.3 100

P4 Excitement (ordinal [1,2,3,4,5,6,7], v4_panss_p4)

v4_panss_p4<-c(v4_clin$v4_panss_p_p4_erregung,v4_con$v4_panss_p_p4_erregung)
v4_panss_p4<-factor(v4_panss_p4, ordered=T)

descT(v4_panss_p4)

##                1    2   3  4   5   <NA>     
## [1,] No. cases 644  44  72 19  2   1005 1786
## [2,] Percent   36.1 2.5 4  1.1 0.1 56.3 100

P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v4_panss_p5)

v4_panss_p5<-c(v4_clin$v4_panss_p_p5_groessenideen,v4_con$v4_panss_p_p5_groessenideen)
v4_panss_p5<-factor(v4_panss_p5, ordered=T)

descT(v4_panss_p5)

##                1   2   3   4   5   <NA>     
## [1,] No. cases 733 21  16  6   5   1005 1786
## [2,] Percent   41  1.2 0.9 0.3 0.3 56.3 100

P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v4_panss_p6)

v4_panss_p6<-c(v4_clin$v4_panss_p_p6_misstr_verfolg,v4_con$v4_panss_p_p6_misstr_verfolg)
v4_panss_p6<-factor(v4_panss_p6, ordered=T)

descT(v4_panss_p6)

##                1    2   3   4  5   6   <NA>     
## [1,] No. cases 656  31  64  18 9   3   1005 1786
## [2,] Percent   36.7 1.7 3.6 1  0.5 0.2 56.3 100

P7 Hostility (ordinal [1,2,3,4,5,6,7], v4_panss_p7)

v4_panss_p7<-c(v4_clin$v4_panss_p_p7_feindseligkeit,v4_con$v4_panss_p_p7_feindseligkeit)
v4_panss_p7<-factor(v4_panss_p7, ordered=T)

descT(v4_panss_p7)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 722  23  30  5   1   1005 1786
## [2,] Percent   40.4 1.3 1.7 0.3 0.1 56.3 100

PANSS Positive sum score (continuous [7-49], v4_panss_sum_pos)

v4_panss_sum_pos<-as.numeric.factor(v4_panss_p1)+
                  as.numeric.factor(v4_panss_p2)+
                  as.numeric.factor(v4_panss_p3)+
                  as.numeric.factor(v4_panss_p4)+
                  as.numeric.factor(v4_panss_p5)+
                  as.numeric.factor(v4_panss_p6)+
                  as.numeric.factor(v4_panss_p7)

summary(v4_panss_sum_pos)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   7.000   7.000   7.000   9.006  10.000  27.000    1005

Negative subscale

N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v4_panss_n1)

v4_panss_n1<-c(v4_clin$v4_panss_n_n1_affektverflachung,v4_con$v4_panss_n_n1_affektverflachung)
v4_panss_n1<-factor(v4_panss_n1, ordered=T)

descT(v4_panss_n1)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 493  67  99  51  67  4   1005 1786
## [2,] Percent   27.6 3.8 5.5 2.9 3.8 0.2 56.3 100

N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v4_panss_n2)

v4_panss_n2<-c(v4_clin$v4_panss_n_n2_emot_rueckzug,v4_con$v4_panss_n_n2_emot_rueckzug)
v4_panss_n2<-factor(v4_panss_n2, ordered=T)

descT(v4_panss_n2)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 568  55  74  57  23  4   1005 1786
## [2,] Percent   31.8 3.1 4.1 3.2 1.3 0.2 56.3 100

N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v4_panss_n3)

v4_panss_n3<-c(v4_clin$v4_panss_n_n3_mang_aff_rapp,v4_con$v4_panss_n_n3_mang_aff_rapp)
v4_panss_n3<-factor(v4_panss_n3, ordered=T)

descT(v4_panss_n3)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 592  49  93  25  15  3   1009 1786
## [2,] Percent   33.1 2.7 5.2 1.4 0.8 0.2 56.5 100

N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v4_panss_n4)

v4_panss_n4<-c(v4_clin$v4_panss_n_n4_soz_pass_apath,v4_con$v4_panss_n_n4_soz_pass_apath)
v4_panss_n4<-factor(v4_panss_n4, ordered=T)

descT(v4_panss_n4)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 550  62  94  37  30  6   1007 1786
## [2,] Percent   30.8 3.5 5.3 2.1 1.7 0.3 56.4 100

N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v4_panss_n5)

v4_panss_n5<-c(v4_clin$v4_panss_n_n5_abstr_denken,v4_con$v4_panss_n_n5_abstr_denken)
v4_panss_n5<-factor(v4_panss_n5, ordered=T)

descT(v4_panss_n5)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 522  82  111 43  15  3   1010 1786
## [2,] Percent   29.2 4.6 6.2 2.4 0.8 0.2 56.6 100

N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v4_panss_n6)

v4_panss_n6<-c(v4_clin$v4_panss_n_n6_spon_fl_sprache,v4_con$v4_panss_n_n6_spon_fl_sprache)
v4_panss_n6<-factor(v4_panss_n6, ordered=T)

descT(v4_panss_n6)

##                1    2   3   4   5  6   <NA>     
## [1,] No. cases 641  29  61  31  17 3   1004 1786
## [2,] Percent   35.9 1.6 3.4 1.7 1  0.2 56.2 100

N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v4_panss_n7)

v4_panss_n7<-c(v4_clin$v4_panss_n_n7_stereotyp_ged,v4_con$v4_panss_n_n7_stereotyp_ged)
v4_panss_n7<-factor(v4_panss_n7, ordered=T)

descT(v4_panss_n7)

##                1    2   3  4   5   <NA>     
## [1,] No. cases 654  33  71 21  1   1006 1786
## [2,] Percent   36.6 1.8 4  1.2 0.1 56.3 100

PANSS Negative sum score (continuous [7-49], v4_panss_sum_neg)

v4_panss_sum_neg<-as.numeric.factor(v4_panss_n1)+
                  as.numeric.factor(v4_panss_n2)+
                  as.numeric.factor(v4_panss_n3)+
                  as.numeric.factor(v4_panss_n4)+
                  as.numeric.factor(v4_panss_n5)+
                  as.numeric.factor(v4_panss_n6)+
                  as.numeric.factor(v4_panss_n7)

summary(v4_panss_sum_neg)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    9.00   11.03   13.00   34.00    1013

General psychopathology subscale

G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v4_panss_g1)

v4_panss_g1<-c(v4_clin$v4_panss_g_g1_sorge_gesundh,v4_con$v4_panss_g_g1_sorge_gesundh)
v4_panss_g1<-factor(v4_panss_g1, ordered=T)

descT(v4_panss_g1)

##                1   2   3   4   5   6   <NA>     
## [1,] No. cases 590 77  76  27  6   1   1009 1786
## [2,] Percent   33  4.3 4.3 1.5 0.3 0.1 56.5 100

G2 Anxiety (ordinal [1,2,3,4,5,6,7], v4_panss_g2)

v4_panss_g2<-c(v4_clin$v4_panss_g_g2_angst,v4_con$v4_panss_g_g2_angst)
v4_panss_g2<-factor(v4_panss_g2, ordered=T)

descT(v4_panss_g2)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 512  64  139 40  24  1007 1786
## [2,] Percent   28.7 3.6 7.8 2.2 1.3 56.4 100

G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v4_panss_g3)

v4_panss_g3<-c(v4_clin$v4_panss_g_g3_schuldgefuehle,v4_con$v4_panss_g_g3_schuldgefuehle)
v4_panss_g3<-factor(v4_panss_g3, ordered=T)

descT(v4_panss_g3)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 606  38  77  41  15  1009 1786
## [2,] Percent   33.9 2.1 4.3 2.3 0.8 56.5 100

G4 Tension (ordinal [1,2,3,4,5,6,7], v4_panss_g4)

v4_panss_g4<-c(v4_clin$v4_panss_g_g4_anspannung,v4_con$v4_panss_g_g4_anspannung)
v4_panss_g4<-factor(v4_panss_g4, ordered=T)

descT(v4_panss_g4)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 558  63  115 32  9   3   1006 1786
## [2,] Percent   31.2 3.5 6.4 1.8 0.5 0.2 56.3 100

G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v4_panss_g5)

v4_panss_g5<-c(v4_clin$v4_panss_g_g5_manier_koerperh,v4_con$v4_panss_g_g5_manier_koerperh)
v4_panss_g5<-factor(v4_panss_g5, ordered=T)

descT(v4_panss_g5)

##                1    2   3   4   6   <NA>     
## [1,] No. cases 724  20  28  5   2   1007 1786
## [2,] Percent   40.5 1.1 1.6 0.3 0.1 56.4 100

G6 Depression (ordinal [1,2,3,4,5,6,7], v4_panss_g6)

v4_panss_g6<-c(v4_clin$v4_panss_g_g6_depression,v4_con$v4_panss_g_g6_depression)
v4_panss_g6<-factor(v4_panss_g6, ordered=T)

descT(v4_panss_g6)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 505  48  108 66  45  5   2   1007 1786
## [2,] Percent   28.3 2.7 6   3.7 2.5 0.3 0.1 56.4 100

G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v4_panss_g7)

v4_panss_g7<-c(v4_clin$v4_panss_g_g7_mot_verlangs,v4_con$v4_panss_g_g7_mot_verlangs)
v4_panss_g7<-factor(v4_panss_g7, ordered=T)

descT(v4_panss_g7)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 584  48  96  47  5   1006 1786
## [2,] Percent   32.7 2.7 5.4 2.6 0.3 56.3 100

G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v4_panss_g8)

v4_panss_g8<-c(v4_clin$v4_panss_g_g8_unkoop_verh,v4_con$v4_panss_g_g8_unkoop_verh)
v4_panss_g8<-factor(v4_panss_g8, ordered=T)

descT(v4_panss_g8)

##                1    2   3   5   <NA>     
## [1,] No. cases 747  16  15  2   1006 1786
## [2,] Percent   41.8 0.9 0.8 0.1 56.3 100

G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v4_panss_g9)

v4_panss_g9<-c(v4_clin$v4_panss_g_g9_ungew_denkinh,v4_con$v4_panss_g_g9_ungew_denkinh)
v4_panss_g9<-factor(v4_panss_g9, ordered=T)

descT(v4_panss_g9)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 657  31  64  19  7   2   1006 1786
## [2,] Percent   36.8 1.7 3.6 1.1 0.4 0.1 56.3 100

G10 Disorientation (ordinal [1,2,3,4,5,6,7], v4_panss_g10)

v4_panss_g10<-c(v4_clin$v4_panss_g_g10_desorient,v4_con$v4_panss_g_g10_desorient)
v4_panss_g10<-factor(v4_panss_g10, ordered=T)

descT(v4_panss_g10)

##                1    2   3   4   <NA>     
## [1,] No. cases 747  23  9   1   1006 1786
## [2,] Percent   41.8 1.3 0.5 0.1 56.3 100

G11 Poor attention (ordinal [1,2,3,4,5,6,7], v4_panss_g11)

v4_panss_g11<-c(v4_clin$v4_panss_g_g11_mang_aufmerks,v4_con$v4_panss_g_g11_mang_aufmerks)
v4_panss_g11<-factor(v4_panss_g11, ordered=T)

descT(v4_panss_g11)

##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 534  55  132 49  1   1   1014 1786
## [2,] Percent   29.9 3.1 7.4 2.7 0.1 0.1 56.8 100

G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v4_panss_g12)

v4_panss_g12<-c(v4_clin$v4_panss_g_g12_mang_urt_einsi,v4_con$v4_panss_g_g12_mang_urt_einsi)

v4_panss_g12<-factor(v4_panss_g12, ordered=T)
descT(v4_panss_g12)

##                1   2   3   4   5   7   <NA>     
## [1,] No. cases 679 37  34  22  5   2   1007 1786
## [2,] Percent   38  2.1 1.9 1.2 0.3 0.1 56.4 100

G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v4_panss_g13)

v4_panss_g13<-c(v4_clin$v4_panss_g_g13_willensschwae,v4_con$v4_panss_g_g13_willensschwae)
v4_panss_g13<-factor(v4_panss_g13, ordered=T)

descT(v4_panss_g13)

##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 676  24  65  11  2   1   1   1006 1786
## [2,] Percent   37.8 1.3 3.6 0.6 0.1 0.1 0.1 56.3 100

G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v4_panss_g14)

v4_panss_g14<-c(v4_clin$v4_panss_g_g14_mang_impulsk,v4_con$v4_panss_g_g14_mang_impulsk)
v4_panss_g14<-factor(v4_panss_g14, ordered=T)

descT(v4_panss_g14)

##                1    2   3   4   6   <NA>     
## [1,] No. cases 687  29  56  4   2   1008 1786
## [2,] Percent   38.5 1.6 3.1 0.2 0.1 56.4 100

G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v4_panss_g15)

v4_panss_g15<-c(v4_clin$v4_panss_g_g15_selbstbezog,v4_con$v4_panss_g_g15_selbstbezog)
v4_panss_g15<-factor(v4_panss_g15, ordered=T)

descT(v4_panss_g15)

##                1    2   3   4   5   <NA>     
## [1,] No. cases 705  32  33  5   3   1008 1786
## [2,] Percent   39.5 1.8 1.8 0.3 0.2 56.4 100

G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v4_panss_g16)

v4_panss_g16<-c(v4_clin$v4_panss_g_g16_aktsoz_vermeid,v4_con$v4_panss_g_g16_aktsoz_vermeid)
v4_panss_g16<-factor(v4_panss_g16, ordered=T)

descT(v4_panss_g16)

##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 624  37  72 26  14  4   1009 1786
## [2,] Percent   34.9 2.1 4  1.5 0.8 0.2 56.5 100

PANSS General Psychopathology sum score (continuous [16-112], v4_panss_sum_gen)

v4_panss_sum_gen<-as.numeric.factor(v4_panss_g1)+
                  as.numeric.factor(v4_panss_g2)+
                  as.numeric.factor(v4_panss_g3)+
                  as.numeric.factor(v4_panss_g4)+
                  as.numeric.factor(v4_panss_g5)+
                  as.numeric.factor(v4_panss_g6)+
                  as.numeric.factor(v4_panss_g7)+
                  as.numeric.factor(v4_panss_g8)+
                  as.numeric.factor(v4_panss_g9)+
                  as.numeric.factor(v4_panss_g10)+
                  as.numeric.factor(v4_panss_g11)+
                  as.numeric.factor(v4_panss_g12)+
                  as.numeric.factor(v4_panss_g13)+
                  as.numeric.factor(v4_panss_g14)+
                  as.numeric.factor(v4_panss_g15)+
                  as.numeric.factor(v4_panss_g16)

summary(v4_panss_sum_gen)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.00   16.00   19.00   22.01   25.00   50.00    1025

Create PANSS Total score (continuous [30-210], v4_panss_sum_tot)

v4_panss_sum_tot<-v4_panss_sum_pos+v4_panss_sum_neg+v4_panss_sum_gen
summary(v4_panss_sum_tot)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   30.00   31.00   36.00   41.88   48.00  100.00    1029

Create dataset

v4_symp_panss<-data.frame(v4_panss_p1,v4_panss_p2,v4_panss_p3,v4_panss_p4,v4_panss_p5,v4_panss_p6,v4_panss_p7,
                          v4_panss_n1,v4_panss_n2,v4_panss_n3,v4_panss_n4,v4_panss_n5,v4_panss_n6,v4_panss_n7,
                          v4_panss_g1,v4_panss_g2,v4_panss_g3,v4_panss_g4,v4_panss_g5,v4_panss_g6,v4_panss_g7,
                          v4_panss_g8,v4_panss_g9,v4_panss_g10,v4_panss_g11,v4_panss_g12,v4_panss_g13,v4_panss_g14,
                          v4_panss_g15,v4_panss_g16,v4_panss_sum_pos,v4_panss_sum_neg,v4_panss_sum_gen,
                          v4_panss_sum_tot)

IDS-C₃₀

For more information on the scale, please see Visit 1

Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v4_idsc_itm1)

v4_idsc_itm1<-c(v4_clin$v4_ids_c_s1_ids1_einschlafschw,v4_con$v4_ids_c_s1_ids1_einschlafschw)
v4_idsc_itm1<-factor(v4_idsc_itm1, ordered=T)

descT(v4_idsc_itm1)

##                0    1   2   3  <NA>     
## [1,] No. cases 569  93  63  53 1008 1786
## [2,] Percent   31.9 5.2 3.5 3  56.4 100

Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v4_idsc_itm2)

v4_idsc_itm2<-c(v4_clin$v4_ids_c_s1_ids2_naechtl_aufw,v4_con$v4_ids_c_s1_ids2_naechtl_aufw)
v4_idsc_itm2<-factor(v4_idsc_itm2, ordered=T)

descT(v4_idsc_itm2)

##                0    1   2   3   <NA>     
## [1,] No. cases 499  114 108 58  1007 1786
## [2,] Percent   27.9 6.4 6   3.2 56.4 100

Item 3 Early morning insomnia (ordinal [0,1,2,3], v4_idsc_itm3)

v4_idsc_itm3<-c(v4_clin$v4_ids_c_s1_ids3_frueh_aufw,v4_con$v4_ids_c_s1_ids3_frueh_aufw)
v4_idsc_itm3<-factor(v4_idsc_itm3, ordered=T)

descT(v4_idsc_itm3)

##                0   1   2   3   <NA>     
## [1,] No. cases 643 58  47  29  1009 1786
## [2,] Percent   36  3.2 2.6 1.6 56.5 100

Item 4 Hypersomnia (ordinal [0,1,2,3], v4_idsc_itm4)

v4_idsc_itm4<-c(v4_clin$v4_ids_c_s1_ids4_hypersomnie,v4_con$v4_ids_c_s1_ids4_hypersomnie)
v4_idsc_itm4<-factor(v4_idsc_itm4, ordered=T)

descT(v4_idsc_itm4)

##                0    1   2   3   <NA>     
## [1,] No. cases 522  176 65  16  1007 1786
## [2,] Percent   29.2 9.9 3.6 0.9 56.4 100

Item 5 Mood (sad) (ordinal [0,1,2,3], v4_idsc_itm5)

v4_idsc_itm5<-c(v4_clin$v4_ids_c_s1_ids5_stimmung_trgk,v4_con$v4_ids_c_s1_ids5_stimmung_trgk)
v4_idsc_itm5<-factor(v4_idsc_itm5, ordered=T)

descT(v4_idsc_itm5)

##                0    1   2   3   <NA>     
## [1,] No. cases 499  172 83  25  1007 1786
## [2,] Percent   27.9 9.6 4.6 1.4 56.4 100

Item 6 Mood (irritable) (ordinal [0,1,2,3], v4_idsc_itm6)

v4_idsc_itm6<-c(v4_clin$v4_ids_c_s1_ids6_stimmung_grzt,v4_con$v4_ids_c_s1_ids6_stimmung_grzt)
v4_idsc_itm6<-factor(v4_idsc_itm6, ordered=T)

descT(v4_idsc_itm6)

##                0    1   2   3   <NA>     
## [1,] No. cases 555  161 47  15  1008 1786
## [2,] Percent   31.1 9   2.6 0.8 56.4 100

Item 7 Mood (anxious) (ordinal [0,1,2,3], v4_idsc_itm7)

v4_idsc_itm7<-c(v4_clin$v4_ids_c_s1_ids7_stimmung_agst,v4_con$v4_ids_c_s1_ids7_stimmung_agst)
v4_idsc_itm7<-factor(v4_idsc_itm7, ordered=T)

descT(v4_idsc_itm7)

##                0    1    2   3   <NA>     
## [1,] No. cases 508  180  66  24  1008 1786
## [2,] Percent   28.4 10.1 3.7 1.3 56.4 100

Item 8 Reactivity of mood (ordinal [0,1,2,3], v4_idsc_itm8)

v4_idsc_itm8<-c(v4_clin$v4_ids_c_s1_ids8_reakt_stimmung,v4_con$v4_ids_c_s1_ids8_reakt_stimmung)
v4_idsc_itm8<-factor(v4_idsc_itm8, ordered=T)

descT(v4_idsc_itm8)

##                0    1  2   3   <NA>     
## [1,] No. cases 637  90 41  10  1008 1786
## [2,] Percent   35.7 5  2.3 0.6 56.4 100

Item 9 Mood Variation (ordinal [0,1,2,3], v4_idsc_itm9)

v4_idsc_itm9<-c(v4_clin$v4_ids_c_s1_ids9_stimmungsschw,v4_con$v4_ids_c_s1_ids9_stimmungsschw)
v4_idsc_itm9<-factor(v4_idsc_itm9, ordered=T)

descT(v4_idsc_itm9)

##                0    1   2   3   <NA>     
## [1,] No. cases 637  56  23  61  1009 1786
## [2,] Percent   35.7 3.1 1.3 3.4 56.5 100

Item 9A (categorical [M, A, N], v4_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).

v4_idsc_itm9a_pre<-c(v4_clin$v4_ids_c_s1_ids9a_stimmungsschw,v4_con$v4_ids_c_s1_ids9a_stimmungsschw)

v4_idsc_itm9a<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==1, "M", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==2, "A", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==3, "N", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-factor(v4_idsc_itm9a, ordered=F)

descT(v4_idsc_itm9a)

##                -999 A   M   N  <NA>     
## [1,] No. cases 637  12  87  17 1033 1786
## [2,] Percent   35.7 0.7 4.9 1  57.8 100

Item 9B (dichotomous, v4_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.

v4_idsc_itm9b_pre<-c(v4_clin$v4_ids_c_s1_ids9b_stimmungsschw,v4_con$v4_ids_c_s1_ids9b_stimmungsschw)

v4_idsc_itm9b<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm9b<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9b_pre==0, "N", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9b))
v4_idsc_itm9b<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9b_pre==1, "Y", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9b))
v4_idsc_itm9b<-factor(v4_idsc_itm9b, ordered=F)

descT(v4_idsc_itm9b)

##                -999 N   Y  <NA>     
## [1,] No. cases 637  42  53 1054 1786
## [2,] Percent   35.7 2.4 3  59   100

Item 10 Quality of mood (ordinal [0,1,2,3], v4_idsc_itm10)

v4_idsc_itm10<-c(v4_clin$v4_ids_c_s1_ids10_quali_stimmung,v4_con$v4_ids_c_s1_ids10_quali_stimmung)
v4_idsc_itm10<-factor(v4_idsc_itm10, ordered=T)

descT(v4_idsc_itm10)

##                0   1   2   3   <NA>     
## [1,] No. cases 678 42  25  25  1016 1786
## [2,] Percent   38  2.4 1.4 1.4 56.9 100

Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.

The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.

Item 11 (ordinal [0,1,2,3], v4_idsc_itm11)

v4_idsc_app_verm<-c(v4_clin$v4_ids_c_s2_ids11_appetit_verm,v4_con$v4_ids_c_s2_ids11_appetit_verm)
v4_idsc_app_gest<-c(v4_clin$v4_ids_c_s2_ids12_appetit_steig,v4_con$v4_ids_c_s2_ids12_appetit_steig)

v4_idsc_itm11<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm11<-ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==F, -999,                
                  ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==T,          
                         v4_idsc_app_verm, 
                      ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &    
                     (v4_idsc_app_verm>v4_idsc_app_gest), v4_idsc_app_verm,                                            ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &                                                         (v4_idsc_app_gest>=v4_idsc_app_verm),-999,v4_idsc_itm11)))))

#Important: do not code as factor, see after calculation of sum score!
descT(v4_idsc_itm11)

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 228  463  62  19  5   1009 1786
## [2,] Percent   12.8 25.9 3.5 1.1 0.3 56.5 100

Item 12 (ordinal [0,1,2,3], v4_idsc_itm12)

v4_idsc_itm12<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm12<-ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==F,    
                         v4_idsc_app_gest,                
                  ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==T,          
                         -999, 
                      ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &    
                     (v4_idsc_app_verm>v4_idsc_app_gest), -999,                                            
                     ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &                                                         (v4_idsc_app_gest>=v4_idsc_app_verm),
                            v4_idsc_app_gest,v4_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v4_idsc_itm12)

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 549  110 73  26  19  1009 1786
## [2,] Percent   30.7 6.2 4.1 1.5 1.1 56.5 100

Item 13 (ordinal [0,1,2,3], v4_idsc_itm13)

v4_idsc_gew_abn<-c(v4_clin$v4_ids_c_s2_ids13_gewichtsabn,v4_con$v4_ids_c_s2_ids13_gewichtsabn)
v4_idsc_gew_zun<-c(v4_clin$v4_ids_c_s2_ids14_gewichtszun,v4_con$v4_ids_c_s2_ids14_gewichtszun)

v4_idsc_itm13<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm13<-ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==F, -999,                
                  ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==T,          
                         v4_idsc_gew_abn, 
                      ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F &    
                     (v4_idsc_gew_abn>v4_idsc_gew_zun), v4_idsc_gew_abn,                                            ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F & (v4_idsc_gew_zun >= v4_idsc_gew_abn),-999,v4_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v4_idsc_itm13)

##                -999 0    1   2   3   <NA>     
## [1,] No. cases 250  437  41  34  16  1008 1786
## [2,] Percent   14   24.5 2.3 1.9 0.9 56.4 100

Item 14 (ordinal [0,1,2,3], v4_idsc_itm14)

v4_idsc_itm14<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm14<-ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==F,    
                         v4_idsc_gew_zun,                
                  ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==T,          
                         -999, 
                      ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F &    
                     (v4_idsc_gew_abn>v4_idsc_gew_zun), -999,                                            
                     ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F &                                                         (v4_idsc_gew_zun>=v4_idsc_gew_abn),
                            v4_idsc_gew_zun,v4_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v4_idsc_itm14)

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 528  139 62  28  21  1008 1786
## [2,] Percent   29.6 7.8 3.5 1.6 1.2 56.4 100

Item 15 Concentration/decision making (ordinal [0,1,2,3], v4_idsc_itm15)

v4_idsc_itm15<-c(v4_clin$v4_ids_c_s2_ids15_konz_entscheid,v4_con$v4_ids_c_s2_ids15_konz_entscheid)
v4_idsc_itm15<-factor(v4_idsc_itm15, ordered=T)

descT(v4_idsc_itm15)

##                0    1    2   3   <NA>     
## [1,] No. cases 443  203  110 21  1009 1786
## [2,] Percent   24.8 11.4 6.2 1.2 56.5 100

Item 16 Outlook (self) (ordinal [0,1,2,3], v4_idsc_itm16)

v4_idsc_itm16<-c(v4_clin$v4_ids_c_s2_ids16_selbstbild,v4_con$v4_ids_c_s2_ids16_selbstbild)
v4_idsc_itm16<-factor(v4_idsc_itm16, ordered=T)

descT(v4_idsc_itm16)

##                0    1   2   3   <NA>     
## [1,] No. cases 580  118 33  46  1009 1786
## [2,] Percent   32.5 6.6 1.8 2.6 56.5 100

Item 17 Outlook (future) (ordinal [0,1,2,3], v4_idsc_itm17)

v4_idsc_itm17<-c(v4_clin$v4_ids_c_s2_ids17_zukunftssicht,v4_con$v4_ids_c_s2_ids17_zukunftssicht)
v4_idsc_itm17<-factor(v4_idsc_itm17, ordered=T)

descT(v4_idsc_itm17)

##                0    1    2   3   <NA>     
## [1,] No. cases 528  185  55  8   1010 1786
## [2,] Percent   29.6 10.4 3.1 0.4 56.6 100

Item 18 Suicidal ideation (ordinal [0,1,2,3], v4_idsc_itm18)

v4_idsc_itm18<-c(v4_clin$v4_ids_c_s2_ids18_selbstmordged,v4_con$v4_ids_c_s2_ids18_selbstmordged)
v4_idsc_itm18<-factor(v4_idsc_itm18, ordered=T)

descT(v4_idsc_itm18)

##                0    1   2   3   <NA>     
## [1,] No. cases 704  40  33  2   1007 1786
## [2,] Percent   39.4 2.2 1.8 0.1 56.4 100

Item 19 Involvement (ordinal [0,1,2,3], v4_idsc_itm19)

v4_idsc_itm19<-c(v4_clin$v4_ids_c_s2_ids19_interess_aktiv,v4_con$v4_ids_c_s2_ids19_interess_aktiv)
v4_idsc_itm19<-factor(v4_idsc_itm19, ordered=T)

descT(v4_idsc_itm19)

##                0    1   2   3   <NA>     
## [1,] No. cases 627  100 29  21  1009 1786
## [2,] Percent   35.1 5.6 1.6 1.2 56.5 100

Item 20 Energy/fatigability (ordinal [0,1,2,3], v4_idsc_itm20)

v4_idsc_itm20<-c(v4_clin$v4_ids_c_s2_ids20_energ_ermued,v4_con$v4_ids_c_s2_ids20_energ_ermued)
v4_idsc_itm20<-factor(v4_idsc_itm20, ordered=T)

descT(v4_idsc_itm20)

##                0    1   2   3   <NA>     
## [1,] No. cases 517  154 96  11  1008 1786
## [2,] Percent   28.9 8.6 5.4 0.6 56.4 100

Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v4_idsc_itm21)

v4_idsc_itm21<-c(v4_clin$v4_ids_c_s3_ids21_vergn_genuss,v4_con$v4_ids_c_s3_ids21_vergn_genuss)
v4_idsc_itm21<-factor(v4_idsc_itm21, ordered=T)

descT(v4_idsc_itm21)

##                0   1   2   3   <NA>     
## [1,] No. cases 643 83  38  13  1009 1786
## [2,] Percent   36  4.6 2.1 0.7 56.5 100

Item 22 Sexual interest (ordinal [0,1,2,3], v4_idsc_itm22)

v4_idsc_itm22<-c(v4_clin$v4_ids_c_s3_ids22_sex_interesse,v4_con$v4_ids_c_s3_ids22_sex_interesse)
v4_idsc_itm22<-factor(v4_idsc_itm22, ordered=T)

descT(v4_idsc_itm22)

##                0    1   2   3   <NA>     
## [1,] No. cases 582  55  74  61  1014 1786
## [2,] Percent   32.6 3.1 4.1 3.4 56.8 100

Item 23 Psychomotor slowing (ordinal [0,1,2,3], v4_idsc_itm23)

v4_idsc_itm23<-c(v4_clin$v4_ids_c_s3_ids23_psymo_hemm,v4_con$v4_ids_c_s3_ids23_psymo_hemm)
v4_idsc_itm23<-factor(v4_idsc_itm23, ordered=T)

descT(v4_idsc_itm23)

##                0    1   2  3   <NA>     
## [1,] No. cases 605  133 35 4   1009 1786
## [2,] Percent   33.9 7.4 2  0.2 56.5 100

Item 24 Psychomotor agitation (ordinal [0,1,2,3], v4_idsc_itm24)

v4_idsc_itm24<-c(v4_clin$v4_ids_c_s3_ids24_psymo_agitht,v4_con$v4_ids_c_s3_ids24_psymo_agitht)
v4_idsc_itm24<-factor(v4_idsc_itm24, ordered=T)

descT(v4_idsc_itm24)

##                0    1   2   3   <NA>     
## [1,] No. cases 642  101 27  2   1014 1786
## [2,] Percent   35.9 5.7 1.5 0.1 56.8 100

Item 25 Somatic complaints (ordinal [0,1,2,3], v4_idsc_itm25)

v4_idsc_itm25<-c(v4_clin$v4_ids_c_s3_ids25_som_beschw,v4_con$v4_ids_c_s3_ids25_som_beschw)
v4_idsc_itm25<-factor(v4_idsc_itm25, ordered=T)

descT(v4_idsc_itm25)

##                0    1    2   3   <NA>     
## [1,] No. cases 504  207  49  16  1010 1786
## [2,] Percent   28.2 11.6 2.7 0.9 56.6 100

Item 26 Sympathetic arousal (ordinal [0,1,2,3], v4_idsc_itm26)

v4_idsc_itm26<-c(v4_clin$v4_ids_c_s3_ids26_veg_erreg,v4_con$v4_ids_c_s3_ids26_veg_erreg)
v4_idsc_itm26<-factor(v4_idsc_itm26, ordered=T)

descT(v4_idsc_itm26)

##                0    1   2   3   <NA>     
## [1,] No. cases 570  165 30  11  1010 1786
## [2,] Percent   31.9 9.2 1.7 0.6 56.6 100

Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v4_idsc_itm27)

v4_idsc_itm27<-c(v4_clin$v4_ids_c_s3_ids27_panik_phob,v4_con$v4_ids_c_s3_ids27_panik_phob)
v4_idsc_itm27<-factor(v4_idsc_itm27, ordered=T)

descT(v4_idsc_itm27)

##                0    1   2   3   <NA>     
## [1,] No. cases 689  55  25  9   1008 1786
## [2,] Percent   38.6 3.1 1.4 0.5 56.4 100

Item 28 Gastrointestinal (ordinal [0,1,2,3], v4_idsc_itm28)

v4_idsc_itm28<-c(v4_clin$v4_ids_c_s3_ids28_verdauung,v4_con$v4_ids_c_s3_ids28_verdauung)
v4_idsc_itm28<-factor(v4_idsc_itm28, ordered=T)

descT(v4_idsc_itm28)

##                0    1   2   3   <NA>     
## [1,] No. cases 666  73  31  8   1008 1786
## [2,] Percent   37.3 4.1 1.7 0.4 56.4 100

Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v4_idsc_itm29)

v4_idsc_itm29<-c(v4_clin$v4_ids_c_s3_ids29_pers_bezieh,v4_con$v4_ids_c_s3_ids29_pers_bezieh)
v4_idsc_itm29<-factor(v4_idsc_itm29, ordered=T)

descT(v4_idsc_itm29)

##                0    1  2   3  <NA>     
## [1,] No. cases 620  90 46  18 1012 1786
## [2,] Percent   34.7 5  2.6 1  56.7 100

Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v4_idsc_itm30)

v4_idsc_itm30<-c(v4_clin$v4_ids_c_s3_ids30_schwgf_k_energ,v4_con$v4_ids_c_s3_ids30_schwgf_k_energ)
v4_idsc_itm30<-factor(v4_idsc_itm30, ordered=T)

descT(v4_idsc_itm30)

##                0    1   2  3   <NA>     
## [1,] No. cases 650  78  36 13  1009 1786
## [2,] Percent   36.4 4.4 2  0.7 56.5 100

Create IDS-C₃₀ total score (continuous [0-84], v4_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.

v4_idsc_sum<-as.numeric.factor(v4_idsc_itm1)+
             as.numeric.factor(v4_idsc_itm2)+
             as.numeric.factor(v4_idsc_itm3)+
             as.numeric.factor(v4_idsc_itm4)+
             as.numeric.factor(v4_idsc_itm5)+
             as.numeric.factor(v4_idsc_itm6)+
             as.numeric.factor(v4_idsc_itm7)+
             as.numeric.factor(v4_idsc_itm8)+
             as.numeric.factor(v4_idsc_itm9)+
             as.numeric.factor(v4_idsc_itm10)+
  
 ifelse(is.na(v4_idsc_itm11)==T & is.na(v4_idsc_itm12)==T, NA, 
        ifelse((v4_idsc_itm11==-999 & v4_idsc_itm12!=-999), v4_idsc_itm12,                
              ifelse((v4_idsc_itm11!=-999 & v4_idsc_itm12==-999),v4_idsc_itm11, NA)))+
  
   ifelse(is.na(v4_idsc_itm13)==T & is.na(v4_idsc_itm14)==T, NA, 
        ifelse((v4_idsc_itm13==-999 & v4_idsc_itm14!=-999), v4_idsc_itm14,                
              ifelse((v4_idsc_itm13!=-999 & v4_idsc_itm14==-999),v4_idsc_itm13, NA)))+
                                                  
             as.numeric.factor(v4_idsc_itm15)+
             as.numeric.factor(v4_idsc_itm16)+
             as.numeric.factor(v4_idsc_itm17)+
             as.numeric.factor(v4_idsc_itm18)+
             as.numeric.factor(v4_idsc_itm19)+
             as.numeric.factor(v4_idsc_itm20)+
             as.numeric.factor(v4_idsc_itm21)+
             as.numeric.factor(v4_idsc_itm22)+
             as.numeric.factor(v4_idsc_itm23)+
             as.numeric.factor(v4_idsc_itm24)+
             as.numeric.factor(v4_idsc_itm25)+
             as.numeric.factor(v4_idsc_itm26)+
             as.numeric.factor(v4_idsc_itm27)+
             as.numeric.factor(v4_idsc_itm28)+
             as.numeric.factor(v4_idsc_itm29)+
             as.numeric.factor(v4_idsc_itm30)

summary(v4_idsc_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    3.00    7.00   10.21   14.75   55.00    1052

Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)

v4_idsc_itm11<-factor(v4_idsc_itm11,ordered=T)
v4_idsc_itm12<-factor(v4_idsc_itm12,ordered=T)
v4_idsc_itm13<-factor(v4_idsc_itm13,ordered=T)
v4_idsc_itm14<-factor(v4_idsc_itm14,ordered=T)

Create dataset

v4_symp_ids_c<-data.frame(v4_idsc_itm1,v4_idsc_itm2,v4_idsc_itm3,v4_idsc_itm4,v4_idsc_itm5,v4_idsc_itm6,v4_idsc_itm7,
                          v4_idsc_itm8,v4_idsc_itm9,v4_idsc_itm9a,v4_idsc_itm9b,v4_idsc_itm10,v4_idsc_itm11,v4_idsc_itm12,
                          v4_idsc_itm13,v4_idsc_itm14,v4_idsc_itm15,v4_idsc_itm16,v4_idsc_itm17,v4_idsc_itm18,v4_idsc_itm19,
                          v4_idsc_itm20,v4_idsc_itm21,v4_idsc_itm22,v4_idsc_itm23,v4_idsc_itm24,v4_idsc_itm25,v4_idsc_itm26,
                          v4_idsc_itm27,v4_idsc_itm28,v4_idsc_itm29,v4_idsc_itm30,v4_idsc_sum)

YMRS

For more information on the scale, please see Visit 1

Item 1 Elevated mood (ordinal [0,1,2,3,4], v4_ymrs_itm1)

v4_ymrs_itm1<-c(v4_clin$v4_ymrs_ymrs1_gehob_stimm,v4_con$v4_ymrs_ymrs1_gehob_stimm)
v4_ymrs_itm1<-factor(v4_ymrs_itm1, ordered=T)

descT(v4_ymrs_itm1)

##                0    1   2   <NA>     
## [1,] No. cases 657  85  34  1010 1786
## [2,] Percent   36.8 4.8 1.9 56.6 100

Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v4_ymrs_itm2)

v4_ymrs_itm2<-c(v4_clin$v4_ymrs_ymrs2_gest_aktiv,v4_con$v4_ymrs_ymrs2_gest_aktiv)
v4_ymrs_itm2<-factor(v4_ymrs_itm2, ordered=T)

descT(v4_ymrs_itm2)

##                0   1   2   3   4   <NA>     
## [1,] No. cases 697 55  16  5   1   1012 1786
## [2,] Percent   39  3.1 0.9 0.3 0.1 56.7 100

Item 3 Sexual interest (ordinal [0,1,2,3,4], v4_ymrs_itm3)

v4_ymrs_itm3<-c(v4_clin$v4_ymrs_ymrs3_sex_interesse,v4_con$v4_ymrs_ymrs3_sex_interesse)
v4_ymrs_itm3<-factor(v4_ymrs_itm3, ordered=T)

descT(v4_ymrs_itm3)

##                0    1   2   3   <NA>     
## [1,] No. cases 738  16  16  2   1014 1786
## [2,] Percent   41.3 0.9 0.9 0.1 56.8 100

Item 4 Sleep (ordinal [0,1,2,3,4], v4_ymrs_itm4)

v4_ymrs_itm4<-c(v4_clin$v4_ymrs_ymrs4_schlaf,v4_con$v4_ymrs_ymrs4_schlaf)
v4_ymrs_itm4<-factor(v4_ymrs_itm4, ordered=T)

descT(v4_ymrs_itm4)

##                0    1   2   3   <NA>     
## [1,] No. cases 720  23  22  11  1010 1786
## [2,] Percent   40.3 1.3 1.2 0.6 56.6 100

Item 5 Irritability (ordinal [0,2,4,6,8], v4_ymrs_itm5)

v4_ymrs_itm5<-c(v4_clin$v4_ymrs_ymrs5_reizbarkeit,v4_con$v4_ymrs_ymrs5_reizbarkeit)
v4_ymrs_itm5<-factor(v4_ymrs_itm5, ordered=T)

descT(v4_ymrs_itm5)

##                0    2   4   6   <NA>     
## [1,] No. cases 650  116 9   1   1010 1786
## [2,] Percent   36.4 6.5 0.5 0.1 56.6 100

Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v4_ymrs_itm6)

v4_ymrs_itm6<-c(v4_clin$v4_ymrs_ymrs6_sprechweise,v4_con$v4_ymrs_ymrs6_sprechweise)
v4_ymrs_itm6<-factor(v4_ymrs_itm6, ordered=T)

descT(v4_ymrs_itm6)

##                0    2   4   6   8   <NA>     
## [1,] No. cases 672  57  40  6   1   1010 1786
## [2,] Percent   37.6 3.2 2.2 0.3 0.1 56.6 100

Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v4_ymrs_itm7)

v4_ymrs_itm7<-c(v4_clin$v4_ymrs_ymrs7_sprachstoer,v4_con$v4_ymrs_ymrs7_sprachstoer)
v4_ymrs_itm7<-factor(v4_ymrs_itm7, ordered=T)

descT(v4_ymrs_itm7)

##                0   1   2   <NA>     
## [1,] No. cases 714 50  12  1010 1786
## [2,] Percent   40  2.8 0.7 56.6 100

Item 8 Content (ordinal [0,2,4,6,8], v4_ymrs_itm8)

v4_ymrs_itm8<-c(v4_clin$v4_ymrs_ymrs8_inhalte,v4_con$v4_ymrs_ymrs8_inhalte)
v4_ymrs_itm8<-factor(v4_ymrs_itm8, ordered=T)

descT(v4_ymrs_itm8)

##                0    2   4   6   8   <NA>     
## [1,] No. cases 745  12  1   8   10  1010 1786
## [2,] Percent   41.7 0.7 0.1 0.4 0.6 56.6 100

Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v4_ymrs_itm9)

v4_ymrs_itm9<-c(v4_clin$v4_ymrs_ymrs9_exp_aggr_verh,v4_con$v4_ymrs_ymrs9_exp_aggr_verh)
v4_ymrs_itm9<-factor(v4_ymrs_itm9, ordered=T)

descT(v4_ymrs_itm9)

##                0    2   4   6   <NA>     
## [1,] No. cases 753  20  1   1   1011 1786
## [2,] Percent   42.2 1.1 0.1 0.1 56.6 100

Item 10 Appearance (ordinal [0,1,2,3,4], v4_ymrs_itm10)

v4_ymrs_itm10<-c(v4_clin$v4_ymrs_ymrs10_erscheinung,v4_con$v4_ymrs_ymrs10_erscheinung)
v4_ymrs_itm10<-factor(v4_ymrs_itm10, ordered=T)

descT(v4_ymrs_itm10)

##                0    1  2  3   <NA>     
## [1,] No. cases 699  54 17 4   1012 1786
## [2,] Percent   39.1 3  1  0.2 56.7 100

Item 11 Insight (ordinal [0,1,2,3,4], v4_ymrs_itm11)

v4_ymrs_itm11<-c(v4_clin$v4_ymrs_ymrs11_krkh_einsicht,v4_con$v4_ymrs_ymrs11_krkh_einsicht)
v4_ymrs_itm11<-factor(v4_ymrs_itm11, ordered=T)

descT(v4_ymrs_itm11)

##                0    1   2   3   4   <NA>     
## [1,] No. cases 745  16  8   2   4   1011 1786
## [2,] Percent   41.7 0.9 0.4 0.1 0.2 56.6 100

Create YMRS total score (continuous [0-60], v4_ymrs_sum)

v4_ymrs_sum<-(as.numeric.factor(v4_ymrs_itm1)+
        as.numeric.factor(v4_ymrs_itm2)+
        as.numeric.factor(v4_ymrs_itm3)+
        as.numeric.factor(v4_ymrs_itm4)+
        as.numeric.factor(v4_ymrs_itm5)+
        as.numeric.factor(v4_ymrs_itm6)+
        as.numeric.factor(v4_ymrs_itm7)+
        as.numeric.factor(v4_ymrs_itm8)+
        as.numeric.factor(v4_ymrs_itm9)+
        as.numeric.factor(v4_ymrs_itm10)+
        as.numeric.factor(v4_ymrs_itm11))

summary(v4_ymrs_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   1.865   2.000  24.000    1018

Create dataset

v4_symp_ymrs<-data.frame(v4_ymrs_itm1,
                         v4_ymrs_itm2,
                         v4_ymrs_itm3,
                         v4_ymrs_itm4,
                         v4_ymrs_itm5,
                         v4_ymrs_itm6,
                         v4_ymrs_itm7,
                         v4_ymrs_itm8,
                         v4_ymrs_itm9,
                         v4_ymrs_itm10,
                         v4_ymrs_itm11,
                         v4_ymrs_sum)

CGI

Please see Visit 1 for more details and explicit rating instructions.

Illness everity (ordinal [1,2,3,4,5,6,7], v4_cgi_s)

v4_cgi_s<-c(v4_clin$v4_cgi1_cgi1_schweregrad,rep(-999,dim(v4_con)[1]))

v4_cgi_s[v4_cgi_s==0]<- -999
v4_cgi_s<-factor(v4_cgi_s, ordered=T)

descT(v4_cgi_s)

##                -999 1   2   3    4   5   6  7   <NA>     
## [1,] No. cases 467  16  59  187  152 134 35 1   735  1786
## [2,] Percent   26.1 0.9 3.3 10.5 8.5 7.5 2  0.1 41.2 100

Change since last study visit (ordinal [1,2,3,4,5,6,7], v4_cgi_c)

Here, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “very much improved”-1 to “very much worse”-7.

v4_cgi_c<-c(v4_clin$v4_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v4_con)[1]))

v4_cgi_c[v4_cgi_c==0]<- -999
v4_cgi_c<-factor(v4_cgi_c, ordered=T)

descT(v4_cgi_c)

##                -999 1   2   3   4    5   6   7   <NA>     
## [1,] No. cases 493  9   68  108 242  96  16  2   752  1786
## [2,] Percent   27.6 0.5 3.8 6   13.5 5.4 0.9 0.1 42.1 100

GAF (continuous [1-100], v4_gaf)

Please see Visit 1 for more details and explicit rating instructions.

v4_gaf<-c(v4_clin$v4_gaf_gaf_code,v4_con$v4_gaf_gaf_code)
v4_gaf[v4_gaf==0]<- -999

summary(v4_gaf[v4_gaf>0])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   14.00   55.00   68.00   67.14   81.00   99.00    1006

boxplot(v4_gaf[v4_gaf>0 & v1_stat=="CLINICAL"], v4_gaf[v4_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))

Create dataset

v4_ill_sev<-data.frame(v4_cgi_s,v4_cgi_c,v4_gaf)

Visit 4: Neuropsychology (cognitive tests)

There are no differences compared to the test battery assessed in Visit 2 or Visit 3.

General comments on the testing (character, v4_nrpsy_com) If there were no comments, this item was coded -999.

Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v4_nrpsy_lng)

v4_nrpsy_lng<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_nrpsy_lng<-ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==0, "mother tongue", 
                     ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==1, "good", 
                            ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==2, "sufficient", 
                              ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==3, "not sufficient",v4_nrpsy_lng))))
                            
v4_nrpsy_lng<-factor(v4_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))

descT(v4_nrpsy_lng)

##                mother tongue good sufficient not sufficient <NA>     
## [1,] No. cases 763           43   4          1              975  1786
## [2,] Percent   42.7          2.4  0.2        0.1            54.6 100

Motivation of the participant (ordinal [“poor”,“average”,“good”], v4_nrpsy_mtv)

v4_nrpsy_mtv_pre<-c(v4_clin$v4_npu1_np_mot,v4_con$v4_npu_folge_np_mot)

v4_nrpsy_mtv<-ifelse(v4_nrpsy_mtv_pre==0, "poor", 
                  ifelse(v4_nrpsy_mtv_pre==1, "average", 
                      ifelse(v4_nrpsy_mtv_pre==2, "good", NA)))

v4_nrpsy_mtv<-factor(v4_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))

descT(v4_nrpsy_mtv)

##                poor average good <NA>     
## [1,] No. cases 13   65      727  981  1786
## [2,] Percent   0.7  3.6     40.7 54.9 100

VLMT

For a description of the test and the variables, see Visit 2.

Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.

VLMT_introcheck (categorical [0, 1, 9], v4_nrpsy_vlmt_check)

v4_nrpsy_vlmt_check<-c(v4_clin$v4_vlmt_vlmt_introcheck1,v4_con$v4_npu_folge_np_vlmt)
descT(v4_nrpsy_vlmt_check)

##                0   1    9   <NA>     
## [1,] No. cases 49  769  20  948  1786
## [2,] Percent   2.7 43.1 1.1 53.1 100

Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v4_nrpsy_vlmt_corr)

v4_nrpsy_vlmt_corr<-c(v4_clin$v4_vlmt_vlmt3_sw_a5d,v4_con$v4_npu_folge_np_vlmt_gl)
summary(v4_nrpsy_vlmt_corr)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     9.0    43.0    53.0    52.1    63.0    76.0    1002

Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v4_nrpsy_vlmt_lss_d)

v4_nrpsy_vlmt_lss_d<-c(v4_clin$v4_vlmt_vlmt5_aw_ilsd6,v4_con$v4_npu_folge_np_vlmt_vni)
summary(v4_nrpsy_vlmt_lss_d)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -4.000   0.000   1.000   1.588   3.000   9.000    1009

Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v4_nrpsy_vlmt_lss_t)

v4_nrpsy_vlmt_lss_t<-c(v4_clin$v4_vlmt_vlmt6_aw_vwd7,v4_con$v4_npu_folge_np_vlmt_vnzv)
summary(v4_nrpsy_vlmt_lss_t)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -4.000   0.000   1.000   1.788   3.000  14.000    1014

Recognition performance (corrected for falsely recognized words) (continuous [number of words], v4_nrpsy_vlmt_rec)

v4_nrpsy_vlmt_rec<-c(v4_clin$v4_vlmt_vlmt8_kwl,v4_con$v4_npu_folge_np_vlmt_kw)
  summary(v4_nrpsy_vlmt_rec)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -13.00   10.00   13.00   11.71   15.00   15.00    1015

TMT

For a description of the test, see Visit 1.

TMT Part A, time (continuous [seconds], v4_nrpsy_tmt_A_rt)

v4_nrpsy_tmt_A_rt<-c(v4_clin$v4_npu1_tmt_001,v4_con$v4_npu_folge_np_tmt_001)
summary(v4_nrpsy_tmt_A_rt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   20.00   26.00   29.69   36.00  151.00     975

TMT Part A, errors (continuous [number of errors], v4_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).

v4_nrpsy_tmt_A_err<-c(v4_clin$v4_npu1_tmt_af_001,v4_con$v4_npu_folge_np_tmtfehler_001)
summary(v4_nrpsy_tmt_A_err)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.0887  0.0000  3.0000     974

TMT Part B, time (continuous [seconds], v4_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.

v4_nrpsy_tmt_B_rt<-c(v4_clin$v4_npu1_tmt_002,v4_con$v4_npu_folge_tmt_002)
v4_nrpsy_tmt_B_rt[v4_nrpsy_tmt_B_rt>300]<-300

summary(v4_nrpsy_tmt_B_rt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   20.00   46.00   61.00   71.73   84.00  300.00    1009

TMT Part B, errors (continuous [number of errors], v4_nrpsy_tmt_B_err)

v4_nrpsy_tmt_B_err<-c(v4_clin$v4_npu1_tmt_af_002,v4_con$v4_npu_folge_tmt_af_002)
summary(v4_nrpsy_tmt_B_err)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.4845  1.0000  9.0000    1010

Verbal digit span

For a description of the test, see Visit 1.

Forward (continuous [number of items], v4_nrpsy_dgt_sp_frw)

v4_nrpsy_dgt_sp_frw<-c(v4_clin$v4_npu1_zns_001,v4_con$v4_npu_folge_np_wie_001)
summary(v4_nrpsy_dgt_sp_frw)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   4.000   8.000  10.000   9.665  11.000  15.000     987

Backward (continuous [number of items], v4_nrpsy_dgt_sp_bck)

v4_nrpsy_dgt_sp_bck<-c(v4_clin$v4_npu1_zns_002,v4_con$v4_npu_folge_np_wie_002)
summary(v4_nrpsy_dgt_sp_bck)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.000   6.000   6.843   8.000  14.000     988

DST (continuous [number of correct symbols], v4_nrpsy_dg_sym)

For a description of the test, see Visit 1.

v4_introcheck3<-c(v4_clin$v4_npu1_np_introcheck3,v4_con$v4_npu_folge_np_hawier)
v4_nrpsy_dg_sym_pre<-c(v4_clin$v4_npu1_zst_001,v4_con$v4_npu_folge_np_hawier_001)

v4_nrpsy_dg_sym<-ifelse(v4_introcheck3==1, v4_nrpsy_dg_sym_pre, 
                           ifelse(v4_introcheck3==9,-999,
                                  ifelse(v4_introcheck3==0,NA,NA)))

summary(subset(v4_nrpsy_dg_sym,v4_nrpsy_dg_sym>=0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   13.00   53.00   70.00   70.38   88.00  133.00

Create dataset

v4_nrpsy<-data.frame(v4_nrpsy_com,
                     v4_nrpsy_lng,
                     v4_nrpsy_mtv,
                     v4_nrpsy_vlmt_check,
                     v4_nrpsy_vlmt_corr,
                     v4_nrpsy_vlmt_lss_d,
                     v4_nrpsy_vlmt_lss_t,
                     v4_nrpsy_vlmt_rec,
                     v4_nrpsy_tmt_A_rt,
                     v4_nrpsy_tmt_A_err,
                     v4_nrpsy_tmt_B_rt,
                     v4_nrpsy_tmt_B_err,
                     v4_nrpsy_dgt_sp_frw,
                     v4_nrpsy_dgt_sp_bck,
                     v4_nrpsy_dg_sym)

Visit 4: Questionnaires (patient rates her/himself)

Participants were asked to fill out questionnaires on the following topics: current medication adherence (compliance), current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 3 and 4) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1, 2 and 3, all questionnaires are checked on whether they were filled out correctly or not and only those correctly filled-out are included in this dataset.

SF-12

For explanation, please refer to the section in Visit 1

“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v4_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.

v4_sf12_recode(v4_con$v4_sf12_sf_allgemein,"v4_sf12_itm0")

##                -999 1   2   3   4   5   6   7  8   9   10  <NA>     
## [1,] No. cases 1320 1   2   5   5   6   9   36 85  65  33  219  1786
## [2,] Percent   73.9 0.1 0.1 0.3 0.3 0.3 0.5 2  4.8 3.6 1.8 12.3 100

“In general, would you say your health is…” (ordinal [1,2,3,4,5], v4_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.

v4_sf12_recode(v4_con$v4_sf12_sf1,"v4_sf12_itm1")

##                -999 1   2   3   4   5   <NA>     
## [1,] No. cases 1320 58  106 75  11  1   215  1786
## [2,] Percent   73.9 3.2 5.9 4.2 0.6 0.1 12   100

“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”

“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v4_sf12_itm2)

Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v4_sf12_recode(v4_con$v4_sf12_sf2,"v4_sf12_itm2")

##                -999 2   3    <NA>     
## [1,] No. cases 1320 20  231  215  1786
## [2,] Percent   73.9 1.1 12.9 12   100

“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v4_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v4_sf12_recode(v4_con$v4_sf12_sf3,"v4_sf12_itm3")

##                -999 1   2   3    <NA>     
## [1,] No. cases 1320 1   23  227  215  1786
## [2,] Percent   73.9 0.1 1.3 12.7 12   100

During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?

“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v4_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.

v4_sf12_recode(v4_con$v4_sf12_sf4,"v4_sf12_itm4")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 37  212  217  1786
## [2,] Percent   73.9 2.1 11.9 12.2 100

“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v4_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.

v4_sf12_recode(v4_con$v4_sf12_sf5,"v4_sf12_itm5")

##                -999 1  2    <NA>     
## [1,] No. cases 1320 18 229  219  1786
## [2,] Percent   73.9 1  12.8 12.3 100

During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?

“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v4_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.

v4_sf12_recode(v4_con$v4_sf12_sf6,"v4_sf12_itm6")

##                -999 1   2    <NA>     
## [1,] No. cases 1320 24  225  217  1786
## [2,] Percent   73.9 1.3 12.6 12.2 100

“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v4_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.

v4_sf12_recode(v4_con$v4_sf12_sf7,"v4_sf12_itm7")

##                -999 1   2   <NA>     
## [1,] No. cases 1320 16  232 218  1786
## [2,] Percent   73.9 0.9 13  12.2 100

“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v4_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.

v4_sf12_recode(v4_con$v4_sf12_st8,"v4_sf12_itm8")

##                -999 1   2  3   4   5   6   <NA>     
## [1,] No. cases 1320 134 54 33  23  4   1   217  1786
## [2,] Percent   73.9 7.5 3  1.8 1.3 0.2 0.1 12.2 100

The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS

Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.

“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v4_sf12_itm9)

v4_sf12_recode(v4_con$v4_sf12_st9,"v4_sf12_itm9")

##                -999 1   2   3   4   5   <NA>     
## [1,] No. cases 1320 27  152 43  22  5   217  1786
## [2,] Percent   73.9 1.5 8.5 2.4 1.2 0.3 12.2 100

“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v4_sf12_itm10)

v4_sf12_recode(v4_con$v4_sf12_st10,"v4_sf12_itm10")

##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1320 15  100 69  50  13  2   217  1786
## [2,] Percent   73.9 0.8 5.6 3.9 2.8 0.7 0.1 12.2 100

“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v4_sf12_itm11)

v4_sf12_recode(v4_con$v4_sf12_st11,"v4_sf12_itm11")

##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1320 1   5   8   26  114 95  217  1786
## [2,] Percent   73.9 0.1 0.3 0.4 1.5 6.4 5.3 12.2 100

“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v4_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.

There is an error in the phenotype database regarding this item. The answering alternatives 3, 4, and 5 appear as 4, 5, and 6 in the database exports. These errors are corrected below.

v4_sf12_recode(v4_con$v4_sf12_st12,"v4_sf12_itm12")

##                -999 2   4   5   6    <NA>     
## [1,] No. cases 1320 6   11  44  182  223  1786
## [2,] Percent   73.9 0.3 0.6 2.5 10.2 12.5 100

#recode error in phenotype database
v4_sf12_itm12[v4_sf12_itm12==4]<-3
v4_sf12_itm12[v4_sf12_itm12==5]<-4
v4_sf12_itm12[v4_sf12_itm12==6]<-5

descT(v4_sf12_itm12)

##                -999 2   3   4   5    <NA>     
## [1,] No. cases 1320 6   11  44  182  223  1786
## [2,] Percent   73.9 0.3 0.6 2.5 10.2 12.5 100

Create dataset

v4_sf12<-data.frame(v4_sf12_itm0,
                    v4_sf12_itm1,
                    v4_sf12_itm2,
                    v4_sf12_itm3,
                    v4_sf12_itm4,
                    v4_sf12_itm5,
                    v4_sf12_itm6,
                    v4_sf12_itm7,
                    v4_sf12_itm8,
                    v4_sf12_itm9,
                    v4_sf12_itm10,
                    v4_sf12_itm11,
                    v4_sf12_itm12)

Religious beliefs

For a description of the questionnaire, see Visit 1. Controls all have “-999”, as here the questionaire was introduced from the start of data collection.

Religion Christianity (dichotomous, v4_rel_chr)

v4_rel_chris<-c(v4_clin$v4_religion_christ,rep(-999,dim(v4_con)[1]))
v4_rel_chr<-ifelse(v4_rel_chris==1, "Y",ifelse(v4_rel_chris==0,"N",ifelse(v4_rel_chris==-999,"-999",NA)))
descT(v4_rel_chr) 

##                -999 N   Y    <NA>     
## [1,] No. cases 466  41  346  933  1786
## [2,] Percent   26.1 2.3 19.4 52.2 100

Religion Islam (dichotomous, v4_rel_isl)

v4_rel_islam<-c(v4_clin$v4_religion_islam_jn,rep(-999,dim(v4_con)[1]))
v4_rel_isl<-ifelse(v4_rel_islam==1, "Y",ifelse(v4_rel_islam==0,"N",ifelse(v4_rel_islam==-999,"-999",NA)))
descT(v4_rel_isl) 

##                -999 N   Y   <NA>     
## [1,] No. cases 466  138 9   1173 1786
## [2,] Percent   26.1 7.7 0.5 65.7 100

Other religion (categorical,[v4_rel_oth])

v4_rel_var<-c(v4_clin$v4_religion_religion,rep(-999,dim(v4_con)[1]))
v4_rel_oth<-ifelse(v4_rel_var==1, "Judaism",
                   ifelse(v4_rel_var==2, "Hinduism",
                          ifelse(v4_rel_var==3, "Buddhism",
                                 ifelse(v4_rel_var==4, "Other",
                                        ifelse(v4_rel_var==5, "No denomination",
                                              ifelse(v4_rel_var==-999, "-999", NA))))))
descT(v4_rel_oth) 

##                -999 Buddhism Hinduism Judaism No denomination Other <NA>     
## [1,] No. cases 466  8        2        1       110             13    1186 1786
## [2,] Percent   26.1 0.4      0.1      0.1     6.2             0.7   66.4 100

“How actively do you practice your belief?” (ordinal [1,2,3,4,5], v4_rel_act) This is an ordinal item with the following answer possibilities and the assigned gadation: “not at all”-1,“little active”-2,“moderately active”-3,“rather active”-4,“very actively”-5.

v4_rel_act<-c(v4_clin$v4_religion_religion_aktiv,rep(-999,dim(v4_con)[1]))
descT(v4_rel_act)

##                -999 1   2   3   4   5   <NA>     
## [1,] No. cases 466  125 155 127 69  25  819  1786
## [2,] Percent   26.1 7   8.7 7.1 3.9 1.4 45.9 100

Create dataset

v4_rlgn<-data.frame(v4_rel_chr,v4_rel_isl,v4_rel_oth,v4_rel_act)

Medication adherence (compliance)

For a description of the questionnaire, see Visit 1.

Past seven days (ordinal [1,2,3,4,5,6], v4_med_pst_wk)

v4_med_chk<-c(v4_clin$v4_compl_verwer_fragebogen,rep(1,dim(v4_con)[1]))
v4_med_pst_wk_pre<-c(v4_clin$v4_compl_psychopharm_7_tag,rep(-999,dim(v4_con)[1]))
  
v4_med_pst_wk<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_med_pst_wk<-ifelse((is.na(v4_med_chk) | v4_med_chk!=2), 
                      v4_med_pst_wk_pre, v4_med_pst_wk)

descT(v4_med_pst_wk)

##                -999 1    2   3   4   6   <NA>     
## [1,] No. cases 466  484  52  19  1   5   759  1786
## [2,] Percent   26.1 27.1 2.9 1.1 0.1 0.3 42.5 100

Past six months (ordinal [1,2,3,4,5,6], v4_med_pst_sx_mths)

v4_med_pre<-c(v4_clin$v4_compl_psychopharm_6_mon,rep(-999,dim(v4_con)[1]))

v4_med_pst_sx_mths<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_med_pst_sx_mths<-ifelse((is.na(v4_med_chk) | v4_med_chk!=2),
                           v4_med_pre, v4_med_pst_sx_mths)

descT(v4_med_pst_sx_mths)

##                -999 1    2  3   4   6   <NA>     
## [1,] No. cases 466  432  71 45  10  3   759  1786
## [2,] Percent   26.1 24.2 4  2.5 0.6 0.2 42.5 100

Create dataset

v4_med_adh<-data.frame(v4_med_pst_wk,v4_med_pst_sx_mths)

BDI-II

For explanation, please refer to the section in Visit 1

1. Sadness (ordinal [0,1,2,3], v4_bdi2_itm1)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi1_traurigkeit,v4_con$v4_bdi2_s1_bdi1,"v4_bdi2_itm1")

##                0    1   2  3   <NA>     
## [1,] No. cases 609  178 18 12  969  1786
## [2,] Percent   34.1 10  1  0.7 54.3 100

2. Pessimism (ordinal [0,1,2,3], v4_bdi2_itm2)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi2_pessimismus,v4_con$v4_bdi2_s1_bdi2,"v4_bdi2_itm2")

##                0    1   2  3   <NA>     
## [1,] No. cases 627  124 53 12  970  1786
## [2,] Percent   35.1 6.9 3  0.7 54.3 100

3. Past failure (ordinal [0,1,2,3], v4_bdi2_itm3)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi3_versagensgef,v4_con$v4_bdi2_s1_bdi3,"v4_bdi2_itm3")

##                0   1   2   3   <NA>     
## [1,] No. cases 571 152 80  13  970  1786
## [2,] Percent   32  8.5 4.5 0.7 54.3 100

4. Loss of pleasure (ordinal [0,1,2,3], v4_bdi2_itm4)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi4_verlust_freude,v4_con$v4_bdi2_s1_bdi4,"v4_bdi2_itm4")

##                0    1    2   3   <NA>     
## [1,] No. cases 546  203  41  23  973  1786
## [2,] Percent   30.6 11.4 2.3 1.3 54.5 100

5. Guilty feelings (ordinal [0,1,2,3], v4_bdi2_itm5)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi5_schuldgef,v4_con$v4_bdi2_s1_bdi5,"v4_bdi2_itm5")

##                0    1   2   3   <NA>     
## [1,] No. cases 624  157 21  12  972  1786
## [2,] Percent   34.9 8.8 1.2 0.7 54.4 100

6. Punishment feelings (ordinal [0,1,2,3], v4_bdi2_itm6)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi6_bestrafungsgef,v4_con$v4_bdi2_s1_bdi6,"v4_bdi2_itm6")

##                0    1   2   3   <NA>     
## [1,] No. cases 688  91  6   31  970  1786
## [2,] Percent   38.5 5.1 0.3 1.7 54.3 100

7. Self-dislike (ordinal [0,1,2,3], v4_bdi2_itm7)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi7_selbstablehnung,v4_con$v4_bdi2_s1_bdi7,"v4_bdi2_itm7")

##                0    1   2   3   <NA>     
## [1,] No. cases 655  102 48  12  969  1786
## [2,] Percent   36.7 5.7 2.7 0.7 54.3 100

8. Self-criticalness (ordinal [0,1,2,3], v4_bdi2_itm8)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi8_selbstvorwuerfe,v4_con$v4_bdi2_s1_bdi8,"v4_bdi2_itm8")

##                0    1   2   3   <NA>     
## [1,] No. cases 581  174 49  14  968  1786
## [2,] Percent   32.5 9.7 2.7 0.8 54.2 100

9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v4_bdi2_itm9)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi9_selbstmordged,v4_con$v4_bdi2_s1_bdi9,"v4_bdi2_itm9")

##                0    1   2   3   <NA>     
## [1,] No. cases 695  110 9   3   969  1786
## [2,] Percent   38.9 6.2 0.5 0.2 54.3 100

10. Crying (ordinal [0,1,2,3], v4_bdi2_itm10)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi10_weinen,v4_con$v4_bdi2_s1_bdi10,"v4_bdi2_itm10")

##                0    1   2   3   <NA>     
## [1,] No. cases 666  80  15  51  974  1786
## [2,] Percent   37.3 4.5 0.8 2.9 54.5 100

11. Agitation (ordinal [0,1,2,3], v4_bdi2_itm11)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi11_unruhe,v4_con$v4_bdi2_s2_bdi11,"v4_bdi2_itm11")

##                0    1   2  3   <NA>     
## [1,] No. cases 628  155 17 12  974  1786
## [2,] Percent   35.2 8.7 1  0.7 54.5 100

12. Loss of interest (ordinal [0,1,2,3], v4_bdi2_itm12)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi12_interessverl,v4_con$v4_bdi2_s2_bdi12,"v4_bdi2_itm12")

##                0    1   2  3   <NA>     
## [1,] No. cases 595  153 36 29  973  1786
## [2,] Percent   33.3 8.6 2  1.6 54.5 100

13. Indecisiveness (ordinal [0,1,2,3], v4_bdi2_itm13)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi13_entschlussunf,v4_con$v4_bdi2_s2_bdi13,"v4_bdi2_itm13")

##                0   1   2   3   <NA>     
## [1,] No. cases 572 169 44  26  975  1786
## [2,] Percent   32  9.5 2.5 1.5 54.6 100

14. Worthlessness (ordinal [0,1,2,3], v4_bdi2_itm14)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi14_wertlosigkeit,v4_con$v4_bdi2_s2_bdi14,"v4_bdi2_itm14")

##                0    1   2   3   <NA>     
## [1,] No. cases 650  101 51  9   975  1786
## [2,] Percent   36.4 5.7 2.9 0.5 54.6 100

15. Loss of energy (ordinal [0,1,2,3], v4_bdi2_itm15)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi15_energieverlust,v4_con$v4_bdi2_s2_bdi15,"v4_bdi2_itm15")

##                0    1    2   3   <NA>     
## [1,] No. cases 498  235  68  9   976  1786
## [2,] Percent   27.9 13.2 3.8 0.5 54.6 100

16. Changes in sleeping pattern (ordinal [0,1,2,3], v4_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep”. There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.

v4_itm_bdi2_chk<-c(v4_clin$v4_bdi2_s1_verwer_fragebogen,v4_con$v4_bdi2_s1_bdi_korrekt)
v4_itm_bdi2_itm16_clin_con<-c(v4_clin$v4_bdi2_s2_bdi16_schlafgewohn,v4_con$v4_bdi2_s2_bdi16)

v4_bdi2_itm16<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])

v4_bdi2_itm16<-ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & v4_itm_bdi2_itm16_clin_con==0, 0,
                ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                           (v4_itm_bdi2_itm16_clin_con==1 | v4_itm_bdi2_itm16_clin_con==100), 1, 
                 ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                               (v4_itm_bdi2_itm16_clin_con==2 | v4_itm_bdi2_itm16_clin_con==200), 2, 
                  ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                               (v4_itm_bdi2_itm16_clin_con==3 | v4_itm_bdi2_itm16_clin_con==300), 3, v4_bdi2_itm16))))  

v4_bdi2_itm16<-factor(v4_bdi2_itm16,ordered=T)
descT(v4_bdi2_itm16)

##                0    1    2   3   <NA>     
## [1,] No. cases 474  247  57  33  975  1786
## [2,] Percent   26.5 13.8 3.2 1.8 54.6 100

17. Irritability (ordinal [0,1,2,3], v4_bdi2_itm17)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi17_reizbarkeit,v4_con$v4_bdi2_s2_bdi17,"v4_bdi2_itm17")

##                0    1   2   3   <NA>     
## [1,] No. cases 656  129 19  8   974  1786
## [2,] Percent   36.7 7.2 1.1 0.4 54.5 100

18. Change in appetite (ordinal [0,1,2,3], v4_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.

v4_itm_bdi2_itm18_clin_con<-c(v4_clin$v4_bdi2_s2_bdi18_appetit,v4_con$v4_bdi2_s2_bdi18)
v4_bdi2_itm18<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])

v4_bdi2_itm18<-ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & v4_itm_bdi2_itm18_clin_con==0, 0,
                ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                           (v4_itm_bdi2_itm18_clin_con==1 | v4_itm_bdi2_itm18_clin_con==100), 1, 
                 ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                               (v4_itm_bdi2_itm18_clin_con==2 | v4_itm_bdi2_itm18_clin_con==200), 2, 
                  ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                               (v4_itm_bdi2_itm18_clin_con==3 | v4_itm_bdi2_itm18_clin_con==300), 3, v4_bdi2_itm18))))  

v4_bdi2_itm18<-factor(v4_bdi2_itm18,ordered=T)
descT(v4_bdi2_itm18)

##                0    1    2   3   <NA>     
## [1,] No. cases 558  200  34  19  975  1786
## [2,] Percent   31.2 11.2 1.9 1.1 54.6 100

19. Concentration difficulty (ordinal [0,1,2,3], v4_bdi2_itm19)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi19_konzschw,v4_con$v4_bdi2_s2_bdi19,"v4_bdi2_itm19")

##                0    1    2   3   <NA>     
## [1,] No. cases 506  217  83  6   974  1786
## [2,] Percent   28.3 12.2 4.6 0.3 54.5 100

20. Tiredness or fatigue (ordinal [0,1,2,3], v4_bdi2_itm20)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi20_ermued_ersch,v4_con$v4_bdi2_s2_bdi20,"v4_bdi2_itm20")

##                0    1    2   3   <NA>     
## [1,] No. cases 504  246  47  14  975  1786
## [2,] Percent   28.2 13.8 2.6 0.8 54.6 100

21. Loss of interest in sex (ordinal [0,1,2,3], v4_bdi2_itm21)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi21_sex_interess,v4_con$v4_bdi2_s2_bdi21,"v4_bdi2_itm21")

##                0    1   2   3   <NA>     
## [1,] No. cases 585  112 46  62  981  1786
## [2,] Percent   32.8 6.3 2.6 3.5 54.9 100

BDI-II sum score calculation (continuous [0-63], v4_bdi2_sum)

v4_bdi2_sum<-as.numeric.factor(v4_bdi2_itm1)+
              as.numeric.factor(v4_bdi2_itm2)+
              as.numeric.factor(v4_bdi2_itm3)+
              as.numeric.factor(v4_bdi2_itm4)+
              as.numeric.factor(v4_bdi2_itm5)+
              as.numeric.factor(v4_bdi2_itm6)+
              as.numeric.factor(v4_bdi2_itm7)+
              as.numeric.factor(v4_bdi2_itm8)+
              as.numeric.factor(v4_bdi2_itm9)+
              as.numeric.factor(v4_bdi2_itm10)+
              as.numeric.factor(v4_bdi2_itm11)+
              as.numeric.factor(v4_bdi2_itm12)+
              as.numeric.factor(v4_bdi2_itm13)+
              as.numeric.factor(v4_bdi2_itm14)+
              as.numeric.factor(v4_bdi2_itm15)+
              as.numeric.factor(v4_bdi2_itm16)+
              as.numeric.factor(v4_bdi2_itm17)+
              as.numeric.factor(v4_bdi2_itm18)+
              as.numeric.factor(v4_bdi2_itm19)+
              as.numeric.factor(v4_bdi2_itm20)+
              as.numeric.factor(v4_bdi2_itm21)

summary(v4_bdi2_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   4.000   7.701  11.000  54.000    1007

Create dataset

v4_bdi2<-data.frame(v4_bdi2_itm1,v4_bdi2_itm2,v4_bdi2_itm3,v4_bdi2_itm4,v4_bdi2_itm5,
                    v4_bdi2_itm6,v4_bdi2_itm7,v4_bdi2_itm8,v4_bdi2_itm9,v4_bdi2_itm10,
                    v4_bdi2_itm11,v4_bdi2_itm12,v4_bdi2_itm13,v4_bdi2_itm14,
                    v4_bdi2_itm15,v4_bdi2_itm16,v4_bdi2_itm17,v4_bdi2_itm18,
                    v4_bdi2_itm19,v4_bdi2_itm20,v4_bdi2_itm21, v4_bdi2_sum)

ASRM

For explanation, please refer to the section in Visit 1

1. Positive Mood (ordinal [0,1,2,3,4], v4_asrm_itm1)

v4_asrm_recode(v4_clin$v4_asrm_asrm1_gluecklich,v4_con$v4_asrm_asrm1,"v4_asrm_itm1")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 617  142 33  11  5   978  1786
## [2,] Percent   34.5 8   1.8 0.6 0.3 54.8 100

2 Self-Confidence (ordinal [0,1,2,3,4], v4_asrm_itm2)

v4_asrm_recode(v4_clin$v4_asrm_asrm2_selbstbewusst,v4_con$v4_asrm_asrm2,"v4_asrm_itm2")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 636  140 25  4   2   979  1786
## [2,] Percent   35.6 7.8 1.4 0.2 0.1 54.8 100

3. Sleep (ordinal [0,1,2,3,4], v4_asrm_itm3)

v4_asrm_recode(v4_clin$v4_asrm_asrm3_schlaf,v4_con$v4_asrm_asrm3,"v4_asrm_itm3")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 691  93  15  4   4   979  1786
## [2,] Percent   38.7 5.2 0.8 0.2 0.2 54.8 100

4. Speech (ordinal [0,1,2,3,4], v4_asrm_itm4)

v4_asrm_recode(v4_clin$v4_asrm_asrm4_reden,v4_con$v4_asrm_asrm4,"v4_asrm_itm4")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 667  122 14  3   1   979  1786
## [2,] Percent   37.3 6.8 0.8 0.2 0.1 54.8 100

5. Activity Level (ordinal [0,1,2,3,4], v4_asrm_itm5)

v4_asrm_recode(v4_clin$v4_asrm_asrm5_aktiv,v4_con$v4_asrm_asrm5,"v4_asrm_itm5")

##                0    1   2   3   4   <NA>     
## [1,] No. cases 629  145 24  6   4   978  1786
## [2,] Percent   35.2 8.1 1.3 0.3 0.2 54.8 100

Create ASRM sum score (continuous [0-20],v4_asrm_sum)

v4_asrm_sum<-as.numeric.factor(v4_asrm_itm1)+
            as.numeric.factor(v4_asrm_itm2)+
            as.numeric.factor(v4_asrm_itm3)+
            as.numeric.factor(v4_asrm_itm4)+
            as.numeric.factor(v4_asrm_itm5)

summary(v4_asrm_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    0.00    0.00    1.25    2.00   13.00     983

Create dataset

v4_asrm<-data.frame(v4_asrm_itm1,v4_asrm_itm2,v4_asrm_itm3,v4_asrm_itm4,v4_asrm_itm5,v4_asrm_sum)

MSS

For explanation, please refer to the section in Visit 1

1. “I had more energy” (dichotomous, v4_mss_itm1)

v4_mss_recode(v4_clin$v4_mss_s1_mss1_energie,v4_con$v4_mss_s1_mss1,"v4_mss_itm1")

##                N    Y   <NA>     
## [1,] No. cases 663  138 985  1786
## [2,] Percent   37.1 7.7 55.2 100

2. “I had trouble sitting still” (dichotomous, v4_mss_itm2)

v4_mss_recode(v4_clin$v4_mss_s1_mss2_ruhig_sitzen,v4_con$v4_mss_s1_mss2,"v4_mss_itm2")

##                N    Y   <NA>     
## [1,] No. cases 705  95  986  1786
## [2,] Percent   39.5 5.3 55.2 100

3. “I drove faster” (dichotomous, v4_mss_itm3)

v4_mss_recode(v4_clin$v4_mss_s1_mss3_auto_fahren,v4_con$v4_mss_s1_mss3,"v4_mss_itm3")

##                N    Y   <NA>     
## [1,] No. cases 752  20  1014 1786
## [2,] Percent   42.1 1.1 56.8 100

4. “I drank more alcoholic beverages” (dichotomous, v4_mss_itm4)

v4_mss_recode(v4_clin$v4_mss_s1_mss4_alkohol,v4_con$v4_mss_s1_mss4,"v4_mss_itm4")

##                N    Y   <NA>     
## [1,] No. cases 736  59  991  1786
## [2,] Percent   41.2 3.3 55.5 100

5. “I changed clothes several times a day” (dichotomous, v4_mss_itm5)

v4_mss_recode(v4_clin$v4_mss_s1_mss5_umziehen, v4_con$v4_mss_s1_mss5,"v4_mss_itm5")

##                N    Y   <NA>     
## [1,] No. cases 749  49  988  1786
## [2,] Percent   41.9 2.7 55.3 100

6. “I wore brighter clothes/make-up” (dichotomous, v4_mss_itm6)

v4_mss_recode(v4_clin$v4_mss_s1_mss6_bunter,v4_con$v4_mss_s1_mss6,"v4_mss_itm6")

##                N   Y   <NA>     
## [1,] No. cases 768 31  987  1786
## [2,] Percent   43  1.7 55.3 100

7. “I played music louder” (dichotomous, v4_mss_itm7)

v4_mss_recode(v4_clin$v4_mss_s1_mss7_musik_lauter,v4_con$v4_mss_s1_mss7,"v4_mss_itm7")

##                N    Y   <NA>     
## [1,] No. cases 706  94  986  1786
## [2,] Percent   39.5 5.3 55.2 100

8. “I ate faster than usual” (dichotomous, v4_mss_itm8)

v4_mss_recode(v4_clin$v4_mss_s1_mss8_hastiger_essen,v4_con$v4_mss_s1_mss8,"v4_mss_itm8")

##                N    Y   <NA>     
## [1,] No. cases 724  76  986  1786
## [2,] Percent   40.5 4.3 55.2 100

9. “I ate more than usual” (dichotomous, v4_mss_itm9)

v4_mss_recode(v4_clin$v4_mss_s1_mss9_mehr_essen,v4_con$v4_mss_s1_mss9,"v4_mss_itm9")

##                N    Y   <NA>     
## [1,] No. cases 656  142 988  1786
## [2,] Percent   36.7 8   55.3 100

10. “I slept fewer hours than usual” (dichotomous, v4_mss_itm10)

v4_mss_recode(v4_clin$v4_mss_s1_mss10_weniger_schlaf,v4_con$v4_mss_s1_mss10,"v4_mss_itm10")

##                N    Y   <NA>     
## [1,] No. cases 717  83  986  1786
## [2,] Percent   40.1 4.6 55.2 100

11. “I started things that I didn’t finish” (dichotomous, v4_mss_itm11)

v4_mss_recode(v4_clin$v4_mss_s1_mss11_unbeendet,v4_con$v4_mss_s1_mss11,"v4_mss_itm11")

##                N    Y   <NA>     
## [1,] No. cases 657  143 986  1786
## [2,] Percent   36.8 8   55.2 100

12. “I gave away my own possessions” (dichotomous, v4_mss_itm12)

v4_mss_recode(v4_clin$v4_mss_s1_mss12_weggeben,v4_con$v4_mss_s1_mss12,"v4_mss_itm12")

##                N    Y   <NA>     
## [1,] No. cases 743  57  986  1786
## [2,] Percent   41.6 3.2 55.2 100

13. “I bought gifts for people” (dichotomous, v4_mss_itm13)

v4_mss_recode(v4_clin$v4_mss_s1_mss13_geschenke,v4_con$v4_mss_s1_mss13,"v4_mss_itm13")

##                N    Y   <NA>     
## [1,] No. cases 739  60  987  1786
## [2,] Percent   41.4 3.4 55.3 100

14. “I spent money more freely” (dichotomous, v4_mss_itm14)

v4_mss_recode(v4_clin$v4_mss_s1_mss14_mehr_geld,v4_con$v4_mss_s1_mss14,"v4_mss_itm14")

##                N   Y   <NA>     
## [1,] No. cases 643 157 986  1786
## [2,] Percent   36  8.8 55.2 100

15. “I accumulated debts” (dichotomous, v4_mss_itm15)

v4_mss_recode(v4_clin$v4_mss_s1_mss15_schulden,v4_con$v4_mss_s1_mss15,"v4_mss_itm15")

##                N    Y   <NA>     
## [1,] No. cases 757  43  986  1786
## [2,] Percent   42.4 2.4 55.2 100

16. “I made unwise business decisions” (dichotomous, v4_mss_itm16)

v4_mss_recode(v4_clin$v4_mss_s1_mss16_unkluge_entsch,v4_con$v4_mss_s1_mss16,"v4_mss_itm16")

##                N    Y   <NA>     
## [1,] No. cases 767  32  987  1786
## [2,] Percent   42.9 1.8 55.3 100

17. “I partied more” (dichotomous, v4_mss_itm17)

v4_mss_recode(v4_clin$v4_mss_s1_mss17_parties,v4_con$v4_mss_s1_mss17,"v4_mss_itm17")

##                N    Y   <NA>     
## [1,] No. cases 769  32  985  1786
## [2,] Percent   43.1 1.8 55.2 100

18. “I enjoyed flirting” (dichotomous, v4_mss_itm18)

v4_mss_recode(v4_clin$v4_mss_s1_mss18_flirten,v4_con$v4_mss_s1_mss18,"v4_mss_itm18")

##                N    Y   <NA>     
## [1,] No. cases 760  38  988  1786
## [2,] Percent   42.6 2.1 55.3 100

19. “I masturbated more often” (dichotomous, v4_mss_itm19)

v4_mss_recode(v4_clin$v4_mss_s2_mss19_selbstbefried,v4_con$v4_mss_s2_mss19,"v4_mss_itm19")

##                N    Y   <NA>     
## [1,] No. cases 755  32  999  1786
## [2,] Percent   42.3 1.8 55.9 100

20. “I was more interested in sex than usual” (dichotomous, v4_mss_itm20)

v4_mss_recode(v4_clin$v4_mss_s2_mss20_sex_interess,v4_con$v4_mss_s2_mss20,"v4_mss_itm20")

##                N    Y   <NA>     
## [1,] No. cases 726  60  1000 1786
## [2,] Percent   40.6 3.4 56   100

21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v4_mss_itm21)

v4_mss_recode(v4_clin$v4_mss_s2_mss21_sexpartner,v4_con$v4_mss_s2_mss21,"v4_mss_itm21")

##                N    Y   <NA>     
## [1,] No. cases 781  7   998  1786
## [2,] Percent   43.7 0.4 55.9 100

22. “I spent more time on the phone” (dichotomous, v4_mss_itm22)

v4_mss_recode(v4_clin$v4_mss_s2_mss22_mehr_telefon,v4_con$v4_mss_s2_mss22,"v4_mss_itm22")

##                N    Y   <NA>     
## [1,] No. cases 694  94  998  1786
## [2,] Percent   38.9 5.3 55.9 100

23. “I spoke louder than usual” (dichotomous, v4_mss_itm23)

v4_mss_recode(v4_clin$v4_mss_s2_mss23_sprache_lauter,v4_con$v4_mss_s2_mss23,"v4_mss_itm23")

##                N   Y  <NA>     
## [1,] No. cases 732 53 1001 1786
## [2,] Percent   41  3  56   100

24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v4_mss_itm24)

v4_mss_recode(v4_clin$v4_mss_s2_mss24_spr_schneller,v4_con$v4_mss_s2_mss24,"v4_mss_itm24")

##                N    Y   <NA>     
## [1,] No. cases 743  48  995  1786
## [2,] Percent   41.6 2.7 55.7 100

25. “1 enjoyed punning or rhyming” (dichotomous, v4_mss_itm25)

v4_mss_recode(v4_clin$v4_mss_s2_mss25_witze,v4_con$v4_mss_s2_mss25,"v4_mss_itm25")

##                N    Y   <NA>     
## [1,] No. cases 729  61  996  1786
## [2,] Percent   40.8 3.4 55.8 100

26. “I butted into conversations” (dichotomous, v4_mss_itm26)

v4_mss_recode(v4_clin$v4_mss_s2_mss26_einmischen,v4_con$v4_mss_s2_mss26,"v4_mss_itm26")

##                N    Y  <NA>     
## [1,] No. cases 756  35 995  1786
## [2,] Percent   42.3 2  55.7 100

27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v4_mss_itm27)

v4_mss_recode(v4_clin$v4_mss_s2_mss27_red_pausenlos,v4_con$v4_mss_s2_mss27,"v4_mss_itm27")

##                N    Y   <NA>     
## [1,] No. cases 769  21  996  1786
## [2,] Percent   43.1 1.2 55.8 100

28. “I enjoyed being the centre of attention” (dichotomous, v4_mss_itm28)

v4_mss_recode(v4_clin$v4_mss_s2_mss28_mittelpunkt,v4_con$v4_mss_s2_mss28,"v4_mss_itm28")

##                N    Y   <NA>     
## [1,] No. cases 748  42  996  1786
## [2,] Percent   41.9 2.4 55.8 100

29. “I liked to joke and laugh” (dichotomous, v4_mss_itm29)

v4_mss_recode(v4_clin$v4_mss_s2_mss29_herumalbern,v4_con$v4_mss_s2_mss29,"v4_mss_itm29")

##                N    Y   <NA>     
## [1,] No. cases 708  83  995  1786
## [2,] Percent   39.6 4.6 55.7 100

30. “People found me entertaining” (dichotomous, v4_mss_itm30)

v4_mss_recode(v4_clin$v4_mss_s2_mss30_unterhaltsamer,v4_con$v4_mss_s2_mss30,"v4_mss_itm30")

##                N    Y   <NA>     
## [1,] No. cases 739  50  997  1786
## [2,] Percent   41.4 2.8 55.8 100

31. “I felt as if I was on top of the world” (dichotomous, v4_mss_itm31)

v4_mss_recode(v4_clin$v4_mss_s2_mss31_obenauf,v4_con$v4_mss_s2_mss31,"v4_mss_itm31")

##                N    Y   <NA>     
## [1,] No. cases 739  49  998  1786
## [2,] Percent   41.4 2.7 55.9 100

32. “I was more cheerful than my usual self” (dichotomous, v4_mss_itm32)

v4_mss_recode(v4_clin$v4_mss_s2_mss32_froehlicher,v4_con$v4_mss_s2_mss32,"v4_mss_itm32")

##                N    Y   <NA>     
## [1,] No. cases 686  102 998  1786
## [2,] Percent   38.4 5.7 55.9 100

33. “Other people got on my nerves” (dichotomous, v4_mss_itm33)

v4_mss_recode(v4_clin$v4_mss_s2_mss33_ungeduldiger,v4_con$v4_mss_s2_mss33,"v4_mss_itm33")

##                N    Y   <NA>     
## [1,] No. cases 640  150 996  1786
## [2,] Percent   35.8 8.4 55.8 100

34. “I was getting into arguments” (dichotomous, v4_mss_itm34)

v4_mss_recode(v4_clin$v4_mss_s2_mss34_streiten,v4_con$v4_mss_s2_mss34,"v4_mss_itm34")

##                N    Y   <NA>     
## [1,] No. cases 735  56  995  1786
## [2,] Percent   41.2 3.1 55.7 100

35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v4_mss_itm35)

v4_mss_recode(v4_clin$v4_mss_s2_mss35_ideen,v4_con$v4_mss_s2_mss35,"v4_mss_itm35")

##                N    Y   <NA>     
## [1,] No. cases 687  103 996  1786
## [2,] Percent   38.5 5.8 55.8 100

36. “My thoughts raced through my mind” (dichotomous, v4_mss_itm36)

v4_mss_recode(v4_clin$v4_mss_s2_mss36_gedanken,v4_con$v4_mss_s2_mss36,"v4_mss_itm36")

##                N    Y   <NA>     
## [1,] No. cases 626  161 999  1786
## [2,] Percent   35.1 9   55.9 100

37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v4_mss_itm37)

v4_mss_recode(v4_clin$v4_mss_s2_mss37_konzentration,v4_con$v4_mss_s2_mss37,"v4_mss_itm37")

##                N    Y   <NA>     
## [1,] No. cases 689  101 996  1786
## [2,] Percent   38.6 5.7 55.8 100

38. “I thought I was an especially important person” (dichotomous, v4_mss_itm38)

v4_mss_recode(v4_clin$v4_mss_s2_mss38_etw_besonderes,v4_con$v4_mss_s2_mss38,"v4_mss_itm38")

##                N    Y   <NA>     
## [1,] No. cases 744  45  997  1786
## [2,] Percent   41.7 2.5 55.8 100

39. “I thought I could change the world” (dichotomous, v4_mss_itm39)

v4_mss_recode(v4_clin$v4_mss_s2_mss39_welt_veraender,v4_con$v4_mss_s2_mss39,"v4_mss_itm39")

##                N    Y   <NA>     
## [1,] No. cases 763  28  995  1786
## [2,] Percent   42.7 1.6 55.7 100

40. “I thought I was right most of the time” (dichotomous, v4_mss_itm40)

v4_mss_recode(v4_clin$v4_mss_s2_mss40_recht_haben,v4_con$v4_mss_s2_mss40,"v4_mss_itm40")

##                N    Y   <NA>     
## [1,] No. cases 759  29  998  1786
## [2,] Percent   42.5 1.6 55.9 100

41. “I thought I was superior to others” (dichotomous, v4_mss_itm41)

v4_mss_recode(v4_clin$v4_mss_s3_mss41_ueberlegen,v4_con$v4_mss_s3_mss41,"v4_mss_itm41")

##                N    Y   <NA>     
## [1,] No. cases 773  23  990  1786
## [2,] Percent   43.3 1.3 55.4 100

42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v4_mss_itm42)

v4_mss_recode(v4_clin$v4_mss_s3_mss42_uebermut,v4_con$v4_mss_s3_mss42,"v4_mss_itm42")

##                N    Y   <NA>     
## [1,] No. cases 757  39  990  1786
## [2,] Percent   42.4 2.2 55.4 100

43. “I thought I knew what other people were thinking” (dichotomous, v4_mss_itm43)

v4_mss_recode(v4_clin$v4_mss_s3_mss43_ged_lesen_akt,v4_con$v4_mss_s3_mss43,"v4_mss_itm43")

##                N    Y   <NA>     
## [1,] No. cases 752  44  990  1786
## [2,] Percent   42.1 2.5 55.4 100

44. “I thought other people knew what I was thinking” (dichotomous, v4_mss_itm44)

v4_mss_recode(v4_clin$v4_mss_s3_mss44_ged_lesen_pas,v4_con$v4_mss_s3_mss44,"v4_mss_itm44")

##                N    Y   <NA>     
## [1,] No. cases 756  40  990  1786
## [2,] Percent   42.3 2.2 55.4 100

45. “I thought someone wanted to harm me” (dichotomous, v4_mss_itm45)

v4_mss_recode(v4_clin$v4_mss_s3_mss45_etw_antun,v4_con$v4_mss_s3_mss45,"v4_mss_itm45")

##                N    Y   <NA>     
## [1,] No. cases 762  34  990  1786
## [2,] Percent   42.7 1.9 55.4 100

46. “I heard voices when people weren’t there” (dichotomous, v4_mss_itm46)

v4_mss_recode(v4_clin$v4_mss_s3_mss46_stimmen,v4_con$v4_mss_s3_mss46,"v4_mss_itm46")

##                N    Y  <NA>     
## [1,] No. cases 742  53 991  1786
## [2,] Percent   41.5 3  55.5 100

47. “I had false beliefs concerning who I was” (dichotomous, v4_mss_itm47)

v4_mss_recode(v4_clin$v4_mss_s3_mss47_jmd_anders,v4_con$v4_mss_s3_mss47,"v4_mss_itm47")

##                N    Y  <NA>     
## [1,] No. cases 778  17 991  1786
## [2,] Percent   43.6 1  55.5 100

48. “I knew I was getting ill” (dichotomous, v4_mss_itm48)

v4_mss_recode(v4_clin$v4_mss_s3_mss48_krank_einsicht,v4_con$v4_mss_s3_mss48,"v4_mss_itm48")

##                N    Y   <NA>     
## [1,] No. cases 713  75  998  1786
## [2,] Percent   39.9 4.2 55.9 100

Create MSS sum score (continuous [0-48],v4_mss_sum)

v4_mss_sum<-ifelse(v4_mss_itm1=="Y",1,0)+
            ifelse(v4_mss_itm2=="Y",1,0)+
            ifelse(v4_mss_itm3=="Y",1,0)+
            ifelse(v4_mss_itm4=="Y",1,0)+
            ifelse(v4_mss_itm5=="Y",1,0)+
            ifelse(v4_mss_itm6=="Y",1,0)+
            ifelse(v4_mss_itm7=="Y",1,0)+
            ifelse(v4_mss_itm8=="Y",1,0)+
            ifelse(v4_mss_itm9=="Y",1,0)+
            ifelse(v4_mss_itm10=="Y",1,0)+
            ifelse(v4_mss_itm11=="Y",1,0)+
            ifelse(v4_mss_itm12=="Y",1,0)+
            ifelse(v4_mss_itm13=="Y",1,0)+
            ifelse(v4_mss_itm14=="Y",1,0)+
            ifelse(v4_mss_itm15=="Y",1,0)+
            ifelse(v4_mss_itm16=="Y",1,0)+
            ifelse(v4_mss_itm17=="Y",1,0)+
            ifelse(v4_mss_itm18=="Y",1,0)+
            ifelse(v4_mss_itm19=="Y",1,0)+
            ifelse(v4_mss_itm20=="Y",1,0)+
            ifelse(v4_mss_itm21=="Y",1,0)+
            ifelse(v4_mss_itm22=="Y",1,0)+
            ifelse(v4_mss_itm23=="Y",1,0)+
            ifelse(v4_mss_itm24=="Y",1,0)+
            ifelse(v4_mss_itm25=="Y",1,0)+
            ifelse(v4_mss_itm26=="Y",1,0)+
            ifelse(v4_mss_itm27=="Y",1,0)+
            ifelse(v4_mss_itm28=="Y",1,0)+
            ifelse(v4_mss_itm29=="Y",1,0)+
            ifelse(v4_mss_itm30=="Y",1,0)+
            ifelse(v4_mss_itm31=="Y",1,0)+
            ifelse(v4_mss_itm32=="Y",1,0)+
            ifelse(v4_mss_itm33=="Y",1,0)+
            ifelse(v4_mss_itm34=="Y",1,0)+
            ifelse(v4_mss_itm35=="Y",1,0)+
            ifelse(v4_mss_itm36=="Y",1,0)+
            ifelse(v4_mss_itm37=="Y",1,0)+
            ifelse(v4_mss_itm38=="Y",1,0)+
            ifelse(v4_mss_itm39=="Y",1,0)+
            ifelse(v4_mss_itm40=="Y",1,0)+
            ifelse(v4_mss_itm41=="Y",1,0)+
            ifelse(v4_mss_itm42=="Y",1,0)+
            ifelse(v4_mss_itm43=="Y",1,0)+
            ifelse(v4_mss_itm44=="Y",1,0)+
            ifelse(v4_mss_itm45=="Y",1,0)+
            ifelse(v4_mss_itm46=="Y",1,0)+
            ifelse(v4_mss_itm47=="Y",1,0)+
            ifelse(v4_mss_itm48=="Y",1,0)

summary(v4_mss_sum)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   2.000   3.538   5.000  36.000    1065

Create dataset

v4_mss<-data.frame(v4_mss_itm1,v4_mss_itm2,v4_mss_itm3,v4_mss_itm4,v4_mss_itm5,v4_mss_itm6,
                   v4_mss_itm7,v4_mss_itm8,v4_mss_itm9,v4_mss_itm10,v4_mss_itm11,
                   v4_mss_itm12,v4_mss_itm13,v4_mss_itm14,v4_mss_itm15,v4_mss_itm16,
                   v4_mss_itm17,v4_mss_itm18,v4_mss_itm19,v4_mss_itm20,v4_mss_itm21,
                   v4_mss_itm22,v4_mss_itm23,v4_mss_itm24,v4_mss_itm25,v4_mss_itm26,
                   v4_mss_itm27,v4_mss_itm28,v4_mss_itm29,v4_mss_itm30,v4_mss_itm31,
                   v4_mss_itm32,v4_mss_itm33,v4_mss_itm34,v4_mss_itm35,v4_mss_itm36,
                   v4_mss_itm37,v4_mss_itm38,v4_mss_itm39,v4_mss_itm40,v4_mss_itm41,
                   v4_mss_itm42,v4_mss_itm43,v4_mss_itm44,v4_mss_itm45,v4_mss_itm46,
                   v4_mss_itm47,v4_mss_itm48, v4_mss_sum)

LEQ

For explanation, please refer to the section in Visit 1

Health

1. “Major personal illness or injury”

1A Nature (dichotomous [“good”,“bad”], v4_leq_A_1A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq1a_schw_krankh,v4_con$v4_leq_a_leq1a,"v4_leq_A_1A")

##                -999 bad good <NA>     
## [1,] No. cases 569  166 26   1025 1786
## [2,] Percent   31.9 9.3 1.5  57.4 100

1B Impact (ordinal [0,1,2,3], v4_leq_A_1B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq1e_schw_krankh,v4_con$v4_leq_a_leq1e,"v4_leq_A_1B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 567  12  38  67  77  1025 1786
## [2,] Percent   31.7 0.7 2.1 3.8 4.3 57.4 100

2. “Major change in eating habits”

2A Nature (dichotomous [“good”,“bad”], v4_leq_A_2A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq2a_ernaehrung,v4_con$v4_leq_a_leq2a,"v4_leq_A_2A")

##                -999 bad good <NA>     
## [1,] No. cases 583  75  103  1025 1786
## [2,] Percent   32.6 4.2 5.8  57.4 100

2B Impact (ordinal [0,1,2,3], v4_leq_A_2B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq2e_ernaehrung,v4_con$v4_leq_a_leq2e,"v4_leq_A_2B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 583  13  49  71 45  1025 1786
## [2,] Percent   32.6 0.7 2.7 4  2.5 57.4 100

3. “Major change in sleeping habits”

3A Nature (dichotomous [“good”,“bad”], v4_leq_A_3A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq3a_schlaf,v4_con$v4_leq_a_leq3a,"v4_leq_A_3A")

##                -999 bad good <NA>     
## [1,] No. cases 574  112 75   1025 1786
## [2,] Percent   32.1 6.3 4.2  57.4 100

3B Impact (ordinal [0,1,2,3], v4_leq_A_3B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq3e_schlaf,v4_con$v4_leq_a_leq3e,"v4_leq_A_3B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 573  20  52  57  59  1025 1786
## [2,] Percent   32.1 1.1 2.9 3.2 3.3 57.4 100

4. “Major change in usual type and/or amount of recreation”

4A Nature (dichotomous [“good”,“bad”], v4_leq_A_4A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq4a_freizeit,v4_con$v4_leq_a_leq4a,"v4_leq_A_4A")

##                -999 bad good <NA>     
## [1,] No. cases 560  73  128  1025 1786
## [2,] Percent   31.4 4.1 7.2  57.4 100

4B Impact (ordinal [0,1,2,3], v4_leq_A_4B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq4e_freizeit,v4_con$v4_leq_a_leq4e,"v4_leq_A_4B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 559  14  48  77  63  1025 1786
## [2,] Percent   31.3 0.8 2.7 4.3 3.5 57.4 100

5. “Major dental work”

5A Nature (dichotomous [“good”,“bad”], v4_leq_A_5A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq5a_zahnarzt,v4_con$v4_leq_a_leq5a,"v4_leq_A_5A")

##                -999 bad good <NA>     
## [1,] No. cases 642  55  64   1025 1786
## [2,] Percent   35.9 3.1 3.6  57.4 100

5B Impact (ordinal [0,1,2,3], v4_leq_A_5B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq5e_zahnarzt,v4_con$v4_leq_a_leq5e,"v4_leq_A_5B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 637  31  42  30  21  1025 1786
## [2,] Percent   35.7 1.7 2.4 1.7 1.2 57.4 100

6. “(Female) Pregnancy”

6A Nature (dichotomous [“good”,“bad”], v4_leq_A_6A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq6a_schwanger,v4_con$v4_leq_a_leq6a,"v4_leq_A_6A")

##                -999 good <NA>     
## [1,] No. cases 755  6    1025 1786
## [2,] Percent   42.3 0.3  57.4 100

6B Impact (ordinal [0,1,2,3], v4_leq_A_6B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq6e_schwanger,v4_con$v4_leq_a_leq6e,"v4_leq_A_6B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 754  1   1   5   1025 1786
## [2,] Percent   42.2 0.1 0.1 0.3 57.4 100

7. “(Female) Miscarriage or abortion”

7A Nature (dichotomous [“good”,“bad”], v4_leq_A_7A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq7a_fehlg_abtr,v4_con$v4_leq_a_leq7a,"v4_leq_A_7A")

##                -999 <NA>     
## [1,] No. cases 761  1025 1786
## [2,] Percent   42.6 57.4 100

7B Impact (ordinal [0,1,2,3], v4_leq_A_7B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq7e_fehlg_abtr,v4_con$v4_leq_a_leq7e,"v4_leq_A_7B")

##                -999 0   <NA>     
## [1,] No. cases 760  1   1025 1786
## [2,] Percent   42.6 0.1 57.4 100

8. “(Female) Started menopause”

8A Nature (dichotomous [“good”,“bad”], v4_leq_A_8A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq8a_wechseljahre,v4_con$v4_leq_a_leq8a,"v4_leq_A_8A")

##                -999 bad good <NA>     
## [1,] No. cases 735  19  7    1025 1786
## [2,] Percent   41.2 1.1 0.4  57.4 100

8B Impact (ordinal [0,1,2,3], v4_leq_A_8B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq8e_wechseljahre,v4_con$v4_leq_a_leq8e,"v4_leq_A_8B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 733  5   8   8   7   1025 1786
## [2,] Percent   41   0.3 0.4 0.4 0.4 57.4 100

9. “Major difficulties with birth control pills or devices”

9A Nature (dichotomous [“good”,“bad”], v4_leq_A_9A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq9a_verhuetung,v4_con$v4_leq_a_leq9a,"v4_leq_A_9A")

##                -999 bad good <NA>     
## [1,] No. cases 752  3   6    1025 1786
## [2,] Percent   42.1 0.2 0.3  57.4 100

9B Impact (ordinal [0,1,2,3], v4_leq_A_9B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq9e_verhuetung,v4_con$v4_leq_a_leq9e,"v4_leq_A_9B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 750  5   5   1   1025 1786
## [2,] Percent   42   0.3 0.3 0.1 57.4 100

Create dataset

v4_leq_A<-data.frame(v4_leq_A_1A,v4_leq_A_1B,v4_leq_A_2A,v4_leq_A_2B,v4_leq_A_3A,
                     v4_leq_A_3B,v4_leq_A_4A,v4_leq_A_4B,v4_leq_A_5A,v4_leq_A_5B,
                     v4_leq_A_6A,v4_leq_A_6B,v4_leq_A_7A,v4_leq_A_7B,v4_leq_A_8A,
                     v4_leq_A_8B,v4_leq_A_9A,v4_leq_A_9B)

Work

10. “Difficulty finding a job”

10A Nature (dichotomous [“good”,“bad”], v4_leq_B_10A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq10a_arbeitssuche,v4_con$v4_leq_b_leq10a,"v4_leq_B_10A")

##                -999 bad good <NA>     
## [1,] No. cases 676  71  14   1025 1786
## [2,] Percent   37.8 4   0.8  57.4 100

10B Impact (ordinal [0,1,2,3], v4_leq_B_10B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq10e_arbeitssuche,v4_con$v4_leq_b_leq10e,"v4_leq_B_10B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 674  10  22  28  27  1025 1786
## [2,] Percent   37.7 0.6 1.2 1.6 1.5 57.4 100

11. “Beginning work outside the home”

11A Nature (dichotomous [“good”,“bad”], v4_leq_B_11A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq11a_arbeit_aussen,v4_con$v4_leq_b_leq11a,"v4_leq_B_11A")

##                -999 bad good <NA>     
## [1,] No. cases 680  13  68   1025 1786
## [2,] Percent   38.1 0.7 3.8  57.4 100

11B Impact (ordinal [0,1,2,3], v4_leq_B_11B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq11e_arbeit_aussen,v4_con$v4_leq_b_leq11e,"v4_leq_B_11B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 678  10  19  23  31  1025 1786
## [2,] Percent   38   0.6 1.1 1.3 1.7 57.4 100

12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v4_leq_B_12A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq12a_arbeitswechs,v4_con$v4_leq_b_leq12a,"v4_leq_B_12A")

##                -999 bad good <NA>     
## [1,] No. cases 659  17  85   1025 1786
## [2,] Percent   36.9 1   4.8  57.4 100

12B Impact (ordinal [0,1,2,3], v4_leq_B_12B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq12e_arbeitswechs,v4_con$v4_leq_b_leq12e,"v4_leq_B_12B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 657  7   19  35 43  1025 1786
## [2,] Percent   36.8 0.4 1.1 2  2.4 57.4 100

13. “Changing your work hours or conditions”

13A Nature (dichotomous [“good”,“bad”], v4_leq_B_13A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq13a_veraend_arb,v4_con$v4_leq_b_leq13a,"v4_leq_B_13A")

##                -999 bad good <NA>     
## [1,] No. cases 626  40  95   1025 1786
## [2,] Percent   35.1 2.2 5.3  57.4 100

13B Impact (ordinal [0,1,2,3], v4_leq_B_13B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq13e_veraend_arb,v4_con$v4_leq_b_leq13e,"v4_leq_B_13B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 625  6   41  43  46  1025 1786
## [2,] Percent   35   0.3 2.3 2.4 2.6 57.4 100

14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v4_leq_B_14A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq14a_veraend_ba,v4_con$v4_leq_b_leq14a,"v4_leq_B_14A")

##                -999 bad good <NA>     
## [1,] No. cases 619  27  115  1025 1786
## [2,] Percent   34.7 1.5 6.4  57.4 100

14B Impact (ordinal [0,1,2,3], v4_leq_B_14B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq14e_veraend_ba,v4_con$v4_leq_b_leq14e,"v4_leq_B_14B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 617  9   37  57  41  1025 1786
## [2,] Percent   34.5 0.5 2.1 3.2 2.3 57.4 100

15. “Troubles at work with your employer or co-worker”

15A Nature (dichotomous [“good”,“bad”], v4_leq_B_15A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq15a_schw_arbeit,v4_con$v4_leq_b_leq15a,"v4_leq_B_15A")

##                -999 bad good <NA>     
## [1,] No. cases 682  66  13   1025 1786
## [2,] Percent   38.2 3.7 0.7  57.4 100

15B Impact (ordinal [0,1,2,3], v4_leq_B_15B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq15e_schw_arbeit,v4_con$v4_leq_b_leq15e,"v4_leq_B_15B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 681  8   29  28  15  1025 1786
## [2,] Percent   38.1 0.4 1.6 1.6 0.8 57.4 100

16. “Major business readjustment”

16A Nature (dichotomous [“good”,“bad”], v4_leq_B_16A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq16a_betr_reorg,v4_con$v4_leq_b_leq16a,"v4_leq_B_16A")

##                -999 bad good <NA>     
## [1,] No. cases 731  19  11   1025 1786
## [2,] Percent   40.9 1.1 0.6  57.4 100

16B Impact (ordinal [0,1,2,3], v4_leq_B_16B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq16e_betr_reorg,v4_con$v4_leq_b_leq16e,"v4_leq_B_16B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 729  5   10  7   10  1025 1786
## [2,] Percent   40.8 0.3 0.6 0.4 0.6 57.4 100

17. “Being fired or laid off from work”

17A Nature (dichotomous [“good”,“bad”], v4_leq_B_17A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq17a_kuendigung,v4_con$v4_leq_b_leq17a,"v4_leq_B_17A")

##                -999 bad good <NA>     
## [1,] No. cases 731  16  14   1025 1786
## [2,] Percent   40.9 0.9 0.8  57.4 100

17B Impact (ordinal [0,1,2,3], v4_leq_B_17B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq17e_kuendigung,v4_con$v4_leq_b_leq17e,"v4_leq_B_17B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 730  8   4   6   13  1025 1786
## [2,] Percent   40.9 0.4 0.2 0.3 0.7 57.4 100

18. “Retirement from work”

18A Nature (dichotomous [“good”,“bad”], v4_leq_B_18A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq18a_ende_beruf,v4_con$v4_leq_b_leq18a,"v4_leq_B_18A")

##                -999 bad good <NA>     
## [1,] No. cases 744  4   13   1025 1786
## [2,] Percent   41.7 0.2 0.7  57.4 100

18B Impact (ordinal [0,1,2,3], v4_leq_B_18B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq18e_ende_beruf,v4_con$v4_leq_b_leq18e,"v4_leq_B_18B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 743  1   5   2   10  1025 1786
## [2,] Percent   41.6 0.1 0.3 0.1 0.6 57.4 100

19. “Taking courses by mail or studying at home to help you in your work”

19A Nature (dichotomous [“good”,“bad”], v4_leq_B_19A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq19a_fortbildung,v4_con$v4_leq_b_leq19a,"v4_leq_B_19A")

##                -999 bad good <NA>     
## [1,] No. cases 716  7   38   1025 1786
## [2,] Percent   40.1 0.4 2.1  57.4 100

19B Impact (ordinal [0,1,2,3], v4_leq_B_19B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq19e_fortbildung,v4_con$v4_leq_b_leq19e,"v4_leq_B_19B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 714  6   15  14  12  1025 1786
## [2,] Percent   40   0.3 0.8 0.8 0.7 57.4 100

v4_leq_B<-data.frame(v4_leq_B_10A,v4_leq_B_10B,v4_leq_B_11A,v4_leq_B_11B,v4_leq_B_12A,
                     v4_leq_B_12B,v4_leq_B_13A,v4_leq_B_13B,v4_leq_B_14A,v4_leq_B_14B,
                     v4_leq_B_15A,v4_leq_B_15B,v4_leq_B_16A,v4_leq_B_16B,v4_leq_B_17A,
                     v4_leq_B_17B,v4_leq_B_18A,v4_leq_B_18B,v4_leq_B_19A,v4_leq_B_19B)

School

20. “Beginning or ceasing school, college, or training program”

20A Nature (dichotomous [“good”,“bad”], v4_leq_C_20A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq20a_beginn_ende,v4_con$v4_leq_c_d_leq20a,"v4_leq_C_20A")

##                -999 bad good <NA>     
## [1,] No. cases 713  2   46   1025 1786
## [2,] Percent   39.9 0.1 2.6  57.4 100

20B Impact (ordinal [0,1,2,3], v4_leq_C_20B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq20e_beginn_ende,v4_con$v4_leq_c_d_leq20e,"v4_leq_C_20B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 712  1   9   15  24  1025 1786
## [2,] Percent   39.9 0.1 0.5 0.8 1.3 57.4 100

21. “Change of school, college, or training program”

21A Nature (dichotomous [“good”,“bad”], v4_leq_C_21A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq21a_schulwechsel,v4_con$v4_leq_c_d_leq21a,"v4_leq_C_21A")

##                -999 bad good <NA>     
## [1,] No. cases 749  2   10   1025 1786
## [2,] Percent   41.9 0.1 0.6  57.4 100

21B Impact (ordinal [0,1,2,3], v4_leq_C_21B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq21e_schulwechsel,v4_con$v4_leq_c_d_leq21e,"v4_leq_C_21B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 748  1   2   2   8   1025 1786
## [2,] Percent   41.9 0.1 0.1 0.1 0.4 57.4 100

22. “Change in career goal or academic major”

A Nature (dichotomous [“good”,“bad”], v4_leq_C_22A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq22a_aend_karriere,v4_con$v4_leq_c_d_leq22a,"v4_leq_C_22A")

##                -999 bad good <NA>     
## [1,] No. cases 739  2   20   1025 1786
## [2,] Percent   41.4 0.1 1.1  57.4 100

B Impact (ordinal [0,1,2,3], v4_leq_C_22B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq22e_aend_karriere,v4_con$v4_leq_c_d_leq22e,"v4_leq_C_22B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 738  1   5   7   10  1025 1786
## [2,] Percent   41.3 0.1 0.3 0.4 0.6 57.4 100

23. “Problem in school, college, or training program”

23A Nature (dichotomous [“good”,“bad”], v4_leq_C_23A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq23a_schulprob,v4_con$v4_leq_c_d_leq23a,"v4_leq_C_23A")

##                -999 bad good <NA>     
## [1,] No. cases 747  12  2    1025 1786
## [2,] Percent   41.8 0.7 0.1  57.4 100

23B Impact (ordinal [0,1,2,3], v4_leq_C_23B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq23e_schulprob,v4_con$v4_leq_c_d_leq23e,"v4_leq_C_23B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 746  1   4   3   7   1025 1786
## [2,] Percent   41.8 0.1 0.2 0.2 0.4 57.4 100

Create dataset

v4_leq_C<-data.frame(v4_leq_C_20A,v4_leq_C_20B,v4_leq_C_21A,v4_leq_C_21B,v4_leq_C_22A,v4_leq_C_22B,v4_leq_C_23A,v4_leq_C_23B)

Residence

24. “Difficulty finding housing”

24A Nature (dichotomous [“good”,“bad”], v4_leq_D_24A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq24a_schw_wsuche,v4_con$v4_leq_c_d_leq24a,"v4_leq_D_24A")

##                -999 bad good <NA>     
## [1,] No. cases 714  39  8    1025 1786
## [2,] Percent   40   2.2 0.4  57.4 100

24B Impact (ordinal [0,1,2,3], v4_leq_D_24B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq24e_schw_wsuche,v4_con$v4_leq_c_d_leq24e,"v4_leq_D_24B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 712  4   9   15  21  1025 1786
## [2,] Percent   39.9 0.2 0.5 0.8 1.2 57.4 100

25. “Changing residence within the same town or city”

A Nature (dichotomous [“good”,“bad”], v4_leq_D_25A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq25a_umzug_nah,v4_con$v4_leq_c_d_leq25a,"v4_leq_D_25A")

##                -999 bad good <NA>     
## [1,] No. cases 709  5   47   1025 1786
## [2,] Percent   39.7 0.3 2.6  57.4 100

B Impact (ordinal [0,1,2,3], v4_leq_D_25B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq25e_umzug_nah,v4_con$v4_leq_c_d_leq25e,"v4_leq_D_25B")

##                -999 0   1   2  3   <NA>     
## [1,] No. cases 707  3   6   18 27  1025 1786
## [2,] Percent   39.6 0.2 0.3 1  1.5 57.4 100

26. “Moving to a different town, city, state, or country”

26A Nature (dichotomous [“good”,“bad”], v4_leq_D_26A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq26a_umzug_fern,v4_con$v4_leq_c_d_leq26a,"v4_leq_D_26A")

##                -999 bad good <NA>     
## [1,] No. cases 740  4   17   1025 1786
## [2,] Percent   41.4 0.2 1    57.4 100

26B Impact (ordinal [0,1,2,3], v4_leq_D_26B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq26e_umzug_fern,v4_con$v4_leq_c_d_leq26e,"v4_leq_D_26B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 739  2   2   5   13  1025 1786
## [2,] Percent   41.4 0.1 0.1 0.3 0.7 57.4 100

27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”

27A Nature (dichotomous [“good”,“bad”], v4_leq_D_27A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq27a_veraend_lu,v4_con$v4_leq_c_d_leq27a,"v4_leq_D_27A")

##                -999 bad good <NA>     
## [1,] No. cases 666  34  61   1025 1786
## [2,] Percent   37.3 1.9 3.4  57.4 100

27B Impact (ordinal [0,1,2,3], v4_leq_D_27B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq27e_veraend_lu,v4_con$v4_leq_c_d_leq27e,"v4_leq_D_27B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 663  1   19  32  46  1025 1786
## [2,] Percent   37.1 0.1 1.1 1.8 2.6 57.4 100

Create dataset

v4_leq_D<-data.frame(v4_leq_D_24A,v4_leq_D_24B,v4_leq_D_25A,v4_leq_D_25B,v4_leq_D_26A,
                     v4_leq_D_26B,v4_leq_D_27A,v4_leq_D_27B)

Love and marriage

28. “Began a new, close, personal relationship”

28A Nature (dichotomous [“good”,“bad”], v4_leq_E_28A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq28a_neue_bez,v4_con$v4_leq_e_leq28a,"v4_leq_E_28A")

##                -999 bad good <NA>     
## [1,] No. cases 708  4   49   1025 1786
## [2,] Percent   39.6 0.2 2.7  57.4 100

28B Impact (ordinal [0,1,2,3], v4_leq_E_28B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq28e_neue_bez,v4_con$v4_leq_e_leq28e,"v4_leq_E_28B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 707  3   9   14  28  1025 1786
## [2,] Percent   39.6 0.2 0.5 0.8 1.6 57.4 100

29. “Became engaged”

29A Nature (dichotomous [“good”,“bad”], v4_leq_E_29A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq29a_verlobung,v4_con$v4_leq_e_leq29a,"v4_leq_E_29A")

##                -999 bad good <NA>     
## [1,] No. cases 753  1   7    1025 1786
## [2,] Percent   42.2 0.1 0.4  57.4 100

29B Impact (ordinal [0,1,2,3], v4_leq_E_29B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq29e_verlobung,v4_con$v4_leq_e_leq29e,"v4_leq_E_29B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 752  2   3   4   1025 1786
## [2,] Percent   42.1 0.1 0.2 0.2 57.4 100

30. “Girlfriend or boyfriend problems”

30A Nature (dichotomous [“good”,“bad”], v4_leq_E_30A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq30a_prob_partner,v4_con$v4_leq_e_leq30a,"v4_leq_E_30A")

##                -999 bad good <NA>     
## [1,] No. cases 702  54  5    1025 1786
## [2,] Percent   39.3 3   0.3  57.4 100

30B Impact (ordinal [0,1,2,3], v4_leq_E_30B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq30e_prob_partner,v4_con$v4_leq_e_leq30e,"v4_leq_E_30B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 701  7   15  16  22  1025 1786
## [2,] Percent   39.2 0.4 0.8 0.9 1.2 57.4 100

31. “Breaking up with a girlfriend or breaking an engagement”

31A Nature (dichotomous [“good”,“bad”], v4_leq_E_31A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq31a_trennung,v4_con$v4_leq_e_leq31a,"v4_leq_E_31A")

##                -999 bad good <NA>     
## [1,] No. cases 730  22  9    1025 1786
## [2,] Percent   40.9 1.2 0.5  57.4 100

31B Impact (ordinal [0,1,2,3], v4_leq_E_31B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq31e_trennung,v4_con$v4_leq_e_leq31e,"v4_leq_E_31B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 729  3   6   7   16  1025 1786
## [2,] Percent   40.8 0.2 0.3 0.4 0.9 57.4 100

32. “(Male) Wife or girlfriend’s pregnancy”

32A Nature (dichotomous [“good”,“bad”], v4_leq_E_32A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq32a_schwanger_p,v4_con$v4_leq_e_leq32a,"v4_leq_E_32A")

##                -999 good <NA>     
## [1,] No. cases 758  3    1025 1786
## [2,] Percent   42.4 0.2  57.4 100

32B Impact (ordinal [0,1,2,3], v4_leq_E_32B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq32e_schwanger_p,v4_con$v4_leq_e_leq32e,"v4_leq_E_32B")

##                -999 2   3   <NA>     
## [1,] No. cases 758  2   1   1025 1786
## [2,] Percent   42.4 0.1 0.1 57.4 100

33. “(Male) Wife or girlfriend having a miscarriage or abortion”

33A Nature (dichotomous [“good”,“bad”], v4_leq_E_33A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq33a_fehlg_abtr_p,v4_con$v4_leq_e_leq33a,"v4_leq_E_33A")

##                -999 <NA>     
## [1,] No. cases 761  1025 1786
## [2,] Percent   42.6 57.4 100

33B Impact (ordinal [0,1,2,3], v4_leq_E_33B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq33e_fehlg_abtr_p,v4_con$v4_leq_e_leq33e,"v4_leq_E_33B")

##                -999 <NA>     
## [1,] No. cases 761  1025 1786
## [2,] Percent   42.6 57.4 100

34. “Getting married (or beginning to live with someone)”

34A Nature (dichotomous [“good”,“bad”], v4_leq_E_34A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq34a_heirat,v4_con$v4_leq_e_leq34a,"v4_leq_E_34A")

##                -999 good <NA>     
## [1,] No. cases 750  11   1025 1786
## [2,] Percent   42   0.6  57.4 100

34B Impact (ordinal [0,1,2,3], v4_leq_E_34B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq34e_heirat,v4_con$v4_leq_e_leq34e,"v4_leq_E_34B")

##                -999 0   2   3   <NA>     
## [1,] No. cases 749  1   6   5   1025 1786
## [2,] Percent   41.9 0.1 0.3 0.3 57.4 100

35. “A change in closeness with your partner”

35A Nature (dichotomous [“good”,“bad”], v4_leq_E_35A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq35a_veraend_naehe,v4_con$v4_leq_e_leq35a,"v4_leq_E_35A")

##                -999 bad good <NA>     
## [1,] No. cases 694  23  44   1025 1786
## [2,] Percent   38.9 1.3 2.5  57.4 100

35B Impact (ordinal [0,1,2,3], v4_leq_E_35B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq35e_veraend_naehe,v4_con$v4_leq_e_leq35e,"v4_leq_E_35B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 693  3   12  20  33  1025 1786
## [2,] Percent   38.8 0.2 0.7 1.1 1.8 57.4 100

36. “Infidelity”

36A Nature (dichotomous [“good”,“bad”], v4_leq_E_36A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq36a_untreue,v4_con$v4_leq_e_leq36a,"v4_leq_E_36A")

##                -999 bad good <NA>     
## [1,] No. cases 752  6   3    1025 1786
## [2,] Percent   42.1 0.3 0.2  57.4 100

36B Impact (ordinal [0,1,2,3], v4_leq_E_36B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq36e_untreue,v4_con$v4_leq_e_leq36e,"v4_leq_E_36B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 751  2   2   3   3   1025 1786
## [2,] Percent   42   0.1 0.1 0.2 0.2 57.4 100

37. “Trouble with in-laws”

37A Nature (dichotomous [“good”,“bad”], v4_leq_E_37A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq37a_konf_schwiege,v4_con$v4_leq_e_leq37a,"v4_leq_E_37A")

##                -999 bad good <NA>     
## [1,] No. cases 748  12  1    1025 1786
## [2,] Percent   41.9 0.7 0.1  57.4 100

37B Impact (ordinal [0,1,2,3], v4_leq_E_37B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq37e_konf_schwiege,v4_con$v4_leq_e_leq37e,"v4_leq_E_37B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 747  1   5   7   1   1025 1786
## [2,] Percent   41.8 0.1 0.3 0.4 0.1 57.4 100

38. “Separation from spouse or partner due to conflict”

38A Nature (dichotomous [“good”,“bad”], v4_leq_E_38A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq38a_trennung_str,v4_con$v4_leq_e_leq38a,"v4_leq_E_38A")

##                -999 bad good <NA>     
## [1,] No. cases 751  6   4    1025 1786
## [2,] Percent   42   0.3 0.2  57.4 100

38B Impact (ordinal [0,1,2,3], v4_leq_E_38B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq38e_trennung_str,v4_con$v4_leq_e_leq38e,"v4_leq_E_38B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 750  2   3   6   1025 1786
## [2,] Percent   42   0.1 0.2 0.3 57.4 100

39. “Separation from spouse or partner due to work, travel, etc.”

39A Nature (dichotomous [“good”,“bad”], v4_leq_E_39A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq39a_trennung_ber,v4_con$v4_leq_e_leq39a,"v4_leq_E_39A")

##                -999 bad good <NA>     
## [1,] No. cases 755  3   3    1025 1786
## [2,] Percent   42.3 0.2 0.2  57.4 100

39B Impact (ordinal [0,1,2,3], v4_leq_E_39B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq39e_trennung_ber,v4_con$v4_leq_e_leq39e,"v4_leq_E_39B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 754  1   1   1   4   1025 1786
## [2,] Percent   42.2 0.1 0.1 0.1 0.2 57.4 100

40. “Reconciliation with spouse or partner”

40A Nature (dichotomous [“good”,“bad”], v4_leq_E_40A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq40a_versoehnung,v4_con$v4_leq_e_leq40a,"v4_leq_E_40A")

##                -999 bad good <NA>     
## [1,] No. cases 745  1   15   1025 1786
## [2,] Percent   41.7 0.1 0.8  57.4 100

40B Impact (ordinal [0,1,2,3], v4_leq_E_40B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq40a_versoehnung,v4_con$v4_leq_e_leq40e,"v4_leq_E_40B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 745  1   11  4   1025 1786
## [2,] Percent   41.7 0.1 0.6 0.2 57.4 100

41. “Divorce”

41A Nature (dichotomous [“good”,“bad”], v4_leq_E_41A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq41a_scheidung,v4_con$v4_leq_e_leq41a,"v4_leq_E_41A")

##                -999 bad <NA>     
## [1,] No. cases 759  2   1025 1786
## [2,] Percent   42.5 0.1 57.4 100

41B Impact (ordinal [0,1,2,3], v4_leq_E_41B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq41e_scheidung,v4_con$v4_leq_e_leq41e,"v4_leq_E_41B")

##                -999 0   3   <NA>     
## [1,] No. cases 758  1   2   1025 1786
## [2,] Percent   42.4 0.1 0.1 57.4 100

42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”

42A Nature (dichotomous [“good”,“bad”], v4_leq_E_42A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq42a_veraend_taet,v4_con$v4_leq_e_leq42a,"v4_leq_E_42A")

##                -999 bad good <NA>     
## [1,] No. cases 729  10  22   1025 1786
## [2,] Percent   40.8 0.6 1.2  57.4 100

42B Impact (ordinal [0,1,2,3], v4_leq_E_42B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq42e_veraend_taet,v4_con$v4_leq_e_leq42e,"v4_leq_E_42B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 728  3   7   13  10  1025 1786
## [2,] Percent   40.8 0.2 0.4 0.7 0.6 57.4 100

Create dataset

v4_leq_E<-data.frame(v4_leq_E_28A,v4_leq_E_28B,v4_leq_E_29A,v4_leq_E_29B,v4_leq_E_30A,
                     v4_leq_E_30B,v4_leq_E_31A,v4_leq_E_31B,v4_leq_E_32A,v4_leq_E_32B,
                     v4_leq_E_33A,v4_leq_E_33B,v4_leq_E_34A,v4_leq_E_34B,v4_leq_E_35A,
                     v4_leq_E_35B,v4_leq_E_36A,v4_leq_E_36B,v4_leq_E_37A,v4_leq_E_37B,
                     v4_leq_E_38A,v4_leq_E_38B,v4_leq_E_39A,v4_leq_E_39B,v4_leq_E_40A,
                     v4_leq_E_40B,v4_leq_E_41A,v4_leq_E_41B,v4_leq_E_42A,v4_leq_E_42B)

Family and close friends

43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”

43A Nature (dichotomous [“good”,“bad”], v4_leq_F_43A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq43a_neu_fmitglied,v4_con$v4_leq_f_g_leq43a,"v4_leq_F_43A")

##                -999 bad good <NA>     
## [1,] No. cases 723  2   36   1025 1786
## [2,] Percent   40.5 0.1 2    57.4 100

43B Impact (ordinal [0,1,2,3], v4_leq_F_43B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq43e_neu_fmitglied,v4_con$v4_leq_f_g_leq43e,"v4_leq_F_43B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 722  3   11  2   23  1025 1786
## [2,] Percent   40.4 0.2 0.6 0.1 1.3 57.4 100

44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”

44A Nature (dichotomous [“good”,“bad”], v4_leq_F_44A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq44a_auszug_fm,v4_con$v4_leq_f_g_leq44a,"v4_leq_F_44A")

##                -999 bad good <NA>     
## [1,] No. cases 750  5   6    1025 1786
## [2,] Percent   42   0.3 0.3  57.4 100

44B Impact (ordinal [0,1,2,3], v4_leq_F_44B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq44e_auszug_fm,v4_con$v4_leq_f_g_leq44e,"v4_leq_F_44B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 748  2   3   4   4   1025 1786
## [2,] Percent   41.9 0.1 0.2 0.2 0.2 57.4 100

45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”

45A Nature (dichotomous [“good”,“bad”], v4_leq_F_45A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq45a_gz_verh_fm,v4_con$v4_leq_f_g_leq45a,"v4_leq_F_45A")

##                -999 bad good <NA>     
## [1,] No. cases 652  98  11   1025 1786
## [2,] Percent   36.5 5.5 0.6  57.4 100

45B Impact (ordinal [0,1,2,3], v4_leq_F_45B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq45e_gz_verh_fm,v4_con$v4_leq_f_g_leq45e,"v4_leq_F_45B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 649  7   26  34  45  1025 1786
## [2,] Percent   36.3 0.4 1.5 1.9 2.5 57.4 100

46. “Death of spouse or partner”

46A Nature (dichotomous [“good”,“bad”], v4_leq_F_46A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq46a_tod_partner,v4_con$v4_leq_f_g_leq46a,"v4_leq_F_46A")

##                -999 bad good <NA>     
## [1,] No. cases 759  1   1    1025 1786
## [2,] Percent   42.5 0.1 0.1  57.4 100

46B Impact (ordinal [0,1,2,3], v4_leq_F_46B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq46e_tod_partner,v4_con$v4_leq_f_g_leq46e,"v4_leq_F_46B")

##                -999 0   1   2   <NA>     
## [1,] No. cases 758  1   1   1   1025 1786
## [2,] Percent   42.4 0.1 0.1 0.1 57.4 100

47. “Death of a child”

47A Nature (dichotomous [“good”,“bad”], v4_leq_F_47A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq47a_tod_kind,v4_con$v4_leq_f_g_leq47a,"v4_leq_F_47A")

##                -999 <NA>     
## [1,] No. cases 761  1025 1786
## [2,] Percent   42.6 57.4 100

47B Impact (ordinal [0,1,2,3], v4_leq_F_47B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq47e_tod_kind,v4_con$v4_leq_f_g_leq47e,"v4_leq_F_47B")

##                -999 0   <NA>     
## [1,] No. cases 760  1   1025 1786
## [2,] Percent   42.6 0.1 57.4 100

48. “Death of family member or close friend”

48A Nature (dichotomous [“good”,“bad”], v4_leq_F_48A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq48a_tod_fm_ef,v4_con$v4_leq_f_g_leq48a,"v4_leq_F_48A")

##                -999 bad good <NA>     
## [1,] No. cases 696  61  4    1025 1786
## [2,] Percent   39   3.4 0.2  57.4 100

48B Impact (ordinal [0,1,2,3], v4_leq_F_48B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq48e_tod_fm_ef,v4_con$v4_leq_f_g_leq48e,"v4_leq_F_48B")

##                -999 0   1  2  3   <NA>     
## [1,] No. cases 695  6   18 17 25  1025 1786
## [2,] Percent   38.9 0.3 1  1  1.4 57.4 100

49. “Birth of a grandchild”

49A Nature (dichotomous [“good”,“bad”], v4_leq_F_49A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq49a_geb_enkel,v4_con$v4_leq_f_g_leq49a,"v4_leq_F_49A")

##                -999 good <NA>     
## [1,] No. cases 745  16   1025 1786
## [2,] Percent   41.7 0.9  57.4 100

49B Impact (ordinal [0,1,2,3], v4_leq_F_49B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq49e_geb_enkel,v4_con$v4_leq_f_g_leq49e,"v4_leq_F_49B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 744  4   2   4   7   1025 1786
## [2,] Percent   41.7 0.2 0.1 0.2 0.4 57.4 100

50. “Change in marital status of your parents”

50A Nature (dichotomous [“good”,“bad”], v4_leq_F_50A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq50a_fstand_eltern,v4_con$v4_leq_f_g_leq50a,"v4_leq_F_50A")

##                -999 bad good <NA>     
## [1,] No. cases 749  7   5    1025 1786
## [2,] Percent   41.9 0.4 0.3  57.4 100

50B Impact (ordinal [0,1,2,3], v4_leq_F_50B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq50e_fstand_eltern,v4_con$v4_leq_f_g_leq50e,"v4_leq_F_50B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 748  3   6   3   1   1025 1786
## [2,] Percent   41.9 0.2 0.3 0.2 0.1 57.4 100

Create dataset

v4_leq_F<-data.frame(v4_leq_F_43A,v4_leq_F_43B,v4_leq_F_44A,v4_leq_F_44B,v4_leq_F_45A,
                     v4_leq_F_45B,v4_leq_F_46A,v4_leq_F_46B,v4_leq_F_47A,v4_leq_F_47B,
                     v4_leq_F_48A,v4_leq_F_48B,v4_leq_F_49A,v4_leq_F_49B,v4_leq_F_50A,
                     v4_leq_F_50B)

Parenting

51. “Change in child care arrangements”

51A Nature (dichotomous [“good”,“bad”], v4_leq_G_51A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq51a_kindbetr,v4_con$v4_leq_f_g_leq51a,"v4_leq_G_51A")

##                -999 bad good <NA>     
## [1,] No. cases 749  4   8    1025 1786
## [2,] Percent   41.9 0.2 0.4  57.4 100

51B Impact (ordinal [0,1,2,3], v4_leq_G_51B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq51e_kindbetr,v4_con$v4_leq_f_g_leq51e,"v4_leq_G_51B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 748  3   4   4   2   1025 1786
## [2,] Percent   41.9 0.2 0.2 0.2 0.1 57.4 100

52. “Conflicts with spouse or partner about parenting”

52A Nature (dichotomous [“good”,“bad”], v4_leq_G_52A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq52a_konf_eschaft,v4_con$v4_leq_f_g_leq52a,"v4_leq_G_52A")

##                -999 bad good <NA>     
## [1,] No. cases 742  16  3    1025 1786
## [2,] Percent   41.5 0.9 0.2  57.4 100

52B Impact (ordinal [0,1,2,3], v4_leq_G_52B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq52e_konf_eschaft,v4_con$v4_leq_f_g_leq52e,"v4_leq_G_52B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 741  4   6   6   4   1025 1786
## [2,] Percent   41.5 0.2 0.3 0.3 0.2 57.4 100

53. “Conflicts with child’s grandparents (or other important person) about parenting”

53A Nature (dichotomous [“good”,“bad”], v4_leq_G_53A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq53a_konf_geltern,v4_con$v4_leq_f_g_leq53a,"v4_leq_G_53A")

##                -999 bad <NA>     
## [1,] No. cases 756  5   1025 1786
## [2,] Percent   42.3 0.3 57.4 100

53B Impact (ordinal [0,1,2,3], v4_leq_G_53B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq53e_konf_geltern,v4_con$v4_leq_f_g_leq53e,"v4_leq_G_53B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 755  1   3   2   1025 1786
## [2,] Percent   42.3 0.1 0.2 0.1 57.4 100

54. “Taking on full responsibility for parenting as a single parent”

54A Nature (dichotomous [“good”,“bad”], v4_leq_G_54A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq54a_alleinerz,v4_con$v4_leq_f_g_leq54a,"v4_leq_G_54A")

##                -999 bad good <NA>     
## [1,] No. cases 758  1   2    1025 1786
## [2,] Percent   42.4 0.1 0.1  57.4 100

54B Impact (ordinal [0,1,2,3], v4_leq_G_54B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq54e_alleinerz,v4_con$v4_leq_f_g_leq54e,"v4_leq_G_54B")

##                -999 0   1   3   <NA>     
## [1,] No. cases 757  1   2   1   1025 1786
## [2,] Percent   42.4 0.1 0.1 0.1 57.4 100

55. “Custody battles with former spouse or partner”

55A Nature (dichotomous [“good”,“bad”], v4_leq_G_55A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq55a_sorgerecht,v4_con$v4_leq_f_g_leq55a,"v4_leq_G_55A")

##                -999 bad good <NA>     
## [1,] No. cases 754  6   1    1025 1786
## [2,] Percent   42.2 0.3 0.1  57.4 100

55B Impact (ordinal [0,1,2,3], v4_leq_G_55B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq55e_sorgerecht,v4_con$v4_leq_f_g_leq55e,"v4_leq_G_55B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 753  2   1   4   1   1025 1786
## [2,] Percent   42.2 0.1 0.1 0.2 0.1 57.4 100

Create dataset

v4_leq_G<-data.frame(v4_leq_G_51A,v4_leq_G_51B,v4_leq_G_52A,v4_leq_G_52B,v4_leq_G_53A,
                     v4_leq_G_53B,v4_leq_G_54A,v4_leq_G_54B,v4_leq_G_55A,v4_leq_G_55B)

Personal or social

56. “Major personal achievement”

56A Nature (dichotomous [“good”,“bad”], v4_leq_H_56A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq56a_pers_leistung,v4_con$v4_leq_h_leq56a,"v4_leq_H_56A")

##                -999 bad good <NA>     
## [1,] No. cases 619  12  130  1025 1786
## [2,] Percent   34.7 0.7 7.3  57.4 100

56B Impact (ordinal [0,1,2,3], v4_leq_H_56B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq56e_pers_leistung,v4_con$v4_leq_h_leq56e,"v4_leq_H_56B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 617  13  33  52  46  1025 1786
## [2,] Percent   34.5 0.7 1.8 2.9 2.6 57.4 100

57. “Major decision regarding your immediate future”

57A Nature (dichotomous [“good”,“bad”], v4_leq_H_57A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq57a_wicht_entsch,v4_con$v4_leq_h_leq57a,"v4_leq_H_57A")

##                -999 bad good <NA>     
## [1,] No. cases 574  22  165  1025 1786
## [2,] Percent   32.1 1.2 9.2  57.4 100

57B Impact (ordinal [0,1,2,3], v4_leq_H_57B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq57e_wicht_entsch,v4_con$v4_leq_h_leq57e,"v4_leq_H_57B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 571  8   23  83  76  1025 1786
## [2,] Percent   32   0.4 1.3 4.6 4.3 57.4 100

58. “Change in your personal habits (your dress, life-style, hobbies, etc.)”

58A Nature (dichotomous [“good”,“bad”], v4_leq_H_58A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq58a_pers_gewohn,v4_con$v4_leq_h_leq58a,"v4_leq_H_58A")

##                -999 bad good <NA>     
## [1,] No. cases 637  23  101  1025 1786
## [2,] Percent   35.7 1.3 5.7  57.4 100

58B Impact (ordinal [0,1,2,3], v4_leq_H_58B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq58e_pers_gewohn,v4_con$v4_leq_h_leq58e,"v4_leq_H_58B")

##                -999 0   1  2   3   <NA>     
## [1,] No. cases 634  8   35 51  33  1025 1786
## [2,] Percent   35.5 0.4 2  2.9 1.8 57.4 100

59. “Change in your religious beliefs”

59A Nature (dichotomous [“good”,“bad”], v4_leq_H_59A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq59a_relig_ueberz,v4_con$v4_leq_h_leq59a,"v4_leq_H_59A")

##                -999 bad good <NA>     
## [1,] No. cases 734  1   26   1025 1786
## [2,] Percent   41.1 0.1 1.5  57.4 100

59B Impact (ordinal [0,1,2,3], v4_leq_H_59B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq59e_relig_ueberz,v4_con$v4_leq_h_leq59e,"v4_leq_H_59B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 731  13  3   9   5   1025 1786
## [2,] Percent   40.9 0.7 0.2 0.5 0.3 57.4 100

60. “Change in your political beliefs”

60A Nature (dichotomous [“good”,“bad”], v4_leq_H_60A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq60a_pol_ansichten,v4_clin$v4_leq_h_leq60a,"v4_leq_H_60A")

## Warning in (is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con != 2) & is.na(leq_con_old_name) == : Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes

## Warning in (is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con != 2) & is.na(leq_con_old_name): Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes

##                -999 bad good <NA>     
## [1,] No. cases 728  9   24   1025 1786
## [2,] Percent   40.8 0.5 1.3  57.4 100

60B Impact (ordinal [0,1,2,3], v4_leq_H_60B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq60e_pol_ansichten,v4_con$v4_leq_h_leq60e,"v4_leq_H_60B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 717  13  14  10  7   1025 1786
## [2,] Percent   40.1 0.7 0.8 0.6 0.4 57.4 100

61. “Loss or damage of personal property”

61A Nature (dichotomous [“good”,“bad”], v4_leq_H_61A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq61a_pers_eigent,v4_con$v4_leq_h_leq61a,"v4_leq_H_61A")

##                -999 bad good <NA>     
## [1,] No. cases 712  45  4    1025 1786
## [2,] Percent   39.9 2.5 0.2  57.4 100

61B Impact (ordinal [0,1,2,3], v4_leq_H_61B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq61e_pers_eigent,v4_con$v4_leq_h_leq61e,"v4_leq_H_61B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 710  10  19  13  9   1025 1786
## [2,] Percent   39.8 0.6 1.1 0.7 0.5 57.4 100

62. “Took a vacation”

62A Nature (dichotomous [“good”,“bad”], v4_leq_H_62A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq62a_erholungsurl,v4_con$v4_leq_h_leq62a,"v4_leq_H_62A")

##                -999 bad good <NA>     
## [1,] No. cases 553  6   202  1025 1786
## [2,] Percent   31   0.3 11.3 57.4 100

62B Impact (ordinal [0,1,2,3], v4_leq_H_62B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq62e_erholungsurl,v4_con$v4_leq_h_leq62e,"v4_leq_H_62B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 549  16  52  83  61  1025 1786
## [2,] Percent   30.7 0.9 2.9 4.6 3.4 57.4 100

63. “Took a trip other than a vacation”

63A Nature (dichotomous [“good”,“bad”], v4_leq_H_63A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq63a_reise_andere,v4_con$v4_leq_h_leq63a,"v4_leq_H_63A")

##                -999 bad good <NA>     
## [1,] No. cases 669  3   89   1025 1786
## [2,] Percent   37.5 0.2 5    57.4 100

63B Impact (ordinal [0,1,2,3], v4_leq_H_63B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq63e_reise_andere,v4_con$v4_leq_h_leq63e,"v4_leq_H_63B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 668  7   27  34  25  1025 1786
## [2,] Percent   37.4 0.4 1.5 1.9 1.4 57.4 100

64. “Change in family get-togethers”

64A Nature (dichotomous [“good”,“bad”], v4_leq_H_64A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq64a_familientreff,v4_con$v4_leq_h_leq64a,"v4_leq_H_64A")

##                -999 bad good <NA>     
## [1,] No. cases 697  12  52   1025 1786
## [2,] Percent   39   0.7 2.9  57.4 100

64B Impact (ordinal [0,1,2,3], v4_leq_H_64B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq64e_familientreff,v4_con$v4_leq_h_leq64e,"v4_leq_H_64B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 696  5   20  22  18 1025 1786
## [2,] Percent   39   0.3 1.1 1.2 1  57.4 100

65. “Change in your social activities (clubs, movies, visiting)”

65A Nature (dichotomous [“good”,“bad”], v4_leq_H_65A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq65a_ges_unternehm,v4_con$v4_leq_h_leq65a,"v4_leq_H_65A")

##                -999 bad good <NA>     
## [1,] No. cases 677  21  63   1025 1786
## [2,] Percent   37.9 1.2 3.5  57.4 100

65B Impact (ordinal [0,1,2,3], v4_leq_H_65B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq65e_ges_unternehm,v4_con$v4_leq_h_leq65e,"v4_leq_H_65B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 675  5   24  42  15  1025 1786
## [2,] Percent   37.8 0.3 1.3 2.4 0.8 57.4 100

66. “Made new friends”

66A Nature (dichotomous [“good”,“bad”], v4_leq_H_66A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq66a_neue_freunds,v4_con$v4_leq_h_leq66a,"v4_leq_H_66A")

##                -999 bad good <NA>     
## [1,] No. cases 590  3   168  1025 1786
## [2,] Percent   33   0.2 9.4  57.4 100

66B Impact (ordinal [0,1,2,3], v4_leq_H_66B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq66e_neue_freunds,v4_con$v4_leq_h_leq66e,"v4_leq_H_66B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 588  9   52  75  37  1025 1786
## [2,] Percent   32.9 0.5 2.9 4.2 2.1 57.4 100

67. “Broke up with a friend”

67A Nature (dichotomous [“good”,“bad”], v4_leq_H_67A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq67a_ende_freunds,v4_con$v4_leq_h_leq67a,"v4_leq_H_67A")

##                -999 bad good <NA>     
## [1,] No. cases 688  45  28   1025 1786
## [2,] Percent   38.5 2.5 1.6  57.4 100

67B Impact (ordinal [0,1,2,3], v4_leq_H_67B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq67e_ende_freunds,v4_con$v4_leq_h_leq67e,"v4_leq_H_67B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 687  6   23  23  22  1025 1786
## [2,] Percent   38.5 0.3 1.3 1.3 1.2 57.4 100

68. “Acquired or lost a pet”

68A Nature (dichotomous [“good”,“bad”], v4_leq_H_68A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq68a_haustier,v4_con$v4_leq_h_leq68a,"v4_leq_H_68A")

##                -999 bad good <NA>     
## [1,] No. cases 722  15  24   1025 1786
## [2,] Percent   40.4 0.8 1.3  57.4 100

68B Impact (ordinal [0,1,2,3], v4_leq_H_68B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq68e_haustier,v4_con$v4_leq_h_leq68e,"v4_leq_H_68B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 720  3   5   11  22  1025 1786
## [2,] Percent   40.3 0.2 0.3 0.6 1.2 57.4 100

Create dataset

v4_leq_H<-data.frame(v4_leq_H_56A,v4_leq_H_56B,v4_leq_H_57A,v4_leq_H_57B,v4_leq_H_58A,
                     v4_leq_H_58B,v4_leq_H_59A,v4_leq_H_59B,v4_leq_H_60A,v4_leq_H_60B,
                     v4_leq_H_61A,v4_leq_H_61B,v4_leq_H_62A,v4_leq_H_62B,v4_leq_H_63A,
                     v4_leq_H_63B,v4_leq_H_64A,v4_leq_H_64B,v4_leq_H_65A,v4_leq_H_65B,
                     v4_leq_H_66A,v4_leq_H_66B,v4_leq_H_67A,v4_leq_H_67B,v4_leq_H_68A,
                     v4_leq_H_68B)

Financial

69. “Major change in finances (increased or decreased income)”

69A Nature (dichotomous [“good”,“bad”], v4_leq_I_69A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq69a_finanz_sit,v4_con$v4_leq_i_j_k_leq69a,"v4_leq_I_69A")

##                -999 bad good <NA>     
## [1,] No. cases 576  90  95   1025 1786
## [2,] Percent   32.3 5   5.3  57.4 100

69B Impact (ordinal [0,1,2,3], v4_leq_I_69B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq69e_finanz_sit,v4_con$v4_leq_i_j_k_leq69e,"v4_leq_I_69B")

##                -999 0   1   2   3  <NA>     
## [1,] No. cases 574  6   41  68  72 1025 1786
## [2,] Percent   32.1 0.3 2.3 3.8 4  57.4 100

70. “Took on a moderate purchase, such as TV, car, freezer, etc.”

70A Nature (dichotomous [“good”,“bad”], v4_leq_I_70A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq70a_finanz_verpfl,v4_con$v4_leq_i_j_k_leq70a,"v4_leq_I_70A")

##                -999 bad good <NA>     
## [1,] No. cases 712  20  29   1025 1786
## [2,] Percent   39.9 1.1 1.6  57.4 100

70B Impact (ordinal [0,1,2,3], v4_leq_I_70B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq70e_finanz_verpfl,v4_con$v4_leq_i_j_k_leq70e,"v4_leq_I_70B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 710  9   21  15  6   1025 1786
## [2,] Percent   39.8 0.5 1.2 0.8 0.3 57.4 100

71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”

71A Nature (dichotomous [“good”,“bad”], v4_leq_I_71A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq71a_hypothek,v4_con$v4_leq_i_j_k_leq71a,"v4_leq_I_71A")

##                -999 bad good <NA>     
## [1,] No. cases 747  6   8    1025 1786
## [2,] Percent   41.8 0.3 0.4  57.4 100

71B Impact (ordinal [0,1,2,3], v4_leq_I_71B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq71e_hypothek,v4_con$v4_leq_i_j_k_leq71e,"v4_leq_I_71B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 745  2   4   4   6   1025 1786
## [2,] Percent   41.7 0.1 0.2 0.2 0.3 57.4 100

72. “Experienced a foreclosure on a mortgage or loan”

72A Nature (dichotomous [“good”,“bad”], v4_leq_I_72A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq72a_hypoth_kuend,v4_con$v4_leq_i_j_k_leq72a,"v4_leq_I_72A")

##                -999 bad good <NA>     
## [1,] No. cases 752  4   5    1025 1786
## [2,] Percent   42.1 0.2 0.3  57.4 100

72B Impact (ordinal [0,1,2,3], v4_leq_I_72B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq72e_hypoth_kuend,v4_con$v4_leq_i_j_k_leq72e,"v4_leq_I_72B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 751  2   1   4   3   1025 1786
## [2,] Percent   42   0.1 0.1 0.2 0.2 57.4 100

73. “Credit rating difficulties”

73A Nature (dichotomous [“good”,“bad”], v4_leq_I_73A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq73a_kreditwuerdk,v4_con$v4_leq_i_j_k_leq73a,"v4_leq_I_73A")

##                -999 bad good <NA>     
## [1,] No. cases 739  20  2    1025 1786
## [2,] Percent   41.4 1.1 0.1  57.4 100

73B Impact (ordinal [0,1,2,3], v4_leq_I_73B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq73e_kreditwuerdk,v4_con$v4_leq_i_j_k_leq73e,"v4_leq_I_73B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 738  2   10  5   6   1025 1786
## [2,] Percent   41.3 0.1 0.6 0.3 0.3 57.4 100

Create dataset

v4_leq_I<-data.frame(v4_leq_I_69A,v4_leq_I_69B,v4_leq_I_70A,v4_leq_I_70B,v4_leq_I_71A,
                     v4_leq_I_71B,v4_leq_I_72A,v4_leq_I_72B,v4_leq_I_73A,v4_leq_I_73B)

Crime and legal matters

74. “Being robbed or victim of identity theft”

74A Nature (dichotomous [“good”,“bad”], v4_leq_J_74A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq74a_opf_diebstahl,v4_con$v4_leq_i_j_k_leq74a,"v4_leq_J_74A")

##                -999 bad <NA>     
## [1,] No. cases 736  25  1025 1786
## [2,] Percent   41.2 1.4 57.4 100

74B Impact (ordinal [0,1,2,3], v4_leq_J_74B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq74e_opf_diebstahl,v4_con$v4_leq_i_j_k_leq74e,"v4_leq_J_74B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 735  5   8   8   5   1025 1786
## [2,] Percent   41.2 0.3 0.4 0.4 0.3 57.4 100

75. “Being a victim of a violent act (rape, assault, etc.)”

75A Nature (dichotomous [“good”,“bad”], v4_leq_J_75A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq75a_opf_gewalttat,v4_con$v4_leq_i_j_k_leq75a,"v4_leq_J_75A")

##                -999 bad <NA>     
## [1,] No. cases 753  8   1025 1786
## [2,] Percent   42.2 0.4 57.4 100

75B Impact (ordinal [0,1,2,3], v4_leq_J_75B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq75e_opf_gewalttat,v4_con$v4_leq_i_j_k_leq75e,"v4_leq_J_75B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 752  4   1   2   2   1025 1786
## [2,] Percent   42.1 0.2 0.1 0.1 0.1 57.4 100

76. “Involved in an accident”

76A Nature (dichotomous [“good”,“bad”], v4_leq_J_76A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq76a_unfall,v4_con$v4_leq_i_j_k_leq76a,"v4_leq_J_76A")

##                -999 bad good <NA>     
## [1,] No. cases 740  19  2    1025 1786
## [2,] Percent   41.4 1.1 0.1  57.4 100

76B Impact (ordinal [0,1,2,3], v4_leq_J_76B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq76e_unfall,v4_con$v4_leq_i_j_k_leq76e,"v4_leq_J_76B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 738  13  4   4   2   1025 1786
## [2,] Percent   41.3 0.7 0.2 0.2 0.1 57.4 100

77. “Involved in a law suit”

77A Nature (dichotomous [“good”,“bad”], v4_leq_J_77A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq77a_rechtsstreit,v4_con$v4_leq_i_j_k_leq77a,"v4_leq_J_77A")

##                -999 bad good <NA>     
## [1,] No. cases 725  27  9    1025 1786
## [2,] Percent   40.6 1.5 0.5  57.4 100

77B Impact (ordinal [0,1,2,3], v4_leq_J_77B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq77e_rechtsstreit,v4_con$v4_leq_i_j_k_leq77e,"v4_leq_J_77B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 723  6   14  9   9   1025 1786
## [2,] Percent   40.5 0.3 0.8 0.5 0.5 57.4 100

78. “Involved in a minor violation of the law (traffic tickets, disturbing the peace, etc)”

78A Nature (dichotomous [“good”,“bad”], v4_leq_J_78A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq78a_owi,v4_con$v4_leq_i_j_k_leq78a,"v4_leq_J_78A")

##                -999 bad good <NA>     
## [1,] No. cases 732  27  2    1025 1786
## [2,] Percent   41   1.5 0.1  57.4 100

78B Impact (ordinal [0,1,2,3], v4_leq_J_78B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq78e_owi,v4_con$v4_leq_i_j_k_leq78e,"v4_leq_J_78B")

##                -999 0   1   2   3   <NA>     
## [1,] No. cases 731  7   15  7   1   1025 1786
## [2,] Percent   40.9 0.4 0.8 0.4 0.1 57.4 100

79. “Legal troubles resulting in your being arrested or held in jail”

79A Nature (dichotomous [“good”,“bad”], v4_leq_J_79A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq79a_konf_gesetz,v4_con$v4_leq_i_j_k_leq79a,"v4_leq_J_79A")

##                -999 bad <NA>     
## [1,] No. cases 758  3   1025 1786
## [2,] Percent   42.4 0.2 57.4 100

79B Impact (ordinal [0,1,2,3], v4_leq_J_79B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq79e_konf_gesetz,v4_con$v4_leq_i_j_k_leq79e,"v4_leq_J_79B")

##                -999 0   1   <NA>     
## [1,] No. cases 757  3   1   1025 1786
## [2,] Percent   42.4 0.2 0.1 57.4 100

Create dataset

v4_leq_J<-data.frame(v4_leq_J_74A,v4_leq_J_74B,v4_leq_J_75A,v4_leq_J_75B,v4_leq_J_76A,
                     v4_leq_J_76B,v4_leq_J_77A,v4_leq_J_77B,v4_leq_J_78A,v4_leq_J_78B,
                     v4_leq_J_79A,v4_leq_J_79B)

Create LEQ dataset

v4_leq<-data.frame(v4_leq_A,v4_leq_B,v4_leq_C,v4_leq_D,v4_leq_E,v4_leq_F,v4_leq_G,
                   v4_leq_H,v4_leq_I,v4_leq_J)

WHOQOL-BREF

For explanation, please refer to the section in Visit 1

1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v4_whoqol_itm1)

v4_quol_recode(v4_clin$v4_whoqol_bref_who1_lebensqualitaet,v4_con$v4_whoqol_bref_who1,"v4_whoqol_itm1",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 11  52  202  363  174 984  1786
## [2,] Percent   0.6 2.9 11.3 20.3 9.7 55.1 100

2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v4_whoqol_itm2)”

v4_quol_recode(v4_clin$v4_whoqol_bref_who2_gesundheit,v4_con$v4_whoqol_bref_who2,"v4_whoqol_itm2",0)

##                1   2   3   4    5   NA's     
## [1,] No. cases 30  145 168 329  130 984  1786
## [2,] Percent   1.7 8.1 9.4 18.4 7.3 55.1 100

3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v4_whoqol_itm3)

Coding reversed so that higher scores mean less impairment by pain.

v4_quol_recode(v4_clin$v4_whoqol_bref_who3_schmerzen,v4_con$v4_whoqol_bref_who3,"v4_whoqol_itm3",1)

##                1   2   3   4   5    NA's     
## [1,] No. cases 9   44  76  157 511  989  1786
## [2,] Percent   0.5 2.5 4.3 8.8 28.6 55.4 100

4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v4_whoqol_itm4)”

Coding reversed so that higher scores mean less dependence on medical treatment.

v4_quol_recode(v4_clin$v4_whoqol_bref_who4_med_behand,v4_con$v4_whoqol_bref_who4,"v4_whoqol_itm4",1)

##                1   2   3   4   5    NA's     
## [1,] No. cases 73  143 109 166 302  993  1786
## [2,] Percent   4.1 8   6.1 9.3 16.9 55.6 100

5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v4_whoqol_itm5)

v4_quol_recode(v4_clin$v4_whoqol_bref_who5_lebensgenuss,v4_con$v4_whoqol_bref_who5,"v4_whoqol_itm5",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 21  84  206  345  136 994  1786
## [2,] Percent   1.2 4.7 11.5 19.3 7.6 55.7 100

6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v4_whoqol_itm6)

v4_quol_recode(v4_clin$v4_whoqol_bref_who6_lebenssinn,v4_con$v4_whoqol_bref_who6,"v4_whoqol_itm6",0)

##                1   2  3   4   5    NA's     
## [1,] No. cases 31  72 167 303 212  1001 1786
## [2,] Percent   1.7 4  9.4 17  11.9 56   100

7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v4_whoqol_itm7)

v4_quol_recode(v4_clin$v4_whoqol_bref_who7_konzentration,v4_con$v4_whoqol_bref_who7,"v4_whoqol_itm7",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 13  103 273  330  78  989  1786
## [2,] Percent   0.7 5.8 15.3 18.5 4.4 55.4 100

8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v4_whoqol_itm8)

v4_quol_recode(v4_clin$v4_whoqol_bref_who8_sicherheit,v4_con$v4_whoqol_bref_who8,"v4_whoqol_itm8",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 13  47  173 378  185  990  1786
## [2,] Percent   0.7 2.6 9.7 21.2 10.4 55.4 100

9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v4_whoqol_itm9)

v4_quol_recode(v4_clin$v4_whoqol_bref_who9_umweltbed,v4_con$v4_whoqol_bref_who9,"v4_whoqol_itm9",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 11  24  153 402  206  990  1786
## [2,] Percent   0.6 1.3 8.6 22.5 11.5 55.4 100

10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v4_whoqol_itm10)

v4_quol_recode(v4_clin$v4_whoqol_bref_who10_energie,v4_con$v4_whoqol_bref_who10,"v4_whoqol_itm10",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 19  55  188  313  224  987  1786
## [2,] Percent   1.1 3.1 10.5 17.5 12.5 55.3 100

11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v4_whoqol_itm11)

v4_quol_recode(v4_clin$v4_whoqol_bref_who11_aussehen,v4_con$v4_whoqol_bref_who11,"v4_whoqol_itm11",0)

##                1   2  3   4    5   NA's     
## [1,] No. cases 19  54 176 333  214 990  1786
## [2,] Percent   1.1 3  9.9 18.6 12  55.4 100

12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v4_whoqol_itm12)

v4_quol_recode(v4_clin$v4_whoqol_bref_who12_genug_geld,v4_con$v4_whoqol_bref_who12,"v4_whoqol_itm12",0)

##                1   2   3    4    5    NA's     
## [1,] No. cases 21  102 180  288  206  989  1786
## [2,] Percent   1.2 5.7 10.1 16.1 11.5 55.4 100

13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v4_whoqol_itm13)

v4_quol_recode(v4_clin$v4_whoqol_bref_who13_infozugang,v4_con$v4_whoqol_bref_who13,"v4_whoqol_itm13",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 4   16  78  281  418  989  1786
## [2,] Percent   0.2 0.9 4.4 15.7 23.4 55.4 100

14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v4_whoqol_itm14)

v4_quol_recode(v4_clin$v4_whoqol_bref_who14_freizeitaktiv,v4_con$v4_whoqol_bref_who14,"v4_whoqol_itm14",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 7   46  175 281  289  988  1786
## [2,] Percent   0.4 2.6 9.8 15.7 16.2 55.3 100

15. “How well are you able to get around? (ordinal [1,2,3,4,5], v4_whoqol_itm15)”

v4_quol_recode(v4_clin$v4_whoqol_bref_who15_fortbewegung,v4_con$v4_whoqol_bref_who15,"v4_whoqol_itm15",0)

##                1   2  3   4    5    NA's     
## [1,] No. cases 3   36 119 275  365  988  1786
## [2,] Percent   0.2 2  6.7 15.4 20.4 55.3 100

16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v4_whoqol_itm16)

v4_quol_recode(v4_clin$v4_whoqol_bref_who16_schlaf,v4_con$v4_whoqol_bref_who16,"v4_whoqol_itm16",0)

##                1   2   3   4    5   NA's     
## [1,] No. cases 32  120 121 383  148 982  1786
## [2,] Percent   1.8 6.7 6.8 21.4 8.3 55   100

17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v4_whoqol_itm17)

v4_quol_recode(v4_clin$v4_whoqol_bref_who17_alltag,v4_con$v4_whoqol_bref_who17,"v4_whoqol_itm17",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 20  99  135 338  210  984  1786
## [2,] Percent   1.1 5.5 7.6 18.9 11.8 55.1 100

18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v4_whoqol_itm18)

v4_quol_recode(v4_clin$v4_whoqol_bref_who18_arbeitsfhgk,v4_con$v4_whoqol_bref_who18,"v4_whoqol_itm18",0)

##                1  2   3   4    5   NA's     
## [1,] No. cases 54 128 152 302  165 985  1786
## [2,] Percent   3  7.2 8.5 16.9 9.2 55.2 100

19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v4_whoqol_itm19)

v4_quol_recode(v4_clin$v4_whoqol_bref_who19_selbstzufried,v4_con$v4_whoqol_bref_who19,"v4_whoqol_itm19",0)

##                1   2   3   4    5   NA's     
## [1,] No. cases 28  95  169 382  129 983  1786
## [2,] Percent   1.6 5.3 9.5 21.4 7.2 55   100

20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v4_whoqol_itm20)

v4_quol_recode(v4_clin$v4_whoqol_bref_who20_pers_bezieh,v4_con$v4_whoqol_bref_who20,"v4_whoqol_itm20",0)

##                1  2   3   4    5    NA's     
## [1,] No. cases 17 75  153 367  184  990  1786
## [2,] Percent   1  4.2 8.6 20.5 10.3 55.4 100

21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v4_whoqol_itm21)

v4_quol_recode(v4_clin$v4_whoqol_bref_who21_sexualleben,v4_con$v4_whoqol_bref_who21,"v4_whoqol_itm21",0)

##                1   2   3    4    5   NA's     
## [1,] No. cases 78  110 229  243  130 996  1786
## [2,] Percent   4.4 6.2 12.8 13.6 7.3 55.8 100

22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v4_whoqol_itm22)

v4_quol_recode(v4_clin$v4_whoqol_bref_who22_freunde,v4_con$v4_whoqol_bref_who22,"v4_whoqol_itm22",0)

##                1  2   3   4    5    NA's     
## [1,] No. cases 17 49  170 346  220  984  1786
## [2,] Percent   1  2.7 9.5 19.4 12.3 55.1 100

23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v4_whoqol_itm23)

v4_quol_recode(v4_clin$v4_whoqol_bref_who23_wohnbeding,v4_con$v4_whoqol_bref_who23,"v4_whoqol_itm23",0)

##                1  2   3   4   5    NA's     
## [1,] No. cases 18 59  125 340 261  983  1786
## [2,] Percent   1  3.3 7   19  14.6 55   100

24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v4_whoqol_itm24)

v4_quol_recode(v4_clin$v4_whoqol_bref_who24_gesundhdiens,v4_con$v4_whoqol_bref_who24,"v4_whoqol_itm24",0)

##                1   2   3   4    5    NA's     
## [1,] No. cases 13  22  78  366  326  981  1786
## [2,] Percent   0.7 1.2 4.4 20.5 18.3 54.9 100

25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v4_whoqol_itm25)

v4_quol_recode(v4_clin$v4_whoqol_bref_who25_transport,v4_con$v4_whoqol_bref_who25,"v4_whoqol_itm25",0)

##                1   2  3   4    5   NA's     
## [1,] No. cases 13  36 73  359  321 984  1786
## [2,] Percent   0.7 2  4.1 20.1 18  55.1 100

26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v4_whoqol_itm26)

Coding reversed so that higher scores mean symptoms less often.

v4_quol_recode(v4_clin$v4_whoqol_bref_who26_neg_gefuehle,v4_con$v4_whoqol_bref_who26,"v4_whoqol_itm26",1)

##                1   2   3   4    5    NA's     
## [1,] No. cases 21  105 165 326  185  984  1786
## [2,] Percent   1.2 5.9 9.2 18.3 10.4 55.1 100

WHOQOL-BREF domain scores

Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).

Global (continuous [4-20],v4_whoqol_dom_glob)

v4_whoqol_dom_glob_df<-data.frame(as.numeric(v4_whoqol_itm1),as.numeric(v4_whoqol_itm2))

v4_who_glob_no_nas<-rowSums(is.na(v4_whoqol_dom_glob_df))

v4_whoqol_dom_glob<-ifelse((v4_who_glob_no_nas==0) | (v4_who_glob_no_nas==1), 
                            rowMeans(v4_whoqol_dom_glob_df,na.rm=T)*4,NA)

v4_whoqol_dom_glob<-round(v4_whoqol_dom_glob,2)

summary(v4_whoqol_dom_glob)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   16.00   14.54   16.00   20.00     981

Physical Health (continuous [4-20],v4_whoqol_dom_phys)

v4_whoqol_dom_phys_df<-data.frame(as.numeric(v4_whoqol_itm3),as.numeric(v4_whoqol_itm10),as.numeric(v4_whoqol_itm16),as.numeric(v4_whoqol_itm15),as.numeric(v4_whoqol_itm17),as.numeric(v4_whoqol_itm4),as.numeric(v4_whoqol_itm18))

v4_who_phys_no_nas<-rowSums(is.na(v4_whoqol_dom_phys_df))

v4_whoqol_dom_phys<-ifelse((v4_who_phys_no_nas==0) | (v4_who_phys_no_nas==1), 
                            rowMeans(v4_whoqol_dom_phys_df,na.rm=T)*4,NA)

v4_whoqol_dom_phys<-round(v4_whoqol_dom_phys,2)

summary(v4_whoqol_dom_phys)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.14   13.14   16.00   15.39   17.71   20.00     987

Psychological (continuous [4-20],v4_whoqol_dom_psy)

v4_whoqol_dom_psy_df<-data.frame(as.numeric(v4_whoqol_itm5),as.numeric(v4_whoqol_itm7),as.numeric(v4_whoqol_itm19),as.numeric(v4_whoqol_itm11),as.numeric(v4_whoqol_itm26),as.numeric(v4_whoqol_itm6))

v4_who_psy_no_nas<-rowSums(is.na(v4_whoqol_dom_psy_df))

v4_whoqol_dom_psy<-ifelse((v4_who_psy_no_nas==0) | (v4_who_psy_no_nas==1), 
                            rowMeans(v4_whoqol_dom_psy_df,na.rm=T)*4,NA)

v4_whoqol_dom_psy<-round(v4_whoqol_dom_psy,2)

summary(v4_whoqol_dom_psy)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.67   15.33   14.63   16.67   20.00     991

Social relationships (continuous [4-20],v4_whoqol_dom_soc)

v4_whoqol_dom_soc_df<-data.frame(as.numeric(v4_whoqol_itm20),as.numeric(v4_whoqol_itm22),as.numeric(v4_whoqol_itm21))

v4_who_soc_no_nas<-rowSums(is.na(v4_whoqol_dom_soc_df))

v4_whoqol_dom_soc<-ifelse((v4_who_soc_no_nas==0) | (v4_who_soc_no_nas==1), 
                            rowMeans(v4_whoqol_dom_soc_df,na.rm=T)*4,NA)

v4_whoqol_dom_soc<-round(v4_whoqol_dom_soc,2)

summary(v4_whoqol_dom_soc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.62   17.33   20.00     985

Environment (continuous [4-20],v4_whoqol_dom_env)

v4_whoqol_dom_env_df<-data.frame(as.numeric(v4_whoqol_itm8),as.numeric(v4_whoqol_itm23),as.numeric(v4_whoqol_itm12),as.numeric(v4_whoqol_itm24),as.numeric(v4_whoqol_itm13),as.numeric(v4_whoqol_itm14),as.numeric(v4_whoqol_itm9),as.numeric(v4_whoqol_itm25))

v4_who_env_no_nas<-rowSums(is.na(v4_whoqol_dom_env_df))

v4_whoqol_dom_env<-ifelse((v4_who_env_no_nas==0) | (v4_who_env_no_nas==1), 
                            rowMeans(v4_whoqol_dom_env_df,na.rm=T)*4,NA)

v4_whoqol_dom_env<-round(v4_whoqol_dom_env,2)

summary(v4_whoqol_dom_env)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00   14.50   16.50   16.11   18.00   20.00     989

Create dataset

v4_whoqol<-data.frame(v4_whoqol_itm1,v4_whoqol_itm2,v4_whoqol_itm3,v4_whoqol_itm4,
                      v4_whoqol_itm5,v4_whoqol_itm6,v4_whoqol_itm7,v4_whoqol_itm8,
                      v4_whoqol_itm9,v4_whoqol_itm10,v4_whoqol_itm11,v4_whoqol_itm12,
                      v4_whoqol_itm13,v4_whoqol_itm14,v4_whoqol_itm15,v4_whoqol_itm16,
                      v4_whoqol_itm17,v4_whoqol_itm18,v4_whoqol_itm19,v4_whoqol_itm20,
                      v4_whoqol_itm21,v4_whoqol_itm22,v4_whoqol_itm23,v4_whoqol_itm24,
                      v4_whoqol_itm25,v4_whoqol_itm26,v4_whoqol_dom_glob,
                      v4_whoqol_dom_phys,v4_whoqol_dom_psy,v4_whoqol_dom_soc,
                      v4_whoqol_dom_env)

Visit 4: Create dataframe

v4_df<-data.frame(v4_id,
                  v4_rec,
                  v4_clin_ill_ep,
                  v4_con_problems,
                  v4_dem,
                  v4_opcrit,
                  v4_leprcp,
                  v4_suic,
                  v4_med,
                  v4_subst,
                  v4_symp_panss,
                  v4_symp_ids_c,
                  v4_symp_ymrs,
                  v4_ill_sev,
                  v4_nrpsy,
                  v4_sf12,
                  v4_rlgn,
                  v4_med_adh,
                  v4_bdi2,
                  v4_asrm,
                  v4_mss,
                  v4_leq,
                  v4_whoqol)

Merge Visits

ctmp1<-merge(x=v1_df, y=v2_df, by.x="v1_id", by.y="v2_id", all.x=T)
ctmp2<-merge(x=ctmp1, y=v3_df, by.x="v1_id", by.y="v3_id", all.x=T)
phen<-merge(x=ctmp2, y=v4_df, by.x="v1_id", by.y="v4_id",all.x=T)

Available Biological Analysis Data

To simplify the process of data analysis and subject selection, we here provide the IDs of individuals that have been included in various biological analyses (e.g. all sample that were whole-genome genotyped have an ID in the column “gsa_id”).

IMPORTANT: Please note that the ID codes in the biological data may be in a slightly different format (e.g. all uppercase).**

PsychChip analysis IDs (character, psyc_id)

1446 individuals contained in this dataset have been genotyped on the Illumina PsychChip (https://www.illumina.com/products/by-type/microarray-kits/infinium-psycharray.html).

Importantly, related individuals exist in the dataset.

There also exists an imputed version of the dataset.

## [1] 1457

#make a dataframe for all analysis ids
ids<-data.frame(v1_id)
ids$v1_id<-as.character(v1_id)

ids<-merge(x=ids,y=psyc_id,all.x=T, by.x="v1_id",by.y="id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$psyc_id)==F))

## [1] 1446

GSA Chip analysis IDs (character, gsa_id)

A total of 1764 individuals contained in this phenotype dataset have been genotyped on the Illumina GSAChip (https://emea.illumina.com/products/by-type/microarray-kits/infinium-global-screening.html).

IMPORTANT:
1. Some individuals were removed during QC of the imputed data.
2. Related individuals exist in the original genotype files.
3. Most individuals were genotyped of version 3 of the GSA chip, but some participants (MImicSS study) were genotyped with an earlier version of the chip.

#merge to dataframe ids 
ids<-merge(x=ids,y=gsa_ids,all.x=T, by="v1_id")

#how many people in the dataset have a gsa_id
table(is.na(ids$gsa_id)==F)

## 
## FALSE  TRUE 
##    22  1764

Exome analysis IDs (character, exome_id)

The exomes of 104 bipolar PsyCourse individuals were sequenced in the context of a larger study.

#merge to dataframe ids 
ids<-merge(x=ids,y=ex_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$exome_id)==F)

## 
## FALSE  TRUE 
##  1682   104

SmallRNAome seq analysis IDs (character, v1_smRNAome_id)

The smallRNAomes of a total of 1526 individuals (1169 clinical and 357 control participants) contained in this dataset were sequenced from biomaterial collected AT THE FIRST VISIT. The variable gives the names of the corresponding .fastq files. The dummy variables “v2_smRNAome_id”, v3_smRNAome_id” and “v4_epic_id” are also created below to enable to include data properly in the long format dataset.

## New names:
## • `` -> `...12`

#merge to dataframe ids 
ids<-merge(x=ids,y=smRNAome_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$v1_smRNAome_id)==F)

## 
## FALSE  TRUE 
##   260  1526

Epigenetic analysis IDs Comes et al., 2020 (character, v1_epic_id and v3_epic_id)

In this analysis, 96 biploar individuals were analyzed at two measurement points (visit 1 and visit 3) using the Illumina EPIC array. The dummy variables “v2_epic_id” and “v4_epic_id” are also created below to enable to include data properly in the dataset in long format.

## [1] 96  2

#merge to dataframe ids 
ids<-merge(x=ids,y=v1_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_epic_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$v3_epic_id)==F)

## 
## FALSE  TRUE 
##  1690    96

Epigenetic analysis IDs (MulioBio) (character, v1_epic2_id)

In this analysis, DNA samples from 192 PsyCourse individuals collected at the first visit were analyzed using the Illumina EPIC array version 2. The individuals analyzed belong to two clusters that differed in their longitudinal course during the PsyCourse Study, and were identified using the longmixr toolbox. This analysis was funded by the MulioBio grant. The dummy variables “v2_epic2_id”,“v3_epic2_id” and “v4_epic2_id” are also created below to enable to include data properly in the dataset in long format.

#merge to dataframe ids 
ids<-merge(x=ids,y=epic2_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$v1_epic2_id)==F)

## 
## FALSE  TRUE 
##  1594   192

RB1CC1 Sanger sequening analysis IDs (character, rb1cc1_id)

In this analysis, the RB1CC1 gene was sequenced in 63 clinical participants (Schizophrenia: n=60, Schizophreniform Disorder: n=1, Schizoaffective Disorder: n=1, Bipolar-I Disorder: n=1).

#merge to dataframe ids 
ids<-merge(x=ids,y=rb1cc1_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$rb1cc1_id)==F)

## 
## FALSE  TRUE 
##  1723    63

Sequencing analysis of Xq28, distal (character, xq_id)

The locus Xq28, distal, putatively associated with schizophrenia, was sequenced on the Illumina HiSeq 2500 platform in the context of PsyCourse Proposal 064 in n=217 individuals with schizophrenia-spectrum disorders.

#merge to dataframe ids 
ids<-merge(x=ids,y=xq_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$xq_id)==F)

## 
## FALSE  TRUE 
##  1569   217

Lexogene mRNA NGS analysis IDs (character, v1_lexo_id and v3_lexo_id)

The mRNA transcriptomes of 542 individuals suffering from schizophrenia-spectrum disorders are contained in this dataset (538 from visit 1, 4 from visit 3) were sequenced. The dummy variables “v2_lexo_id” and “v4_lexo_id” are also created below to enable to include data properly in the long dataset.

#merge to dataframe ids
ids<-merge(x=ids,y=v1_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_lexo_seq_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$v1_lexo_id)==F)

## 
## FALSE  TRUE 
##  1248   538

Proteomics analysis IDs (character, v1_prot_id, v2_prot_id, v3_prot_id, v4_prot_id)

In a pilot study, a plasma proteome profiling pipeline was applied to 220 PsyCourse participants. Of these, 74 were from visit 1 (Bipolar-I: 40, Bipolar-II: 4 Schizoaffective Disorder: 11, Schizophrenia: 19), 37 from visit 2 (Bipolar-I: 13, Bipolar-II: 5, Recurrent Depression: 1, Schizoaffective Disorder: 1, Schizophrenia: 16, Schizophreniform Disorder: 1), 72 from visit 3 (Bipolar-I: 37, Bipolar-II: 5, Schizoaffective Disorder: 11, Schizophrenia: 19), 36 from visit 4 (Bipolar-I: 13, Bipolar-II: 4, Recurrent Depression: 1, Schizoaffective Disorder: 1, Schizophrenia: 16, Schizophreniform Disorder: 1), and one from an extra study visit between regular visits. The aforementioned individual is not contained in the dataset.
We do not have analysis IDs from these individuals, if they are contained in the analysis, the respective field contains a “Y”.

## 
##  1  2  3  4 ZV 
## 74 37 72 36  1

#merge to dataframe ids
ids<-merge(x=ids,y=v1_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_prot_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$v1_prot_id)==F)

## 
## FALSE  TRUE 
##  1712    74

table(is.na(ids$v2_prot_id)==F)

## 
## FALSE  TRUE 
##  1749    37

table(is.na(ids$v3_prot_id)==F)

## 
## FALSE  TRUE 
##  1714    72

table(is.na(ids$v4_prot_id)==F)

## 
## FALSE  TRUE 
##  1750    36

Protein profiling analysis IDs (character, v1_ab_prof_id, v2_ab_prof_id, v3_ab_prof_id, v4_ab_prof_id)

In a total of 224 PsyCourse individuals (mainly Bipolar Disorder and Schizophrenia-Spectrum; 214 from visit 1, 9 from visit 2, and 1 from visit 3), a selected panel of ~100 serum proteins was determined using a set of 155 antibodies in a high-throughput antibody-based assay. This suspension bead array technology enabled a multiplexed protein profiling of these proteins.

#merge to dataframe ids
ids<-merge(x=ids,y=v1_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_ab_prof_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
table(is.na(ids$v1_ab_prof_id)==F)

## 
## FALSE  TRUE 
##  1572   214

table(is.na(ids$v2_ab_prof_id)==F)

## 
## FALSE  TRUE 
##  1777     9

table(is.na(ids$v3_ab_prof_id)==F)

## 
## FALSE  TRUE 
##  1785     1

table(is.na(ids$v4_ab_prof_id)==F)

## 
## FALSE 
##  1786

Lipidomics profiling analysis IDs (character, v1_lip_id, v2_lip_id, v3_lip_id, v4_lip_id)

Plasma lipid profiles were analyzed for a total of 620 PsyCourse individuals (partly from multiple visits), 410 from visit 1, 270 from visit 2, 49 from visit 3, 26 from visit 4. Diagnoses include schizophrenia and bipolar disorder, as well as controls. We do not have ID codes from these individuals, if they are contained in the analysis, the respective field contains a “Y”. )

## [1] 270   2

#merge to dataframe ids
ids<-merge(x=ids,y=v1_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_lip_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_lip_id)==F))

## [1] 410

length(subset(ids$v1_id,is.na(ids$v2_lip_id)==F))

## [1] 270

length(subset(ids$v1_id,is.na(ids$v3_lip_id)==F))

## [1] 49

length(subset(ids$v1_id,is.na(ids$v4_lip_id)==F))

## [1] 26

Merge to get PsyCourse wide format dataset, version 6.0

Save PsyCourse 6.0 wide format dataset

save(psycrs6.0_wd, file="230614_v6.0_psycourse_wd.RData")

Write wide format .csv file

write.table(psycrs6.0_wd,file="230614_v6.0_psycourse_wd.csv", quote=F, row.names=F, col.names=T, sep="\t") 

Create long format dataset

To create a dataset in long format, it has to be determined which variables are assessed at one, two, three and four visits in the PsyCourse 6.0 dataset. Subsequently, one has to integrate variables that were measured at two or three measurement points with those assessed at four points in time.

For variables that were repeatedly measured at the three follow-up visits, dummy first measurement point variables are created, all coded -999.

Only four variables were measured at two times, and these are items on religion. These items do not assess change, but were only added at a later measurement point so that people who did not have the chance to complete it at the first measurement point could also be assessed (the questionnaire was introduced some time after the study had started). Below, these variables are collapsed into cross-sectional variables.

Get a list of variables measured one, two, three, or four times. These are identified by counting the variables that are similarly named after the “_” character.

#get variables names measured one time
crs<-names(subset(table(substring(names(psycrs6.0_wd),4)),table(substring(names(psycrs6.0_wd),4))==1))
length(crs)

## [1] 221

#get variables names measured two times
lng2<-names(subset(table(substring(names(psycrs6.0_wd),4)),table(substring(names(psycrs6.0_wd),4))==2))
length(lng2)

## [1] 5

#get variables names measured three times
lng3<-names(subset(table(substring(names(psycrs6.0_wd),4)),table(substring(names(psycrs6.0_wd),4))==3))
length(lng3)

## [1] 147

#get variables names measured four times
lng4<-names(subset(table(substring(names(psycrs6.0_wd),4)),table(substring(names(psycrs6.0_wd),4))==4))
length(lng4)

## [1] 412

#Check whether the sum of these variable is equal to the total number of variables in the wide dataset
length(c(crs,lng2,lng2,lng3,lng3,lng3,lng4,lng4,lng4,lng4))==dim(psycrs6.0_wd)[2]

## [1] TRUE

Variable names included in lng3 (measured only three times)

After inspection, these variables and saw that variables that were asked on follow-up but not the first visit: For each variable name, create a “v1_” variable filled with -999.

#Modify items in lng3 so that each vector element has a "v1_" added in front of it
lng3_new_v1_varnames<-paste("v1",lng3, sep="_")

#Add new variables to psycrs6.0_wd and fill them with -999
psycrs6.0_wd[lng3_new_v1_varnames] <- -999

Variable names included in lng2 (measured only two times): religion variables (see introductory text above)

These four variables are the religion variables, the questionnaire of which is asked at visit 1 but also at visit 4.

If a participant anwswered the questionnaire already in v1, use this data, if not and v4 data exisit and is not -999, use v4 data

psycrs6.0_wd$v1_rel_act<-ifelse(is.na(psycrs6.0_wd$v1_rel_act) & 
                                  is.na(psycrs6.0_wd$v4_rel_act)==F & 
                                    psycrs6.0_wd$v4_rel_act!=-999,psycrs6.0_wd$v4_rel_act,psycrs6.0_wd$v1_rel_act)

psycrs6.0_wd$v1_rel_chr<-ifelse(is.na(psycrs6.0_wd$v1_rel_chr) & 
                                  is.na(psycrs6.0_wd$v4_rel_chr)==F & 
                                    psycrs6.0_wd$v4_rel_chr!=-999,psycrs6.0_wd$v4_rel_chr,psycrs6.0_wd$v1_rel_chr)

psycrs6.0_wd$v1_rel_isl<-as.factor(ifelse(is.na(psycrs6.0_wd$v1_rel_isl) & 
                                            is.na(psycrs6.0_wd$v4_rel_isl)==F & 
                                    psycrs6.0_wd$v4_rel_isl!=-999,as.character(psycrs6.0_wd$v4_rel_isl),as.character(psycrs6.0_wd$v1_rel_isl)))

psycrs6.0_wd$v1_rel_oth<-as.factor(ifelse(is.na(psycrs6.0_wd$v1_rel_oth) & is.na(psycrs6.0_wd$v4_rel_oth)==F & 
                                    psycrs6.0_wd$v4_rel_oth!=-999,as.character(psycrs6.0_wd$v4_rel_oth),as.character(psycrs6.0_wd$v1_rel_oth)))

Remove variables v4_rel_act, v4_rel_chr, v4_rel_isl, v4_rel_oth from psycrs6.0_wd

psycrs6.0_wd$v4_rel_act<-NULL
psycrs6.0_wd$v4_rel_chr<-NULL
psycrs6.0_wd$v4_rel_isl<-NULL
psycrs6.0_wd$v4_rel_oth<-NULL

After the modifications done above, determine longitudinally measured (four time points) and cross-sectionally (less than four times) measured variables

#get variables names measured four times
lng4_cor<-names(subset(table(substring(names(psycrs6.0_wd),4)),table(substring(names(psycrs6.0_wd),4))==4))
length(lng4_cor)

## [1] 559

#get variables names measured three times
lng3_cor<-names(subset(table(substring(names(psycrs6.0_wd),4)),table(substring(names(psycrs6.0_wd),4))==3))
length(lng3_cor)

## [1] 0

#get variables names measured two times
lng2_cor<-names(subset(table(substring(names(psycrs6.0_wd),4)),table(substring(names(psycrs6.0_wd),4))==2))
length(lng2_cor)

## [1] 1

#get variables names measured one time
crs_cor<-names(subset(table(substring(names(psycrs6.0_wd),4)),table(substring(names(psycrs6.0_wd),4))==1))
length(crs_cor) 

## [1] 225

#Check whether the sum of these variable is equal to the total number of variables in the wide dataset
length(c(crs_cor,lng2_cor,lng2_cor,lng3_cor,lng3_cor,lng3_cor,lng4_cor,lng4_cor,lng4_cor,lng4_cor))==dim(psycrs6.0_wd)[2]

## [1] TRUE

Make a vector with all names of the longitdinally measured variables

lng4_cor_v1<-paste("v1",lng4_cor,sep="_")
lng4_cor_v2<-paste("v2",lng4_cor,sep="_")
lng4_cor_v3<-paste("v3",lng4_cor,sep="_")
lng4_cor_v4<-paste("v4",lng4_cor,sep="_")

names_lng<-c(lng4_cor_v1,lng4_cor_v2,lng4_cor_v3,lng4_cor_v4)

Format from wide to long

Split psycrs6.0_wd in longitudinal and cross-sectional variables

long<-subset(psycrs6.0_wd,select=names_lng)

#change names of longitudinally measured variables, so that visit info comes at the end
names(long)<-paste(substring(names(long),4),substr(names(long),2,2),sep=".")  

#sort dataframe
long<-long[,sort(names(long))]
dim(long)

## [1] 1786 2236

#create a dataframe with cross-sectionally measured variables
cross<-subset(psycrs6.0_wd,select=!(names(psycrs6.0_wd)%in%names_lng))
dim(cross)

## [1] 1786  227

Reunite long and cross

psycrs6.0_wd2<-cbind(cross,long)
dim(psycrs6.0_wd2) 

## [1] 1786 2463

Reshape dataframme from long to wide

IMPORTANT: the column “visit” contains the time information

psycrs6.0_ln<-reshape(data=psycrs6.0_wd2,
                       direction="long",
                       varying=names(long),
                       timevar="visit",
                       sep=".")

dim(psycrs6.0_ln) 

## [1] 7144  788

#Remove the last column that contains only consective numbers for each time point, and can safely be removed
psycrs6.0_ln<-psycrs6.0_ln[,-dim(psycrs6.0_ln)[2]] 

#Is the number of rows four times that of the long dataframe? 
dim(psycrs6.0_ln)[1]==dim(psycrs6.0_wd2)[1]*4 

## [1] TRUE

Save PsyCourse 6.0 long format dataset

save(psycrs6.0_ln, file="230614_v6.0_psycourse_ln.RData")

Write long format .csv file

write.table(psycrs6.0_ln,file="230614_v6.0_psycourse_ln.csv", quote=F, row.names=F, col.names=T, sep="\t") 

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1. The ALDA scala should only be assessed in clinical participants with a diagnosis of bipolar disorder↩︎

2. Self-reported weight is assessed at each study visit↩︎

3. Data not included in the present dataset, but were used to exclude control participants↩︎

4. Included during the course of the study, also included in Visit 4 to get information from people that did not fill out this cross-sectional questionnaire in Visit 1↩︎