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")