This codebook (data dictionary) and the accompanying phenotype dataset were created using R version 3.6.3 on a machine running Ubuntu 18.04. 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 PsyCourse 4.1 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:
PsyCourse is purely observational, i.e. there is no intervention at baseline, at or between the follow-ups.
PsyCourse is very heterogeneous in terms of participants.
Longitudinal measurements were approximately six months aparts, 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 occurrred (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:
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.
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.
The original CRFs are in German language, so they are of little use for most foreign colleagues.
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.
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:
Variables in this dataset have one of the following scale levels:
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.
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 “200715_v4.1_psycourse_wd.RData”, the long format dataset is contained in the file “200715_v4.1_psycourse_ln.RData”.
Also, .csv files are provided (“200715_v4.1_psycourse_wd.csv” and "200715_v4.1_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 R, 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:
The datasets containing the raw data on illicit drugs have the following names:
A number of biological analyses have been carried out. Information on these can be found in this section of the codebook.
This dataset was created from exports of our phenotype databases. Recruitment and data entry into these databases is complete. Many, but not all of these source data have been checked for data entry errors.
Thus, the dataset may contain errors, even though we do our best to avoid these, and we are still actively working on some of the source data.
The re-coding of many variables also 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.
Some basic QC steps were implemented, but, again, please be sure to inspect the data you are analyzing thorougly.
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 are also published there.
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 the centers LMU Munich and UMG Göttingen. Now, all psychiatric rating scales (PANSS, IDS-C30, YMRS, and GAF) are 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, as is the CAPE-42 (also introduced later). These control participants were recruited in the center UMG Göttingen.
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 |
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.
library("sjmisc") #neccessary for row_sum function
## Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!
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]}
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 #important only with clinical participants!
itm_cts_con<-ifelse((is.na(itm_cts_chk_con) | itm_cts_chk_con!=2),
cts_con_old_name,NA)
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)}
## [1] 1320
## [1] 466
Clinical participants
Control participants
Conbine 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 20
## [1,] No. cases 19 39 23 76 111 62 32 8 13 36 13 254 378 104 100 166
## [2,] Percent 1.1 2.2 1.3 4.3 6.2 3.5 1.8 0.4 0.7 2 0.7 14.2 21.2 5.8 5.6 9.3
## 21 22 23 25
## [1,] 147 102 47 56 1786
## [2,] 8.2 5.7 2.6 3.1 100
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 were de-identified due to data protection requirements.
Clinical participants
Control participants
Conbine 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)
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
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.
## 8.00 28.00 40.00 40.86 52.00 86.00
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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.
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)
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
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
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
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
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)
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$v1_con_cntr_brth))
v1_cntr_brth<-as.factor(c(v1_clin_cntr_brth,v1_con_cntr_brth))
descT(v1_cntr_brth)
## Afghanistan Ägypten anderes Land Argentinien Äthiopien
## [1,] No. cases 1 1 1 1 2
## [2,] Percent 0.1 0.1 0.1 0.1 0.1
## Australien Belarus (Weißrussland) Bosnien und Herzegowina Brasilien
## [1,] 1 1 3 1
## [2,] 0.1 0.1 0.2 0.1
## Deutschland Eritrea Estland Finnland Frankreich Griechenland Irak
## [1,] 1370 2 1 1 2 1 2
## [2,] 76.7 0.1 0.1 0.1 0.1 0.1 0.1
## Iran, Islamische Republik Italien Kasachstan Kirgisistan Kroatien Marokko
## [1,] 3 1 8 2 4 1
## [2,] 0.2 0.1 0.4 0.1 0.2 0.1
## Mazedonien, ehem. jugoslawische Republik Moldawien (Republik Moldau)
## [1,] 2 1
## [2,] 0.1 0.1
## Mongolei Niederlande Nigeria Österreich Pakistan Polen Rumänien
## [1,] 1 1 1 153 1 27 14
## [2,] 0.1 0.1 0.1 8.6 0.1 1.5 0.8
## Russische Föderation Senegal Serbien Slowakei Slowenien Sri Lanka
## [1,] 11 1 9 1 1 1
## [2,] 0.6 0.1 0.5 0.1 0.1 0.1
## Südafrika Tadschikistan Thailand Tschechische Republik Türkei Ukraine
## [1,] 1 1 1 4 9 1
## [2,] 0.1 0.1 0.1 0.2 0.5 0.1
## Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 2 1 2
## [2,] 0.1 0.1 0.1
## Vereinigtes Königreich Großbritannien und Nordirland Vietnam <NA>
## [1,] 2 1 126 1786
## [2,] 0.1 0.1 7.1 100
v1_clin_cntr_brth_m<-v1_clin$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$v1_demo2_gebortm==1,
"Deutschland",as.character(v1_con_cntr_brth_m))
v1_cntr_brth_m<-as.factor(c(v1_clin_cntr_brth_m,v1_con_cntr_brth_m))
descT(v1_cntr_brth_m)
## Afghanistan Algerien anderes Land Argentinien Äthiopien
## [1,] No. cases 1 1 2 1 2
## [2,] Percent 0.1 0.1 0.1 0.1 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Brasilien Bulgarien
## [1,] 2 1 6 1 2
## [2,] 0.1 0.1 0.3 0.1 0.1
## Chile Dänemark Deutschland Eritrea Estland Finnland Frankreich
## [1,] 2 1 1075 2 2 1 2
## [2,] 0.1 0.1 60.2 0.1 0.1 0.1 0.1
## Griechenland Indien Indonesien Irak Iran, Islamische Republik Irland
## [1,] 1 1 2 2 6 1
## [2,] 0.1 0.1 0.1 0.1 0.3 0.1
## Israel Italien Japan Kasachstan Kenia Kirgisistan
## [1,] 1 9 1 9 1 1
## [2,] 0.1 0.5 0.1 0.5 0.1 0.1
## Korea, Republik (Südkorea) Kroatien
## [1,] 1 10
## [2,] 0.1 0.6
## Libysch-Arabische Dschamahirija (Libyen) Litauen Luxemburg Marokko
## [1,] 1 1 3 1
## [2,] 0.1 0.1 0.2 0.1
## Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3 1 2 1
## [2,] 0.2 0.1 0.1 0.1
## Nigeria Norwegen Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 1 1 199 1 97 16 25
## [2,] 0.1 0.1 11.1 0.1 5.4 0.9 1.4
## Schweiz (Confoederatio Helvetica) Senegal Serbien Singapur Slowakei
## [1,] 2 1 13 1 3
## [2,] 0.1 0.1 0.7 0.1 0.2
## Slowenien Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine
## [1,] 5 3 3 1 24 20 10
## [2,] 0.3 0.2 0.2 0.1 1.3 1.1 0.6
## Ungarn Usbekistan Vereinigtes Königreich Großbritannien und Nordirland
## [1,] 12 1 3
## [2,] 0.7 0.1 0.2
## <NA>
## [1,] 181 1786
## [2,] 10.1 100
v1_clin_cntr_brth_f<-v1_clin$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$v1_demo2_gebortv==1,
"Deutschland",as.character(v1_con_cntr_brth_f))
v1_cntr_brth_f<-as.factor(c(v1_clin_cntr_brth_f,v1_con_cntr_brth_f))
descT(v1_cntr_brth_f)
## Afghanistan Ägypten anderes Land Argentinien Armenien Äthiopien
## [1,] No. cases 1 1 4 3 1 1
## [2,] Percent 0.1 0.1 0.2 0.2 0.1 0.1
## Australien Belarus (Weißrussland) Belgien Bosnien und Herzegowina
## [1,] 2 1 2 4
## [2,] 0.1 0.1 0.1 0.2
## Bulgarien Chile Deutschland Dominikanische Republik Eritrea Estland
## [1,] 3 3 1034 1 3 3
## [2,] 0.2 0.2 57.9 0.1 0.2 0.2
## Frankreich Griechenland Indien Indonesien Irak Iran, Islamische Republik
## [1,] 5 1 1 1 2 6
## [2,] 0.3 0.1 0.1 0.1 0.1 0.3
## Israel Italien Japan Jemen Kasachstan Kenia Korea, Republik (Südkorea)
## [1,] 1 10 2 1 8 1 1
## [2,] 0.1 0.6 0.1 0.1 0.4 0.1 0.1
## Kroatien Libanon Luxemburg Marokko
## [1,] 5 1 1 2
## [2,] 0.3 0.1 0.1 0.1
## Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Nigeria Norwegen
## [1,] 3 1 2 2 2
## [2,] 0.2 0.1 0.1 0.1 0.1
## Österreich Pakistan Palästinensische Autonomiegebiete Polen Rumänien
## [1,] 209 2 1 114 14
## [2,] 11.7 0.1 0.1 6.4 0.8
## Russische Föderation Senegal Serbien Slowakei Slowenien Spanien Sri Lanka
## [1,] 32 1 9 2 3 1 2
## [2,] 1.8 0.1 0.5 0.1 0.2 0.1 0.1
## Tadschikistan Thailand Tschechische Republik Türkei Ukraine Ungarn
## [1,] 1 1 23 24 8 7
## [2,] 0.1 0.1 1.3 1.3 0.4 0.4
## Usbekistan Vereinigte Staaten von Amerika
## [1,] 1 5
## [2,] 0.1 0.3
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 4 196 1786
## [2,] 0.2 11 100
v1_clin_cntr_brth_gmm<-v1_clin$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$v1_demo2_gebortgmm==1,
"Deutschland",as.character(v1_con_cntr_brth_gmm))
v1_cntr_brth_gmm<-as.factor(c(v1_clin_cntr_brth_gmm,v1_con_cntr_brth_gmm))
descT(v1_cntr_brth_gmm)
## Afghanistan anderes Land Argentinien Äthiopien
## [1,] No. cases 1 2 1 1
## [2,] Percent 0.1 0.1 0.1 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien Chile
## [1,] 2 1 7 1 2
## [2,] 0.1 0.1 0.4 0.1 0.1
## China, Volksrepublik Dänemark Deutschland Eritrea Estland Finnland
## [1,] 1 2 893 2 2 2
## [2,] 0.1 0.1 50 0.1 0.1 0.1
## Frankreich Georgien Griechenland Indien Indonesien Irak
## [1,] 1 1 3 2 1 2
## [2,] 0.1 0.1 0.2 0.1 0.1 0.1
## Iran, Islamische Republik Israel Italien Japan Kasachstan Kenia Kolumbien
## [1,] 6 2 9 1 5 1 1
## [2,] 0.3 0.1 0.5 0.1 0.3 0.1 0.1
## Korea, Republik (Südkorea) Kroatien Litauen Luxemburg Marokko
## [1,] 1 10 2 3 1
## [2,] 0.1 0.6 0.1 0.2 0.1
## Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3 1 1 3
## [2,] 0.2 0.1 0.1 0.2
## Nigeria Norwegen Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 1 1 183 1 127 12 21
## [2,] 0.1 0.1 10.2 0.1 7.1 0.7 1.2
## Schweden Schweiz (Confoederatio Helvetica) Senegal Serbien Slowakei
## [1,] 1 3 1 13 2
## [2,] 0.1 0.2 0.1 0.7 0.1
## Slowenien Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine
## [1,] 6 4 3 1 34 18 16
## [2,] 0.3 0.2 0.2 0.1 1.9 1 0.9
## Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 15 1 1
## [2,] 0.8 0.1 0.1
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 3 338 1786
## [2,] 0.2 18.9 100
v1_clin_cntr_brth_gfm<-v1_clin$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$v1_demo2_gebortgmv==1,
"Deutschland",as.character(v1_con_cntr_brth_gfm))
v1_cntr_brth_gfm<-as.factor(c(v1_clin_cntr_brth_gfm,v1_con_cntr_brth_gfm))
descT(v1_cntr_brth_gfm)
## Afghanistan anderes Land Argentinien Aserbaidschan Äthiopien
## [1,] No. cases 1 2 1 1 1
## [2,] Percent 0.1 0.1 0.1 0.1 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien Chile
## [1,] 3 1 7 1 2
## [2,] 0.2 0.1 0.4 0.1 0.1
## China, Volksrepublik Dänemark Deutschland Eritrea Estland Finnland
## [1,] 2 1 821 2 2 1
## [2,] 0.1 0.1 46 0.1 0.1 0.1
## Frankreich Georgien Griechenland Indien Indonesien Irak
## [1,] 3 1 4 2 1 2
## [2,] 0.2 0.1 0.2 0.1 0.1 0.1
## Iran, Islamische Republik Israel Italien Japan Kasachstan Kenia
## [1,] 6 2 7 1 5 1
## [2,] 0.3 0.1 0.4 0.1 0.3 0.1
## Korea, Demokratische Volksrepublik (Nordkorea) Korea, Republik (Südkorea)
## [1,] 1 1
## [2,] 0.1 0.1
## Kroatien Luxemburg Marokko Mazedonien, ehem. jugoslawische Republik
## [1,] 10 3 1 3
## [2,] 0.6 0.2 0.1 0.2
## Mongolei Namibia Niederlande Nigeria Norwegen Österreich Pakistan Polen
## [1,] 1 2 5 1 1 178 1 114
## [2,] 0.1 0.1 0.3 0.1 0.1 10 0.1 6.4
## Rumänien Russische Föderation Schweden Schweiz (Confoederatio Helvetica)
## [1,] 11 26 1 2
## [2,] 0.6 1.5 0.1 0.1
## Senegal Serbien Slowakei Slowenien Spanien Sri Lanka Thailand
## [1,] 1 12 3 5 4 3 1
## [2,] 0.1 0.7 0.2 0.3 0.2 0.2 0.1
## Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 24 19 8 16 1
## [2,] 1.3 1.1 0.4 0.9 0.1
## Vereinigte Staaten von Amerika
## [1,] 1
## [2,] 0.1
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 2 440 1786
## [2,] 0.1 24.6 100
v1_clin_cntr_brth_gmf<-v1_clin$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$v1_demo2_gebortgvm==1,
"Deutschland",as.character(v1_con_cntr_brth_gmf))
v1_cntr_brth_gmf<-as.factor(c(v1_clin_cntr_brth_gmf,v1_con_cntr_brth_gmf))
descT(v1_cntr_brth_gmf)
## Afghanistan Ägypten anderes Land Argentinien Australien
## [1,] No. cases 1 1 5 1 1
## [2,] Percent 0.1 0.1 0.3 0.1 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien Chile
## [1,] 1 1 4 2 3
## [2,] 0.1 0.1 0.2 0.1 0.2
## Dänemark Deutschland Dominikanische Republik Eritrea Estland Frankreich
## [1,] 1 814 1 3 2 6
## [2,] 0.1 45.6 0.1 0.2 0.1 0.3
## Ghana Griechenland Indien Irak Iran, Islamische Republik Israel Italien
## [1,] 1 3 2 2 6 1 10
## [2,] 0.1 0.2 0.1 0.1 0.3 0.1 0.6
## Japan Jemen Kasachstan Kenia Korea, Republik (Südkorea) Kroatien Libanon
## [1,] 2 1 3 1 1 4 2
## [2,] 0.1 0.1 0.2 0.1 0.1 0.2 0.1
## Luxemburg Marokko Mazedonien, ehem. jugoslawische Republik
## [1,] 1 1 3
## [2,] 0.1 0.1 0.2
## Moldawien (Republik Moldau) Mongolei Namibia Niederlande Nigeria Norwegen
## [1,] 2 2 1 2 2 2
## [2,] 0.1 0.1 0.1 0.1 0.1 0.1
## Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 191 1 109 12 31
## [2,] 10.7 0.1 6.1 0.7 1.7
## Schweiz (Confoederatio Helvetica) Senegal Serbien Slowakei Slowenien
## [1,] 1 1 10 2 4
## [2,] 0.1 0.1 0.6 0.1 0.2
## Spanien Sri Lanka Südafrika Thailand Tschechische Republik Türkei Ukraine
## [1,] 3 2 1 1 29 21 8
## [2,] 0.2 0.1 0.1 0.1 1.6 1.2 0.4
## Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 10 1 4
## [2,] 0.6 0.1 0.2
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 4 438 1786
## [2,] 0.2 24.5 100
v1_clin_cntr_brth_gff<-v1_clin$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$v1_demo2_gebortgvv==1,
"Deutschland",as.character(v1_con_cntr_brth_gff))
v1_cntr_brth_gff<-as.factor(c(v1_clin_cntr_brth_gff,v1_con_cntr_brth_gff))
descT(v1_cntr_brth_gff)
## Afghanistan Ägypten Algerien anderes Land Australien
## [1,] No. cases 1 1 1 5 1
## [2,] Percent 0.1 0.1 0.1 0.3 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien Chile
## [1,] 1 2 4 2 4
## [2,] 0.1 0.1 0.2 0.1 0.2
## Deutschland Eritrea Estland Frankreich Ghana Griechenland Indien Irak
## [1,] 847 3 1 5 1 1 2 2
## [2,] 47.4 0.2 0.1 0.3 0.1 0.1 0.1 0.1
## Iran, Islamische Republik Israel Italien Japan Jemen Kasachstan Kenia
## [1,] 7 1 9 2 1 3 1
## [2,] 0.4 0.1 0.5 0.1 0.1 0.2 0.1
## Korea, Republik (Südkorea) Kroatien Litauen Luxemburg Marokko
## [1,] 1 5 2 1 1
## [2,] 0.1 0.3 0.1 0.1 0.1
## Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3 1 2 1
## [2,] 0.2 0.1 0.1 0.1
## Nigeria Norwegen Österreich Pakistan Palästinensische Autonomiegebiete
## [1,] 2 2 191 1 2
## [2,] 0.1 0.1 10.7 0.1 0.1
## Polen Rumänien Russische Föderation Senegal Serbien Slowakei Slowenien
## [1,] 109 12 31 1 9 3 5
## [2,] 6.1 0.7 1.7 0.1 0.5 0.2 0.3
## Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine Ungarn
## [1,] 3 2 1 26 22 7 11
## [2,] 0.2 0.1 0.1 1.5 1.2 0.4 0.6
## Usbekistan Vereinigte Staaten von Amerika
## [1,] 1 6
## [2,] 0.1 0.3
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 3 411 1786
## [2,] 0.2 23 100
Create dataset
v1_eth<-data.frame(v1_cntr_brth,v1_cntr_brth_m,v1_cntr_brth_f,v1_cntr_brth_gmm,
v1_cntr_brth_gfm,v1_cntr_brth_gmf,v1_cntr_brth_gff)
This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.
v1_clin_cur_psy_trm<-rep(NA,dim(v1_clin)[1])
v1_con_cur_psy_trm<-rep(NA,dim(v1_con)[1])
v1_clin_cur_psy_trm<-ifelse(v1_clin$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$v1_psyaktuel_aktbehand==1,"4",v1_con_cur_psy_trm))))
v1_cur_psy_trm<-ordered(as.factor(c(v1_clin_cur_psy_trm,v1_con_cur_psy_trm)))
descT(v1_cur_psy_trm)
## 1 2 3 4 <NA>
## [1,] No. cases 476 640 78 555 37 1786
## [2,] Percent 26.7 35.8 4.4 31.1 2.1 100
This is an ordinal scale with four levels: “no”-1, “yes, consultation or short treatment”-2, “yes, continuous treatment for six months or numerous short episodes”-3, “yes, continuous treatment for several years or many short episodes”-4. The option “No information” was coded as “-999”.
v1_clin_outpat_psy_trm<-ifelse(v1_clin$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$v1_psyaktuel_jembehand)
v1_outpat_psy_trm<-factor(c(v1_clin_outpat_psy_trm,v1_con_outpat_psy_trm),ordered=T)
summary(v1_outpat_psy_trm)
## -999 1 2 3 4 NA's
## 27 428 181 151 967 32
If controls were never treated for any mental health reason, they are coded as -999.
v1_age_1st_out_trm<-c(v1_clin$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
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
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
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 50 33 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 50 33 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 50 33
## 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
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
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
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)
The code below creates the following variables that summarize the medication of each individual:
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)
#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$v1_Other_psychiatric<-rowSums(v1_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
#get the following variables from v1_con
#1. Medication name ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name ["_depot_medi_2"]
#4. Depot category ["_depot_kategorie_2"]
#5. Bedarf name ["_bedarf_medi_2"]
#6. Bedarf category ["_bedarf_kategorie_2"]
v1_con_medication_variables_1<-as.data.frame(v1_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v1_con))])
dim(v1_con_medication_variables_1)
## [1] 466 29
#recode the variables that are coded as characters/logicals in the "v1_con_medication_variables_1" as factors
v1_con_medication_variables_1$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$v1_Other_psychiatric<-rowSums(v1_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
Bind v1_clin and v1_con together by rows
v1_drugs<-rbind(v1_clin_med_table,v1_con_med_table)
dim(v1_drugs)
## [1] 1786 6
#check if the id column of v1_drugs and v1_id match
table(droplevels(v1_drugs[,1])==v1_id)
##
## TRUE
## 1786
In control participants, this item is coded “-999”, as it was not assessed.
v1_clin_adv<-ifelse(v1_clin$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
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$v1_medikabehand_medi3_mediaenderung==1,"Y","N")
v1_con_medchange<-rep("-999",dim(v1_con)[1])
v1_medchange<-as.factor(c(v1_clin_medchange,v1_con_medchange))
descT(v1_medchange)
## -999 N Y <NA>
## [1,] No. cases 466 259 595 466 1786
## [2,] Percent 26.1 14.5 33.3 26.1 100
The following two items on medication with lithium are asked in all study vists, in order not to miss participants eligible for the ALDA scale, which is assessed at the last study visit.
v1_clin_lith<-rep(NA,dim(v1_clin)[1])
v1_clin_lith<-ifelse(v1_clin$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
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)
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”
Regular medication: Name of the medication (character)
“v1_medikabehand3_med_medi_199998”/“v1_medikabehand3_med_medi_200705”
Regular medication: Category to which the medication belongs (character)
“v1_medikabehand3_med_kategorie_199998”/“v1_medikabehand3_med_kategorie_200705”
Regular medication: Subcategory to which the medication belongs (character)
“v1_medikabehand3_med_kategorie_sub_199998”/“v1_medikabehand3_med_kategorie_sub_200705”
Regular medication: Psychiatric medication? (0-no, 1-yes) “v1_medikabehand3_med_zusatz_199998”/“v1_medikabehand3_med_zusatz_200705”
Regular medication: Dose in the morning (unitless)
“v1_medikabehand3_s_medi1_morgens_199998”/“v1_medikabehand3_s_medi1_morgens_200705”
Regular medication: Dose at midday (unitless)
“v1_medikabehand3_smedi1_mittags_199998”/“v1_medikabehand3_smedi1_mittags_200705”
Regular medication: Dose in the evening (unitless)
“v1_medikabehand3_smedi1_abends_199998”/“v1_medikabehand3_smedi1_abends_200705”
Regular medication: Dose at night (unitless)
“v1_medikabehand3_smedi1_nachts_199998”/“v1_medikabehand3_smedi1_nachts_200705”
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”
Regular medication: Total dose of the medication per day (unitless)
“v1_medikabehand3_smedi1_gesamtdosis_199998”/“v1_medikabehand3_smedi1_gesamtdosis_200705”
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”
Regular medication: Medication name, if not contained in our catalog (character)
“v1_medikabehand3_medikament_text_199998”/“v1_medikabehand3_medikament_text_200705”
Depot medication: Name of the medication (character) “v1_medikabehand3_depot_medi_200170”/"v1_medikabehand3_depot_medi_201224
Depot medication: Category to which the medication belongs (character) “v1_medikabehand3_depot_kategorie_200170”/"v1_medikabehand3_depot_kategorie_201224
Depot medication: Subcategory to which the medication belongs (character)
“v1_medikabehand3_depot_kategorie_sub_200170”/"v1_medikabehand3_depot_kategorie_sub_201224
Depot medication: Psychiatric medication? (0-no, 1-yes) “v1_medikabehand3_depot_zusatz_200170”/“v1_medikabehand3_depot_zusatz_201224”
Depot medication: Total Dose (unitless) “v1_medikabehand3_s_depot_gesamtdosis_200170”/“v1_medikabehand3_s_depot_gesamtdosis_201224”
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”
Interval, at which the depot medication is given (days) “v1_medikabehand3_s_depot_tage_200170”/“v1_medikabehand3_s_depot_tage_201224”
Medication name, if not contained in our catalog (character) “v1_medikabehand3_medikament_text_200170”/“v1_medikabehand3_medikament_text_201224”
Pro re nata (PRN) medication: Name of the medication (character) “v1_medikabehand3_bedarf_medi_199584”/“v1_medikabehand3_bedarf_medi_201187”
Pro re nata (PRN) medication: Category to which the medication belongs (character)
“v1_medikabehand3_bedarf_kategorie_199584”/“v1_medikabehand3_bedarf_kategorie_201187”
Pro re nata (PRN) medication: Subcategory to which the medication belongs (character) “v1_medikabehand3_bedarf_kategorie_sub_199584”/“v1_medikabehand3_bedarf_kategorie_sub_201187”
Pro re nata (PRN) medication: Psychiatric medication? (0-no, 1-yes) “v1_medikabehand3_bedarf_zusatz_199584”/“v1_medikabehand3_bedarf_zusatz_201187”
Pro re nata (PRN) medication: Total dose up to (unitless) “v1_medikabehand3_s_bedarf_gesamtdosis_199584”/"v1_medikabehand3_s_bedarf_kommentar_201187
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”
Pro re nata (PRN) medication: Comment (character) “v1_medikabehand3_s_bedarf_kommentar_199584”/“v1_medikabehand3_s_bedarf_kommentar_201187”
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$mnppsd,v1_con[,247:391])
names(v1_med_con_orig)[1]<-"v1_id"
Save raw medication datasets of visit 1
save(v1_med_clin_orig, file="200715_v4.1_psycourse_clin_raw_med_visit1.RData")
save(v1_med_con_orig, file="200715_v4.1_psycourse_con_raw_med_visit1.RData")
Write .csv file
write.table(v1_med_clin_orig,file="200715_v4.1_psycourse_clin_raw_med_visit1.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v1_med_con_orig,file="200715_v4.1_psycourse_con_raw_med_visit1.csv", quote=F, row.names=F, col.names=T, sep="\t")
The option “Participant does not know” is coded as “NA”. The option “Participant does not want to disclose information” is coded as “-999”.
v1_clin_fam_hist<-rep(NA,dim(v1_clin)[1])
v1_con_fam_hist<-rep(NA,dim(v1_con)[1])
v1_clin_fam_hist<-ifelse(v1_clin$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$v1_famanam_psyangeh==3,NA,v1_con_fam_hist))))
v1_fam_hist<-as.factor(c(v1_clin_fam_hist,v1_con_fam_hist))
descT(v1_fam_hist)
## -999 N Y <NA>
## [1,] No. cases 10 528 1120 128 1786
## [2,] Percent 0.6 29.6 62.7 7.2 100
Also, in the original assessment of psychiatric history, each affected family member is listed, together with an indication whther the diagnosis is certain or not. This detailed information is available on request.
The method of assessment of physical measures and somatic diseases changed during the course of the study. Selected diseases are now assessed specifically as dichotomous items. Before, it was only assessed whether a participant was ever affected by a group of diseases. If possble, these different phenotypes were harmonized. More detailed information on specific diseases (e.g. “Which type of cancer?”) may be available.
IMPORTANT:
Please note that all of the questions below are self-reported measurements and that interviews were mainly conducted by research/clinical psychologists. For diseases, participants were asked whethe a medical doctor had ever diagnosed them with the disease in question.
Due to the change of assessment of these diseases and the (rather poor) ad-hoc harmonization, a researcher should carefully consider whether these items should be used in research at all.
Height (continuous [centimeters], v1_height)
v1_clin_height<-ifelse(is.na(v1_clin$v1_medwkeii_groesse),v1_clin$v1_mwke_mw1_groesse,v1_clin$v1_medwkeii_groesse)
v1_con_height<-ifelse(is.na(v1_con$v1_mwke_groesse),v1_con$v1_medwkeii_groesse,v1_con$v1_mwke_groesse)
v1_height<-c(v1_clin_height,v1_con_height)
summary(v1_height)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 52.0 167.0 174.0 173.7 180.0 203.0 36
Weight (continuous [kilograms], v1_weight)
v1_clin_weight<-ifelse(is.na(v1_clin$v1_medwkeii_gewicht),v1_clin$v1_mwke_mw2_gewicht,v1_clin$v1_medwkeii_gewicht)
v1_con_weight<-ifelse(is.na(v1_con$v1_medwkeii_gewicht),v1_con$v1_mwke_gewicht,v1_con$v1_medwkeii_gewicht)
v1_weight<-c(v1_clin_weight,v1_con_weight)
summary(v1_weight)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 39.00 68.00 79.00 82.08 93.00 190.00 44
Waist circumference (continouos [centimeters], v1_waist) This item was only recorded in a subset of individuals, because the question was introduced while the study was running.
v1_clin_waist<-v1_clin$v1_medwkeii_mw1_taillenumfang
v1_con_waist<-v1_con$v1_medwkeii_mw1_taillenumfang
v1_waist<-c(v1_clin_waist,v1_con_waist)
summary(v1_waist)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 61.00 75.25 86.00 88.61 99.00 149.00 1280
Body mass index (BMI, continuous [BMI], v1_bmi) We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.
v1_bmi<-round(v1_weight/(v1_height/100)^2,2)
summary(v1_bmi)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 16.18 22.80 25.89 27.40 30.04 576.92 44
Elevated cholesterol or triglyceride levels (dichotomous,v1_chol_trig) Phenotype was harmonized with the previous system.
v1_clin_chol_trig<-ifelse(v1_clin$v1_mwke_ke2_an090_stoffw==2,"N",
ifelse(v1_clin$v1_mwke_ke2_an090_stoffw==1 &
grepl("[Tt]riglyceride|[Cc]holesterin|[Hh]ypercholesterin|[Hh]ypertriglycerid|Hyperlipoproteinämie|Hypercholestrinomie",
v1_clin$v1_mwke_ke2_an090_stoffw_det),"Y","N"))
v1_clin_chol_trig<-ifelse(is.na(v1_clin_chol_trig) & v1_clin$v1_medwkeii_medwkeii_ecot==1,"Y",
ifelse(is.na(v1_clin_chol_trig) & v1_clin$v1_medwkeii_medwkeii_ecot==2,"N",v1_clin_chol_trig))
v1_con_chol_trig<-ifelse(v1_con$v1_mwke_stoffw==2,"N",
ifelse(v1_con$v1_mwke_stoffw==1 &
grepl("[Tt]riglyceride|[Cc]holesterin|[Hh]ypercholesterin|[Hh]ypertriglycerid|Hyperlipoproteinämie|Hypercholestrinomie",
v1_con$v1_mwke_stoffw_t),"Y","N"))
v1_con_chol_trig<-ifelse(is.na(v1_con_chol_trig) & v1_con$v1_medwkeii_medwkeii_ecot==1,"Y",
ifelse(is.na(v1_con_chol_trig) & v1_con$v1_medwkeii_medwkeii_ecot==2,"N",v1_con_chol_trig))
v1_chol_trig<-factor(c(v1_clin_chol_trig,v1_con_chol_trig))
descT(v1_chol_trig)
## N Y <NA>
## [1,] No. cases 1418 203 165 1786
## [2,] Percent 79.4 11.4 9.2 100
Hypertension (dichotomous, v1_hyperten) Phenotype was harmonized with the previous system.
v1_clin_hyperten<-ifelse(v1_clin$v1_mwke_ke1_an010_hke==2,"N",
ifelse(v1_clin$v1_mwke_ke1_an010_hke==1 &
grepl("[Hh]yperton|[Hh]oher Blutdruck|[Bb]luthochdruck",
v1_clin$v1_mwke_ke1_an010_hke_det),"Y","N"))
v1_clin_hyperten<-ifelse(is.na(v1_clin_hyperten) & v1_clin$v1_medwkeii_medwkeii_hyperton==1,"Y",
ifelse(is.na(v1_clin_hyperten) & v1_clin$v1_medwkeii_medwkeii_hyperton==2,"N",v1_clin_hyperten))
v1_con_hyperten<-ifelse(v1_con$v1_mwke_hke==2,"N",
ifelse(v1_con$v1_mwke_hke==1 &
grepl("[Hh]yperton|[Hh]oher Blutdruck|[Bb]luthochdruck",
v1_con$v1_mwke_hke_t),"Y","N"))
v1_con_hyperten<-ifelse(is.na(v1_con_hyperten) & v1_con$v1_medwkeii_medwkeii_hyperton==1,"Y",
ifelse(is.na(v1_con_hyperten) & v1_con$v1_medwkeii_medwkeii_hyperton==2,"N",v1_con_hyperten))
v1_hyperten<-factor(c(v1_clin_hyperten,v1_con_hyperten))
descT(v1_hyperten)
## N Y <NA>
## [1,] No. cases 1345 279 162 1786
## [2,] Percent 75.3 15.6 9.1 100
Angina pectoris (dichotomous, v1_ang_pec) Phenotype was harmonized with the previous system.
v1_clin_ang_pec<-ifelse(v1_clin$v1_mwke_ke1_an010_hke==2,"N",
ifelse(v1_clin$v1_mwke_ke1_an010_hke==1 & grepl("[Aa]ngina [Pp]ectoris",v1_clin$v1_mwke_ke1_an010_hke_det),"Y","N"))
v1_clin_ang_pec<-ifelse(is.na(v1_clin_ang_pec) & v1_clin$v1_medwkeii_medwkeii_angpec==1,"Y",
ifelse(is.na(v1_clin_ang_pec) & v1_clin$v1_medwkeii_medwkeii_angpec==2,"N",v1_clin_ang_pec))
v1_con_ang_pec<-ifelse(v1_con$v1_mwke_hke==2,"N",
ifelse(v1_con$v1_mwke_hke==1 & grepl("[Aa]ngina [Pp]ectoris",v1_con$v1_mwke_hke_t),"Y","N"))
v1_con_ang_pec<-ifelse(is.na(v1_con_ang_pec) & v1_con$v1_medwkeii_medwkeii_angpec==1,"Y",
ifelse(is.na(v1_con_ang_pec) & v1_con$v1_medwkeii_medwkeii_angpec==2,"N",v1_con_ang_pec))
v1_ang_pec<-factor(c(v1_clin_ang_pec,v1_con_ang_pec))
descT(v1_ang_pec)
## N Y <NA>
## [1,] No. cases 1606 18 162 1786
## [2,] Percent 89.9 1 9.1 100
Heart attack (dichotomous, v1_heart_att) Phenotype was harmonized with the previous system.
v1_clin_heart_att<-ifelse(v1_clin$v1_mwke_ke1_an010_hke==2,"N",
ifelse(v1_clin$v1_mwke_ke1_an010_hke==1 & grepl("[Hh]erzinfarkt|[Mm]yokardinfarkt",v1_clin$v1_mwke_ke1_an010_hke_det),"Y","N"))
v1_clin_heart_att<-ifelse(is.na(v1_clin_heart_att) & v1_clin$v1_medwkeii_medwkeii_herzinf==1,"Y",
ifelse(is.na(v1_clin_heart_att) & v1_clin$v1_medwkeii_medwkeii_herzinf==2,"N",v1_clin_heart_att))
v1_con_heart_att<-ifelse(v1_con$v1_mwke_hke==2,"N",
ifelse(v1_con$v1_mwke_hke==1 & grepl("[Hh]erzinfarkt|[Mm]yokardinfarkt",v1_con$v1_mwke_hke_t),"Y","N"))
v1_con_heart_att<-ifelse(is.na(v1_con_heart_att) & v1_con$v1_medwkeii_medwkeii_herzinf==1,"Y",
ifelse(is.na(v1_con_heart_att) & v1_con$v1_medwkeii_medwkeii_herzinf==2,"N",v1_con_heart_att))
v1_heart_att<-factor(c(v1_clin_heart_att,v1_con_heart_att))
descT(v1_heart_att)
## N Y <NA>
## [1,] No. cases 1607 16 163 1786
## [2,] Percent 90 0.9 9.1 100
Stroke (dichotomous, v1_stroke) Phenotype was harmonized with the previous system.
v1_clin_stroke<-ifelse(v1_clin$v1_mwke_ke1_an010_hke==2,"N",
ifelse(v1_clin$v1_mwke_ke1_an010_hke==1 & grepl("[Ss]chlaganfall|[Hh]irnblutung|[Gg]ehirnblutung",v1_clin$v1_mwke_ke1_an010_hke_det),"Y","N"))
v1_clin_stroke<-ifelse(is.na(v1_clin_stroke) & v1_clin$v1_medwkeii_medwkeii_schlag==1,"Y",
ifelse(is.na(v1_clin_stroke) & v1_clin$v1_medwkeii_medwkeii_schlag==2,"N",v1_clin_stroke))
v1_con_stroke<-ifelse(v1_con$v1_mwke_hke==2,"N",
ifelse(v1_con$v1_mwke_hke==1 & grepl("[Ss]chlaganfall|[Hh]irnblutung|[Gg]ehirnblutung",v1_con$v1_mwke_hke_t),"Y","N"))
v1_con_stroke<-ifelse(is.na(v1_con_stroke) & v1_con$v1_medwkeii_medwkeii_schlag==1,"Y",
ifelse(is.na(v1_con_stroke) & v1_con$v1_medwkeii_medwkeii_schlag==2,"N",v1_con_stroke))
v1_stroke<-factor(c(v1_clin_stroke,v1_con_stroke))
descT(v1_stroke)
## N Y <NA>
## [1,] No. cases 1612 12 162 1786
## [2,] Percent 90.3 0.7 9.1 100
Diabetes (dichotomous, v1_diabetes) Phenotype was harmonized with the previous system.
v1_clin_diabetes<-ifelse(v1_clin$v1_mwke_ke2_an090_stoffw==2,"N",
ifelse(v1_clin$v1_mwke_ke2_an090_stoffw==1 &
grepl("[Dd]iabetes|[Zz]ucker",v1_clin$v1_mwke_ke2_an090_stoffw_det),"Y","N"))
v1_clin_diabetes<-ifelse(is.na(v1_clin_diabetes) & v1_clin$v1_medwkeii_medwkeii_zuck==1,"Y",
ifelse(is.na(v1_clin_diabetes) & v1_clin$v1_medwkeii_medwkeii_zuck==2,"N",v1_clin_diabetes))
v1_con_diabetes<-ifelse(v1_con$v1_mwke_stoffw==2,"N",
ifelse(v1_con$v1_mwke_stoffw==1 & grepl("[Dd]iabetes|[Zz]ucker",v1_con$v1_mwke_stoffw_t),"Y","N"))
v1_con_diabetes<-ifelse(is.na(v1_con_diabetes) & v1_con$v1_medwkeii_medwkeii_zuck==1,"Y",
ifelse(is.na(v1_con_diabetes) & v1_con$v1_medwkeii_medwkeii_zuck==2,"N",v1_con_diabetes))
v1_diabetes<-factor(c(v1_clin_diabetes,v1_con_diabetes))
descT(v1_diabetes)
## N Y <NA>
## [1,] No. cases 1505 117 164 1786
## [2,] Percent 84.3 6.6 9.2 100
Hyperthyroidism (dichotomous, v1_hyperthy) Phenotype was harmonized with the previous system.
v1_clin_hyperthy<-ifelse(v1_clin$v1_mwke_ke3_an11_schildd==2,"N",
ifelse(v1_clin$v1_mwke_ke3_an11_schildd==1 &
grepl("[Üü]berfunktion|[Uu]eberfunktion|[Ss]childdrüsenüberfunktion|[Hh]yperthyreose",v1_clin$v1_mwke_ke3_an11_schildd_det),"Y","N"))
v1_clin_hyperthy<-ifelse(is.na(v1_clin_hyperthy) & v1_clin$v1_medwkeii_medwkeii_hypothy==1,"Y",
ifelse(is.na(v1_clin_hyperthy) & v1_clin$v1_medwkeii_medwkeii_hypothy==2,"N",v1_clin_hyperthy))
v1_con_hyperthy<-ifelse(v1_con$v1_mwke_schildd==2,"N",
ifelse(v1_con$v1_mwke_schildd==1 &
grepl("[Üü]berfunktion|[Uu]eberfunktion|[Ss]childdrüsenüberfunktion|[Hh]yperthyreose",v1_con$v1_mwke_schildd_t),"Y","N"))
v1_con_hyperthy<-ifelse(is.na(v1_con_hyperthy) & v1_con$v1_medwkeii_medwkeii_hypothy==1,"Y",
ifelse(is.na(v1_con_hyperthy) & v1_con$v1_medwkeii_medwkeii_hypothy==2,"N",v1_con_hyperthy))
v1_hyperthy<-factor(c(v1_clin_hyperthy,v1_con_hyperthy))
descT(v1_hyperthy)
## N Y <NA>
## [1,] No. cases 1500 119 167 1786
## [2,] Percent 84 6.7 9.4 100
Hypothyroidism (dichotomous, v1_hypothy) Phenotype was harmonized with the previous system.
v1_clin_hypothy<-ifelse(v1_clin$v1_mwke_ke3_an11_schildd==2,"N",
ifelse(v1_clin$v1_mwke_ke3_an11_schildd==1 &
grepl("[Uu]nterfunktion|[Ss]childdrüsenunterfunktion|Hashimoto|[Hh]ypothyreose|[Tt]hyreoiditis|[Ee]ntfernt",v1_clin$v1_mwke_ke3_an11_schildd_det),"Y","N"))
v1_clin_hypothy<-ifelse(is.na(v1_clin_hypothy) & v1_clin$v1_medwkeii_medwkeii_hypothy==1,"Y",
ifelse(is.na(v1_clin_hypothy) & v1_clin$v1_medwkeii_medwkeii_hypothy==2,"N",v1_clin_hypothy))
v1_con_hypothy<-ifelse(v1_con$v1_mwke_schildd==2,"N",
ifelse(v1_con$v1_mwke_schildd==1 &
grepl("[Uu]nterfunktion|[Ss]childdrüsenunterfunktion|Hashimoto|[Hh]ypothyreose|[Tt]hyreoiditis|[Ee]ntfernt",v1_con$v1_mwke_schildd_t),"Y","N"))
v1_con_hypothy<-ifelse(is.na(v1_con_hypothy) & v1_con$v1_medwkeii_medwkeii_hypothy==1,"Y",
ifelse(is.na(v1_con_hypothy) & v1_con$v1_medwkeii_medwkeii_hypothy==2,"N",v1_con_hypothy))
v1_hypothy<-factor(c(v1_clin_hypothy,v1_con_hypothy))
descT(v1_hypothy)
## N Y <NA>
## [1,] No. cases 1352 267 167 1786
## [2,] Percent 75.7 14.9 9.4 100
Osteoporosis (dichotomous, v1_osteopor) Phenotype was harmonized with the previous system.
v1_clin_osteopor<-ifelse(v1_clin$v1_mwke_ke4_an130_rme==2,"N",
ifelse(v1_clin$v1_mwke_ke4_an130_rme==1 &
grepl("[Oo]steoporose|[Kk]nochenschwund",v1_clin$v1_mwke_ke4_an130_rme_det),"Y","N"))
v1_clin_osteopor<-ifelse(is.na(v1_clin_osteopor) & v1_clin$v1_medwkeii_medwkeii_osterop==1,"Y",
ifelse(is.na(v1_clin_osteopor) & v1_clin$v1_medwkeii_medwkeii_osterop==2,"N",v1_clin_osteopor))
v1_con_osteopor<-ifelse(v1_con$v1_mwke_rme==2,"N",
ifelse(v1_con$v1_mwke_rme==1 &
grepl("[Oo]steoporose|[Kk]nochenschwund",v1_con$v1_mwke_rme_t),"Y","N"))
v1_con_osteopor<-ifelse(is.na(v1_con_osteopor) & v1_con$v1_medwkeii_medwkeii_osterop==1,"Y",
ifelse(is.na(v1_con_osteopor) & v1_con$v1_medwkeii_medwkeii_osterop==2,"N",v1_con_osteopor))
v1_osteopor<-factor(c(v1_clin_osteopor,v1_con_osteopor))
descT(v1_osteopor)
## N Y <NA>
## [1,] No. cases 1588 33 165 1786
## [2,] Percent 88.9 1.8 9.2 100
Asthma (dichotomous, v1_asthma) Phenotype was harmonized with the previous system.
v1_clin_asthma<-ifelse(v1_clin$v1_mwke_ke5_an180_lunge==2,"N",
ifelse(v1_clin$v1_mwke_ke5_an180_lunge==1 &
grepl("[Aa]sthma",v1_clin$v1_mwke_ke5_an180_lunge_det),"Y","N"))
v1_clin_asthma<-ifelse(is.na(v1_clin_asthma) & v1_clin$v1_medwkeii_medwkeii_asthma==1,"Y",
ifelse(is.na(v1_clin_asthma) & v1_clin$v1_medwkeii_medwkeii_asthma==2,"N",v1_clin_asthma))
v1_con_asthma<-ifelse(v1_con$v1_mwke_lunge==2,"N",
ifelse(v1_con$v1_mwke_lunge==1 &
grepl("[Aa]sthma",v1_con$v1_mwke_lunge_t),"Y","N"))
v1_con_asthma<-ifelse(is.na(v1_con_asthma) & v1_con$v1_medwkeii_medwkeii_asthma==1,"Y",
ifelse(is.na(v1_con_asthma) & v1_con$v1_medwkeii_medwkeii_asthma==2,"N",v1_con_asthma))
v1_asthma<-factor(c(v1_clin_asthma,v1_con_asthma))
descT(v1_asthma)
## N Y <NA>
## [1,] No. cases 1492 133 161 1786
## [2,] Percent 83.5 7.4 9 100
COPD/chronic Bronchitis (dichotomous, v1_copd) Phenotype was harmonized with the previous system.
v1_clin_copd<-ifelse(v1_clin$v1_mwke_ke5_an180_lunge==2,"N",
ifelse(v1_clin$v1_mwke_ke5_an180_lunge==1 &
grepl("COPD|[Cc]hronische Bronchitis|[Cc]hron. Bronchitis|Chronisch obstruktive Atemwegserkrankung",v1_clin$v1_mwke_ke5_an180_lunge_det),"Y","N"))
v1_clin_copd<-ifelse(is.na(v1_clin_copd) & v1_clin$v1_medwkeii_medwkeii_copd==1,"Y",
ifelse(is.na(v1_clin_copd) & v1_clin$v1_medwkeii_medwkeii_copd==2,"N",v1_clin_copd))
v1_con_copd<-ifelse(v1_con$v1_mwke_lunge==2,"N",
ifelse(v1_con$v1_mwke_lunge==1 &
grepl("COPD|[Cc]hronische Bronchitis|[Cc]hron. Bronchitis|Chronisch obstruktive Atemwegserkrankung",v1_con$v1_mwke_lunge_t),"Y","N"))
v1_con_copd<-ifelse(is.na(v1_con_copd) & v1_con$v1_medwkeii_medwkeii_copd==1,"Y",
ifelse(is.na(v1_con_copd) & v1_con$v1_medwkeii_medwkeii_copd==2,"N",v1_con_copd))
v1_copd<-factor(c(v1_clin_copd,v1_con_copd))
descT(v1_copd)
## N Y <NA>
## [1,] No. cases 1550 73 163 1786
## [2,] Percent 86.8 4.1 9.1 100
Allergies (dichotomus, v1_allerg) Phenotype was harmonized with the previous system.
v1_clin_allerg<-ifelse(v1_clin$v1_mwke_ke6_an200_allerg==1,"Y","N")
v1_clin_allerg<-ifelse(is.na(v1_clin_allerg) & v1_clin$v1_medwkeii_medwkeii_allerg==1,"Y",
ifelse(is.na(v1_clin_allerg) & v1_clin$v1_medwkeii_medwkeii_allerg==2,"N",v1_clin_allerg))
v1_con_allerg<-ifelse(v1_con$v1_mwke_allerg==1,"Y","N")
v1_con_allerg<-ifelse(is.na(v1_con_allerg) & v1_con$v1_medwkeii_medwkeii_allerg==1,"Y",
ifelse(is.na(v1_con_allerg) & v1_con$v1_medwkeii_medwkeii_allerg==2,"N",v1_con_allerg))
v1_allerg<-factor(c(v1_clin_allerg,v1_con_allerg))
descT(v1_allerg)
## N Y <NA>
## [1,] No. cases 950 677 159 1786
## [2,] Percent 53.2 37.9 8.9 100
Neurodermatitis (dichotomous, v1_neuroder) Phenotype was harmonized with the previous system.
v1_clin_neuroder<-ifelse(v1_clin$v1_mwke_ke9_an330_haut==2,"N",
ifelse(v1_clin$v1_mwke_ke9_an330_haut==1 &
grepl("[Nn]eurodermitis",v1_clin$v1_mwke_ke9_an330_haut_det),"Y","N"))
v1_clin_neuroder<-ifelse(is.na(v1_clin_neuroder) & v1_clin$v1_medwkeii_medwkeii_neurod==1,"Y",
ifelse(is.na(v1_clin_neuroder) & v1_clin$v1_medwkeii_medwkeii_neurod==2,"N",v1_clin_neuroder))
v1_con_neuroder<-ifelse(v1_con$v1_mwke_haut==2,"N",
ifelse(v1_con$v1_mwke_haut==1 &
grepl("[Nn]eurodermitis",v1_con$v1_mwke_haut_t),"Y","N"))
v1_con_neuroder<-ifelse(is.na(v1_con_neuroder) & v1_con$v1_medwkeii_medwkeii_neurod==1,"Y",
ifelse(is.na(v1_con_neuroder) & v1_con$v1_medwkeii_medwkeii_neurod==2,"N",v1_con_neuroder))
v1_neuroder<-factor(c(v1_clin_neuroder,v1_con_neuroder))
descT(v1_neuroder)
## N Y <NA>
## [1,] No. cases 1517 102 167 1786
## [2,] Percent 84.9 5.7 9.4 100
Psoriasis (dichotomous, v1_psoriasis) Phenotype was harmonized with the previous system.
v1_clin_psoriasis<-ifelse(v1_clin$v1_mwke_ke9_an330_haut==2,"N",
ifelse(v1_clin$v1_mwke_ke9_an330_haut==1 &
grepl("[Pp]soriasis|[Ss][Cc]huppenflechte",v1_clin$v1_mwke_ke9_an330_haut_det),"Y","N"))
v1_clin_psoriasis<-ifelse(is.na(v1_clin_psoriasis) & v1_clin$v1_medwkeii_medwkeii_neurod==1,"Y",
ifelse(is.na(v1_clin_psoriasis) & v1_clin$v1_medwkeii_medwkeii_neurod==2,"N",v1_clin_psoriasis))
v1_con_psoriasis<-ifelse(v1_con$v1_mwke_haut==2,"N",
ifelse(v1_con$v1_mwke_haut==1 &
grepl("[Ss][Cc]huppenflechte|[Ss][Cc]huppenflechte",v1_con$v1_mwke_haut_t),"Y","N"))
v1_con_psoriasis<-ifelse(is.na(v1_con_psoriasis) & v1_con$v1_medwkeii_medwkeii_neurod==1,"Y",
ifelse(is.na(v1_con_psoriasis) & v1_con$v1_medwkeii_medwkeii_neurod==2,"N",v1_con_psoriasis))
v1_psoriasis<-factor(c(v1_clin_psoriasis,v1_con_psoriasis))
descT(v1_psoriasis)
## N Y <NA>
## [1,] No. cases 1542 77 167 1786
## [2,] Percent 86.3 4.3 9.4 100
Autoimmune diseases (dichotomous, v1_autoimm)
v1_clin_autoimm_yn<-ifelse(v1_clin$v1_medwkeii_medwkeii_autoim==1,"Y","N")
v1_con_autoimm_yn<-ifelse(v1_con$v1_medwkeii_medwkeii_autoim==1,"Y","N")
v1_autoimm<-factor(c(v1_clin_autoimm_yn,v1_con_autoimm_yn))
descT(v1_autoimm)
## N Y <NA>
## [1,] No. cases 571 27 1188 1786
## [2,] Percent 32 1.5 66.5 100
Cancer (dichotomous, v1_cancer) Similar question asked in previous system.Information on the type of cancer is available on request.
v1_clin_cancer<-ifelse(v1_clin$v1_mwke_ke14_an56_krebs==1,"Y","N")
v1_clin_cancer<-ifelse(is.na(v1_clin_cancer) & v1_clin$v1_medwkeii_medwkeii_kreberk==1,"Y",
ifelse(is.na(v1_clin_cancer) & v1_clin$v1_medwkeii_medwkeii_kreberk==2,"N",v1_clin_cancer))
v1_con_cancer<-ifelse(v1_con$v1_mwke_krebs==1,"Y","N")
v1_con_cancer<-ifelse(is.na(v1_con_cancer) & v1_con$v1_medwkeii_medwkeii_kreberk==1,"Y",
ifelse(is.na(v1_con_cancer) & v1_con$v1_medwkeii_medwkeii_kreberk==2,"N",v1_con_cancer))
v1_cancer<-factor(c(v1_clin_cancer,v1_con_cancer))
descT(v1_cancer)
## N Y <NA>
## [1,] No. cases 1542 81 163 1786
## [2,] Percent 86.3 4.5 9.1 100
Stomach ulcer (dichotomous, v1_stom_ulc) Phenotype was harmonized with the previous system.
v1_clin_stom_ulc<-ifelse(v1_clin$v1_medwkeii_medwkeii_maggesch==1,"Y","N")
v1_clin_stom_ulc<-ifelse(is.na(v1_clin_stom_ulc) & v1_clin$v1_mwke_ke7_an270_mde==2,"N",
ifelse(is.na(v1_clin_stom_ulc) & v1_clin$v1_mwke_ke7_an270_mde==1 &
grepl("[Mm]agengeschwür",v1_clin$v1_mwke_ke7_an270_mde_det),"Y",v1_clin_stom_ulc))
v1_con_stom_ulc<-ifelse(v1_con$v1_medwkeii_medwkeii_maggesch==1,"Y","N")
v1_con_stom_ulc<-ifelse(is.na(v1_con_stom_ulc) & v1_con$v1_mwke_mde==2,"N",
ifelse(is.na(v1_con_stom_ulc) & v1_con$v1_mwke_mde==1 &
grepl("[Mm]agengeschwür",v1_con$v1_mwke_mde_t),"Y",v1_con_stom_ulc))
v1_stom_ulc<-factor(c(v1_clin_stom_ulc,v1_con_stom_ulc))
descT(v1_stom_ulc)
## N Y <NA>
## [1,] No. cases 1415 35 336 1786
## [2,] Percent 79.2 2 18.8 100
Kidney failure (dichotomous, v1_kid_fail)
v1_clin_kid_fail<-ifelse(v1_clin$v1_medwkeii_medwkeii_nierver==1,"Y","N")
v1_clin_kid_fail<-ifelse(is.na(v1_clin_kid_fail) & v1_clin$v1_mwke_ke10_an360_nibl==2,"N",
ifelse(is.na(v1_clin_kid_fail) & v1_clin$v1_mwke_ke10_an360_nibl==1 &
grepl("[Nn]ierenversagen",v1_clin$v1_mwke_ke10_an360_nibl_det),"Y",v1_clin_kid_fail))
v1_con_kid_fail<-ifelse(v1_con$v1_medwkeii_medwkeii_nierver==1,"Y","N")
v1_con_kid_fail<-ifelse(is.na(v1_con_kid_fail) & v1_con$v1_mwke_nibl==2,"N",
ifelse(is.na(v1_con_kid_fail) & v1_con$v1_mwke_nibl==1 &
grepl("[Nn]ierenversagen",v1_con$v1_mwke_nibl_t),"Y",v1_con_kid_fail))
v1_kid_fail<-factor(c(v1_clin_kid_fail,v1_con_kid_fail))
descT(v1_kid_fail)
## N Y <NA>
## [1,] No. cases 1493 5 288 1786
## [2,] Percent 83.6 0.3 16.1 100
Kidney-/Gallstone (dichotomous, v1_stone)
v1_clin_stone<-ifelse(v1_clin$v1_medwkeii_medwkeii_nierga==1,"Y","N")
v1_clin_stone<-ifelse(is.na(v1_clin_stone) & v1_clin$v1_mwke_ke10_an360_nibl==2,"N",
ifelse(is.na(v1_clin_stone) & (v1_clin$v1_mwke_ke10_an360_nibl==1 | v1_clin$v1_mwke_ke8_an300_galleb) &
(grepl("[Nn]ierenstein|[Nn]ierensteine",v1_clin$v1_mwke_ke10_an360_nibl_det) |
grepl("[Gg]allenstein|[Gg]allensteine",v1_clin$v1_mwke_ke8_an300_galleb_det)),"Y",v1_clin_stone))
v1_con_stone<-ifelse(v1_con$v1_medwkeii_medwkeii_nierga==1,"Y","N")
v1_con_stone<-ifelse(is.na(v1_con_stone) & v1_con$v1_mwke_nibl==2,"N",
ifelse(is.na(v1_con_stone) & (v1_con$v1_mwke_nibl==1 | v1_con$v1_mwke_galle==1) &
(grepl("[Nn]ierenstein|[Nn]ierensteine",v1_con$v1_mwke_nibl_t) |
grepl("[Gg]allenstein|[Gg]allensteine",v1_con$v1_mwke_galle_t)),"Y",v1_con_stone))
v1_stone<-factor(c(v1_clin_stone,v1_con_stone))
descT(v1_stone)
## N Y <NA>
## [1,] No. cases 1466 51 269 1786
## [2,] Percent 82.1 2.9 15.1 100
Epilepsy (dichotomous, v1_epilepsy)
v1_clin_epilepsy<-ifelse(v1_clin$v1_medwkeii_medwkeii_epile==1,"Y","N")
v1_clin_epilepsy<-ifelse(is.na(v1_clin_epilepsy) & v1_clin$v1_mwke_ke11_an400_npsy==2,"N",
ifelse(is.na(v1_clin_epilepsy) & v1_clin$v1_mwke_ke11_an400_npsy==1 &
grepl("[Ee]pilepsie",v1_clin$v1_mwke_ke11_an400_npsy_det),"Y",v1_clin_epilepsy))
v1_con_epilepsy<-ifelse(v1_con$v1_medwkeii_medwkeii_epile==1,"Y","N")
v1_con_epilepsy<-ifelse(is.na(v1_con_epilepsy) & v1_con$v1_mwke_npsy==2,"N",
ifelse(is.na(v1_con_epilepsy) & v1_con$v1_mwke_npsy==1 &
grepl("[Ee]pilepsie",v1_con$v1_mwke_npsy_t),"Y",v1_con_epilepsy))
v1_epilepsy<-factor(c(v1_clin_epilepsy,v1_con_epilepsy))
descT(v1_epilepsy)
## N Y <NA>
## [1,] No. cases 1366 21 399 1786
## [2,] Percent 76.5 1.2 22.3 100
Migraine (dichotomous, v1_migraine)
v1_clin_migraine<-ifelse(v1_clin$v1_medwkeii_medwkeii_mig==1,"Y","N")
v1_clin_migraine<-ifelse(is.na(v1_clin_migraine) & v1_clin$v1_mwke_ke11_an400_npsy==2,"N",
ifelse(is.na(v1_clin_migraine) & v1_clin$v1_mwke_ke11_an400_npsy==1 &
grepl("[Mm]igräne | Mirgäne | mIGRÄNE",v1_clin$v1_mwke_ke11_an400_npsy_det),"Y",v1_clin_migraine))
v1_con_migraine<-ifelse(v1_con$v1_medwkeii_medwkeii_mig==1,"Y","N")
v1_con_migraine<-ifelse(is.na(v1_con_migraine) & v1_con$v1_mwke_npsy==2,"N",
ifelse(is.na(v1_con_migraine) & v1_con$v1_mwke_npsy==1 &
grepl("[Mm]igräne | Mirgäne | mIGRÄNE",v1_con$v1_mwke_npsy_t),"Y",v1_con_migraine))
v1_migraine<-factor(c(v1_clin_migraine,v1_con_migraine))
descT(v1_migraine)
## N Y <NA>
## [1,] No. cases 1300 83 403 1786
## [2,] Percent 72.8 4.6 22.6 100
Parkinson syndrome (dichotomous, v1_parkinson)
v1_clin_parkinson<-ifelse(v1_clin$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_clin_parkinson<-ifelse(is.na(v1_clin_parkinson) & v1_clin$v1_mwke_ke11_an400_npsy==2,"N",
ifelse(is.na(v1_clin_parkinson) & v1_clin$v1_mwke_ke11_an400_npsy==1 &
grepl("[Pp]arkinson",v1_clin$v1_mwke_ke11_an400_npsy_det),"Y",v1_clin_parkinson))
v1_con_parkinson<-ifelse(v1_con$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_con_parkinson<-ifelse(is.na(v1_con_parkinson) & v1_con$v1_mwke_npsy==2,"N",
ifelse(is.na(v1_con_parkinson) & v1_con$v1_mwke_npsy==1 &
grepl("[Pp]arkinson",v1_con$v1_mwke_npsy_t),"Y",v1_con_parkinson))
v1_parkinson<-factor(c(v1_clin_parkinson,v1_con_parkinson))
descT(v1_parkinson)
## N Y <NA>
## [1,] No. cases 1373 8 405 1786
## [2,] Percent 76.9 0.4 22.7 100
Liver cirrhosis or inflammation (dichotomous, v1_liv_cir_inf)
v1_clin_liv_cir_inf<-ifelse(v1_clin$v1_medwkeii_medwkeii_leberz==1,"Y","N")
v1_clin_liv_cir_inf<-ifelse(is.na(v1_clin_liv_cir_inf) & v1_clin$v1_mwke_ke8_an300_galleb==2,"N",
ifelse(is.na(v1_clin_liv_cir_inf) & v1_clin$v1_mwke_ke8_an300_galleb==1 &
grepl("[Ll]eberzerose | [Ll]eberzirrhose |
[Ll]eberentzündung",v1_clin$v1_mwke_ke8_an300_galleb_det),"Y",v1_clin_liv_cir_inf))
v1_con_liv_cir_inf<-ifelse(v1_con$v1_medwkeii_medwkeii_leberz==1,"Y","N")
v1_con_liv_cir_inf<-ifelse(is.na(v1_con_liv_cir_inf) & v1_con$v1_mwke_galle==2,"N",
ifelse(is.na(v1_con_liv_cir_inf) & v1_con$v1_mwke_galle==1 &
grepl("[Ll]eberzerose | [Ll]eberzirrhose |
[Ll]eberentzündung",v1_con$v1_mwke_galle_t),"Y",v1_con_liv_cir_inf))
v1_liv_cir_inf<-factor(c(v1_clin_liv_cir_inf,v1_con_liv_cir_inf))
descT(v1_liv_cir_inf)
## N Y <NA>
## [1,] No. cases 1499 5 282 1786
## [2,] Percent 83.9 0.3 15.8 100
Traumatic brain injury (dichotomous, v1_tbi)
v1_clin_tbi<-ifelse(v1_clin$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_clin_tbi<-ifelse(is.na(v1_clin_tbi) & v1_clin$v1_mwke_ke11_an400_npsy==2,"N",
ifelse(is.na(v1_clin_tbi) & v1_clin$v1_mwke_ke11_an400_npsy==1 &
grepl("[Ss]chädel-Hirn-Trauma",v1_clin$v1_mwke_ke11_an400_npsy_det),"Y",v1_clin_tbi))
v1_con_tbi<-ifelse(v1_con$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_con_tbi<-ifelse(is.na(v1_con_tbi) & v1_con$v1_mwke_npsy==2,"N",
ifelse(is.na(v1_con_tbi) & v1_con$v1_mwke_npsy==1 &
grepl("[Ss]chädel-Hirn-Trauma",v1_con$v1_mwke_npsy_t),"Y",v1_con_tbi))
v1_tbi<-factor(c(v1_clin_tbi,v1_con_tbi))
descT(v1_tbi)
## N Y <NA>
## [1,] No. cases 1373 4 409 1786
## [2,] Percent 76.9 0.2 22.9 100
Disorders of the eyes or ears (dichotomous, v1_eyear) This item was also asked in the previous method of assessment, with three answering alternatives: “Yes,”No“, and”Don’t know“. The option”Don’t know" was coded as NA, to harmonize it with the new methode of assessment. Near- or farsightedness, according to our guidelines, is not a disease and should not be coded as such. There is more information on the type of disease, available on request.
v1_clin_eyear<-ifelse(v1_clin$v1_mwke_ke12_an440_auohr==1,"Y",ifelse(v1_clin$v1_mwke_ke12_an440_auohr==2,"N",NA))
v1_clin_eyear<-ifelse(is.na(v1_clin_eyear) & v1_clin$v1_medwkeii_medwkeii_auo==1,"Y",
ifelse(is.na(v1_clin_eyear) & v1_clin$v1_medwkeii_medwkeii_auo==2,"N",v1_clin_eyear))
v1_con_eyear<-ifelse(v1_con$v1_mwke_auohr==1,"Y",ifelse(v1_con$v1_mwke_auohr==2,"N",NA))
v1_con_eyear<-ifelse(is.na(v1_con_eyear) & v1_con$v1_medwkeii_medwkeii_auo==1,"Y",
ifelse(is.na(v1_con_eyear) & v1_con$v1_medwkeii_medwkeii_auo==2,"N",v1_con_eyear))
v1_eyear<-factor(c(v1_clin_eyear,v1_con_eyear))
descT(v1_eyear)
## N Y <NA>
## [1,] No. cases 1395 221 170 1786
## [2,] Percent 78.1 12.4 9.5 100
Infectious diseases (dichotomous, v1_inf) As the assessment of this item was modified in the course of the study (at some point interviewers were instructed not to code benign childhood diseases such as chickenpox), this item is NOT included in this version of the dataset anymore. The variable name remains in the codebook, but not in the dataset.
Create dataset
v1_som_dsrdr<-data.frame(v1_height,
v1_weight,
v1_waist,
v1_bmi,
v1_chol_trig,
v1_hyperten,
v1_ang_pec,
v1_heart_att,
v1_stroke,
v1_diabetes,
v1_hyperthy,
v1_hypothy,
v1_osteopor,
v1_asthma,
v1_copd,
v1_allerg,
v1_neuroder,
v1_psoriasis,
v1_autoimm,
v1_cancer,
v1_stom_ulc,
v1_kid_fail,
v1_stone,
v1_epilepsy,
v1_migraine,
v1_parkinson,
v1_liv_cir_inf,
v1_tbi,
v1_eyear)
There is some more information available (whether smokers have stopped smoking more than one year, if yes how many years, how many pipes, cigars and cigarillos), not part of the present dataset.
This is a categorical item with three optional answers: “never”-N, “yes”-Y, and “former” (having smoked in the past but not now)-F. It is required to have stopped smoking for at least three months to qualify as former smoker.
v1_clin_ever_smkd<-ifelse(v1_clin$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_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 219 593 712 262 1786
## [2,] Percent 12.3 33.2 39.9 14.7 100
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$v1_tabalk_ta2_alter_jahre))
v1_age_smk<-factor(c(v1_clin_age_smk,v1_con_age_smk))
summary(v1_age_smk)
## -999 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
## 767 2 2 2 1 9 4 11 36 53 109 120 174 58 89 30
## 20 21 22 23 24 25 26 27 28 29 30 31 32 33 35 36
## 43 23 10 11 7 11 2 3 5 5 13 2 2 2 4 1
## 38 40 44 45 48 53 57 61 NA's
## 1 1 2 1 1 1 1 1 166
In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.
Please note that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur. For people presently do not smoke this question is coded as “-999”.
v1_clin_no_cig<-ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==1, -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$v1_tabalk_tabak1==1, -999,
ifelse(v1_con$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$v1_tabalk_ta3_anz_zig*12,NA))))
v1_no_cig<-c(v1_clin_no_cig,v1_con_no_cig)
summary(v1_no_cig)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -999 -999 1825 3285 7300 23725 503
This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.
v1_alc_pst12_mths<-factor(c(v1_clin$v1_tabalk_ta9_alkkonsum,v1_con$v1_tabalk_alkohol9), ordered=T)
descT(v1_alc_pst12_mths)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 299 356 191 390 206 70 71 203 1786
## [2,] Percent 16.7 19.9 10.7 21.8 11.5 3.9 4 11.4 100
This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.
v1_clin_alc_5orm<-ifelse((v1_clin$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$v1_tabalk_alkohol10))
v1_alc_5orm<-factor(c(v1_clin_alc_5orm,v1_con_alc_5orm), ordered=T)
descT(v1_alc_5orm)
## -999 1 2 3 4 5 6 7 8 9 <NA>
## [1,] No. cases 846 228 106 107 69 70 80 42 17 13 208 1786
## [2,] Percent 47.4 12.8 5.9 6 3.9 3.9 4.5 2.4 1 0.7 11.6 100
The criteria for alcohol dependence are checked, resulting in a dichotomous assessment whether lifetime alcohol dependence is present.
v1_clin_lftm_alc_dep<-ifelse(v1_clin$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
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:
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)
“Number of cannabis drugs ever consumed” (continuous [number], v1_can)
“Number of opioid drugs ever consumed” (continuous [number], v1_opi)
“Number of cocaine drugs ever consumed” (continuous [number], v1_kok)
“Number of hallucinogenic drugs ever consumed” (continuous [number], v1_hal)
“Number of inhalant drugs ever consumed” (continuous [number], v1_inh)
“Number of tranquillizer drugs ever consumed” (continuous [number], v1_tra)
“Number of other drugs ever consumed” (continuous [number], v1_var)
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)
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="200715_v4.1_psycourse_clin_raw_ill_drg_visit1.RData")
save(v1_con_ill_drugs_orig, file="200715_v4.1_psycourse_con_raw_ill_drg_visit1.RData")
Write .csv file
write.table(v1_clin_ill_drugs_orig,file="200715_v4.1_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="200715_v4.1_psycourse_con_raw_ill_drg_visit1.csv", quote=F, row.names=F, col.names=T, sep="\t")
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 inclueded in this dataset, usually coded “Y”-yes, “N”-no and “U”-unknown.
Control participants have “-999” in every SCID variable.
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 10 438 12 4 99 12 101
## [2,] Percent 26.1 0.8 0.6 24.5 0.7 0.2 5.5 0.7 5.7
## 296.89 296.X 298.80 MImicSS
## [1,] 124 443 6 56 1786
## [2,] 6.9 24.8 0.3 3.1 100
DSM-IV diagnoses in human readable form for non-clinical researchers. Please note the following:
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")]<-"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 443 124 6
## [2,] Percent 24.8 6.9 0.3
## Control Depression ICD-10 Schizophrenia Schizoaffective Disorder
## [1,] 466 101 56 99
## [2,] 26.1 5.7 3.1 5.5
## Schizophrenia Schizophreniform Disorder
## [1,] 479 12 1786
## [2,] 26.8 0.7 100
The following items were assessed in clincal participants only!
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:
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:
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:
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$v1_sna_24_hypomane1_a_160_beurteilung==0), -999, NA)))
descT(v1_clin_scid_no_hypomania)
## -999 1 2 3 4 5 6 7 8 10 12 14 15 18 20 30
## [1,] No. cases 1185 19 14 13 7 4 2 6 2 5 1 1 2 1 2 1
## [2,] Percent 89.8 1.4 1.1 1 0.5 0.3 0.2 0.5 0.2 0.4 0.1 0.1 0.2 0.1 0.2 0.1
## 70 75 99 <NA>
## [1,] 1 1 15 38 1320
## [2,] 0.1 0.1 1.1 2.9 100
v1_scid_no_hypomania<-c(v1_clin_scid_no_hypomania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_hypomania)
## -999 1 2 3 4 5 6 7 8 10 12 14 15 18 20 30
## [1,] No. cases 1651 19 14 13 7 4 2 6 2 5 1 1 2 1 2 1
## [2,] Percent 92.4 1.1 0.8 0.7 0.4 0.2 0.1 0.3 0.1 0.3 0.1 0.1 0.1 0.1 0.1 0.1
## 70 75 99 <NA>
## [1,] 1 1 15 38 1786
## [2,] 0.1 0.1 0.8 2.1 100
This section was assessed in all study participants. If at least one item covering delusions (delusion item) or hallucinations (hallucination item) was answered in the affirmative this was coded “Y”, otherwise “N”. Please note that an individual was also coded as “N” if there was insufficient information (“0” on the original SCID item; occuring only in few individuals) or if the symptom was too mild to fulfill criteria (“2” on the original SCID item).
NA means that at least one question was not completed and all other completed questions were coded “N”.
v1_clin_scid_ever_delus<-ifelse((is.na(v1_clin$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$v1_snb_31_prodsymp1_b14_gedankenueber==3),"Y",NA)))
v1_scid_ever_delus<-factor(c(v1_clin_scid_ever_delus,rep(-999,dim(v1_con)[1])))
summary(v1_scid_ever_delus)
## -999 N Y NA's
## 466 333 873 114
v1_clin_scid_ever_halls<-ifelse(v1_clin$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
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
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$v1_snx_112_suizged2_x7_suizid_gedanken,rep(-999,dim(v1_con)[1]))==3, "Y",
v1_scid_evr_suic_ide)))
descT(v1_scid_evr_suic_ide)
## -999 N Y <NA>
## [1,] No. cases 473 298 930 85 1786
## [2,] Percent 26.5 16.7 52.1 4.8 100
This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).
v1_scid_suic_ide<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_scid_suic_ide<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
ifelse(c(v1_clin$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$v1_snx_112_suizged2_x8_suizged_inhalt==4,rep(-999,dim(v1_con)[1])), 4, v1_scid_suic_ide)))))
v1_scid_suic_ide<-factor(v1_scid_suic_ide,ordered=T)
descT(v1_scid_suic_ide)
## -999 1 2 3 4 <NA>
## [1,] No. cases 771 297 124 134 345 115 1786
## [2,] Percent 43.2 16.6 6.9 7.5 19.3 6.4 100
This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3. The item is coded “-999” if skipped (see above).
v1_scid_suic_thght_mth<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_scid_suic_thght_mth<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
ifelse(c(v1_clin$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
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
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
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).
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$v1_snx_111_suizvrs2_x2_suiz_anz,rep(-999,dim(v1_con)[1])), v1_scid_no_suic_attmpt))
v1_scid_no_suic_attmpt<-factor(v1_scid_no_suic_attmpt,ordered=T)
descT(v1_scid_no_suic_attmpt)
## -999 1 2 3 <NA>
## [1,] No. cases 1272 226 97 81 110 1786
## [2,] Percent 71.2 12.7 5.4 4.5 6.2 100
This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4. The item is coded “-999” if skipped (see above).
v1_prep_suic_attp_ord<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_prep_suic_attp_ord<-ifelse(v1_suic_attmpt==1, -999,
ifelse(v1_suic_attmpt>1 &
c(v1_clin$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$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==4, 4,
v1_prep_suic_attp_ord)))))
v1_prep_suic_attp_ord<-factor(v1_prep_suic_attp_ord,ordered=T)
descT(v1_prep_suic_attp_ord)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1272 123 40 71 136 144 1786
## [2,] Percent 71.2 6.9 2.2 4 7.6 8.1 100
This is on ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).
v1_suic_note_attmpt<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_suic_note_attmpt<-ifelse(v1_suic_attmpt==1, -999,
ifelse(v1_suic_attmpt>1 & c(v1_clin$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$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==4, "4",
v1_suic_note_attmpt)))))
v1_suic_note_attmpt<-factor(v1_suic_note_attmpt,ordered=T)
descT(v1_suic_note_attmpt)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1272 260 17 17 92 128 1786
## [2,] Percent 71.2 14.6 1 1 5.2 7.2 100
Create dataset
v1_scid<-data.frame(v1_scid_dsm_dx,
v1_scid_dsm_dx_cat,
v1_scid_age_MDE,
v1_scid_no_MDE,
v1_scid_age_mania,
v1_scid_no_mania,
v1_scid_age_hypomania,
v1_scid_no_hypomania,
v1_scid_ever_halls,
v1_scid_ever_delus,
v1_scid_ever_psyc,
v1_scid_age_fst_psyc,
v1_scid_yr_fst_psyc,
v1_scid_evr_suic_ide,
v1_scid_suic_ide,
v1_scid_suic_thght_mth,
v1_scid_suic_note_thgts,
v1_suic_attmpt,
v1_scid_no_suic_attmpt,
v1_prep_suic_attp_ord,
v1_suic_note_attmpt)
The PANSS (Kay, Fiszbein, & Opler, 1987) is a rating scale measuring positive and negative symptoms in schizophrenia. It has three subscales: positive, negative and gereral psychopathology symptoms. Each item is rated on an ordinal scale from one to seven with the following gradation: “absent”-1, “minimal”-2, “mild”-3, “moderate”-4, “moderate severe”-5, “severe”-6, “extreme”-7. On all items, higher scores mean more severe symptoms. The ratings refer to the past seven days. Please find the items below.
P1 Delusions (ordinal [1,2,3,4,5,6,7], v1_panss_p1)
v1_panss_p1<-c(v1_clin$v1_panss_p_p1_wahnideen,v1_con$v1_panss_p_p1_wahnideen)
v1_panss_p1<-factor(v1_panss_p1, ordered=T)
descT(v1_panss_p1)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1114 107 150 119 63 54 179 1786
## [2,] Percent 62.4 6 8.4 6.7 3.5 3 10 100
P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v1_panss_p2)
v1_panss_p2<-c(v1_clin$v1_panss_p_p2_form_denkst,v1_con$v1_panss_p_p2_form_denkst)
v1_panss_p2<-factor(v1_panss_p2, ordered=T)
descT(v1_panss_p2)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 1023 183 225 117 51 8 1 178 1786
## [2,] Percent 57.3 10.2 12.6 6.6 2.9 0.4 0.1 10 100
P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v1_panss_p3)
v1_panss_p3<-c(v1_clin$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$v1_panss_p_p4_erregung)
v1_panss_p4<-factor(v1_panss_p4, ordered=T)
descT(v1_panss_p4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1096 160 268 60 17 3 182 1786
## [2,] Percent 61.4 9 15 3.4 1 0.2 10.2 100
P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v1_panss_p5)
v1_panss_p5<-c(v1_clin$v1_panss_p_p5_groessenideen,v1_con$v1_panss_p_p5_groessenideen)
v1_panss_p5<-factor(v1_panss_p5, ordered=T)
descT(v1_panss_p5)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1349 104 91 36 19 6 181 1786
## [2,] Percent 75.5 5.8 5.1 2 1.1 0.3 10.1 100
P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v1_panss_p6)
v1_panss_p6<-c(v1_clin$v1_panss_p_p6_misstr_verfolg,v1_con$v1_panss_p_p6_misstr_verfolg)
v1_panss_p6<-factor(v1_panss_p6, ordered=T)
descT(v1_panss_p6)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 1149 155 192 61 34 13 1 181 1786
## [2,] Percent 64.3 8.7 10.8 3.4 1.9 0.7 0.1 10.1 100
P7 Hostility (ordinal [1,2,3,4,5,6,7], v1_panss_p7)
v1_panss_p7<-c(v1_clin$v1_panss_p_p7_feindseligkeit,v1_con$v1_panss_p_p7_feindseligkeit)
v1_panss_p7<-factor(v1_panss_p7, ordered=T)
descT(v1_panss_p7)
## 1 2 3 4 5 <NA>
## [1,] No. cases 1400 104 78 18 4 182 1786
## [2,] Percent 78.4 5.8 4.4 1 0.2 10.2 100
PANSS Positive sum score (continuous [7-49], v1_panss_sum_pos)
v1_panss_sum_pos<-as.numeric.factor(v1_panss_p1)+
as.numeric.factor(v1_panss_p2)+
as.numeric.factor(v1_panss_p3)+
as.numeric.factor(v1_panss_p4)+
as.numeric.factor(v1_panss_p5)+
as.numeric.factor(v1_panss_p6)+
as.numeric.factor(v1_panss_p7)
summary(v1_panss_sum_pos)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.00 7.00 8.00 10.66 13.00 35.00 190
N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v1_panss_n1)
v1_panss_n1<-c(v1_clin$v1_panss_n_n1_affektverflachung,v1_con$v1_panss_n_n1_affektverflachung)
v1_panss_n1<-factor(v1_panss_n1, ordered=T)
descT(v1_panss_n1)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 897 188 229 156 114 18 1 183 1786
## [2,] Percent 50.2 10.5 12.8 8.7 6.4 1 0.1 10.2 100
N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v1_panss_n2)
v1_panss_n2<-c(v1_clin$v1_panss_n_n2_emot_rueckzug,v1_con$v1_panss_n_n2_emot_rueckzug)
v1_panss_n2<-factor(v1_panss_n2, ordered=T)
descT(v1_panss_n2)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 974 179 208 193 36 10 1 185 1786
## [2,] Percent 54.5 10 11.6 10.8 2 0.6 0.1 10.4 100
N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v1_panss_n3)
v1_panss_n3<-c(v1_clin$v1_panss_n_n3_mang_aff_rapp,v1_con$v1_panss_n_n3_mang_aff_rapp)
v1_panss_n3<-factor(v1_panss_n3, ordered=T)
descT(v1_panss_n3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1090 181 211 89 28 4 183 1786
## [2,] Percent 61 10.1 11.8 5 1.6 0.2 10.2 100
N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v1_panss_n4)
v1_panss_n4<-c(v1_clin$v1_panss_n_n4_soz_pass_apath,v1_con$v1_panss_n_n4_soz_pass_apath)
v1_panss_n4<-factor(v1_panss_n4, ordered=T)
descT(v1_panss_n4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 995 150 274 122 51 9 185 1786
## [2,] Percent 55.7 8.4 15.3 6.8 2.9 0.5 10.4 100
N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v1_panss_n5)
v1_panss_n5<-c(v1_clin$v1_panss_n_n5_abstr_denken,v1_con$v1_panss_n_n5_abstr_denken)
v1_panss_n5<-factor(v1_panss_n5, ordered=T)
descT(v1_panss_n5)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 994 180 260 101 39 16 1 195 1786
## [2,] Percent 55.7 10.1 14.6 5.7 2.2 0.9 0.1 10.9 100
N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v1_panss_n6)
v1_panss_n6<-c(v1_clin$v1_panss_n_n6_spon_fl_sprache,v1_con$v1_panss_n_n6_spon_fl_sprache)
v1_panss_n6<-factor(v1_panss_n6, ordered=T)
descT(v1_panss_n6)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1195 144 149 78 34 3 183 1786
## [2,] Percent 66.9 8.1 8.3 4.4 1.9 0.2 10.2 100
N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v1_panss_n7)
v1_panss_n7<-c(v1_clin$v1_panss_n_n7_stereotyp_ged,v1_con$v1_panss_n_n7_stereotyp_ged)
v1_panss_n7<-factor(v1_panss_n7, ordered=T)
descT(v1_panss_n7)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1230 151 163 43 11 5 183 1786
## [2,] Percent 68.9 8.5 9.1 2.4 0.6 0.3 10.2 100
PANSS Negative sum score (continuous [7-49], v1_panss_sum_neg)
v1_panss_sum_neg<-as.numeric.factor(v1_panss_n1)+
as.numeric.factor(v1_panss_n2)+
as.numeric.factor(v1_panss_n3)+
as.numeric.factor(v1_panss_n4)+
as.numeric.factor(v1_panss_n5)+
as.numeric.factor(v1_panss_n6)+
as.numeric.factor(v1_panss_n7)
summary(v1_panss_sum_neg)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.00 7.00 10.00 12.07 16.00 38.00 213
G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v1_panss_g1)
v1_panss_g1<-c(v1_clin$v1_panss_g_g1_sorge_gesundh,v1_con$v1_panss_g_g1_sorge_gesundh)
v1_panss_g1<-factor(v1_panss_g1, ordered=T)
descT(v1_panss_g1)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 1083 196 192 87 35 9 3 181 1786
## [2,] Percent 60.6 11 10.8 4.9 2 0.5 0.2 10.1 100
G2 Anxiety (ordinal [1,2,3,4,5,6,7], v1_panss_g2)
v1_panss_g2<-c(v1_clin$v1_panss_g_g2_angst,v1_con$v1_panss_g_g2_angst)
v1_panss_g2<-factor(v1_panss_g2, ordered=T)
descT(v1_panss_g2)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 946 159 310 135 48 6 1 181 1786
## [2,] Percent 53 8.9 17.4 7.6 2.7 0.3 0.1 10.1 100
G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v1_panss_g3)
v1_panss_g3<-c(v1_clin$v1_panss_g_g3_schuldgefuehle,v1_con$v1_panss_g_g3_schuldgefuehle)
v1_panss_g3<-factor(v1_panss_g3, ordered=T)
descT(v1_panss_g3)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 1075 176 196 123 30 5 1 180 1786
## [2,] Percent 60.2 9.9 11 6.9 1.7 0.3 0.1 10.1 100
G4 Tension (ordinal [1,2,3,4,5,6,7], v1_panss_g4)
v1_panss_g4<-c(v1_clin$v1_panss_g_g4_anspannung,v1_con$v1_panss_g_g4_anspannung)
v1_panss_g4<-factor(v1_panss_g4, ordered=T)
descT(v1_panss_g4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 931 239 287 118 23 6 182 1786
## [2,] Percent 52.1 13.4 16.1 6.6 1.3 0.3 10.2 100
G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v1_panss_g5)
v1_panss_g5<-c(v1_clin$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$v1_panss_g_g6_depression)
v1_panss_g6<-factor(v1_panss_g6, ordered=T)
descT(v1_panss_g6)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 822 170 280 199 114 16 4 181 1786
## [2,] Percent 46 9.5 15.7 11.1 6.4 0.9 0.2 10.1 100
G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v1_panss_g7)
v1_panss_g7<-c(v1_clin$v1_panss_g_g7_mot_verlangs,v1_con$v1_panss_g_g7_mot_verlangs)
v1_panss_g7<-factor(v1_panss_g7, ordered=T)
descT(v1_panss_g7)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1017 186 268 107 23 5 180 1786
## [2,] Percent 56.9 10.4 15 6 1.3 0.3 10.1 100
G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v1_panss_g8)
v1_panss_g8<-c(v1_clin$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$v1_panss_g_g9_ungew_denkinh)
v1_panss_g9<-factor(v1_panss_g9, ordered=T)
descT(v1_panss_g9)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 1190 100 175 81 47 12 1 180 1786
## [2,] Percent 66.6 5.6 9.8 4.5 2.6 0.7 0.1 10.1 100
G10 Disorientation (ordinal [1,2,3,4,5,6,7], v1_panss_g10)
v1_panss_g10<-c(v1_clin$v1_panss_g_g10_desorient,v1_con$v1_panss_g_g10_desorient)
v1_panss_g10<-factor(v1_panss_g10, ordered=T)
descT(v1_panss_g10)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1419 108 67 5 4 2 181 1786
## [2,] Percent 79.5 6 3.8 0.3 0.2 0.1 10.1 100
G11 Poor attention (ordinal [1,2,3,4,5,6,7], v1_panss_g11)
v1_panss_g11<-c(v1_clin$v1_panss_g_g11_mang_aufmerks,v1_con$v1_panss_g_g11_mang_aufmerks)
v1_panss_g11<-factor(v1_panss_g11, ordered=T)
descT(v1_panss_g11)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 889 186 359 134 28 3 187 1786
## [2,] Percent 49.8 10.4 20.1 7.5 1.6 0.2 10.5 100
G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v1_panss_g12)
v1_panss_g12<-c(v1_clin$v1_panss_g_g12_mang_urt_einsi,v1_con$v1_panss_g_g12_mang_urt_einsi)
v1_panss_g12<-factor(v1_panss_g12, ordered=T)
descT(v1_panss_g12)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 1260 135 110 74 17 8 1 181 1786
## [2,] Percent 70.5 7.6 6.2 4.1 1 0.4 0.1 10.1 100
G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v1_panss_g13)
v1_panss_g13<-c(v1_clin$v1_panss_g_g13_willensschwae,v1_con$v1_panss_g_g13_willensschwae)
v1_panss_g13<-factor(v1_panss_g13, ordered=T)
descT(v1_panss_g13)
## 1 2 3 4 5 <NA>
## [1,] No. cases 1317 118 124 41 2 184 1786
## [2,] Percent 73.7 6.6 6.9 2.3 0.1 10.3 100
G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v1_panss_g14)
v1_panss_g14<-c(v1_clin$v1_panss_g_g14_mang_impulsk,v1_con$v1_panss_g_g14_mang_impulsk)
v1_panss_g14<-factor(v1_panss_g14, ordered=T)
descT(v1_panss_g14)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1326 109 142 21 1 2 185 1786
## [2,] Percent 74.2 6.1 8 1.2 0.1 0.1 10.4 100
G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v1_panss_g15)
v1_panss_g15<-c(v1_clin$v1_panss_g_g15_selbstbezog,v1_con$v1_panss_g_g15_selbstbezog)
v1_panss_g15<-factor(v1_panss_g15, ordered=T)
descT(v1_panss_g15)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1317 128 110 42 8 1 180 1786
## [2,] Percent 73.7 7.2 6.2 2.4 0.4 0.1 10.1 100
G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v1_panss_g16)
v1_panss_g16<-c(v1_clin$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 <NA>
## [1,] No. cases 1109 155 224 75 34 6 183 1786
## [2,] Percent 62.1 8.7 12.5 4.2 1.9 0.3 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 74.00 219
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 58.00 141.00 256
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)
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$v1_ids_c_s1_ids1_einschlafschw)
v1_idsc_itm1<-factor(v1_idsc_itm1, ordered=T)
descT(v1_idsc_itm1)
## 0 1 2 3 <NA>
## [1,] No. cases 1112 246 124 113 191 1786
## [2,] Percent 62.3 13.8 6.9 6.3 10.7 100
Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v1_idsc_itm2)
v1_idsc_itm2<-c(v1_clin$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$v1_ids_c_s1_ids3_frueh_aufw)
v1_idsc_itm3<-factor(v1_idsc_itm3, ordered=T)
descT(v1_idsc_itm3)
## 0 1 2 3 <NA>
## [1,] No. cases 1259 123 108 98 198 1786
## [2,] Percent 70.5 6.9 6 5.5 11.1 100
Item 4 Hypersomnia (ordinal [0,1,2,3], v1_idsc_itm4)
v1_idsc_itm4<-c(v1_clin$v1_ids_c_s1_ids4_hypersomnie,v1_con$v1_ids_c_s1_ids4_hypersomnie)
v1_idsc_itm4<-factor(v1_idsc_itm4, ordered=T)
descT(v1_idsc_itm4)
## 0 1 2 3 <NA>
## [1,] No. cases 1074 330 154 30 198 1786
## [2,] Percent 60.1 18.5 8.6 1.7 11.1 100
Item 5 Mood (sad) (ordinal [0,1,2,3], v1_idsc_itm5)
v1_idsc_itm5<-c(v1_clin$v1_ids_c_s1_ids5_stimmung_trgk,v1_con$v1_ids_c_s1_ids5_stimmung_trgk)
v1_idsc_itm5<-factor(v1_idsc_itm5, ordered=T)
descT(v1_idsc_itm5)
## 0 1 2 3 <NA>
## [1,] No. cases 914 434 149 92 197 1786
## [2,] Percent 51.2 24.3 8.3 5.2 11 100
Item 6 Mood (irritable) (ordinal [0,1,2,3], v1_idsc_itm6)
v1_idsc_itm6<-c(v1_clin$v1_ids_c_s1_ids6_stimmung_grzt,v1_con$v1_ids_c_s1_ids6_stimmung_grzt)
v1_idsc_itm6<-factor(v1_idsc_itm6, ordered=T)
descT(v1_idsc_itm6)
## 0 1 2 3 <NA>
## [1,] No. cases 1173 324 60 36 193 1786
## [2,] Percent 65.7 18.1 3.4 2 10.8 100
Item 7 Mood (anxious) (ordinal [0,1,2,3], v1_idsc_itm7)
v1_idsc_itm7<-c(v1_clin$v1_ids_c_s1_ids7_stimmung_agst,v1_con$v1_ids_c_s1_ids7_stimmung_agst)
v1_idsc_itm7<-factor(v1_idsc_itm7, ordered=T)
descT(v1_idsc_itm7)
## 0 1 2 3 <NA>
## [1,] No. cases 1020 349 143 78 196 1786
## [2,] Percent 57.1 19.5 8 4.4 11 100
Item 8 Reactivity of mood (ordinal [0,1,2,3], v1_idsc_itm8)
v1_idsc_itm8<-c(v1_clin$v1_ids_c_s1_ids8_reakt_stimmung,v1_con$v1_ids_c_s1_ids8_reakt_stimmung)
v1_idsc_itm8<-factor(v1_idsc_itm8, ordered=T)
descT(v1_idsc_itm8)
## 0 1 2 3 <NA>
## [1,] No. cases 1259 206 80 47 194 1786
## [2,] Percent 70.5 11.5 4.5 2.6 10.9 100
Item 9 Mood Variation (ordinal [0,1,2,3], v1_idsc_itm9)
v1_idsc_itm9<-c(v1_clin$v1_ids_c_s1_ids9_stimmungsschw,v1_con$v1_ids_c_s1_ids9_stimmungsschw)
v1_idsc_itm9<-factor(v1_idsc_itm9, ordered=T)
descT(v1_idsc_itm9)
## 0 1 2 3 <NA>
## [1,] No. cases 1189 150 88 159 200 1786
## [2,] Percent 66.6 8.4 4.9 8.9 11.2 100
Item 9A (categorical [M, A, N], v1_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).
v1_idsc_itm9a_pre<-c(v1_clin$v1_ids_c_s1_ids9a_stimmungsschw,v1_con$v1_ids_c_s1_ids9a_stimmungsschw)
v1_idsc_itm9a<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==1, "M", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==2, "A", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==3, "N", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-factor(v1_idsc_itm9a, ordered=F)
descT(v1_idsc_itm9a)
## -999 A M N <NA>
## [1,] No. cases 1189 18 168 75 336 1786
## [2,] Percent 66.6 1 9.4 4.2 18.8 100
Item 9B (dichotomous, v1_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.
v1_idsc_itm9b_pre<-c(v1_clin$v1_ids_c_s1_ids9b_stimmungsschw,v1_con$v1_ids_c_s1_ids9b_stimmungsschw)
v1_idsc_itm9b<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm9b<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9b_pre==0, "N", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9b))
v1_idsc_itm9b<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9b_pre==1, "Y", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9b))
v1_idsc_itm9b<-factor(v1_idsc_itm9b, ordered=F)
descT(v1_idsc_itm9b)
## -999 N Y <NA>
## [1,] No. cases 1189 117 97 383 1786
## [2,] Percent 66.6 6.6 5.4 21.4 100
Item 10 Quality of mood (ordinal [0,1,2,3], v1_idsc_itm10)
v1_idsc_itm10<-c(v1_clin$v1_ids_c_s1_ids10_quali_stimmung,v1_con$v1_ids_c_s1_ids10_quali_stimmung)
v1_idsc_itm10<-factor(v1_idsc_itm10, ordered=T)
descT(v1_idsc_itm10)
## 0 1 2 3 <NA>
## [1,] No. cases 1247 117 64 145 213 1786
## [2,] Percent 69.8 6.6 3.6 8.1 11.9 100
Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.
The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.
Item 11 (ordinal [0,1,2,3], v1_idsc_itm11)
v1_idsc_app_verm<-c(v1_clin$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$v1_ids_c_s2_ids12_appetit_steig)
v1_idsc_itm11<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm11<-ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==T, NA,
ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==F, -999,
ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==T,
v1_idsc_app_verm,
ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_verm>v1_idsc_app_gest), v1_idsc_app_verm,
ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_gest>=v1_idsc_app_verm),-999,v1_idsc_itm11)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v1_idsc_itm11)
## -999 0 1 2 3 <NA>
## [1,] No. cases 454 950 146 27 12 197 1786
## [2,] Percent 25.4 53.2 8.2 1.5 0.7 11 100
Item 12 (ordinal [0,1,2,3], v1_idsc_itm12)
v1_idsc_itm12<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm12<-ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==T, NA,
ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==F,
v1_idsc_app_gest,
ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==T,
-999,
ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_verm>v1_idsc_app_gest), -999,
ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_gest>=v1_idsc_app_verm), v1_idsc_app_gest,v1_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v1_idsc_itm12)
## -999 0 1 2 3 <NA>
## [1,] No. cases 1135 145 184 78 47 197 1786
## [2,] Percent 63.5 8.1 10.3 4.4 2.6 11 100
Item 13 (ordinal [0,1,2,3], v1_idsc_itm13)
v1_idsc_gew_abn<-c(v1_clin$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$v1_ids_c_s2_ids14_gewichtszun)
v1_idsc_itm13<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm13<-ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==T, NA,
ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==F, -999,
ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==T, v1_idsc_gew_abn,
ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_abn>v1_idsc_gew_zun), v1_idsc_gew_abn, ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_zun >= v1_idsc_gew_abn),-999,v1_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v1_idsc_itm13)
## -999 0 1 2 3 <NA>
## [1,] No. cases 491 892 75 79 44 205 1786
## [2,] Percent 27.5 49.9 4.2 4.4 2.5 11.5 100
Item 14 (ordinal [0,1,2,3], v1_idsc_itm14)
v1_idsc_itm14<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm14<-ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==T, NA,
ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==F,
v1_idsc_gew_zun,
ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==T, -999,
ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_abn>v1_idsc_gew_zun), -999,
ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_zun>=v1_idsc_gew_abn), v1_idsc_gew_zun,v1_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v1_idsc_itm14)
## -999 0 1 2 3 <NA>
## [1,] No. cases 1090 202 114 101 74 205 1786
## [2,] Percent 61 11.3 6.4 5.7 4.1 11.5 100
Item 15 Concentration/decision making (ordinal [0,1,2,3], v1_idsc_itm15)
v1_idsc_itm15<-c(v1_clin$v1_ids_c_s2_ids15_konz_entscheid,v1_con$v1_ids_c_s2_ids15_konz_entscheid)
v1_idsc_itm15<-factor(v1_idsc_itm15, ordered=T)
descT(v1_idsc_itm15)
## 0 1 2 3 <NA>
## [1,] No. cases 850 379 288 67 202 1786
## [2,] Percent 47.6 21.2 16.1 3.8 11.3 100
Item 16 Outlook (self) (ordinal [0,1,2,3], v1_idsc_itm16)
v1_idsc_itm16<-c(v1_clin$v1_ids_c_s2_ids16_selbstbild,v1_con$v1_ids_c_s2_ids16_selbstbild)
v1_idsc_itm16<-factor(v1_idsc_itm16, ordered=T)
descT(v1_idsc_itm16)
## 0 1 2 3 <NA>
## [1,] No. cases 1136 241 105 109 195 1786
## [2,] Percent 63.6 13.5 5.9 6.1 10.9 100
Item 17 Outlook (future) (ordinal [0,1,2,3], v1_idsc_itm17)
v1_idsc_itm17<-c(v1_clin$v1_ids_c_s2_ids17_zukunftssicht,v1_con$v1_ids_c_s2_ids17_zukunftssicht)
v1_idsc_itm17<-factor(v1_idsc_itm17, ordered=T)
descT(v1_idsc_itm17)
## 0 1 2 3 <NA>
## [1,] No. cases 999 405 156 30 196 1786
## [2,] Percent 55.9 22.7 8.7 1.7 11 100
Item 18 Suicidal ideation (ordinal [0,1,2,3], v1_idsc_itm18)
v1_idsc_itm18<-c(v1_clin$v1_ids_c_s2_ids18_selbstmordged,v1_con$v1_ids_c_s2_ids18_selbstmordged)
v1_idsc_itm18<-factor(v1_idsc_itm18, ordered=T)
descT(v1_idsc_itm18)
## 0 1 2 3 <NA>
## [1,] No. cases 1422 94 69 9 192 1786
## [2,] Percent 79.6 5.3 3.9 0.5 10.8 100
Item 19 Involvement (ordinal [0,1,2,3], v1_idsc_itm19)
v1_idsc_itm19<-c(v1_clin$v1_ids_c_s2_ids19_interess_aktiv,v1_con$v1_ids_c_s2_ids19_interess_aktiv)
v1_idsc_itm19<-factor(v1_idsc_itm19, ordered=T)
descT(v1_idsc_itm19)
## 0 1 2 3 <NA>
## [1,] No. cases 1204 285 57 44 196 1786
## [2,] Percent 67.4 16 3.2 2.5 11 100
Item 20 Energy/fatigability (ordinal [0,1,2,3], v1_idsc_itm20)
v1_idsc_itm20<-c(v1_clin$v1_ids_c_s2_ids20_energ_ermued,v1_con$v1_ids_c_s2_ids20_energ_ermued)
v1_idsc_itm20<-factor(v1_idsc_itm20, ordered=T)
descT(v1_idsc_itm20)
## 0 1 2 3 <NA>
## [1,] No. cases 961 403 185 44 193 1786
## [2,] Percent 53.8 22.6 10.4 2.5 10.8 100
Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v1_idsc_itm21)
v1_idsc_itm21<-c(v1_clin$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$v1_ids_c_s3_ids22_sex_interesse)
v1_idsc_itm22<-factor(v1_idsc_itm22, ordered=T)
descT(v1_idsc_itm22)
## 0 1 2 3 <NA>
## [1,] No. cases 1107 110 214 151 204 1786
## [2,] Percent 62 6.2 12 8.5 11.4 100
Item 23 Psychomotor slowing (ordinal [0,1,2,3], v1_idsc_itm23)
v1_idsc_itm23<-c(v1_clin$v1_ids_c_s3_ids23_psymo_hemm,v1_con$v1_ids_c_s3_ids23_psymo_hemm)
v1_idsc_itm23<-factor(v1_idsc_itm23, ordered=T)
descT(v1_idsc_itm23)
## 0 1 2 3 <NA>
## [1,] No. cases 1209 308 64 8 197 1786
## [2,] Percent 67.7 17.2 3.6 0.4 11 100
Item 24 Psychomotor agitation (ordinal [0,1,2,3], v1_idsc_itm24)
v1_idsc_itm24<-c(v1_clin$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$v1_ids_c_s3_ids25_som_beschw)
v1_idsc_itm25<-factor(v1_idsc_itm25, ordered=T)
descT(v1_idsc_itm25)
## 0 1 2 3 <NA>
## [1,] No. cases 1115 361 81 29 200 1786
## [2,] Percent 62.4 20.2 4.5 1.6 11.2 100
Item 26 Sympathetic arousal (ordinal [0,1,2,3], v1_idsc_itm26)
v1_idsc_itm26<-c(v1_clin$v1_ids_c_s3_ids26_veg_erreg,v1_con$v1_ids_c_s3_ids26_veg_erreg)
v1_idsc_itm26<-factor(v1_idsc_itm26, ordered=T)
descT(v1_idsc_itm26)
## 0 1 2 3 <NA>
## [1,] No. cases 1147 334 88 20 197 1786
## [2,] Percent 64.2 18.7 4.9 1.1 11 100
Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v1_idsc_itm27)
v1_idsc_itm27<-c(v1_clin$v1_ids_c_s3_ids27_panik_phob,v1_con$v1_ids_c_s3_ids27_panik_phob)
v1_idsc_itm27<-factor(v1_idsc_itm27, ordered=T)
descT(v1_idsc_itm27)
## 0 1 2 3 <NA>
## [1,] No. cases 1354 156 55 24 197 1786
## [2,] Percent 75.8 8.7 3.1 1.3 11 100
Item 28 Gastrointestinal (ordinal [0,1,2,3], v1_idsc_itm28)
v1_idsc_itm28<-c(v1_clin$v1_ids_c_s3_ids28_verdauung,v1_con$v1_ids_c_s3_ids28_verdauung)
v1_idsc_itm28<-factor(v1_idsc_itm28, ordered=T)
descT(v1_idsc_itm28)
## 0 1 2 3 <NA>
## [1,] No. cases 1317 161 77 33 198 1786
## [2,] Percent 73.7 9 4.3 1.8 11.1 100
Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v1_idsc_itm29)
v1_idsc_itm29<-c(v1_clin$v1_ids_c_s3_ids29_pers_bezieh,v1_con$v1_ids_c_s3_ids29_pers_bezieh)
v1_idsc_itm29<-factor(v1_idsc_itm29, ordered=T)
descT(v1_idsc_itm29)
## 0 1 2 3 <NA>
## [1,] No. cases 1263 218 79 31 195 1786
## [2,] Percent 70.7 12.2 4.4 1.7 10.9 100
Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v1_idsc_itm30)
v1_idsc_itm30<-c(v1_clin$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 63.0 328
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)
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$v1_ymrs_ymrs1_gehob_stimm)
v1_ymrs_itm1<-factor(v1_ymrs_itm1, ordered=T)
descT(v1_ymrs_itm1)
## 0 1 2 3 4 <NA>
## [1,] No. cases 1270 159 84 21 5 247 1786
## [2,] Percent 71.1 8.9 4.7 1.2 0.3 13.8 100
Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v1_ymrs_itm2)
v1_ymrs_itm2<-c(v1_clin$v1_ymrs_ymrs2_gest_aktiv,v1_con$v1_ymrs_ymrs2_gest_aktiv)
v1_ymrs_itm2<-factor(v1_ymrs_itm2, ordered=T)
descT(v1_ymrs_itm2)
## 0 1 2 3 4 <NA>
## [1,] No. cases 1303 137 67 27 3 249 1786
## [2,] Percent 73 7.7 3.8 1.5 0.2 13.9 100
Item 3 Sexual interest (ordinal [0,1,2,3,4], v1_ymrs_itm3)
v1_ymrs_itm3<-c(v1_clin$v1_ymrs_ymrs3_sex_interesse,v1_con$v1_ymrs_ymrs3_sex_interesse)
v1_ymrs_itm3<-factor(v1_ymrs_itm3, ordered=T)
descT(v1_ymrs_itm3)
## 0 1 2 3 <NA>
## [1,] No. cases 1431 62 31 10 252 1786
## [2,] Percent 80.1 3.5 1.7 0.6 14.1 100
Item 4 Sleep (ordinal [0,1,2,3,4], v1_ymrs_itm4)
v1_ymrs_itm4<-c(v1_clin$v1_ymrs_ymrs4_schlaf,v1_con$v1_ymrs_ymrs4_schlaf)
v1_ymrs_itm4<-factor(v1_ymrs_itm4, ordered=T)
descT(v1_ymrs_itm4)
## 0 1 2 3 4 <NA>
## [1,] No. cases 1364 86 45 41 2 248 1786
## [2,] Percent 76.4 4.8 2.5 2.3 0.1 13.9 100
Item 5 Irritability (ordinal [0,2,4,6,8], v1_ymrs_itm5)
v1_ymrs_itm5<-c(v1_clin$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$v1_ymrs_ymrs6_sprechweise)
v1_ymrs_itm6<-factor(v1_ymrs_itm6, ordered=T)
descT(v1_ymrs_itm6)
## 0 2 4 6 8 <NA>
## [1,] No. cases 1300 112 85 38 3 248 1786
## [2,] Percent 72.8 6.3 4.8 2.1 0.2 13.9 100
Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v1_ymrs_itm7)
v1_ymrs_itm7<-c(v1_clin$v1_ymrs_ymrs7_sprachstoer,v1_con$v1_ymrs_ymrs7_sprachstoer)
v1_ymrs_itm7<-factor(v1_ymrs_itm7, ordered=T)
descT(v1_ymrs_itm7)
## 0 1 2 3 <NA>
## [1,] No. cases 1303 163 61 9 250 1786
## [2,] Percent 73 9.1 3.4 0.5 14 100
Item 8 Content (ordinal [0,2,4,6,8], v1_ymrs_itm8)
v1_ymrs_itm8<-c(v1_clin$v1_ymrs_ymrs8_inhalte,v1_con$v1_ymrs_ymrs8_inhalte)
v1_ymrs_itm8<-factor(v1_ymrs_itm8, ordered=T)
descT(v1_ymrs_itm8)
## 0 2 4 6 8 <NA>
## [1,] No. cases 1407 71 15 21 21 251 1786
## [2,] Percent 78.8 4 0.8 1.2 1.2 14.1 100
Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v1_ymrs_itm9)
v1_ymrs_itm9<-c(v1_clin$v1_ymrs_ymrs9_exp_aggr_verh,v1_con$v1_ymrs_ymrs9_exp_aggr_verh)
v1_ymrs_itm9<-factor(v1_ymrs_itm9, ordered=T)
descT(v1_ymrs_itm9)
## 0 2 4 <NA>
## [1,] No. cases 1460 68 5 253 1786
## [2,] Percent 81.7 3.8 0.3 14.2 100
Item 10 Appearance (ordinal [0,1,2,3,4], v1_ymrs_itm10)
v1_ymrs_itm10<-c(v1_clin$v1_ymrs_ymrs10_erscheinung,v1_con$v1_ymrs_ymrs10_erscheinung)
v1_ymrs_itm10<-factor(v1_ymrs_itm10, ordered=T)
descT(v1_ymrs_itm10)
## 0 1 2 3 <NA>
## [1,] No. cases 1382 129 23 3 249 1786
## [2,] Percent 77.4 7.2 1.3 0.2 13.9 100
Item 11 Insight (ordinal [0,1,2,3,4], v1_ymrs_itm11)
v1_ymrs_itm11<-c(v1_clin$v1_ymrs_ymrs11_krkh_einsicht,v1_con$v1_ymrs_ymrs11_krkh_einsicht)
v1_ymrs_itm11<-factor(v1_ymrs_itm11, ordered=T)
descT(v1_ymrs_itm11)
## 0 1 2 3 4 <NA>
## [1,] No. cases 1425 52 29 11 8 261 1786
## [2,] Percent 79.8 2.9 1.6 0.6 0.4 14.6 100
Create YMRS total score (continuous [0-60], v1_ymrs_sum)
v1_ymrs_sum<-(as.numeric.factor(v1_ymrs_itm1)+
as.numeric.factor(v1_ymrs_itm2)+
as.numeric.factor(v1_ymrs_itm3)+
as.numeric.factor(v1_ymrs_itm4)+
as.numeric.factor(v1_ymrs_itm5)+
as.numeric.factor(v1_ymrs_itm6)+
as.numeric.factor(v1_ymrs_itm7)+
as.numeric.factor(v1_ymrs_itm8)+
as.numeric.factor(v1_ymrs_itm9)+
as.numeric.factor(v1_ymrs_itm10)+
as.numeric.factor(v1_ymrs_itm11))
summary(v1_ymrs_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 0.000 2.557 3.000 39.000 286
Create dataset
v1_symp_ymrs<-data.frame(v1_ymrs_itm1,
v1_ymrs_itm2,
v1_ymrs_itm3,
v1_ymrs_itm4,
v1_ymrs_itm5,
v1_ymrs_itm6,
v1_ymrs_itm7,
v1_ymrs_itm8,
v1_ymrs_itm9,
v1_ymrs_itm10,
v1_ymrs_itm11,
v1_ymrs_sum)
The CGI (see e.g. Busner & Targum, 2007) measures illness severity. The degree of impairment is to be quantified on a scale from zero to seven. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “normal, not at all ill”-1 to “extremely ill”-7. Please note that in all other study visits, the improvement scale (whether or not there was improvement compared to the last study visit) is also assessed. All control subjects also have -999 in this variable.
v1_cgi_s<-c(v1_clin$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
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.
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
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)
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 1555 130 33 3 65 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 1404 210 1786
## [2,] Percent 1.5 8.1 78.6 11.8 100
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 34.73 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
## 0.0000 0.0000 0.0000 0.1342 0.0000 5.0000 102
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 79.62 96.00 300.00 153
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 0.5902 1.0000 17.0000 168
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 9.447 11.000 16.000 125
Histogram
hist(v1_nrpsy_dgt_sp_frw,breaks=c(1:16), main="Digit-span forward", xlab="Score",ylab="Number of Individuals")
Backward (continuous [number of items], v1_nrpsy_dgt_sp_bck)
v1_nrpsy_dgt_sp_bck<-c(v1_clin$v1_npu_zns_002,v1_con$v1_npu_np_wie_002)
summary(v1_nrpsy_dgt_sp_bck)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 5.000 6.000 6.364 8.000 14.000 128
Histogram
hist(v1_nrpsy_dgt_sp_bck, breaks=c(1:14), xlim=c(0,14), main="Digit-span backward", xlab="Score",ylab="Number of Individuals")
This test measures processing speed. The participant is presented with rows of numbers and an empty space below each number. In these empty spaces, the participant is asked to fill in symbols that match the number above it. The respective number-symbole association is given at the top of the test sheet. It is measured how many correct symbols the participant can fill in during a 120 second period. Participants that only partially completed the test were excluded and are coded as -999.
v1_introcheck3<-c(v1_clin$v1_npu_np_introcheck3,v1_con$v1_npu_np_hawier)
v1_nrpsy_dg_sym_pre<-c(v1_clin$v1_npu_zst_001,v1_con$v1_npu_np_hawier_001)
v1_nrpsy_dg_sym<-ifelse(v1_introcheck3==1, v1_nrpsy_dg_sym_pre,
ifelse(v1_introcheck3==9,-999,
ifelse(v1_introcheck3==0,NA,NA)))
summary(v1_nrpsy_dg_sym[v1_nrpsy_dg_sym>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 50.00 63.00 63.93 78.00 133.00 142
Histogram
hist(v1_nrpsy_dg_sym[v1_nrpsy_dg_sym>=0], breaks=c(1:133), main="Digit-Symbol-Test", xlab="Number of correct symbols")
This test assesses crystallized IQ. Crystallized IQ rises with advancing age. In this test, subjects are presented with 37 sets of five words each. Four “words” of each set are artificial (i.e. do not exist in the German language), one word really exists. They are instructed that they may be familiar with one word in each set, and asked to cross out that word. The known words start with easy ones and their difficulty increases. The sum score of correctly identified real words is the final score.
Important: only persons with German as a native language and who completed the test are included in the present dataset, those who were excluded due to these criteria are coded -999.
v1_introcheck4<-c(v1_clin$v1_npu_np_introcheck4,v1_con$v1_npu_np_mwtb)
v1_nrpsy_mwtb_pre<-c(v1_clin$v1_npu_mwt_001,v1_con$v1_npu_np_mwtb_001)
v1_nrpsy_mwtb<-ifelse((v1_introcheck4=="1" & v1_nrpsy_lng=="mother tongue"),v1_nrpsy_mwtb_pre,-999)
#Set one participant with zero recognized words to NA - this person either misunderstood the instructions or
#gave wrong answers on purpose
v1_nrpsy_mwtb[v1_nrpsy_mwtb==0]<-NA
summary(v1_nrpsy_mwtb[v1_nrpsy_mwtb>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 26.00 29.00 28.44 32.00 37.00 62
Histogram
hist(v1_nrpsy_mwtb[v1_nrpsy_mwtb>=0], breaks=c(0:37), main="Multiple-Choice Vocabulary Intelligence Test", xlab="Score")
Create dataset
v1_nrpsy<-data.frame(v1_nrpsy_com,
v1_nrpsy_lng,
v1_nrpsy_mtv,
v1_nrpsy_tmt_A_rt,
v1_nrpsy_tmt_A_err,
v1_nrpsy_tmt_B_rt,
v1_nrpsy_tmt_B_err,
v1_nrpsy_dgt_sp_frw,
v1_nrpsy_dgt_sp_bck,
v1_nrpsy_dg_sym,
v1_nrpsy_mwtb)
All participants were asked to fill out questionnaires on the following topics: religious beliefs, current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, current quality of life (WHOQOL-BREF) and personality (big five). Additionally, control participants completed the CAPE-42 questionnaire (Community Assessment of Psycic Experiences) and the Short Form Health Survey (SF-12). Medication adherence (compliance) was only assessed in clinical participants. All questionnaires are briefly explained below. Importantly, there are items in our database assessing whether a questionnaire was filled out correctly. Questionnaires considered unusable are NOT included in this dataset (i.e. are NA).
The CAPE-42 was developed by Jim van Os, Hélène Verdoux and Manon Hanssen. It is based on the PDI-21 and PDI-40 developed by Emmanuelle Peters et al (2004). It asesses psychotic-like experiences and was only assessed in control subjects. All items have a part A (“Never”,“Sometimes”,“Often”,“Nearly always”; coded 0-3, repectively) and a part B, which is to be answered if the answer to the corresponding part A item was not “Never” and asks how distressed the participant was by this experience (“Not distressed”,“A bit distressed”,“Quite distressed” or “Very distressed”; coded 0-3, repectively).
“Do you ever feel sad?” (ordinal [0,1,2,3], v1_cape_itm1A)
v1_cape_recode(v1_con$v1_cape_cape_hsjtraugefa1,"v1_cape_itm1A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 7 256 35 1 167 1786
## [2,] Percent 73.9 0.4 14.3 2 0.1 9.4 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm1B)
v1_cape_recode(v1_con$v1_cape_cape_hsjtraugefb1,"v1_cape_itm1B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 124 143 25 1 173 1786
## [2,] Percent 73.9 6.9 8 1.4 0.1 9.7 100
“Do you ever feel as if people seem to drop hints about you or say things with a double meaning?” (ordinal [0,1,2,3], v1_cape_itm2A)
v1_cape_recode(v1_con$v1_cape_cape_anspapersa1,"v1_cape_itm2A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 94 186 16 4 166 1786
## [2,] Percent 73.9 5.3 10.4 0.9 0.2 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm2B)
v1_cape_recode(v1_con$v1_cape_cape_anspapersb1,"v1_cape_itm2B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 59 111 28 8 260 1786
## [2,] Percent 73.9 3.3 6.2 1.6 0.4 14.6 100
“Do you ever feel that you are not a very animated person?” (ordinal [0,1,2,3], v1_cape_itm3A)
v1_cape_recode(v1_con$v1_cape_cape_nlebha1,"v1_cape_itm3A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 185 99 13 3 166 1786
## [2,] Percent 73.9 10.4 5.5 0.7 0.2 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm3B)
v1_cape_recode(v1_con$v1_cape_cape_nlebhb1,"v1_cape_itm3B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 59 47 9 351 1786
## [2,] Percent 73.9 3.3 2.6 0.5 19.7 100
“Do you ever feel that you are not much of a talker when you are conversing with other people?” (ordinal [0,1,2,3], v1_cape_itm4A)
v1_cape_recode(v1_con$v1_cape_cape_nsaga1,"v1_cape_itm4A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 126 140 30 4 166 1786
## [2,] Percent 73.9 7.1 7.8 1.7 0.2 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm4B)
v1_cape_recode(v1_con$v1_cape_cape_nsagb1,"v1_cape_itm4B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 92 72 10 292 1786
## [2,] Percent 73.9 5.2 4 0.6 16.3 100
“Do you ever feel as if things in magazines or on TV were written especially for you?” (ordinal [0,1,2,3], v1_cape_itm5A)
v1_cape_recode(v1_con$v1_cape_cape_auszeita1,"v1_cape_itm5A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 258 36 6 166 1786
## [2,] Percent 73.9 14.4 2 0.3 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm5B)
v1_cape_recode(v1_con$v1_cape_cape_auszeitb1,"v1_cape_itm5B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 33 7 2 424 1786
## [2,] Percent 73.9 1.8 0.4 0.1 23.7 100
“Do you ever feel as if some people are not what they seem to be?” (ordinal [0,1,2,3], v1_cape_itm6A)
v1_cape_recode(v1_con$v1_cape_cape_geflnswsea1,"v1_cape_itm6A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 40 184 70 6 166 1786
## [2,] Percent 73.9 2.2 10.3 3.9 0.3 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm6B)
v1_cape_recode(v1_con$v1_cape_cape_geflnswseb1,"v1_cape_itm6B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 108 127 18 6 207 1786
## [2,] Percent 73.9 6 7.1 1 0.3 11.6 100
“Do you ever feel as if you are being persecuted in some way?” (ordinal [0,1,2,3], v1_cape_itm7A)
v1_cape_recode(v1_con$v1_cape_cape_verfa1,"v1_cape_itm7A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 267 32 1 166 1786
## [2,] Percent 73.9 14.9 1.8 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm7B)
v1_cape_recode(v1_con$v1_cape_cape_verfb1,"v1_cape_itm7B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 16 11 6 433 1786
## [2,] Percent 73.9 0.9 0.6 0.3 24.2 100
“Do you ever feel that you experience few or no emotions at important events?” (ordinal [0,1,2,3], v1_cape_itm8A)
v1_cape_recode(v1_con$v1_cape_cape_kgefa1,"v1_cape_itm8A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 195 89 13 2 167 1786
## [2,] Percent 73.9 10.9 5 0.7 0.1 9.4 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm8B)
v1_cape_recode(v1_con$v1_cape_cape_kgefb1,"v1_cape_itm8B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 59 40 4 1 362 1786
## [2,] Percent 73.9 3.3 2.2 0.2 0.1 20.3 100
“Do you ever feel pessimistic about everything?” (ordinal [0,1,2,3], v1_cape_itm9A)
v1_cape_recode(v1_con$v1_cape_cape_negseha1,"v1_cape_itm9A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 191 98 10 1 166 1786
## [2,] Percent 73.9 10.7 5.5 0.6 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm9B)
v1_cape_recode(v1_con$v1_cape_cape_negsehb1,"v1_cape_itm9B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 21 66 17 5 357 1786
## [2,] Percent 73.9 1.2 3.7 1 0.3 20 100
“Do you ever feel as if there is a conspiracy against you?” (ordinal [0,1,2,3], v1_cape_itm10A)
v1_cape_recode(v1_con$v1_cape_cape_kompla1,"v1_cape_itm10A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 260 39 1 166 1786
## [2,] Percent 73.9 14.6 2.2 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm10B)
v1_cape_recode(v1_con$v1_cape_cape_komplb1,"v1_cape_itm10B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 5 23 9 3 426 1786
## [2,] Percent 73.9 0.3 1.3 0.5 0.2 23.9 100
“Do you ever feel as if you are destined to be someone very important?” (ordinal [0,1,2,3], v1_cape_itm11A)
v1_cape_recode(v1_con$v1_cape_cape_bestwpa1,"v1_cape_itm11A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 230 61 9 1 165 1786
## [2,] Percent 73.9 12.9 3.4 0.5 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm11B)
v1_cape_recode(v1_con$v1_cape_cape_bestwpb1,"v1_cape_itm11B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 66 4 1 395 1786
## [2,] Percent 73.9 3.7 0.2 0.1 22.1 100
“Do you ever feel as if there is no future for you?” (ordinal [0,1,2,3], v1_cape_itm12A)
v1_cape_recode(v1_con$v1_cape_cape_keinza1,"v1_cape_itm12A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 244 51 5 166 1786
## [2,] Percent 73.9 13.7 2.9 0.3 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm12B)
v1_cape_recode(v1_con$v1_cape_cape_keinzb1,"v1_cape_itm12B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 11 30 10 5 410 1786
## [2,] Percent 73.9 0.6 1.7 0.6 0.3 23 100
“Do you ever feel that you are a very special or unusual person?” (ordinal [0,1,2,3], v1_cape_itm13A)
v1_cape_recode(v1_con$v1_cape_cape_gefaupersa1,"v1_cape_itm13A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 169 98 29 5 165 1786
## [2,] Percent 73.9 9.5 5.5 1.6 0.3 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm13B)
v1_cape_recode(v1_con$v1_cape_cape_gefaupersb1,"v1_cape_itm13B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 111 16 3 336 1786
## [2,] Percent 73.9 6.2 0.9 0.2 18.8 100
“Do you ever feel as if you do not want to live anymore?” (ordinal [0,1,2,3], v1_cape_itm14A)
v1_cape_recode(v1_con$v1_cape_cape_nileba1,"v1_cape_itm14A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 234 63 2 167 1786
## [2,] Percent 73.9 13.1 3.5 0.1 9.4 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm14B)
v1_cape_recode(v1_con$v1_cape_cape_nileba1,"v1_cape_itm14B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 234 63 2 167 1786
## [2,] Percent 73.9 13.1 3.5 0.1 9.4 100
“Do you ever think that people can communicate telepathically?” (ordinal [0,1,2,3], v1_cape_itm15A)
v1_cape_recode(v1_con$v1_cape_cape_telea1,"v1_cape_itm15A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 214 69 14 3 166 1786
## [2,] Percent 73.9 12 3.9 0.8 0.2 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm15B)
v1_cape_recode(v1_con$v1_cape_cape_teleb1,"v1_cape_itm15B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 81 4 1 380 1786
## [2,] Percent 73.9 4.5 0.2 0.1 21.3 100
“Do you ever feel that you have no interest to be with other people?” (ordinal [0,1,2,3], v1_cape_itm16A)
v1_cape_recode(v1_con$v1_cape_cape_kbedgesa1,"v1_cape_itm16A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 97 179 23 2 165 1786
## [2,] Percent 73.9 5.4 10 1.3 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm16B)
v1_cape_recode(v1_con$v1_cape_cape_kbedgesb1,"v1_cape_itm16B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 179 20 5 262 1786
## [2,] Percent 73.9 10 1.1 0.3 14.7 100
“Do you ever feel as if electrical devices such as computers can influence the way you think?” (ordinal [0,1,2,3], v1_cape_itm17A)
v1_cape_recode(v1_con$v1_cape_cape_elegeggeda1,"v1_cape_itm17A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 278 18 4 1 165 1786
## [2,] Percent 73.9 15.6 1 0.2 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm17B)
v1_cape_recode(v1_con$v1_cape_cape_elegeggedb1,"v1_cape_itm17B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 8 10 4 444 1786
## [2,] Percent 73.9 0.4 0.6 0.2 24.9 100
“Do you ever feel that you are lacking in motivation to do things?” (ordinal [0,1,2,3], v1_cape_itm18A)
v1_cape_recode(v1_con$v1_cape_cape_motfehla1,"v1_cape_itm18A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 55 201 39 6 165 1786
## [2,] Percent 73.9 3.1 11.3 2.2 0.3 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm18B)
v1_cape_recode(v1_con$v1_cape_cape_motfehlb1,"v1_cape_itm18B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 77 115 41 12 221 1786
## [2,] Percent 73.9 4.3 6.4 2.3 0.7 12.4 100
“Do you ever cry about nothing?” (ordinal [0,1,2,3], v1_cape_itm19A)
v1_cape_recode(v1_con$v1_cape_cape_ougewa1,"v1_cape_itm19A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 243 56 1 1 165 1786
## [2,] Percent 73.9 13.6 3.1 0.1 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm19B)
v1_cape_recode(v1_con$v1_cape_cape_ougewb1,"v1_cape_itm19B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 34 18 6 408 1786
## [2,] Percent 73.9 1.9 1 0.3 22.8 100
“Do you believe in the power of witchcraft, voodoo or the occult?” (ordinal [0,1,2,3], v1_cape_itm20A)
v1_cape_recode(v1_con$v1_cape_cape_hexvoa1,"v1_cape_itm20A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 247 39 8 7 165 1786
## [2,] Percent 73.9 13.8 2.2 0.4 0.4 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm20B)
v1_cape_recode(v1_con$v1_cape_cape_hexvob1,"v1_cape_itm20B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 46 5 1 1 413 1786
## [2,] Percent 73.9 2.6 0.3 0.1 0.1 23.1 100
“Do you ever feel that you are lacking in energy?” (ordinal [0,1,2,3], v1_cape_itm21A)
v1_cape_recode(v1_con$v1_cape_cape_energiela1,"v1_cape_itm21A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 73 205 21 2 165 1786
## [2,] Percent 73.9 4.1 11.5 1.2 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm21B)
v1_cape_recode(v1_con$v1_cape_cape_energielb1,"v1_cape_itm21B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 79 106 32 10 239 1786
## [2,] Percent 73.9 4.4 5.9 1.8 0.6 13.4 100
“Do you ever feel that people look at you oddly because of your appearance?” (ordinal [0,1,2,3], v1_cape_itm22A)
v1_cape_recode(v1_con$v1_cape_cape_sonda1,"v1_cape_itm22A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 163 119 18 1 165 1786
## [2,] Percent 73.9 9.1 6.7 1 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm22B)
v1_cape_recode(v1_con$v1_cape_cape_sondb1,"v1_cape_itm22B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 65 59 11 2 329 1786
## [2,] Percent 73.9 3.6 3.3 0.6 0.1 18.4 100
“Do you ever feel that your mind is empty?” (ordinal [0,1,2,3], v1_cape_itm23A)
v1_cape_recode(v1_con$v1_cape_cape_kopfleera1,"v1_cape_itm23A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 173 122 5 166 1786
## [2,] Percent 73.9 9.7 6.8 0.3 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm23B)
v1_cape_recode(v1_con$v1_cape_cape_kopfleerb1,"v1_cape_itm23B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 54 60 7 5 340 1786
## [2,] Percent 73.9 3 3.4 0.4 0.3 19 100
“Do you ever feel as if the thoughts in your head are being taken away from you?” (ordinal [0,1,2,3], v1_cape_itm24A)
v1_cape_recode(v1_con$v1_cape_cape_gedaka1,"v1_cape_itm24A")
## -999 0 1 <NA>
## [1,] No. cases 1320 290 11 165 1786
## [2,] Percent 73.9 16.2 0.6 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm24B)
v1_cape_recode(v1_con$v1_cape_cape_gedakb1,"v1_cape_itm24B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 6 4 1 455 1786
## [2,] Percent 73.9 0.3 0.2 0.1 25.5 100
“Do you ever feel that you are spending all your days doing nothing?” (ordinal [0,1,2,3], v1_cape_itm25A)
v1_cape_recode(v1_con$v1_cape_cape_tagoetuna1,"v1_cape_itm25A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 151 132 16 2 165 1786
## [2,] Percent 73.9 8.5 7.4 0.9 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm25B)
v1_cape_recode(v1_con$v1_cape_cape_tagoetunb1,"v1_cape_itm25B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 61 55 26 7 317 1786
## [2,] Percent 73.9 3.4 3.1 1.5 0.4 17.7 100
“Do you ever feel as if the thoughts in your head are not your own?” (ordinal [0,1,2,3], v1_cape_itm26A)
v1_cape_recode(v1_con$v1_cape_cape_gedneiga1,"v1_cape_itm26A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 281 18 1 166 1786
## [2,] Percent 73.9 15.7 1 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm26B)
v1_cape_recode(v1_con$v1_cape_cape_gedneigb1,"v1_cape_itm26B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 7 5 5 2 447 1786
## [2,] Percent 73.9 0.4 0.3 0.3 0.1 25 100
" Do you ever feel that your feelings are lacking in intensity?" (ordinal [0,1,2,3], v1_cape_itm27A)
v1_cape_recode(v1_con$v1_cape_cape_gefinta1,"v1_cape_itm27A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 207 85 8 1 165 1786
## [2,] Percent 73.9 11.6 4.8 0.4 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm27B)
v1_cape_recode(v1_con$v1_cape_cape_gefintb1,"v1_cape_itm27B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 39 40 12 3 372 1786
## [2,] Percent 73.9 2.2 2.2 0.7 0.2 20.8 100
“Have your thoughts ever been so vivid that you were worried other people would hear them?” (ordinal [0,1,2,3], v1_cape_itm28A)
v1_cape_recode(v1_con$v1_cape_cape_lebhfa1,"v1_cape_itm28A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 283 15 2 1 165 1786
## [2,] Percent 73.9 15.8 0.8 0.1 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm28B)
v1_cape_recode(v1_con$v1_cape_cape_lebhfb1,"v1_cape_itm28B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 13 4 1 448 1786
## [2,] Percent 73.9 0.7 0.2 0.1 25.1 100
“Do you ever feel that you are lacking in spontaneity?” (ordinal [0,1,2,3], v1_cape_itm29A)
v1_cape_recode(v1_con$v1_cape_cape_sponfehla1,"v1_cape_itm29A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 158 125 16 2 165 1786
## [2,] Percent 73.9 8.8 7 0.9 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm29B)
v1_cape_recode(v1_con$v1_cape_cape_sponfehlb1,"v1_cape_itm29B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 50 77 13 2 324 1786
## [2,] Percent 73.9 2.8 4.3 0.7 0.1 18.1 100
“Do you ever hear your own thoughts being echoed back to you?” (ordinal [0,1,2,3], v1_cape_itm30A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa1,"v1_cape_itm30A")
## -999 0 1 <NA>
## [1,] No. cases 1320 285 16 165 1786
## [2,] Percent 73.9 16 0.9 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm30B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob1,"v1_cape_itm30B")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 11 4 1 450 1786
## [2,] Percent 73.9 0.6 0.2 0.1 25.2 100
“Do you ever feel as if you are under the control of some force or power other than yourself?” (ordinal [0,1,2,3], v1_cape_itm31A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa2,"v1_cape_itm31A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 287 11 2 166 1786
## [2,] Percent 73.9 16.1 0.6 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm31B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob2,"v1_cape_itm31B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 5 4 3 1 453 1786
## [2,] Percent 73.9 0.3 0.2 0.2 0.1 25.4 100
“Do you ever feel that your emotions are blunted?” (ordinal [0,1,2,3], v1_cape_itm32A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa3,"v1_cape_itm32A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 206 83 8 3 166 1786
## [2,] Percent 73.9 11.5 4.6 0.4 0.2 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm32B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob3,"v1_cape_itm32B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 43 37 9 4 373 1786
## [2,] Percent 73.9 2.4 2.1 0.5 0.2 20.9 100
“Do you ever hear voices when you are alone?” (ordinal [0,1,2,3], v1_cape_itm33A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa4,"v1_cape_itm33A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 296 4 1 165 1786
## [2,] Percent 73.9 16.6 0.2 0.1 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm33B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob4,"v1_cape_itm33B")
## -999 0 1 3 <NA>
## [1,] No. cases 1320 3 1 1 461 1786
## [2,] Percent 73.9 0.2 0.1 0.1 25.8 100
“Do you ever hear voices talking to each other when you are alone?” (ordinal [0,1,2,3], v1_cape_itm34A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa5,"v1_cape_itm34A")
## -999 0 <NA>
## [1,] No. cases 1320 300 166 1786
## [2,] Percent 73.9 16.8 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm34B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob5,"v1_cape_itm34B")
## -999 <NA>
## [1,] No. cases 1320 466 1786
## [2,] Percent 73.9 26.1 100
“Do you ever feel that you are neglecting your appearance or personal hygiene?” (ordinal [0,1,2,3], v1_cape_itm35A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa6,"v1_cape_itm35A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 243 52 6 165 1786
## [2,] Percent 73.9 13.6 2.9 0.3 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm35B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob6,"v1_cape_itm35B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 25 23 8 2 408 1786
## [2,] Percent 73.9 1.4 1.3 0.4 0.1 22.8 100
“Do you ever feel that you can never get things done?” (ordinal [0,1,2,3], v1_cape_itm36A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa7,"v1_cape_itm36A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 153 128 17 3 165 1786
## [2,] Percent 73.9 8.6 7.2 1 0.2 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm36B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob7,"v1_cape_itm36B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 28 74 29 17 318 1786
## [2,] Percent 73.9 1.6 4.1 1.6 1 17.8 100
“Do you ever feel that you have only few hobbies or interests?” (ordinal [0,1,2,3], v1_cape_itm37A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa8,"v1_cape_itm37A")
## -999 0 1 2 <NA>
## [1,] No. cases 1320 224 72 5 165 1786
## [2,] Percent 73.9 12.5 4 0.3 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm37B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob8,"v1_cape_itm37B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 35 36 5 1 389 1786
## [2,] Percent 73.9 2 2 0.3 0.1 21.8 100
“Do you ever feel guilty?” (ordinal [0,1,2,3], v1_cape_itm38A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa9,"v1_cape_itm38A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 46 226 24 5 165 1786
## [2,] Percent 73.9 2.6 12.7 1.3 0.3 9.2 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm38B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob9,"v1_cape_itm38B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 58 128 48 21 211 1786
## [2,] Percent 73.9 3.2 7.2 2.7 1.2 11.8 100
“Do you ever feel like a failure?” (ordinal [0,1,2,3], v1_cape_itm39A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa10,"v1_cape_itm39A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 156 128 14 2 166 1786
## [2,] Percent 73.9 8.7 7.2 0.8 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm39B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob10,"v1_cape_itm39B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 35 65 26 18 322 1786
## [2,] Percent 73.9 2 3.6 1.5 1 18 100
“Do you ever feel tense?” (ordinal [0,1,2,3], v1_cape_itm40A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa11,"v1_cape_itm40A")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 68 188 42 2 166 1786
## [2,] Percent 73.9 3.8 10.5 2.4 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm40B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob11,"v1_cape_itm40B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1320 116 93 18 3 236 1786
## [2,] Percent 73.9 6.5 5.2 1 0.2 13.2 100
“Do you ever feel as if a double has taken the place of a family member, friend or acquaintance?” (ordinal [0,1,2,3], v1_cape_itm41A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa12,"v1_cape_itm41A")
## -999 0 1 <NA>
## [1,] No. cases 1320 298 2 166 1786
## [2,] Percent 73.9 16.7 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm41B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob12,"v1_cape_itm41B")
## -999 0 2 <NA>
## [1,] No. cases 1320 1 1 464 1786
## [2,] Percent 73.9 0.1 0.1 26 100
“Do you ever see objects, people or animals that other people cannot see?” (ordinal [0,1,2,3], v1_cape_itm42A)
v1_cape_recode(v1_con$v1_cape_cape_gedechoa13,"v1_cape_itm42A")
## -999 0 1 3 <NA>
## [1,] No. cases 1320 292 7 1 166 1786
## [2,] Percent 73.9 16.3 0.4 0.1 9.3 100
“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm42B)
v1_cape_recode(v1_con$v1_cape_cape_gedechob13,"v1_cape_itm42B")
## -999 0 <NA>
## [1,] No. cases 1320 8 458 1786
## [2,] Percent 73.9 0.4 25.6 100
Create dataset
v1_cape<-data.frame(v1_cape_itm1A,v1_cape_itm1B,
v1_cape_itm2A,v1_cape_itm2B,
v1_cape_itm3A,v1_cape_itm3B,
v1_cape_itm4A,v1_cape_itm4B,
v1_cape_itm5A,v1_cape_itm5B,
v1_cape_itm6A,v1_cape_itm6B,
v1_cape_itm7A,v1_cape_itm7B,
v1_cape_itm8A,v1_cape_itm8B,
v1_cape_itm9A,v1_cape_itm9B,
v1_cape_itm10A,v1_cape_itm10B,
v1_cape_itm11A,v1_cape_itm11B,
v1_cape_itm12A,v1_cape_itm12B,
v1_cape_itm13A,v1_cape_itm13B,
v1_cape_itm14A,v1_cape_itm14B,
v1_cape_itm15A,v1_cape_itm15B,
v1_cape_itm16A,v1_cape_itm16B,
v1_cape_itm17A,v1_cape_itm17B,
v1_cape_itm18A,v1_cape_itm18B,
v1_cape_itm19A,v1_cape_itm19B,
v1_cape_itm20A,v1_cape_itm20B,
v1_cape_itm21A,v1_cape_itm21B,
v1_cape_itm22A,v1_cape_itm22B,
v1_cape_itm23A,v1_cape_itm23B,
v1_cape_itm24A,v1_cape_itm24B,
v1_cape_itm25A,v1_cape_itm25B,
v1_cape_itm26A,v1_cape_itm26B,
v1_cape_itm27A,v1_cape_itm27B,
v1_cape_itm28A,v1_cape_itm28B,
v1_cape_itm29A,v1_cape_itm29B,
v1_cape_itm30A,v1_cape_itm30B,
v1_cape_itm31A,v1_cape_itm31B,
v1_cape_itm32A,v1_cape_itm32B,
v1_cape_itm33A,v1_cape_itm33B,
v1_cape_itm34A,v1_cape_itm34B,
v1_cape_itm35A,v1_cape_itm35B,
v1_cape_itm36A,v1_cape_itm36B,
v1_cape_itm37A,v1_cape_itm37B,
v1_cape_itm38A,v1_cape_itm38B,
v1_cape_itm39A,v1_cape_itm39B,
v1_cape_itm40A,v1_cape_itm40B,
v1_cape_itm41A,v1_cape_itm41B,
v1_cape_itm42A,v1_cape_itm42B)
The SF-12 is a short instrument to assess health-related quality of life.
“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v1_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.
v1_sf12_recode(v1_con$v1_sf12_sf_allgemein,"v1_sf12_itm0")
## -999 1 2 3 4 5 6 7 8 9 10 <NA>
## [1,] No. cases 1320 1 2 6 13 10 19 67 129 118 57 44 1786
## [2,] Percent 73.9 0.1 0.1 0.3 0.7 0.6 1.1 3.8 7.2 6.6 3.2 2.5 100
“In general, would you say your health is…” (ordinal [1,2,3,4,5], v1_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.
v1_sf12_recode(v1_con$v1_sf12_sf1,"v1_sf12_itm1")
## -999 1 2 3 4 5 <NA>
## [1,] No. cases 1320 90 227 122 13 1 13 1786
## [2,] Percent 73.9 5 12.7 6.8 0.7 0.1 0.7 100
“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”
“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v1_sf12_itm2) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.
v1_sf12_recode(v1_con$v1_sf12_sf2,"v1_sf12_itm2")
## -999 1 2 3 <NA>
## [1,] No. cases 1320 2 36 414 14 1786
## [2,] Percent 73.9 0.1 2 23.2 0.8 100
“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v1_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.
v1_sf12_recode(v1_con$v1_sf12_sf3,"v1_sf12_itm3")
## -999 1 2 3 <NA>
## [1,] No. cases 1320 3 49 401 13 1786
## [2,] Percent 73.9 0.2 2.7 22.5 0.7 100
During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?
“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v1_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.
v1_sf12_recode(v1_con$v1_sf12_sf4,"v1_sf12_itm4")
## -999 1 2 <NA>
## [1,] No. cases 1320 55 397 14 1786
## [2,] Percent 73.9 3.1 22.2 0.8 100
“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v1_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.
v1_sf12_recode(v1_con$v1_sf12_sf5,"v1_sf12_itm5")
## -999 1 2 <NA>
## [1,] No. cases 1320 32 416 18 1786
## [2,] Percent 73.9 1.8 23.3 1 100
During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?
“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v1_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.
v1_sf12_recode(v1_con$v1_sf12_sf6,"v1_sf12_itm6")
## -999 1 2 <NA>
## [1,] No. cases 1320 28 425 13 1786
## [2,] Percent 73.9 1.6 23.8 0.7 100
“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v1_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.
v1_sf12_recode(v1_con$v1_sf12_sf7,"v1_sf12_itm7")
## -999 1 2 <NA>
## [1,] No. cases 1320 22 430 14 1786
## [2,] Percent 73.9 1.2 24.1 0.8 100
“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v1_sf12_itm8) Answering alternatives are the following: “None”-1 to “Extremely”-6.
v1_sf12_recode(v1_con$v1_sf12_st8,"v1_sf12_itm8")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 262 85 58 35 9 1 16 1786
## [2,] Percent 73.9 14.7 4.8 3.2 2 0.5 0.1 0.9 100
The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS
Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.
“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v1_sf12_itm9)
v1_sf12_recode(v1_con$v1_sf12_st9,"v1_sf12_itm9")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 35 270 98 35 12 2 14 1786
## [2,] Percent 73.9 2 15.1 5.5 2 0.7 0.1 0.8 100
“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v1_sf12_itm10)
v1_sf12_recode(v1_con$v1_sf12_st10,"v1_sf12_itm10")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 27 173 150 75 20 3 18 1786
## [2,] Percent 73.9 1.5 9.7 8.4 4.2 1.1 0.2 1 100
“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v1_sf12_itm11)
v1_sf12_recode(v1_con$v1_sf12_st11,"v1_sf12_itm11")
## -999 2 3 4 5 6 <NA>
## [1,] No. cases 1320 2 10 67 230 140 17 1786
## [2,] Percent 73.9 0.1 0.6 3.8 12.9 7.8 1 100
“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [1,2,3,4,5], v1_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.
There is an error in the phenotype database regarding this item. The answering alternatives 3, 4, and 5 appear as 4, 5, and 6 in the database exports. These errors are corrected below.
v1_sf12_recode(v1_con$v1_sf12_st12,"v1_sf12_itm12")
## -999 2 4 5 6 <NA>
## [1,] No. cases 1320 7 29 90 327 13 1786
## [2,] Percent 73.9 0.4 1.6 5 18.3 0.7 100
#recode error in phenotype database
v1_sf12_itm12[v1_sf12_itm12==4]<-3
v1_sf12_itm12[v1_sf12_itm12==5]<-4
v1_sf12_itm12[v1_sf12_itm12==6]<-5
descT(v1_sf12_itm12)
## -999 2 3 4 5 <NA>
## [1,] No. cases 1320 7 29 90 327 13 1786
## [2,] Percent 73.9 0.4 1.6 5 18.3 0.7 100
Create dataset
v1_sf12<-data.frame(v1_sf12_itm0,
v1_sf12_itm1,
v1_sf12_itm2,
v1_sf12_itm3,
v1_sf12_itm4,
v1_sf12_itm5,
v1_sf12_itm6,
v1_sf12_itm7,
v1_sf12_itm8,
v1_sf12_itm9,
v1_sf12_itm10,
v1_sf12_itm11,
v1_sf12_itm12)
#INCLUDE v2_sf12_itm12 when issues are settled
This self-created questionnaire asks about whether the participant belongs to a certain belief system and how actively she or he practices this belief. The first two questions are about Christianity and Islam. In a third question, other belief systems such are Judaism, Hinduism, Buddhism, Other (specify) and No religious denomination are assessed. There are also mode fine-grained distinctions concerning Christianity and Islan, but these are not included in the present dataset. The second item assesses how actively the belief is practiced. Because this questionnaire was introduced after data collection started, it is included in Visit 4 as well for those participants that were not assessed in Visit 1. In control participants, the questionnaire is assessed in Visit 1.
Religion Christianity (dichotomous, v1_rel_chr)
v1_rel_chris<-c(v1_clin$v1_religion_christ,v1_con$v1_religion_christ_jn)
v1_rel_chr<-ifelse(v1_rel_chris==1, "Y","N")
descT(v1_rel_chr)
## N Y <NA>
## [1,] No. cases 76 748 962 1786
## [2,] Percent 4.3 41.9 53.9 100
Religion Islam (dichotomous, v1_rel_isl)
v1_rel_islam<-c(v1_clin$v1_religion_islam_jn,v1_con$v1_religion_islam_jn)
v1_rel_isl<-ifelse(v1_rel_islam==1, "Y","N")
descT(v1_rel_isl)
## N Y <NA>
## [1,] No. cases 211 24 1551 1786
## [2,] Percent 11.8 1.3 86.8 100
Other religion (categorical,[v1_rel_oth])
v1_rel_var<-c(v1_clin$v1_religion_religion,v1_con$v1_religion_religion)
v1_rel_oth<-ifelse(v1_rel_var==1, "Judaism",
ifelse(v1_rel_var==2, "Hinduism",
ifelse(v1_rel_var==3, "Buddhism",
ifelse(v1_rel_var==4, "Other",
ifelse(v1_rel_var==5, "No denomination",NA)))))
descT(v1_rel_oth)
## Buddhism Judaism No denomination Other <NA>
## [1,] No. cases 15 1 280 6 1484 1786
## [2,] Percent 0.8 0.1 15.7 0.3 83.1 100
“How actively do you practice your belief?” (ordinal [1,2,3,4,5], v1_rel_act)
This is an ordinal item with the following answer possibilities and the assigned gradation: “not at all”-1,“little active”-2,“moderately active”-3,“rather active”-4,“very actively”-5.
v1_rel_act<-c(v1_clin$v1_religion_religion_aktiv,v1_con$v1_religion_aktiv)
descT(v1_rel_act)
## 1 2 3 4 5 <NA>
## [1,] No. cases 364 301 176 88 51 806 1786
## [2,] Percent 20.4 16.9 9.9 4.9 2.9 45.1 100
Create dataset
v1_rlgn<-data.frame(v1_rel_chr,v1_rel_isl,v1_rel_oth,v1_rel_act)
This questionnaire asks whether psychopharmacological medication was taken as prescribed. We developed this questionnaire ourselves. The past seven days and the past six months are assessed. Both items have the following gradation: “everyday, exactly as prescribed”-1, “everyday, but not always as prescribed”-2, “regularly, but not every day”-3, “sometimes, but not regularly”-4, “seldom”-5, “not at all”-6. Control participants are coded -999.
Past seven days (ordinal [1,2,3,4,5,6], v1_med_pst_wk)
v1_med_chk<-c(v1_clin$v1_compl_verwer_fragebogen,rep(1,dim(v1_con)[1]))
v1_med_pst_wk_pre<-c(v1_clin$v1_compl_psychopharm_7_tag,rep(-999,dim(v1_con)[1]))
v1_med_pst_wk<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_med_pst_wk<-ifelse((is.na(v1_med_chk) | v1_med_chk!=2),
v1_med_pst_wk_pre, v1_med_pst_wk)
descT(v1_med_pst_wk)
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 466 988 88 21 5 3 14 201 1786
## [2,] Percent 26.1 55.3 4.9 1.2 0.3 0.2 0.8 11.3 100
Past six months (ordinal [1,2,3,4,5,6], v1_med_pst_sx_mths)
v1_med_pre<-c(v1_clin$v1_compl_psychopharm_6_mon,rep(-999,dim(v1_con)[1]))
v1_med_pst_sx_mths<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_med_pst_sx_mths<-ifelse((is.na(v1_med_chk) | v1_med_chk!=2),
v1_med_pre, v1_med_pst_sx_mths)
descT(v1_med_pst_sx_mths)
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 466 730 149 107 44 24 49 217 1786
## [2,] Percent 26.1 40.9 8.3 6 2.5 1.3 2.7 12.2 100
Create dataset
v1_med_adh<-data.frame(v1_med_pst_wk,v1_med_pst_sx_mths)
The German translation of the BDI-II (Hautzinger, Keller, & Kühner, 2006) asesses depressive symptoms. Patients are supposed to pick the answer that best describes how they have been feeling during the past two weeks. Each item is rated from zero to three, except item 16 (sleep) and item 18 (apppetite), for which seven alternatives exist (described below). With all items, higher scores mean more depressive symptomatology. For clinically meaningful threshold values see sum score calculation at the end of thhis section.
1. Sadness (ordinal [0,1,2,3], v1_bdi2_itm1)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi1_traurigkeit,v1_con$v1_bdi2_s1_bdi1,"v1_bdi2_itm1")
## 0 1 2 3 <NA>
## [1,] No. cases 990 486 64 29 217 1786
## [2,] Percent 55.4 27.2 3.6 1.6 12.2 100
2. Pessimism (ordinal [0,1,2,3], v1_bdi2_itm2)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi2_pessimismus,v1_con$v1_bdi2_s1_bdi2,"v1_bdi2_itm2")
## 0 1 2 3 <NA>
## [1,] No. cases 1075 326 121 43 221 1786
## [2,] Percent 60.2 18.3 6.8 2.4 12.4 100
3. Past failure (ordinal [0,1,2,3], v1_bdi2_itm3)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi3_versagensgef,v1_con$v1_bdi2_s1_bdi3,"v1_bdi2_itm3")
## 0 1 2 3 <NA>
## [1,] No. cases 940 328 248 54 216 1786
## [2,] Percent 52.6 18.4 13.9 3 12.1 100
4. Loss of pleasure (ordinal [0,1,2,3], v1_bdi2_itm4)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi4_verlust_freude,v1_con$v1_bdi2_s1_bdi4,"v1_bdi2_itm4")
## 0 1 2 3 <NA>
## [1,] No. cases 857 491 163 58 217 1786
## [2,] Percent 48 27.5 9.1 3.2 12.2 100
5. Guilty feelings (ordinal [0,1,2,3], v1_bdi2_itm5)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi5_schuldgef,v1_con$v1_bdi2_s1_bdi5,"v1_bdi2_itm5")
## 0 1 2 3 <NA>
## [1,] No. cases 1007 451 66 45 217 1786
## [2,] Percent 56.4 25.3 3.7 2.5 12.2 100
6. Punishment feelings (ordinal [0,1,2,3], v1_bdi2_itm6)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi6_bestrafungsgef,v1_con$v1_bdi2_s1_bdi6,"v1_bdi2_itm6")
## 0 1 2 3 <NA>
## [1,] No. cases 1177 238 37 114 220 1786
## [2,] Percent 65.9 13.3 2.1 6.4 12.3 100
7. Self-dislike (ordinal [0,1,2,3], v1_bdi2_itm7)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi7_selbstablehnung,v1_con$v1_bdi2_s1_bdi7,"v1_bdi2_itm7")
## 0 1 2 3 <NA>
## [1,] No. cases 1091 277 154 41 223 1786
## [2,] Percent 61.1 15.5 8.6 2.3 12.5 100
8. Self-criticalness (ordinal [0,1,2,3], v1_bdi2_itm8)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi8_selbstvorwuerfe,v1_con$v1_bdi2_s1_bdi8,"v1_bdi2_itm8")
## 0 1 2 3 <NA>
## [1,] No. cases 901 458 152 51 224 1786
## [2,] Percent 50.4 25.6 8.5 2.9 12.5 100
9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v1_bdi2_itm9)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi9_selbstmordged,v1_con$v1_bdi2_s1_bdi9,"v1_bdi2_itm9")
## 0 1 2 3 <NA>
## [1,] No. cases 1240 292 23 13 218 1786
## [2,] Percent 69.4 16.3 1.3 0.7 12.2 100
10. Crying (ordinal [0,1,2,3], v1_bdi2_itm10)
v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi10_weinen,v1_con$v1_bdi2_s1_bdi10,"v1_bdi2_itm10")
## 0 1 2 3 <NA>
## [1,] No. cases 1118 228 66 154 220 1786
## [2,] Percent 62.6 12.8 3.7 8.6 12.3 100
11. Agitation (ordinal [0,1,2,3], v1_bdi2_itm11)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi11_unruhe,v1_con$v1_bdi2_s2_bdi11,"v1_bdi2_itm11")
## 0 1 2 3 <NA>
## [1,] No. cases 986 423 88 53 236 1786
## [2,] Percent 55.2 23.7 4.9 3 13.2 100
12. Loss of interest (ordinal [0,1,2,3], v1_bdi2_itm12)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi12_interessverl,v1_con$v1_bdi2_s2_bdi12,"v1_bdi2_itm12")
## 0 1 2 3 <NA>
## [1,] No. cases 1006 353 114 76 237 1786
## [2,] Percent 56.3 19.8 6.4 4.3 13.3 100
13. Indecisiveness (ordinal [0,1,2,3], v1_bdi2_itm13)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi13_entschlussunf,v1_con$v1_bdi2_s2_bdi13,"v1_bdi2_itm13")
## 0 1 2 3 <NA>
## [1,] No. cases 927 408 130 87 234 1786
## [2,] Percent 51.9 22.8 7.3 4.9 13.1 100
14. Worthlessness (ordinal [0,1,2,3], v1_bdi2_itm14)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi14_wertlosigkeit,v1_con$v1_bdi2_s2_bdi14,"v1_bdi2_itm14")
## 0 1 2 3 <NA>
## [1,] No. cases 1086 252 167 43 238 1786
## [2,] Percent 60.8 14.1 9.4 2.4 13.3 100
15. Loss of energy (ordinal [0,1,2,3], v1_bdi2_itm15)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi15_energieverlust,v1_con$v1_bdi2_s2_bdi15,"v1_bdi2_itm15")
## 0 1 2 3 <NA>
## [1,] No. cases 738 599 183 26 240 1786
## [2,] Percent 41.3 33.5 10.2 1.5 13.4 100
16. Changes in sleeping pattern (ordinal [0,1,2,3], v1_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep". There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.
v1_itm_bdi2_chk<-c(v1_clin$v1_bdi2_s1_verwer_fragebogen,v1_con$v1_bdi2_s1_bdi_korrekt)
v1_itm_bdi2_itm16_clin_con<-c(v1_clin$v1_bdi2_s2_bdi16_schlafgewohn,v1_con$v1_bdi2_s2_bdi16)
v1_bdi2_itm16<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_bdi2_itm16<-ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & v1_itm_bdi2_itm16_clin_con==0, 0,
ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) &
(v1_itm_bdi2_itm16_clin_con==1 | v1_itm_bdi2_itm16_clin_con==100), 1,
ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) &
(v1_itm_bdi2_itm16_clin_con==2 | v1_itm_bdi2_itm16_clin_con==200), 2,
ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) &
(v1_itm_bdi2_itm16_clin_con==3 | v1_itm_bdi2_itm16_clin_con==300), 3, v1_bdi2_itm16))))
v1_bdi2_itm16<-factor(v1_bdi2_itm16,ordered=T)
descT(v1_bdi2_itm16)
## 0 1 2 3 <NA>
## [1,] No. cases 665 577 191 115 238 1786
## [2,] Percent 37.2 32.3 10.7 6.4 13.3 100
17. Irritability (ordinal [0,1,2,3], v1_bdi2_itm17)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi17_reizbarkeit,v1_con$v1_bdi2_s2_bdi17,"v1_bdi2_itm17")
## 0 1 2 3 <NA>
## [1,] No. cases 1143 335 50 23 235 1786
## [2,] Percent 64 18.8 2.8 1.3 13.2 100
18. Change in appetite (ordinal [0,1,2,3], v1_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.
v1_itm_bdi2_itm18_clin_con<-c(v1_clin$v1_bdi2_s2_bdi18_appetit,v1_con$v1_bdi2_s2_bdi18)
v1_bdi2_itm18<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_bdi2_itm18<-ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & v1_itm_bdi2_itm18_clin_con==0, 0,
ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) &
(v1_itm_bdi2_itm18_clin_con==1 | v1_itm_bdi2_itm18_clin_con==100), 1,
ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) &
(v1_itm_bdi2_itm18_clin_con==2 | v1_itm_bdi2_itm18_clin_con==200), 2,
ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) &
(v1_itm_bdi2_itm18_clin_con==3 | v1_itm_bdi2_itm18_clin_con==300), 3, v1_bdi2_itm18))))
v1_bdi2_itm18<-factor(v1_bdi2_itm18,ordered=T)
descT(v1_bdi2_itm18)
## 0 1 2 3 <NA>
## [1,] No. cases 908 474 108 58 238 1786
## [2,] Percent 50.8 26.5 6 3.2 13.3 100
19. Concentration difficulty (ordinal [0,1,2,3], v1_bdi2_itm19)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi19_konzschw,v1_con$v1_bdi2_s2_bdi19,"v1_bdi2_itm19")
## 0 1 2 3 <NA>
## [1,] No. cases 807 448 265 31 235 1786
## [2,] Percent 45.2 25.1 14.8 1.7 13.2 100
20. Tiredness or fatigue (ordinal [0,1,2,3], v1_bdi2_itm20)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi20_ermued_ersch,v1_con$v1_bdi2_s2_bdi20,"v1_bdi2_itm20")
## 0 1 2 3 <NA>
## [1,] No. cases 791 553 168 39 235 1786
## [2,] Percent 44.3 31 9.4 2.2 13.2 100
21. Loss of interest in sex (ordinal [0,1,2,3], v1_bdi2_itm21)
v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi21_sex_interess,v1_con$v1_bdi2_s2_bdi21,"v1_bdi2_itm21")
## 0 1 2 3 <NA>
## [1,] No. cases 937 309 149 147 244 1786
## [2,] Percent 52.5 17.3 8.3 8.2 13.7 100
BDI-II sum score calculation (continuous [0-63], v1_bdi2_sum) The following cut-off values are generally considered to be meaningful:
Please note that if one or more of BDI-II items are missing, this will result in the sum score to become NA.
v1_bdi2_sum<-as.numeric.factor(v1_bdi2_itm1)+
as.numeric.factor(v1_bdi2_itm2)+
as.numeric.factor(v1_bdi2_itm3)+
as.numeric.factor(v1_bdi2_itm4)+
as.numeric.factor(v1_bdi2_itm5)+
as.numeric.factor(v1_bdi2_itm6)+
as.numeric.factor(v1_bdi2_itm7)+
as.numeric.factor(v1_bdi2_itm8)+
as.numeric.factor(v1_bdi2_itm9)+
as.numeric.factor(v1_bdi2_itm10)+
as.numeric.factor(v1_bdi2_itm11)+
as.numeric.factor(v1_bdi2_itm12)+
as.numeric.factor(v1_bdi2_itm13)+
as.numeric.factor(v1_bdi2_itm14)+
as.numeric.factor(v1_bdi2_itm15)+
as.numeric.factor(v1_bdi2_itm16)+
as.numeric.factor(v1_bdi2_itm17)+
as.numeric.factor(v1_bdi2_itm18)+
as.numeric.factor(v1_bdi2_itm19)+
as.numeric.factor(v1_bdi2_itm20)+
as.numeric.factor(v1_bdi2_itm21)
summary(v1_bdi2_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 2.00 8.00 11.16 17.00 59.00 313
Create dataset
v1_bdi2<-data.frame(v1_bdi2_itm1,v1_bdi2_itm2,v1_bdi2_itm3,v1_bdi2_itm4,v1_bdi2_itm5,
v1_bdi2_itm6,v1_bdi2_itm7,v1_bdi2_itm8,v1_bdi2_itm9,v1_bdi2_itm10,
v1_bdi2_itm11,v1_bdi2_itm12,v1_bdi2_itm13,v1_bdi2_itm14,
v1_bdi2_itm15,v1_bdi2_itm16,v1_bdi2_itm17,v1_bdi2_itm18,
v1_bdi2_itm19,v1_bdi2_itm20,v1_bdi2_itm21,v1_bdi2_sum)
The ASRM (Altman, Hedeker, Peterson, & Davis, 1997) assesses symptoms of mania during the past week. All items are scored from zero to four with higher scores indicating more mania symptoms.
1. Positive Mood (ordinal [0,1,2,3,4], v1_asrm_itm1)
v1_asrm_recode(v1_clin$v1_asrm_asrm1_gluecklich,v1_con$v1_asrm_asrm1,"v1_asrm_itm1")
## 0 1 2 3 4 <NA>
## [1,] No. cases 993 366 89 53 12 273 1786
## [2,] Percent 55.6 20.5 5 3 0.7 15.3 100
2 Self-Confidence (ordinal [0,1,2,3,4], v1_asrm_itm2)
v1_asrm_recode(v1_clin$v1_asrm_asrm2_selbstbewusst,v1_con$v1_asrm_asrm2,"v1_asrm_itm2")
## 0 1 2 3 4 <NA>
## [1,] No. cases 1031 318 97 48 18 274 1786
## [2,] Percent 57.7 17.8 5.4 2.7 1 15.3 100
3. Sleep (ordinal [0,1,2,3,4], v1_asrm_itm3)
v1_asrm_recode(v1_clin$v1_asrm_asrm3_schlaf,v1_con$v1_asrm_asrm3,"v1_asrm_itm3")
## 0 1 2 3 4 <NA>
## [1,] No. cases 1169 234 56 37 16 274 1786
## [2,] Percent 65.5 13.1 3.1 2.1 0.9 15.3 100
4. Speech (ordinal [0,1,2,3,4], v1_asrm_itm4)
v1_asrm_recode(v1_clin$v1_asrm_asrm4_reden,v1_con$v1_asrm_asrm4,"v1_asrm_itm4")
## 0 1 2 3 4 <NA>
## [1,] No. cases 1084 304 77 37 14 270 1786
## [2,] Percent 60.7 17 4.3 2.1 0.8 15.1 100
5. Activity Level (ordinal [0,1,2,3,4], v1_asrm_itm5)
v1_asrm_recode(v1_clin$v1_asrm_asrm5_aktiv,v1_con$v1_asrm_asrm5,"v1_asrm_itm5")
## 0 1 2 3 4 <NA>
## [1,] No. cases 1014 350 91 37 25 269 1786
## [2,] Percent 56.8 19.6 5.1 2.1 1.4 15.1 100
Create ASRM sum scoresum score (continuous [0-20],v1_asrm_sum)
v1_asrm_sum<-as.numeric.factor(v1_asrm_itm1)+
as.numeric.factor(v1_asrm_itm2)+
as.numeric.factor(v1_asrm_itm3)+
as.numeric.factor(v1_asrm_itm4)+
as.numeric.factor(v1_asrm_itm5)
summary(v1_asrm_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 1.000 2.216 3.000 20.000 283
Create dataset
v1_asrm<-data.frame(v1_asrm_itm1,v1_asrm_itm2,v1_asrm_itm3,v1_asrm_itm4,v1_asrm_itm5,v1_asrm_sum)
Forty-eight statements, each of which asks for a mania symtom and should to be answered “Yes” or No" (Shugar, Schertzer, Toner, & Di Gasbarro, 1992). Measures mania symptoms during the past month. All questions have the same direction, “Yes” indicating mania symptom present.
1. “I had more energy” (dichotomous, v1_mss_itm1)
v1_mss_recode(v1_clin$v1_mss_s1_mss1_energie,v1_con$v1_mss_s1_mss1,"v1_mss_itm1")
## N Y <NA>
## [1,] No. cases 1129 375 282 1786
## [2,] Percent 63.2 21 15.8 100
2. “I had trouble sitting still” (dichotomous, v1_mss_itm2)
v1_mss_recode(v1_clin$v1_mss_s1_mss2_ruhig_sitzen,v1_con$v1_mss_s1_mss2,"v1_mss_itm2")
## N Y <NA>
## [1,] No. cases 1168 333 285 1786
## [2,] Percent 65.4 18.6 16 100
3. “I drove faster” (dichotomous, v1_mss_itm3)
v1_mss_recode(v1_clin$v1_mss_s1_mss3_auto_fahren,v1_con$v1_mss_s1_mss3,"v1_mss_itm3")
## N Y <NA>
## [1,] No. cases 1348 77 361 1786
## [2,] Percent 75.5 4.3 20.2 100
4. “I drank more alcoholic beverages” (dichotomous, v1_mss_itm4)
v1_mss_recode(v1_clin$v1_mss_s1_mss4_alkohol,v1_con$v1_mss_s1_mss4,"v1_mss_itm4")
## N Y <NA>
## [1,] No. cases 1350 136 300 1786
## [2,] Percent 75.6 7.6 16.8 100
5. “I changed clothes several times a day” (dichotomous, v1_mss_itm5)
v1_mss_recode(v1_clin$v1_mss_s1_mss5_umziehen, v1_con$v1_mss_s1_mss5,"v1_mss_itm5")
## N Y <NA>
## [1,] No. cases 1317 181 288 1786
## [2,] Percent 73.7 10.1 16.1 100
6. “I wore brighter clothes/make-up” (dichotomous, v1_mss_itm6)
v1_mss_recode(v1_clin$v1_mss_s1_mss6_bunter,v1_con$v1_mss_s1_mss6,"v1_mss_itm6")
## N Y <NA>
## [1,] No. cases 1374 122 290 1786
## [2,] Percent 76.9 6.8 16.2 100
7. “I played music louder” (dichotomous, v1_mss_itm7)
v1_mss_recode(v1_clin$v1_mss_s1_mss7_musik_lauter,v1_con$v1_mss_s1_mss7,"v1_mss_itm7")
## N Y <NA>
## [1,] No. cases 1218 285 283 1786
## [2,] Percent 68.2 16 15.8 100
8. “I ate faster than usual” (dichotomous, v1_mss_itm8)
v1_mss_recode(v1_clin$v1_mss_s1_mss8_hastiger_essen,v1_con$v1_mss_s1_mss8,"v1_mss_itm8")
## N Y <NA>
## [1,] No. cases 1263 242 281 1786
## [2,] Percent 70.7 13.5 15.7 100
9. “I ate more than usual” (dichotomous, v1_mss_itm9)
v1_mss_recode(v1_clin$v1_mss_s1_mss9_mehr_essen,v1_con$v1_mss_s1_mss9,"v1_mss_itm9")
## N Y <NA>
## [1,] No. cases 1132 371 283 1786
## [2,] Percent 63.4 20.8 15.8 100
10. “I slept fewer hours than usual” (dichotomous, v1_mss_itm10)
v1_mss_recode(v1_clin$v1_mss_s1_mss10_weniger_schlaf,v1_con$v1_mss_s1_mss10,"v1_mss_itm10")
## N Y <NA>
## [1,] No. cases 1243 254 289 1786
## [2,] Percent 69.6 14.2 16.2 100
11. “I started things that I didn’t finish” (dichotomous, v1_mss_itm11)
v1_mss_recode(v1_clin$v1_mss_s1_mss11_unbeendet,v1_con$v1_mss_s1_mss11,"v1_mss_itm11")
## N Y <NA>
## [1,] No. cases 1090 413 283 1786
## [2,] Percent 61 23.1 15.8 100
12. “I gave away my own possessions” (dichotomous, v1_mss_itm12)
v1_mss_recode(v1_clin$v1_mss_s1_mss12_weggeben,v1_con$v1_mss_s1_mss12,"v1_mss_itm12")
## N Y <NA>
## [1,] No. cases 1290 213 283 1786
## [2,] Percent 72.2 11.9 15.8 100
13. “I bought gifts for people” (dichotomous, v1_mss_itm13)
v1_mss_recode(v1_clin$v1_mss_s1_mss13_geschenke,v1_con$v1_mss_s1_mss13,"v1_mss_itm13")
## N Y <NA>
## [1,] No. cases 1325 177 284 1786
## [2,] Percent 74.2 9.9 15.9 100
14. “I spent money more freely” (dichotomous, v1_mss_itm14)
v1_mss_recode(v1_clin$v1_mss_s1_mss14_mehr_geld,v1_con$v1_mss_s1_mss14,"v1_mss_itm14")
## N Y <NA>
## [1,] No. cases 1107 398 281 1786
## [2,] Percent 62 22.3 15.7 100
15. “I accumulated debts” (dichotomous, v1_mss_itm15)
v1_mss_recode(v1_clin$v1_mss_s1_mss15_schulden,v1_con$v1_mss_s1_mss15,"v1_mss_itm15")
## N Y <NA>
## [1,] No. cases 1380 124 282 1786
## [2,] Percent 77.3 6.9 15.8 100
16. “I made unwise business decisions” (dichotomous, v1_mss_itm16)
v1_mss_recode(v1_clin$v1_mss_s1_mss16_unkluge_entsch,v1_con$v1_mss_s1_mss16,"v1_mss_itm16")
## N Y <NA>
## [1,] No. cases 1421 80 285 1786
## [2,] Percent 79.6 4.5 16 100
17. “I partied more” (dichotomous, v1_mss_itm17)
v1_mss_recode(v1_clin$v1_mss_s1_mss17_parties,v1_con$v1_mss_s1_mss17,"v1_mss_itm17")
## N Y <NA>
## [1,] No. cases 1393 110 283 1786
## [2,] Percent 78 6.2 15.8 100
18. “I enjoyed flirting” (dichotomous, v1_mss_itm18)
v1_mss_recode(v1_clin$v1_mss_s1_mss18_flirten,v1_con$v1_mss_s1_mss18,"v1_mss_itm18")
## N Y <NA>
## [1,] No. cases 1340 167 279 1786
## [2,] Percent 75 9.4 15.6 100
19. “I masturbated more often” (dichotomous, v1_mss_itm19)
v1_mss_recode(v1_clin$v1_mss_s2_mss19_selbstbefried,v1_con$v1_mss_s2_mss19,"v1_mss_itm19")
## N Y <NA>
## [1,] No. cases 1359 115 312 1786
## [2,] Percent 76.1 6.4 17.5 100
20. “I was more interested in sex than usual” (dichotomous, v1_mss_itm20)
v1_mss_recode(v1_clin$v1_mss_s2_mss20_sex_interess,v1_con$v1_mss_s2_mss20,"v1_mss_itm20")
## N Y <NA>
## [1,] No. cases 1301 175 310 1786
## [2,] Percent 72.8 9.8 17.4 100
21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v1_mss_itm21)
v1_mss_recode(v1_clin$v1_mss_s2_mss21_sexpartner,v1_con$v1_mss_s2_mss21,"v1_mss_itm21")
## N Y <NA>
## [1,] No. cases 1431 45 310 1786
## [2,] Percent 80.1 2.5 17.4 100
22. “I spent more time on the phone” (dichotomous, v1_mss_itm22)
v1_mss_recode(v1_clin$v1_mss_s2_mss22_mehr_telefon,v1_con$v1_mss_s2_mss22,"v1_mss_itm22")
## N Y <NA>
## [1,] No. cases 1209 276 301 1786
## [2,] Percent 67.7 15.5 16.9 100
23. “I spoke louder than usual” (dichotomous, v1_mss_itm23)
v1_mss_recode(v1_clin$v1_mss_s2_mss23_sprache_lauter,v1_con$v1_mss_s2_mss23,"v1_mss_itm23")
## N Y <NA>
## [1,] No. cases 1289 197 300 1786
## [2,] Percent 72.2 11 16.8 100
24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v1_mss_itm24)
v1_mss_recode(v1_clin$v1_mss_s2_mss24_spr_schneller,v1_con$v1_mss_s2_mss24,"v1_mss_itm24")
## N Y <NA>
## [1,] No. cases 1321 163 302 1786
## [2,] Percent 74 9.1 16.9 100
25. “1 enjoyed punning or rhyming” (dichotomous, v1_mss_itm25)
v1_mss_recode(v1_clin$v1_mss_s2_mss25_witze,v1_con$v1_mss_s2_mss25,"v1_mss_itm25")
## N Y <NA>
## [1,] No. cases 1293 192 301 1786
## [2,] Percent 72.4 10.8 16.9 100
26. “I butted into conversations” (dichotomous, v1_mss_itm26)
v1_mss_recode(v1_clin$v1_mss_s2_mss26_einmischen,v1_con$v1_mss_s2_mss26,"v1_mss_itm26")
## N Y <NA>
## [1,] No. cases 1331 155 300 1786
## [2,] Percent 74.5 8.7 16.8 100
27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v1_mss_itm27)
v1_mss_recode(v1_clin$v1_mss_s2_mss27_red_pausenlos,v1_con$v1_mss_s2_mss27,"v1_mss_itm27")
## N Y <NA>
## [1,] No. cases 1399 84 303 1786
## [2,] Percent 78.3 4.7 17 100
28. “I enjoyed being the centre of attention” (dichotomous, v1_mss_itm28)
v1_mss_recode(v1_clin$v1_mss_s2_mss28_mittelpunkt,v1_con$v1_mss_s2_mss28,"v1_mss_itm28")
## N Y <NA>
## [1,] No. cases 1332 152 302 1786
## [2,] Percent 74.6 8.5 16.9 100
29. “I liked to joke and laugh” (dichotomous, v1_mss_itm29)
v1_mss_recode(v1_clin$v1_mss_s2_mss29_herumalbern,v1_con$v1_mss_s2_mss29,"v1_mss_itm29")
## N Y <NA>
## [1,] No. cases 1216 267 303 1786
## [2,] Percent 68.1 14.9 17 100
30. “People found me entertaining” (dichotomous, v1_mss_itm30)
v1_mss_recode(v1_clin$v1_mss_s2_mss30_unterhaltsamer,v1_con$v1_mss_s2_mss30,"v1_mss_itm30")
## N Y <NA>
## [1,] No. cases 1273 202 311 1786
## [2,] Percent 71.3 11.3 17.4 100
31. “I felt as if I was on top of the world” (dichotomous, v1_mss_itm31)
v1_mss_recode(v1_clin$v1_mss_s2_mss31_obenauf,v1_con$v1_mss_s2_mss31,"v1_mss_itm31")
## N Y <NA>
## [1,] No. cases 1302 180 304 1786
## [2,] Percent 72.9 10.1 17 100
32. “I was more cheerful than my usual self” (dichotomous, v1_mss_itm32)
v1_mss_recode(v1_clin$v1_mss_s2_mss32_froehlicher,v1_con$v1_mss_s2_mss32,"v1_mss_itm32")
## N Y <NA>
## [1,] No. cases 1154 329 303 1786
## [2,] Percent 64.6 18.4 17 100
33. “Other people got on my nerves” (dichotomous, v1_mss_itm33)
v1_mss_recode(v1_clin$v1_mss_s2_mss33_ungeduldiger,v1_con$v1_mss_s2_mss33,"v1_mss_itm33")
## N Y <NA>
## [1,] No. cases 1054 430 302 1786
## [2,] Percent 59 24.1 16.9 100
34. “I was getting into arguments” (dichotomous, v1_mss_itm34)
v1_mss_recode(v1_clin$v1_mss_s2_mss34_streiten,v1_con$v1_mss_s2_mss34,"v1_mss_itm34")
## N Y <NA>
## [1,] No. cases 1284 194 308 1786
## [2,] Percent 71.9 10.9 17.2 100
35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v1_mss_itm35)
v1_mss_recode(v1_clin$v1_mss_s2_mss35_ideen,v1_con$v1_mss_s2_mss35,"v1_mss_itm35")
## N Y <NA>
## [1,] No. cases 1134 349 303 1786
## [2,] Percent 63.5 19.5 17 100
36. “My thoughts raced through my mind” (dichotomous, v1_mss_itm36)
v1_mss_recode(v1_clin$v1_mss_s2_mss36_gedanken,v1_con$v1_mss_s2_mss36,"v1_mss_itm36")
## N Y <NA>
## [1,] No. cases 992 491 303 1786
## [2,] Percent 55.5 27.5 17 100
37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v1_mss_itm37)
v1_mss_recode(v1_clin$v1_mss_s2_mss37_konzentration,v1_con$v1_mss_s2_mss37,"v1_mss_itm37")
## N Y <NA>
## [1,] No. cases 1182 298 306 1786
## [2,] Percent 66.2 16.7 17.1 100
38. “I thought I was an especially important person” (dichotomous, v1_mss_itm38)
v1_mss_recode(v1_clin$v1_mss_s2_mss38_etw_besonderes,v1_con$v1_mss_s2_mss38,"v1_mss_itm38")
## N Y <NA>
## [1,] No. cases 1323 163 300 1786
## [2,] Percent 74.1 9.1 16.8 100
39. “I thought I could change the world” (dichotomous, v1_mss_itm39)
v1_mss_recode(v1_clin$v1_mss_s2_mss39_welt_veraender,v1_con$v1_mss_s2_mss39,"v1_mss_itm39")
## N Y <NA>
## [1,] No. cases 1338 143 305 1786
## [2,] Percent 74.9 8 17.1 100
40. “I thought I was right most of the time” (dichotomous, v1_mss_itm40)
v1_mss_recode(v1_clin$v1_mss_s2_mss40_recht_haben,v1_con$v1_mss_s2_mss40,"v1_mss_itm40")
## N Y <NA>
## [1,] No. cases 1355 129 302 1786
## [2,] Percent 75.9 7.2 16.9 100
41. “I thought I was superior to others” (dichotomous, v1_mss_itm41)
v1_mss_recode(v1_clin$v1_mss_s3_mss41_ueberlegen,v1_con$v1_mss_s3_mss41,"v1_mss_itm41")
## N Y <NA>
## [1,] No. cases 1380 94 312 1786
## [2,] Percent 77.3 5.3 17.5 100
42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v1_mss_itm42)
v1_mss_recode(v1_clin$v1_mss_s3_mss42_uebermut,v1_con$v1_mss_s3_mss42,"v1_mss_itm42")
## N Y <NA>
## [1,] No. cases 1315 158 313 1786
## [2,] Percent 73.6 8.8 17.5 100
43. “I thought I knew what other people were thinking” (dichotomous, v1_mss_itm43)
v1_mss_recode(v1_clin$v1_mss_s3_mss43_ged_lesen_akt,v1_con$v1_mss_s3_mss43,"v1_mss_itm43")
## N Y <NA>
## [1,] No. cases 1295 178 313 1786
## [2,] Percent 72.5 10 17.5 100
44. “I thought other people knew what I was thinking” (dichotomous, v1_mss_itm44)
v1_mss_recode(v1_clin$v1_mss_s3_mss44_ged_lesen_pas,v1_con$v1_mss_s3_mss44,"v1_mss_itm44")
## N Y <NA>
## [1,] No. cases 1345 126 315 1786
## [2,] Percent 75.3 7.1 17.6 100
45. “I thought someone wanted to harm me” (dichotomous, v1_mss_itm45)
v1_mss_recode(v1_clin$v1_mss_s3_mss45_etw_antun,v1_con$v1_mss_s3_mss45,"v1_mss_itm45")
## N Y <NA>
## [1,] No. cases 1347 126 313 1786
## [2,] Percent 75.4 7.1 17.5 100
46. “I heard voices when people weren’t there” (dichotomous, v1_mss_itm46)
v1_mss_recode(v1_clin$v1_mss_s3_mss46_stimmen,v1_con$v1_mss_s3_mss46,"v1_mss_itm46")
## N Y <NA>
## [1,] No. cases 1332 141 313 1786
## [2,] Percent 74.6 7.9 17.5 100
47. “I had false beliefs concerning who I was” (dichotomous, v1_mss_itm47)
v1_mss_recode(v1_clin$v1_mss_s3_mss47_jmd_anders,v1_con$v1_mss_s3_mss47,"v1_mss_itm47")
## N Y <NA>
## [1,] No. cases 1412 62 312 1786
## [2,] Percent 79.1 3.5 17.5 100
48. “I knew I was getting ill” (dichotomous, v1_mss_itm48)
v1_mss_recode(v1_clin$v1_mss_s3_mss48_krank_einsicht,v1_con$v1_mss_s3_mss48,"v1_mss_itm48")
## N Y <NA>
## [1,] No. cases 1142 317 327 1786
## [2,] Percent 63.9 17.7 18.3 100
Create MSS sum score (continuous [0-48],v1_mss_sum) Please note that if one or more of MSS items are missing, this will result in the sum score to become NA.
v1_mss_sum<-ifelse(v1_mss_itm1=="Y",1,0)+
ifelse(v1_mss_itm2=="Y",1,0)+
ifelse(v1_mss_itm3=="Y",1,0)+
ifelse(v1_mss_itm4=="Y",1,0)+
ifelse(v1_mss_itm5=="Y",1,0)+
ifelse(v1_mss_itm6=="Y",1,0)+
ifelse(v1_mss_itm7=="Y",1,0)+
ifelse(v1_mss_itm8=="Y",1,0)+
ifelse(v1_mss_itm9=="Y",1,0)+
ifelse(v1_mss_itm10=="Y",1,0)+
ifelse(v1_mss_itm11=="Y",1,0)+
ifelse(v1_mss_itm12=="Y",1,0)+
ifelse(v1_mss_itm13=="Y",1,0)+
ifelse(v1_mss_itm14=="Y",1,0)+
ifelse(v1_mss_itm15=="Y",1,0)+
ifelse(v1_mss_itm16=="Y",1,0)+
ifelse(v1_mss_itm17=="Y",1,0)+
ifelse(v1_mss_itm18=="Y",1,0)+
ifelse(v1_mss_itm19=="Y",1,0)+
ifelse(v1_mss_itm20=="Y",1,0)+
ifelse(v1_mss_itm21=="Y",1,0)+
ifelse(v1_mss_itm22=="Y",1,0)+
ifelse(v1_mss_itm23=="Y",1,0)+
ifelse(v1_mss_itm24=="Y",1,0)+
ifelse(v1_mss_itm25=="Y",1,0)+
ifelse(v1_mss_itm26=="Y",1,0)+
ifelse(v1_mss_itm27=="Y",1,0)+
ifelse(v1_mss_itm28=="Y",1,0)+
ifelse(v1_mss_itm29=="Y",1,0)+
ifelse(v1_mss_itm30=="Y",1,0)+
ifelse(v1_mss_itm31=="Y",1,0)+
ifelse(v1_mss_itm32=="Y",1,0)+
ifelse(v1_mss_itm33=="Y",1,0)+
ifelse(v1_mss_itm34=="Y",1,0)+
ifelse(v1_mss_itm35=="Y",1,0)+
ifelse(v1_mss_itm36=="Y",1,0)+
ifelse(v1_mss_itm37=="Y",1,0)+
ifelse(v1_mss_itm38=="Y",1,0)+
ifelse(v1_mss_itm39=="Y",1,0)+
ifelse(v1_mss_itm40=="Y",1,0)+
ifelse(v1_mss_itm41=="Y",1,0)+
ifelse(v1_mss_itm42=="Y",1,0)+
ifelse(v1_mss_itm43=="Y",1,0)+
ifelse(v1_mss_itm44=="Y",1,0)+
ifelse(v1_mss_itm45=="Y",1,0)+
ifelse(v1_mss_itm46=="Y",1,0)+
ifelse(v1_mss_itm47=="Y",1,0)+
ifelse(v1_mss_itm48=="Y",1,0)
summary(v1_mss_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 4.000 5.905 8.000 39.000 562
Create dataset
v1_mss<-data.frame(v1_mss_itm1,v1_mss_itm2,v1_mss_itm3,v1_mss_itm4,v1_mss_itm5,v1_mss_itm6,
v1_mss_itm7,v1_mss_itm8,v1_mss_itm9,v1_mss_itm10,v1_mss_itm11,
v1_mss_itm12,v1_mss_itm13,v1_mss_itm14,v1_mss_itm15,v1_mss_itm16,
v1_mss_itm17,v1_mss_itm18,v1_mss_itm19,v1_mss_itm20,v1_mss_itm21,
v1_mss_itm22,v1_mss_itm23,v1_mss_itm24,v1_mss_itm25,v1_mss_itm26,
v1_mss_itm27,v1_mss_itm28,v1_mss_itm29,v1_mss_itm30,v1_mss_itm31,
v1_mss_itm32,v1_mss_itm33,v1_mss_itm34,v1_mss_itm35,v1_mss_itm36,
v1_mss_itm37,v1_mss_itm38,v1_mss_itm39,v1_mss_itm40,v1_mss_itm41,
v1_mss_itm42,v1_mss_itm43,v1_mss_itm44,v1_mss_itm45,v1_mss_itm46,
v1_mss_itm47,v1_mss_itm48, v1_mss_sum)
In this questionnaire (Norbeck, 1984; Sarason, Johnson, & Siegel, 1978) many possible life events are listed (e.g. “Difficulties finding work”) from the following areas: health, work, school, residence, love and marriage, family and close friends, parenting, personal or social, financial, crime and legal matters, and other. Participants are supposed to answer only to those life events which they have experienced during the past six months. For these particular events, participants were asked to rate:
As participants usually only experience relatively few life events during the follow-up period, most of the items are not filled out. We have coded empty items as “-999” in people that filled out the questionnaire correctly. In participants that did not fill out the questionaire at all or filled it out obviously wrong (e.g. answering every question, regardless whether they experienced the life event or not), all items are “NA”.
The questionaire is divided in ten separate sections (A-“Health”, B-“Work”, C-“School”, D-“Residence”, E-“Love and marriage”, F-“Family and close friends”, G-“Parenting”, H-“Personal or social”, I-“Financial”, J-“Crime and legal matters”). The respective sections are contained in the item name.
1. “Major personal illness or injury”
1A Nature (dichotomous [“good”,“bad”], v1_leq_A_1A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq1a_schw_krankh,v1_con$v1_leq_a_leq1a,"v1_leq_A_1A")
## -999 bad good <NA>
## [1,] No. cases 932 431 85 338 1786
## [2,] Percent 52.2 24.1 4.8 18.9 100
1B Impact (ordinal [0,1,2,3], v1_leq_A_1B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq1e_schw_krankh,v1_con$v1_leq_a_leq1e,"v1_leq_A_1B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 922 25 39 118 344 338 1786
## [2,] Percent 51.6 1.4 2.2 6.6 19.3 18.9 100
2. “Major change in eating habits”
2A Nature (dichotomous [“good”,“bad”], v1_leq_A_2A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq2a_ernaehrung,v1_con$v1_leq_a_leq2a,"v1_leq_A_2A")
## -999 bad good <NA>
## [1,] No. cases 1005 204 239 338 1786
## [2,] Percent 56.3 11.4 13.4 18.9 100
2B Impact (ordinal [0,1,2,3], v1_leq_A_2B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq2e_ernaehrung,v1_con$v1_leq_a_leq2e,"v1_leq_A_2B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 992 39 92 186 139 338 1786
## [2,] Percent 55.5 2.2 5.2 10.4 7.8 18.9 100
3. “Major change in sleeping habits”
3A Nature (dichotomous [“good”,“bad”], v1_leq_A_3A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq3a_schlaf,v1_con$v1_leq_a_leq3a,"v1_leq_A_3A")
## -999 bad good <NA>
## [1,] No. cases 938 344 166 338 1786
## [2,] Percent 52.5 19.3 9.3 18.9 100
3B Impact (ordinal [0,1,2,3], v1_leq_A_3B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq3e_schlaf,v1_con$v1_leq_a_leq3e,"v1_leq_A_3B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 926 27 111 182 202 338 1786
## [2,] Percent 51.8 1.5 6.2 10.2 11.3 18.9 100
4. “Major change in usual type and/or amount of recreation”
4A Nature (dichotomous [“good”,“bad”], v1_leq_A_4A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq4a_freizeit,v1_con$v1_leq_a_leq4a,"v1_leq_A_4A")
## -999 bad good <NA>
## [1,] No. cases 885 269 294 338 1786
## [2,] Percent 49.6 15.1 16.5 18.9 100
4B Impact (ordinal [0,1,2,3], v1_leq_A_4B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq4e_freizeit,v1_con$v1_leq_a_leq4e,"v1_leq_A_4B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 874 35 109 228 202 338 1786
## [2,] Percent 48.9 2 6.1 12.8 11.3 18.9 100
5. “Major dental work”
5A Nature (dichotomous [“good”,“bad”], v1_leq_A_5A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq5a_zahnarzt,v1_con$v1_leq_a_leq5a,"v1_leq_A_5A")
## -999 bad good <NA>
## [1,] No. cases 1222 93 133 338 1786
## [2,] Percent 68.4 5.2 7.4 18.9 100
5B Impact (ordinal [0,1,2,3], v1_leq_A_5B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq5e_zahnarzt,v1_con$v1_leq_a_leq5e,"v1_leq_A_5B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1205 66 52 68 57 338 1786
## [2,] Percent 67.5 3.7 2.9 3.8 3.2 18.9 100
6. “(Female) Pregnancy”
6A Nature (dichotomous [“good”,“bad”], v1_leq_A_6A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq6a_schwanger,v1_con$v1_leq_a_leq6a,"v1_leq_A_6A")
## -999 bad good <NA>
## [1,] No. cases 1423 7 18 338 1786
## [2,] Percent 79.7 0.4 1 18.9 100
6B Impact (ordinal [0,1,2,3], v1_leq_A_6B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq6e_schwanger,v1_con$v1_leq_a_leq6e,"v1_leq_A_6B")
## -999 0 2 3 <NA>
## [1,] No. cases 1421 9 3 15 338 1786
## [2,] Percent 79.6 0.5 0.2 0.8 18.9 100
7. “(Female) Miscarriage or abortion”
7A Nature (dichotomous [“good”,“bad”], v1_leq_A_7A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq7a_fehlg_abtr,v1_con$v1_leq_a_leq7a,"v1_leq_A_7A")
## -999 bad good <NA>
## [1,] No. cases 1435 9 4 338 1786
## [2,] Percent 80.3 0.5 0.2 18.9 100
7B Impact (ordinal [0,1,2,3], v1_leq_A_7B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq7e_fehlg_abtr,v1_con$v1_leq_a_leq7e,"v1_leq_A_7B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1433 6 2 1 6 338 1786
## [2,] Percent 80.2 0.3 0.1 0.1 0.3 18.9 100
8. “(Female) Started menopause”
8A Nature (dichotomous [“good”,“bad”], v1_leq_A_8A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq8a_wechseljahre,v1_con$v1_leq_a_leq8a,"v1_leq_A_8A")
## -999 bad good <NA>
## [1,] No. cases 1391 38 19 338 1786
## [2,] Percent 77.9 2.1 1.1 18.9 100
8B Impact (ordinal [0,1,2,3], v1_leq_A_8B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq8e_wechseljahre,v1_con$v1_leq_a_leq8e,"v1_leq_A_8B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1387 12 10 21 18 338 1786
## [2,] Percent 77.7 0.7 0.6 1.2 1 18.9 100
9. “Major difficulties with birth control pills or devices”
9A Nature (dichotomous [“good”,“bad”], v1_leq_A_9A)
v1_leq_a_recode(v1_clin$v1_leq_a_leq9a_verhuetung,v1_con$v1_leq_a_leq9a,"v1_leq_A_9A")
## -999 bad good <NA>
## [1,] No. cases 1396 38 14 338 1786
## [2,] Percent 78.2 2.1 0.8 18.9 100
9B Impact (ordinal [0,1,2,3], v1_leq_A_9B)
v1_leq_b_recode(v1_clin$v1_leq_a_leq9e_verhuetung,v1_con$v1_leq_a_leq9e,"v1_leq_A_9B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1392 16 12 9 19 338 1786
## [2,] Percent 77.9 0.9 0.7 0.5 1.1 18.9 100
Create dataset
v1_leq_A<-data.frame(v1_leq_A_1A,v1_leq_A_1B,v1_leq_A_2A,v1_leq_A_2B,v1_leq_A_3A,
v1_leq_A_3B,v1_leq_A_4A,v1_leq_A_4B,v1_leq_A_5A,v1_leq_A_5B,
v1_leq_A_6A,v1_leq_A_6B,v1_leq_A_7A,v1_leq_A_7B,v1_leq_A_8A,
v1_leq_A_8B,v1_leq_A_9A,v1_leq_A_9B)
10. “Difficulty finding a job”
10A Nature (dichotomous [“good”,“bad”], v1_leq_B_10A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq10a_arbeitssuche,v1_con$v1_leq_b_leq10a,"v1_leq_B_10A")
## -999 bad good <NA>
## [1,] No. cases 1193 204 51 338 1786
## [2,] Percent 66.8 11.4 2.9 18.9 100
10B Impact (ordinal [0,1,2,3], v1_leq_B_10B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq10e_arbeitssuche,v1_con$v1_leq_b_leq10e,"v1_leq_B_10B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1184 25 46 68 125 338 1786
## [2,] Percent 66.3 1.4 2.6 3.8 7 18.9 100
11. “Beginning work outside the home”
11A Nature (dichotomous [“good”,“bad”], v1_leq_B_11A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq11a_arbeit_aussen,v1_con$v1_leq_b_leq11a,"v1_leq_B_11A")
## -999 bad good <NA>
## [1,] No. cases 1225 73 150 338 1786
## [2,] Percent 68.6 4.1 8.4 18.9 100
11B Impact (ordinal [0,1,2,3], v1_leq_B_11B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq11e_arbeit_aussen,v1_con$v1_leq_b_leq11e,"v1_leq_B_11B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1220 25 42 66 95 338 1786
## [2,] Percent 68.3 1.4 2.4 3.7 5.3 18.9 100
12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v1_leq_B_12A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq12a_arbeitswechs,v1_con$v1_leq_b_leq12a,"v1_leq_B_12A")
## -999 bad good <NA>
## [1,] No. cases 1196 62 190 338 1786
## [2,] Percent 67 3.5 10.6 18.9 100
12B Impact (ordinal [0,1,2,3], v1_leq_B_12B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq12e_arbeitswechs,v1_con$v1_leq_b_leq12e,"v1_leq_B_12B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1193 14 48 87 106 338 1786
## [2,] Percent 66.8 0.8 2.7 4.9 5.9 18.9 100
13. “Changing your work hours or conditions”
13A Nature (dichotomous [“good”,“bad”], v1_leq_B_13A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq13a_veraend_arb,v1_con$v1_leq_b_leq13a,"v1_leq_B_13A")
## -999 bad good <NA>
## [1,] No. cases 1149 113 186 338 1786
## [2,] Percent 64.3 6.3 10.4 18.9 100
13B Impact (ordinal [0,1,2,3], v1_leq_B_13B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq13e_veraend_arb,v1_con$v1_leq_b_leq13e,"v1_leq_B_13B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1146 14 75 102 111 338 1786
## [2,] Percent 64.2 0.8 4.2 5.7 6.2 18.9 100
14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v1_leq_B_14A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq14a_veraend_ba,v1_con$v1_leq_b_leq14a,"v1_leq_B_14A")
## -999 bad good <NA>
## [1,] No. cases 1141 92 215 338 1786
## [2,] Percent 63.9 5.2 12 18.9 100
14B Impact (ordinal [0,1,2,3], v1_leq_B_14B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq14e_veraend_ba,v1_con$v1_leq_b_leq14e,"v1_leq_B_14B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1140 19 67 102 120 338 1786
## [2,] Percent 63.8 1.1 3.8 5.7 6.7 18.9 100
15. “Troubles at work with your employer or co-worker”
15A Nature (dichotomous [“good”,“bad”], v1_leq_B_15A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq15a_schw_arbeit,v1_con$v1_leq_b_leq15a,"v1_leq_B_15A")
## -999 bad good <NA>
## [1,] No. cases 1223 201 24 338 1786
## [2,] Percent 68.5 11.3 1.3 18.9 100
15B Impact (ordinal [0,1,2,3], v1_leq_B_15B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq15e_schw_arbeit,v1_con$v1_leq_b_leq15e,"v1_leq_B_15B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1220 27 55 64 82 338 1786
## [2,] Percent 68.3 1.5 3.1 3.6 4.6 18.9 100
16. “Major business readjustment”
16A Nature (dichotomous [“good”,“bad”], v1_leq_B_16A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq16a_betr_reorg,v1_con$v1_leq_b_leq16a,"v1_leq_B_16A")
## -999 bad good <NA>
## [1,] No. cases 1374 41 33 338 1786
## [2,] Percent 76.9 2.3 1.8 18.9 100
16B Impact (ordinal [0,1,2,3], v1_leq_B_16B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq16e_betr_reorg,v1_con$v1_leq_b_leq16e,"v1_leq_B_16B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1370 13 23 19 23 338 1786
## [2,] Percent 76.7 0.7 1.3 1.1 1.3 18.9 100
17. “Being fired or laid off from work”
17A Nature (dichotomous [“good”,“bad”], v1_leq_B_17A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq17a_kuendigung,v1_con$v1_leq_b_leq17a,"v1_leq_B_17A")
## -999 bad good <NA>
## [1,] No. cases 1314 97 37 338 1786
## [2,] Percent 73.6 5.4 2.1 18.9 100
17B Impact (ordinal [0,1,2,3], v1_leq_B_17B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq17e_kuendigung,v1_con$v1_leq_b_leq17e,"v1_leq_B_17B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1312 15 17 31 73 338 1786
## [2,] Percent 73.5 0.8 1 1.7 4.1 18.9 100
18. “Retirement from work”
18A Nature (dichotomous [“good”,“bad”], v1_leq_B_18A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq18a_ende_beruf,v1_con$v1_leq_b_leq18a,"v1_leq_B_18A")
## -999 bad good <NA>
## [1,] No. cases 1367 47 34 338 1786
## [2,] Percent 76.5 2.6 1.9 18.9 100
18B Impact (ordinal [0,1,2,3], v1_leq_B_18B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq18e_ende_beruf,v1_con$v1_leq_b_leq18e,"v1_leq_B_18B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1364 11 7 16 50 338 1786
## [2,] Percent 76.4 0.6 0.4 0.9 2.8 18.9 100
19. “Taking courses by mail or studying at home to help you in your work”
19A Nature (dichotomous [“good”,“bad”], v1_leq_B_19A)
v1_leq_a_recode(v1_clin$v1_leq_b_leq19a_fortbildung,v1_con$v1_leq_b_leq19a,"v1_leq_B_19A")
## -999 bad good <NA>
## [1,] No. cases 1331 21 96 338 1786
## [2,] Percent 74.5 1.2 5.4 18.9 100
19B Impact (ordinal [0,1,2,3], v1_leq_B_19B)
v1_leq_b_recode(v1_clin$v1_leq_b_leq19e_fortbildung,v1_con$v1_leq_b_leq19e,"v1_leq_B_19B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1328 20 17 41 42 338 1786
## [2,] Percent 74.4 1.1 1 2.3 2.4 18.9 100
v1_leq_B<-data.frame(v1_leq_B_10A,v1_leq_B_10B,v1_leq_B_11A,v1_leq_B_11B,v1_leq_B_12A,
v1_leq_B_12B,v1_leq_B_13A,v1_leq_B_13B,v1_leq_B_14A,v1_leq_B_14B,
v1_leq_B_15A,v1_leq_B_15B,v1_leq_B_16A,v1_leq_B_16B,v1_leq_B_17A,
v1_leq_B_17B,v1_leq_B_18A,v1_leq_B_18B,v1_leq_B_19A,v1_leq_B_19B)
20. “Beginning or ceasing school, college, or training program”
20A Nature (dichotomous [“good”,“bad”], v1_leq_C_20A)
v1_leq_a_recode(v1_clin$v1_leq_c_d_leq20a_beginn_ende,v1_con$v1_leq_c_d_leq20a,"v1_leq_C_20A")
## -999 bad good <NA>
## [1,] No. cases 1306 37 105 338 1786
## [2,] Percent 73.1 2.1 5.9 18.9 100
20B Impact (ordinal [0,1,2,3], v1_leq_C_20B)
v1_leq_b_recode(v1_clin$v1_leq_c_d_leq20e_beginn_ende,v1_con$v1_leq_c_d_leq20e,"v1_leq_C_20B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1304 9 20 37 78 338 1786
## [2,] Percent 73 0.5 1.1 2.1 4.4 18.9 100
21. “Change of school, college, or training program”
21A Nature (dichotomous [“good”,“bad”], v1_leq_C_21A)
v1_leq_a_recode(v1_clin$v1_leq_c_d_leq21a_schulwechsel,v1_con$v1_leq_c_d_leq21a,"v1_leq_C_21A")
## -999 bad good <NA>
## [1,] No. cases 1398 13 37 338 1786
## [2,] Percent 78.3 0.7 2.1 18.9 100
21B Impact (ordinal [0,1,2,3], v1_leq_C_21B)
v1_leq_b_recode(v1_clin$v1_leq_c_d_leq21e_schulwechsel,v1_con$v1_leq_c_d_leq21e,"v1_leq_C_21B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1395 7 8 18 20 338 1786
## [2,] Percent 78.1 0.4 0.4 1 1.1 18.9 100
22. “Change in career goal or academic major”
A Nature (dichotomous [“good”,“bad”], v1_leq_C_22A)
v1_leq_a_recode(v1_clin$v1_leq_c_d_leq22a_aend_karriere,v1_con$v1_leq_c_d_leq22a,"v1_leq_C_22A")
## -999 bad good <NA>
## [1,] No. cases 1339 22 87 338 1786
## [2,] Percent 75 1.2 4.9 18.9 100
B Impact (ordinal [0,1,2,3], v1_leq_C_22B)
v1_leq_b_recode(v1_clin$v1_leq_c_d_leq22e_aend_karriere,v1_con$v1_leq_c_d_leq22e,"v1_leq_C_22B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1336 8 19 31 54 338 1786
## [2,] Percent 74.8 0.4 1.1 1.7 3 18.9 100
23. “Problem in school, college, or training program”
23A Nature (dichotomous [“good”,“bad”], v1_leq_C_23A)
v1_leq_a_recode(v1_clin$v1_leq_c_d_leq23a_schulprob,v1_con$v1_leq_c_d_leq23a,"v1_leq_C_23A")
## -999 bad good <NA>
## [1,] No. cases 1355 82 11 338 1786
## [2,] Percent 75.9 4.6 0.6 18.9 100
23B Impact (ordinal [0,1,2,3], v1_leq_C_23B)
v1_leq_b_recode(v1_clin$v1_leq_c_d_leq23e_schulprob,v1_con$v1_leq_c_d_leq23e,"v1_leq_C_23B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1353 9 20 29 37 338 1786
## [2,] Percent 75.8 0.5 1.1 1.6 2.1 18.9 100
Create dataset
v1_leq_C<-data.frame(v1_leq_C_20A,v1_leq_C_20B,v1_leq_C_21A,v1_leq_C_21B,v1_leq_C_22A,v1_leq_C_22B,v1_leq_C_23A,v1_leq_C_23B)
24. “Difficulty finding housing”
24A Nature (dichotomous [“good”,“bad”], v1_leq_D_24A)
v1_leq_a_recode(v1_clin$v1_leq_c_d_leq24a_schw_wsuche,v1_con$v1_leq_c_d_leq24a,"v1_leq_D_24A")
## -999 bad good <NA>
## [1,] No. cases 1265 149 34 338 1786
## [2,] Percent 70.8 8.3 1.9 18.9 100
24B Impact (ordinal [0,1,2,3], v1_leq_D_24B)
v1_leq_b_recode(v1_clin$v1_leq_c_d_leq24e_schw_wsuche,v1_con$v1_leq_c_d_leq24e,"v1_leq_D_24B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1260 23 46 55 64 338 1786
## [2,] Percent 70.5 1.3 2.6 3.1 3.6 18.9 100
25. “Changing residence within the same town or city”
A Nature (dichotomous [“good”,“bad”], v1_leq_D_25A)
v1_leq_a_recode(v1_clin$v1_leq_c_d_leq25a_umzug_nah,v1_con$v1_leq_c_d_leq25a,"v1_leq_D_25A")
## -999 bad good <NA>
## [1,] No. cases 1290 36 122 338 1786
## [2,] Percent 72.2 2 6.8 18.9 100
B Impact (ordinal [0,1,2,3], v1_leq_D_25B)
v1_leq_b_recode(v1_clin$v1_leq_c_d_leq25e_umzug_nah,v1_con$v1_leq_c_d_leq25e,"v1_leq_D_25B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1288 17 30 46 67 338 1786
## [2,] Percent 72.1 1 1.7 2.6 3.8 18.9 100
26. “Moving to a different town, city, state, or country”
26A Nature (dichotomous [“good”,“bad”], v1_leq_D_26A)
v1_leq_a_recode(v1_clin$v1_leq_c_d_leq26a_umzug_fern,v1_con$v1_leq_c_d_leq26a,"v1_leq_D_26A")
## -999 bad good <NA>
## [1,] No. cases 1321 41 86 338 1786
## [2,] Percent 74 2.3 4.8 18.9 100
26B Impact (ordinal [0,1,2,3], v1_leq_D_26B)
v1_leq_b_recode(v1_clin$v1_leq_c_d_leq26e_umzug_fern,v1_con$v1_leq_c_d_leq26e,"v1_leq_D_26B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1315 15 12 36 70 338 1786
## [2,] Percent 73.6 0.8 0.7 2 3.9 18.9 100
27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”
27A Nature (dichotomous [“good”,“bad”], v1_leq_D_27A)
v1_leq_a_recode(v1_clin$v1_leq_c_d_leq27a_veraend_lu,v1_con$v1_leq_c_d_leq27a,"v1_leq_D_27A")
## -999 bad good <NA>
## [1,] No. cases 1166 122 160 338 1786
## [2,] Percent 65.3 6.8 9 18.9 100
27B Impact (ordinal [0,1,2,3], v1_leq_D_27B)
v1_leq_b_recode(v1_clin$v1_leq_c_d_leq27e_veraend_lu,v1_con$v1_leq_c_d_leq27e,"v1_leq_D_27B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1159 22 56 79 132 338 1786
## [2,] Percent 64.9 1.2 3.1 4.4 7.4 18.9 100
Create dataset
v1_leq_D<-data.frame(v1_leq_D_24A,v1_leq_D_24B,v1_leq_D_25A,v1_leq_D_25B,v1_leq_D_26A,
v1_leq_D_26B,v1_leq_D_27A,v1_leq_D_27B)
28. “Began a new, close, personal relationship”
28A Nature (dichotomous [“good”,“bad”], v1_leq_E_28A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq28a_neue_bez,v1_con$v1_leq_e_leq28a,"v1_leq_E_28A")
## -999 bad good <NA>
## [1,] No. cases 1243 32 173 338 1786
## [2,] Percent 69.6 1.8 9.7 18.9 100
28B Impact (ordinal [0,1,2,3], v1_leq_E_28B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq28e_neue_bez,v1_con$v1_leq_e_leq28e,"v1_leq_E_28B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1239 12 32 52 113 338 1786
## [2,] Percent 69.4 0.7 1.8 2.9 6.3 18.9 100
29. “Became engaged”
29A Nature (dichotomous [“good”,“bad”], v1_leq_E_29A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq29a_verlobung,v1_con$v1_leq_e_leq29a,"v1_leq_E_29A")
## -999 bad good <NA>
## [1,] No. cases 1408 10 30 338 1786
## [2,] Percent 78.8 0.6 1.7 18.9 100
29B Impact (ordinal [0,1,2,3], v1_leq_E_29B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq29e_verlobung,v1_con$v1_leq_e_leq29e,"v1_leq_E_29B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1405 6 4 11 22 338 1786
## [2,] Percent 78.7 0.3 0.2 0.6 1.2 18.9 100
30. “Girlfriend or boyfriend problems”
30A Nature (dichotomous [“good”,“bad”], v1_leq_E_30A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq30a_prob_partner,v1_con$v1_leq_e_leq30a,"v1_leq_E_30A")
## -999 bad good <NA>
## [1,] No. cases 1202 219 27 338 1786
## [2,] Percent 67.3 12.3 1.5 18.9 100
30B Impact (ordinal [0,1,2,3], v1_leq_E_30B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq30e_prob_partner,v1_con$v1_leq_e_leq30e,"v1_leq_E_30B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1199 10 47 95 97 338 1786
## [2,] Percent 67.1 0.6 2.6 5.3 5.4 18.9 100
31. “Breaking up with a girlfriend or breaking an engagement”
31A Nature (dichotomous [“good”,“bad”], v1_leq_E_31A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq31a_trennung,v1_con$v1_leq_e_leq31a,"v1_leq_E_31A")
## -999 bad good <NA>
## [1,] No. cases 1295 116 37 338 1786
## [2,] Percent 72.5 6.5 2.1 18.9 100
31B Impact (ordinal [0,1,2,3], v1_leq_E_31B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq31e_trennung,v1_con$v1_leq_e_leq31e,"v1_leq_E_31B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1290 12 26 50 70 338 1786
## [2,] Percent 72.2 0.7 1.5 2.8 3.9 18.9 100
32. “(Male) Wife or girlfriend’s pregnancy”
32A Nature (dichotomous [“good”,“bad”], v1_leq_E_32A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq32a_schwanger_p,v1_con$v1_leq_e_leq32a,"v1_leq_E_32A")
## -999 bad good <NA>
## [1,] No. cases 1433 7 8 338 1786
## [2,] Percent 80.2 0.4 0.4 18.9 100
32B Impact (ordinal [0,1,2,3], v1_leq_E_32B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq32e_schwanger_p,v1_con$v1_leq_e_leq32e,"v1_leq_E_32B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1431 2 3 3 9 338 1786
## [2,] Percent 80.1 0.1 0.2 0.2 0.5 18.9 100
33. “(Male) Wife or girlfriend having a miscarriage or abortion”
33A Nature (dichotomous [“good”,“bad”], v1_leq_E_33A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq33a_fehlg_abtr_p,v1_con$v1_leq_e_leq33a,"v1_leq_E_33A")
## -999 bad good <NA>
## [1,] No. cases 1436 10 2 338 1786
## [2,] Percent 80.4 0.6 0.1 18.9 100
33B Impact (ordinal [0,1,2,3], v1_leq_E_33B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq33e_fehlg_abtr_p,v1_con$v1_leq_e_leq33e,"v1_leq_E_33B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1435 5 2 1 5 338 1786
## [2,] Percent 80.3 0.3 0.1 0.1 0.3 18.9 100
34. “Getting married (or beginning to live with someone)”
34A Nature (dichotomous [“good”,“bad”], v1_leq_E_34A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq34a_heirat,v1_con$v1_leq_e_leq34a,"v1_leq_E_34A")
## -999 bad good <NA>
## [1,] No. cases 1404 5 39 338 1786
## [2,] Percent 78.6 0.3 2.2 18.9 100
34B Impact (ordinal [0,1,2,3], v1_leq_E_34B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq34e_heirat,v1_con$v1_leq_e_leq34e,"v1_leq_E_34B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1399 4 4 14 27 338 1786
## [2,] Percent 78.3 0.2 0.2 0.8 1.5 18.9 100
35. “A change in closeness with your partner”
35A Nature (dichotomous [“good”,“bad”], v1_leq_E_35A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq35a_veraend_naehe,v1_con$v1_leq_e_leq35a,"v1_leq_E_35A")
## -999 bad good <NA>
## [1,] No. cases 1223 129 96 338 1786
## [2,] Percent 68.5 7.2 5.4 18.9 100
35B Impact (ordinal [0,1,2,3], v1_leq_E_35B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq35e_veraend_naehe,v1_con$v1_leq_e_leq35e,"v1_leq_E_35B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1217 7 36 82 106 338 1786
## [2,] Percent 68.1 0.4 2 4.6 5.9 18.9 100
36. “Infidelity”
36A Nature (dichotomous [“good”,“bad”], v1_leq_E_36A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq36a_untreue,v1_con$v1_leq_e_leq36a,"v1_leq_E_36A")
## -999 bad good <NA>
## [1,] No. cases 1383 52 13 338 1786
## [2,] Percent 77.4 2.9 0.7 18.9 100
36B Impact (ordinal [0,1,2,3], v1_leq_E_36B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq36e_untreue,v1_con$v1_leq_e_leq36e,"v1_leq_E_36B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1380 14 12 9 33 338 1786
## [2,] Percent 77.3 0.8 0.7 0.5 1.8 18.9 100
37. “Trouble with in-laws”
37A Nature (dichotomous [“good”,“bad”], v1_leq_E_37A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq37a_konf_schwiege,v1_con$v1_leq_e_leq37a,"v1_leq_E_37A")
## -999 bad good <NA>
## [1,] No. cases 1379 60 9 338 1786
## [2,] Percent 77.2 3.4 0.5 18.9 100
37B Impact (ordinal [0,1,2,3], v1_leq_E_37B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq37e_konf_schwiege,v1_con$v1_leq_e_leq37e,"v1_leq_E_37B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1377 5 21 28 17 338 1786
## [2,] Percent 77.1 0.3 1.2 1.6 1 18.9 100
38. “Separation from spouse or partner due to conflict”
38A Nature (dichotomous [“good”,“bad”], v1_leq_E_38A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq38a_trennung_str,v1_con$v1_leq_e_leq38a,"v1_leq_E_38A")
## -999 bad good <NA>
## [1,] No. cases 1379 51 18 338 1786
## [2,] Percent 77.2 2.9 1 18.9 100
38B Impact (ordinal [0,1,2,3], v1_leq_E_38B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq38e_trennung_str,v1_con$v1_leq_e_leq38e,"v1_leq_E_38B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1379 4 5 21 39 338 1786
## [2,] Percent 77.2 0.2 0.3 1.2 2.2 18.9 100
39. “Separation from spouse or partner due to work, travel, etc.”
39A Nature (dichotomous [“good”,“bad”], v1_leq_E_39A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq39a_trennung_ber,v1_con$v1_leq_e_leq39a,"v1_leq_E_39A")
## -999 bad good <NA>
## [1,] No. cases 1428 19 1 338 1786
## [2,] Percent 80 1.1 0.1 18.9 100
39B Impact (ordinal [0,1,2,3], v1_leq_E_39B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq39e_trennung_ber,v1_con$v1_leq_e_leq39e,"v1_leq_E_39B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1426 7 1 6 8 338 1786
## [2,] Percent 79.8 0.4 0.1 0.3 0.4 18.9 100
40. “Reconciliation with spouse or partner”
40A Nature (dichotomous [“good”,“bad”], v1_leq_E_40A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq40a_versoehnung,v1_con$v1_leq_e_leq40a,"v1_leq_E_40A")
## -999 bad good <NA>
## [1,] No. cases 1369 7 72 338 1786
## [2,] Percent 76.7 0.4 4 18.9 100
40B Impact (ordinal [0,1,2,3], v1_leq_E_40B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq40e_versoehnung,v1_con$v1_leq_e_leq40e,"v1_leq_E_40B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1368 5 14 22 39 338 1786
## [2,] Percent 76.6 0.3 0.8 1.2 2.2 18.9 100
41. “Divorce”
41A Nature (dichotomous [“good”,“bad”], v1_leq_E_41A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq41a_scheidung,v1_con$v1_leq_e_leq41a,"v1_leq_E_41A")
## -999 bad good <NA>
## [1,] No. cases 1422 18 8 338 1786
## [2,] Percent 79.6 1 0.4 18.9 100
41B Impact (ordinal [0,1,2,3], v1_leq_E_41B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq41e_scheidung,v1_con$v1_leq_e_leq41e,"v1_leq_E_41B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1421 5 1 6 15 338 1786
## [2,] Percent 79.6 0.3 0.1 0.3 0.8 18.9 100
42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”
42A Nature (dichotomous [“good”,“bad”], v1_leq_E_42A)
v1_leq_a_recode(v1_clin$v1_leq_e_leq42a_veraend_taet,v1_con$v1_leq_e_leq42a,"v1_leq_E_42A")
## -999 bad good <NA>
## [1,] No. cases 1363 34 51 338 1786
## [2,] Percent 76.3 1.9 2.9 18.9 100
42B Impact (ordinal [0,1,2,3], v1_leq_E_42B)
v1_leq_b_recode(v1_clin$v1_leq_e_leq42e_veraend_taet,v1_con$v1_leq_e_leq42e,"v1_leq_E_42B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1361 10 15 32 30 338 1786
## [2,] Percent 76.2 0.6 0.8 1.8 1.7 18.9 100
Create dataset
v1_leq_E<-data.frame(v1_leq_E_28A,v1_leq_E_28B,v1_leq_E_29A,v1_leq_E_29B,v1_leq_E_30A,
v1_leq_E_30B,v1_leq_E_31A,v1_leq_E_31B,v1_leq_E_32A,v1_leq_E_32B,
v1_leq_E_33A,v1_leq_E_33B,v1_leq_E_34A,v1_leq_E_34B,v1_leq_E_35A,
v1_leq_E_35B,v1_leq_E_36A,v1_leq_E_36B,v1_leq_E_37A,v1_leq_E_37B,
v1_leq_E_38A,v1_leq_E_38B,v1_leq_E_39A,v1_leq_E_39B,v1_leq_E_40A,
v1_leq_E_40B,v1_leq_E_41A,v1_leq_E_41B,v1_leq_E_42A,v1_leq_E_42B)
43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”
43A Nature (dichotomous [“good”,“bad”], v1_leq_F_43A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq43a_neu_fmitglied,v1_con$v1_leq_f_g_leq43a,"v1_leq_F_43A")
## -999 bad good <NA>
## [1,] No. cases 1326 14 108 338 1786
## [2,] Percent 74.2 0.8 6 18.9 100
43B Impact (ordinal [0,1,2,3], v1_leq_F_43B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq43e_neu_fmitglied,v1_con$v1_leq_f_g_leq43e,"v1_leq_F_43B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1324 10 35 30 49 338 1786
## [2,] Percent 74.1 0.6 2 1.7 2.7 18.9 100
44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”
44A Nature (dichotomous [“good”,“bad”], v1_leq_F_44A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq44a_auszug_fm,v1_con$v1_leq_f_g_leq44a,"v1_leq_F_44A")
## -999 bad good <NA>
## [1,] No. cases 1382 30 36 338 1786
## [2,] Percent 77.4 1.7 2 18.9 100
44B Impact (ordinal [0,1,2,3], v1_leq_F_44B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq44e_auszug_fm,v1_con$v1_leq_f_g_leq44e,"v1_leq_F_44B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1379 8 14 25 22 338 1786
## [2,] Percent 77.2 0.4 0.8 1.4 1.2 18.9 100
45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”
45A Nature (dichotomous [“good”,“bad”], v1_leq_F_45A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq45a_gz_verh_fm,v1_con$v1_leq_f_g_leq45a,"v1_leq_F_45A")
## -999 bad good <NA>
## [1,] No. cases 1176 247 25 338 1786
## [2,] Percent 65.8 13.8 1.4 18.9 100
45B Impact (ordinal [0,1,2,3], v1_leq_F_45B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq45e_gz_verh_fm,v1_con$v1_leq_f_g_leq45e,"v1_leq_F_45B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1175 6 44 102 121 338 1786
## [2,] Percent 65.8 0.3 2.5 5.7 6.8 18.9 100
46. “Death of spouse or partner”
46A Nature (dichotomous [“good”,“bad”], v1_leq_F_46A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq46a_tod_partner,v1_con$v1_leq_f_g_leq46a,"v1_leq_F_46A")
## -999 bad good <NA>
## [1,] No. cases 1432 15 1 338 1786
## [2,] Percent 80.2 0.8 0.1 18.9 100
46B Impact (ordinal [0,1,2,3], v1_leq_F_46B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq46e_tod_partner,v1_con$v1_leq_f_g_leq46e,"v1_leq_F_46B")
## -999 0 2 3 <NA>
## [1,] No. cases 1431 3 4 10 338 1786
## [2,] Percent 80.1 0.2 0.2 0.6 18.9 100
47. “Death of a child”
47A Nature (dichotomous [“good”,“bad”], v1_leq_F_47A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq47a_tod_kind,v1_con$v1_leq_f_g_leq47a,"v1_leq_F_47A")
## -999 bad good <NA>
## [1,] No. cases 1434 13 1 338 1786
## [2,] Percent 80.3 0.7 0.1 18.9 100
47B Impact (ordinal [0,1,2,3], v1_leq_F_47B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq47e_tod_kind,v1_con$v1_leq_f_g_leq47e,"v1_leq_F_47B")
## -999 0 3 <NA>
## [1,] No. cases 1434 3 11 338 1786
## [2,] Percent 80.3 0.2 0.6 18.9 100
48. “Death of family member or close friend”
48A Nature (dichotomous [“good”,“bad”], v1_leq_F_48A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq48a_tod_fm_ef,v1_con$v1_leq_f_g_leq48a,"v1_leq_F_48A")
## -999 bad good <NA>
## [1,] No. cases 1326 118 4 338 1786
## [2,] Percent 74.2 6.6 0.2 18.9 100
48B Impact (ordinal [0,1,2,3], v1_leq_F_48B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq48e_tod_fm_ef,v1_con$v1_leq_f_g_leq48e,"v1_leq_F_48B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1324 11 25 40 48 338 1786
## [2,] Percent 74.1 0.6 1.4 2.2 2.7 18.9 100
49. “Birth of a grandchild”
49A Nature (dichotomous [“good”,“bad”], v1_leq_F_49A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq49a_geb_enkel,v1_con$v1_leq_f_g_leq49a,"v1_leq_F_49A")
## -999 bad good <NA>
## [1,] No. cases 1413 4 31 338 1786
## [2,] Percent 79.1 0.2 1.7 18.9 100
49B Impact (ordinal [0,1,2,3], v1_leq_F_49B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq49e_geb_enkel,v1_con$v1_leq_f_g_leq49e,"v1_leq_F_49B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1411 6 6 3 22 338 1786
## [2,] Percent 79 0.3 0.3 0.2 1.2 18.9 100
50. “Change in marital status of your parents”
50A Nature (dichotomous [“good”,“bad”], v1_leq_F_50A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq50a_fstand_eltern,v1_con$v1_leq_f_g_leq50a,"v1_leq_F_50A")
## -999 bad good <NA>
## [1,] No. cases 1412 28 8 338 1786
## [2,] Percent 79.1 1.6 0.4 18.9 100
50B Impact (ordinal [0,1,2,3], v1_leq_F_50B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq50e_fstand_eltern,v1_con$v1_leq_f_g_leq50e,"v1_leq_F_50B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1411 5 7 12 13 338 1786
## [2,] Percent 79 0.3 0.4 0.7 0.7 18.9 100
Create dataset
v1_leq_F<-data.frame(v1_leq_F_43A,v1_leq_F_43B,v1_leq_F_44A,v1_leq_F_44B,v1_leq_F_45A,
v1_leq_F_45B,v1_leq_F_46A,v1_leq_F_46B,v1_leq_F_47A,v1_leq_F_47B,
v1_leq_F_48A,v1_leq_F_48B,v1_leq_F_49A,v1_leq_F_49B,v1_leq_F_50A,
v1_leq_F_50B)
51. “Change in child care arrangements”
51A Nature (dichotomous [“good”,“bad”], v1_leq_G_51A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq51a_kindbetr,v1_con$v1_leq_f_g_leq51a,"v1_leq_G_51A")
## -999 bad good <NA>
## [1,] No. cases 1405 20 23 338 1786
## [2,] Percent 78.7 1.1 1.3 18.9 100
51B Impact (ordinal [0,1,2,3], v1_leq_G_51B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq51e_kindbetr,v1_con$v1_leq_f_g_leq51e,"v1_leq_G_51B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1404 6 5 14 19 338 1786
## [2,] Percent 78.6 0.3 0.3 0.8 1.1 18.9 100
52. “Conflicts with spouse or partner about parenting”
52A Nature (dichotomous [“good”,“bad”], v1_leq_G_52A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq52a_konf_eschaft,v1_con$v1_leq_f_g_leq52a,"v1_leq_G_52A")
## -999 bad good <NA>
## [1,] No. cases 1403 38 7 338 1786
## [2,] Percent 78.6 2.1 0.4 18.9 100
52B Impact (ordinal [0,1,2,3], v1_leq_G_52B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq52e_konf_eschaft,v1_con$v1_leq_f_g_leq52e,"v1_leq_G_52B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1403 5 12 17 11 338 1786
## [2,] Percent 78.6 0.3 0.7 1 0.6 18.9 100
53. “Conflicts with child’s grandparents (or other important person) about parenting”
53A Nature (dichotomous [“good”,“bad”], v1_leq_G_53A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq53a_konf_geltern,v1_con$v1_leq_f_g_leq53a,"v1_leq_G_53A")
## -999 bad good <NA>
## [1,] No. cases 1427 17 4 338 1786
## [2,] Percent 79.9 1 0.2 18.9 100
53B Impact (ordinal [0,1,2,3], v1_leq_G_53B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq53e_konf_geltern,v1_con$v1_leq_f_g_leq53e,"v1_leq_G_53B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1427 3 7 3 8 338 1786
## [2,] Percent 79.9 0.2 0.4 0.2 0.4 18.9 100
54. “Taking on full responsibility for parenting as a single parent”
54A Nature (dichotomous [“good”,“bad”], v1_leq_G_54A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq54a_alleinerz,v1_con$v1_leq_f_g_leq54a,"v1_leq_G_54A")
## -999 bad good <NA>
## [1,] No. cases 1426 12 10 338 1786
## [2,] Percent 79.8 0.7 0.6 18.9 100
54B Impact (ordinal [0,1,2,3], v1_leq_G_54B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq54e_alleinerz,v1_con$v1_leq_f_g_leq54e,"v1_leq_G_54B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1426 3 2 8 9 338 1786
## [2,] Percent 79.8 0.2 0.1 0.4 0.5 18.9 100
55. “Custody battles with former spouse or partner”
55A Nature (dichotomous [“good”,“bad”], v1_leq_G_55A)
v1_leq_a_recode(v1_clin$v1_leq_f_g_leq55a_sorgerecht,v1_con$v1_leq_f_g_leq55a,"v1_leq_G_55A")
## -999 bad good <NA>
## [1,] No. cases 1416 28 4 338 1786
## [2,] Percent 79.3 1.6 0.2 18.9 100
55B Impact (ordinal [0,1,2,3], v1_leq_G_55B)
v1_leq_b_recode(v1_clin$v1_leq_f_g_leq55e_sorgerecht,v1_con$v1_leq_f_g_leq55e,"v1_leq_G_55B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1414 3 11 7 13 338 1786
## [2,] Percent 79.2 0.2 0.6 0.4 0.7 18.9 100
Create dataset
v1_leq_G<-data.frame(v1_leq_G_51A,
v1_leq_G_51B,
v1_leq_G_52A,
v1_leq_G_52B,
v1_leq_G_53A,
v1_leq_G_53B,
v1_leq_G_54A,
v1_leq_G_54B,
v1_leq_G_55A,
v1_leq_G_55B)
69. “Major change in finances (increased or decreased income)”
69A Nature (dichotomous [“good”,“bad”], v1_leq_I_69A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq69a_finanz_sit,v1_con$v1_leq_i_j_k_leq69a,"v1_leq_I_69A")
## -999 bad good <NA>
## [1,] No. cases 1004 256 188 338 1786
## [2,] Percent 56.2 14.3 10.5 18.9 100
69B Impact (ordinal [0,1,2,3], v1_leq_I_69B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq69e_finanz_sit,v1_con$v1_leq_i_j_k_leq69e,"v1_leq_I_69B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1001 16 105 135 191 338 1786
## [2,] Percent 56 0.9 5.9 7.6 10.7 18.9 100
70. “Took on a moderate purchase, such as TV, car, freezer, etc.”
70A Nature (dichotomous [“good”,“bad”], v1_leq_I_70A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq70a_finanz_verpfl,v1_con$v1_leq_i_j_k_leq70a,"v1_leq_I_70A")
## -999 bad good <NA>
## [1,] No. cases 1287 63 98 338 1786
## [2,] Percent 72.1 3.5 5.5 18.9 100
70B Impact (ordinal [0,1,2,3], v1_leq_I_70B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq70e_finanz_verpfl,v1_con$v1_leq_i_j_k_leq70e,"v1_leq_I_70B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1286 22 52 58 30 338 1786
## [2,] Percent 72 1.2 2.9 3.2 1.7 18.9 100
71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”
71A Nature (dichotomous [“good”,“bad”], v1_leq_I_71A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq71a_hypothek,v1_con$v1_leq_i_j_k_leq71a,"v1_leq_I_71A")
## -999 bad good <NA>
## [1,] No. cases 1389 34 25 338 1786
## [2,] Percent 77.8 1.9 1.4 18.9 100
71B Impact (ordinal [0,1,2,3], v1_leq_I_71B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq71e_hypothek,v1_con$v1_leq_i_j_k_leq71e,"v1_leq_I_71B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1386 10 15 17 20 338 1786
## [2,] Percent 77.6 0.6 0.8 1 1.1 18.9 100
72. “Experienced a foreclosure on a mortgage or loan”
72A Nature (dichotomous [“good”,“bad”], v1_leq_I_72A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq72a_hypoth_kuend,v1_con$v1_leq_i_j_k_leq72a,"v1_leq_I_72A")
## -999 bad good <NA>
## [1,] No. cases 1416 13 19 338 1786
## [2,] Percent 79.3 0.7 1.1 18.9 100
72B Impact (ordinal [0,1,2,3], v1_leq_I_72B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq72e_hypoth_kuend,v1_con$v1_leq_i_j_k_leq72e,"v1_leq_I_72B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1416 7 4 6 15 338 1786
## [2,] Percent 79.3 0.4 0.2 0.3 0.8 18.9 100
73. “Credit rating difficulties”
73A Nature (dichotomous [“good”,“bad”], v1_leq_I_73A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq73a_kreditwuerdk,v1_con$v1_leq_i_j_k_leq73a,"v1_leq_I_73A")
## -999 bad good <NA>
## [1,] No. cases 1351 91 6 338 1786
## [2,] Percent 75.6 5.1 0.3 18.9 100
73B Impact (ordinal [0,1,2,3], v1_leq_I_73B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq73e_kreditwuerdk,v1_con$v1_leq_i_j_k_leq73e,"v1_leq_I_73B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1349 12 21 24 42 338 1786
## [2,] Percent 75.5 0.7 1.2 1.3 2.4 18.9 100
Create dataset
v1_leq_I<-data.frame(v1_leq_I_69A,v1_leq_I_69B,v1_leq_I_70A,v1_leq_I_70B,v1_leq_I_71A,
v1_leq_I_71B,v1_leq_I_72A,v1_leq_I_72B,v1_leq_I_73A,v1_leq_I_73B)
74. “Being robbed or victim of identity theft”
74A Nature (dichotomous [“good”,“bad”], v1_leq_J_74A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq74a_opf_diebstahl,v1_con$v1_leq_i_j_k_leq74a,"v1_leq_J_74A")
## -999 bad good <NA>
## [1,] No. cases 1361 78 9 338 1786
## [2,] Percent 76.2 4.4 0.5 18.9 100
74B Impact (ordinal [0,1,2,3], v1_leq_J_74B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq74e_opf_diebstahl,v1_con$v1_leq_i_j_k_leq74e,"v1_leq_J_74B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1358 16 24 20 30 338 1786
## [2,] Percent 76 0.9 1.3 1.1 1.7 18.9 100
75. “Being a victim of a violent act (rape, assault, etc.)”
75A Nature (dichotomous [“good”,“bad”], v1_leq_J_75A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq75a_opf_gewalttat,v1_con$v1_leq_i_j_k_leq75a,"v1_leq_J_75A")
## -999 bad good <NA>
## [1,] No. cases 1404 40 4 338 1786
## [2,] Percent 78.6 2.2 0.2 18.9 100
75B Impact (ordinal [0,1,2,3], v1_leq_J_75B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq75e_opf_gewalttat,v1_con$v1_leq_i_j_k_leq75e,"v1_leq_J_75B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1402 8 3 6 29 338 1786
## [2,] Percent 78.5 0.4 0.2 0.3 1.6 18.9 100
76. “Involved in an accident”
76A Nature (dichotomous [“good”,“bad”], v1_leq_J_76A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq76a_unfall,v1_con$v1_leq_i_j_k_leq76a,"v1_leq_J_76A")
## -999 bad good <NA>
## [1,] No. cases 1384 57 7 338 1786
## [2,] Percent 77.5 3.2 0.4 18.9 100
76B Impact (ordinal [0,1,2,3], v1_leq_J_76B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq76e_unfall,v1_con$v1_leq_i_j_k_leq76e,"v1_leq_J_76B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1378 11 22 13 24 338 1786
## [2,] Percent 77.2 0.6 1.2 0.7 1.3 18.9 100
77. “Involved in a law suit”
77A Nature (dichotomous [“good”,“bad”], v1_leq_J_77A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq77a_rechtsstreit,v1_con$v1_leq_i_j_k_leq77a,"v1_leq_J_77A")
## -999 bad good <NA>
## [1,] No. cases 1338 91 19 338 1786
## [2,] Percent 74.9 5.1 1.1 18.9 100
77B Impact (ordinal [0,1,2,3], v1_leq_J_77B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq77e_rechtsstreit,v1_con$v1_leq_i_j_k_leq77e,"v1_leq_J_77B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1337 13 27 24 47 338 1786
## [2,] Percent 74.9 0.7 1.5 1.3 2.6 18.9 100
78. “Involved in a minor violation of the law (traffic tickets, disturbing the peace, etc)”
78A Nature (dichotomous [“good”,“bad”], v1_leq_J_78A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq78a_owi,v1_con$v1_leq_i_j_k_leq78a,"v1_leq_J_78A")
## -999 bad good <NA>
## [1,] No. cases 1342 99 7 338 1786
## [2,] Percent 75.1 5.5 0.4 18.9 100
78B Impact (ordinal [0,1,2,3], v1_leq_J_78B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq78e_owi,v1_con$v1_leq_i_j_k_leq78e,"v1_leq_J_78B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 1341 32 36 18 21 338 1786
## [2,] Percent 75.1 1.8 2 1 1.2 18.9 100
79. “Legal troubles resulting in your being arrested or held in jail”
79A Nature (dichotomous [“good”,“bad”], v1_leq_J_79A)
v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq79a_konf_gesetz,v1_con$v1_leq_i_j_k_leq79a,"v1_leq_J_79A")
## -999 bad good <NA>
## [1,] No. cases 1421 21 6 338 1786
## [2,] Percent 79.6 1.2 0.3 18.9 100
79B Impact (ordinal [0,1,2,3], v1_leq_J_79B)
v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq79e_konf_gesetz,v1_con$v1_leq_i_j_k_leq79e,"v1_leq_J_79B")
## -999 0 2 3 <NA>
## [1,] No. cases 1420 8 9 11 338 1786
## [2,] Percent 79.5 0.4 0.5 0.6 18.9 100
Create dataset
v1_leq_J<-data.frame(v1_leq_J_74A,v1_leq_J_74B,v1_leq_J_75A,v1_leq_J_75B,v1_leq_J_76A,
v1_leq_J_76B,v1_leq_J_77A,v1_leq_J_77B,v1_leq_J_78A,v1_leq_J_78B,
v1_leq_J_79A,v1_leq_J_79B)
Create LEQ dataset
v1_leq<-data.frame(v1_leq_A,v1_leq_B,v1_leq_C,v1_leq_D,v1_leq_E,v1_leq_F,v1_leq_G,
v1_leq_H,v1_leq_I,v1_leq_J)
The WHOQOL-BREF instrument comprises 26 items, which measure the following broad domains: physical health, psychological health, social relationships, and environment. The past two weeks are assessed. All items are on a five-point scale with the following gradations:
Items 1, 15: “Very poor”-1, “Poor”-2, “Neither poor nor good”-3, “Good”-4, “Very good”-5 Items 2, 16-25: “Very dissatisfied”-1, “dissatisfied”-2, “Neither satisfied nor dissatisfied”-3, “satisfied”-4, “Very satisfied”-5 Items 3-14: “Not at all”-1, “A little”-2, “A moderate amount”-3, “Very much”-4, “An extreme amount”-5 Items 26: “Never”-1, “Seldom”-2, “Quite often”-3, “Very often”-4, “Always”-5
The coding of items number three, four and 26 has been reversed to keep directionality (see below). For all items higher scores now mean higher quality of life. Please see below for subscales (domain scores) of this questionnaire.
1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v1_whoqol_itm1)
v1_quol_recode(v1_clin$v1_whoqol_bref_who1_lebensqualitaet,v1_con$v1_whoqol_bref_who1,"v1_whoqol_itm1",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 53 159 413 605 307 249 1786
## [2,] Percent 3 8.9 23.1 33.9 17.2 13.9 100
2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v1_whoqol_itm2)”
v1_quol_recode(v1_clin$v1_whoqol_bref_who2_gesundheit,v1_con$v1_whoqol_bref_who2,"v1_whoqol_itm2",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 97 326 326 541 240 256 1786
## [2,] Percent 5.4 18.3 18.3 30.3 13.4 14.3 100
3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v1_whoqol_itm3)
Coding reversed so that higher scores mean less impairment by pain.
v1_quol_recode(v1_clin$v1_whoqol_bref_who3_schmerzen,v1_con$v1_whoqol_bref_who3,"v1_whoqol_itm3",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 26 108 135 336 924 257 1786
## [2,] Percent 1.5 6 7.6 18.8 51.7 14.4 100
4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v1_whoqol_itm4)”
Coding reversed so that higher scores mean less dependence on medical treatment.
v1_quol_recode(v1_clin$v1_whoqol_bref_who4_med_behand,v1_con$v1_whoqol_bref_who4,"v1_whoqol_itm4",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 170 300 223 248 587 258 1786
## [2,] Percent 9.5 16.8 12.5 13.9 32.9 14.4 100
5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v1_whoqol_itm5)
v1_quol_recode(v1_clin$v1_whoqol_bref_who5_lebensgenuss,v1_con$v1_whoqol_bref_who5,"v1_whoqol_itm5",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 78 236 401 545 262 264 1786
## [2,] Percent 4.4 13.2 22.5 30.5 14.7 14.8 100
6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v1_whoqol_itm6)
v1_quol_recode(v1_clin$v1_whoqol_bref_who6_lebenssinn,v1_con$v1_whoqol_bref_who6,"v1_whoqol_itm6",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 111 163 301 513 432 266 1786
## [2,] Percent 6.2 9.1 16.9 28.7 24.2 14.9 100
7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v1_whoqol_itm7)
v1_quol_recode(v1_clin$v1_whoqol_bref_who7_konzentration,v1_con$v1_whoqol_bref_who7,"v1_whoqol_itm7",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 46 242 538 579 125 256 1786
## [2,] Percent 2.6 13.5 30.1 32.4 7 14.3 100
8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v1_whoqol_itm8)
v1_quol_recode(v1_clin$v1_whoqol_bref_who8_sicherheit,v1_con$v1_whoqol_bref_who8,"v1_whoqol_itm8",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 67 155 344 625 335 260 1786
## [2,] Percent 3.8 8.7 19.3 35 18.8 14.6 100
9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v1_whoqol_itm9)
v1_quol_recode(v1_clin$v1_whoqol_bref_who9_umweltbed,v1_con$v1_whoqol_bref_who9,"v1_whoqol_itm9",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 28 63 331 723 381 260 1786
## [2,] Percent 1.6 3.5 18.5 40.5 21.3 14.6 100
10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v1_whoqol_itm10)
v1_quol_recode(v1_clin$v1_whoqol_bref_who10_energie,v1_con$v1_whoqol_bref_who10,"v1_whoqol_itm10",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 48 177 374 550 375 262 1786
## [2,] Percent 2.7 9.9 20.9 30.8 21 14.7 100
11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v1_whoqol_itm11)
v1_quol_recode(v1_clin$v1_whoqol_bref_who11_aussehen,v1_con$v1_whoqol_bref_who11,"v1_whoqol_itm11",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 49 138 320 602 408 269 1786
## [2,] Percent 2.7 7.7 17.9 33.7 22.8 15.1 100
12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v1_whoqol_itm12)
v1_quol_recode(v1_clin$v1_whoqol_bref_who12_genug_geld,v1_con$v1_whoqol_bref_who12,"v1_whoqol_itm12",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 93 219 360 453 399 262 1786
## [2,] Percent 5.2 12.3 20.2 25.4 22.3 14.7 100
13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v1_whoqol_itm13)
v1_quol_recode(v1_clin$v1_whoqol_bref_who13_infozugang,v1_con$v1_whoqol_bref_who13,"v1_whoqol_itm13",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 11 50 184 528 748 265 1786
## [2,] Percent 0.6 2.8 10.3 29.6 41.9 14.8 100
14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v1_whoqol_itm14)
v1_quol_recode(v1_clin$v1_whoqol_bref_who14_freizeitaktiv,v1_con$v1_whoqol_bref_who14,"v1_whoqol_itm14",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 19 146 307 494 557 263 1786
## [2,] Percent 1.1 8.2 17.2 27.7 31.2 14.7 100
15. “How well are you able to get around? (ordinal [1,2,3,4,5], v1_whoqol_itm15)”
v1_quol_recode(v1_clin$v1_whoqol_bref_who15_fortbewegung,v1_con$v1_whoqol_bref_who15,"v1_whoqol_itm15",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 8 69 225 527 693 264 1786
## [2,] Percent 0.4 3.9 12.6 29.5 38.8 14.8 100
16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v1_whoqol_itm16)
v1_quol_recode(v1_clin$v1_whoqol_bref_who16_schlaf,v1_con$v1_whoqol_bref_who16,"v1_whoqol_itm16",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 74 266 279 655 271 241 1786
## [2,] Percent 4.1 14.9 15.6 36.7 15.2 13.5 100
17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v1_whoqol_itm17)
v1_quol_recode(v1_clin$v1_whoqol_bref_who17_alltag,v1_con$v1_whoqol_bref_who17,"v1_whoqol_itm17",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 60 248 266 619 350 243 1786
## [2,] Percent 3.4 13.9 14.9 34.7 19.6 13.6 100
18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v1_whoqol_itm18)
v1_quol_recode(v1_clin$v1_whoqol_bref_who18_arbeitsfhgk,v1_con$v1_whoqol_bref_who18,"v1_whoqol_itm18",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 169 297 303 469 289 259 1786
## [2,] Percent 9.5 16.6 17 26.3 16.2 14.5 100
19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v1_whoqol_itm19)
v1_quol_recode(v1_clin$v1_whoqol_bref_who19_selbstzufried,v1_con$v1_whoqol_bref_who19,"v1_whoqol_itm19",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 94 222 356 634 224 256 1786
## [2,] Percent 5.3 12.4 19.9 35.5 12.5 14.3 100
20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v1_whoqol_itm20)
v1_quol_recode(v1_clin$v1_whoqol_bref_who20_pers_bezieh,v1_con$v1_whoqol_bref_who20,"v1_whoqol_itm20",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 68 190 283 689 298 258 1786
## [2,] Percent 3.8 10.6 15.8 38.6 16.7 14.4 100
21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v1_whoqol_itm21)
v1_quol_recode(v1_clin$v1_whoqol_bref_who21_sexualleben,v1_con$v1_whoqol_bref_who21,"v1_whoqol_itm21",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 206 264 437 412 201 266 1786
## [2,] Percent 11.5 14.8 24.5 23.1 11.3 14.9 100
22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v1_whoqol_itm22)
v1_quol_recode(v1_clin$v1_whoqol_bref_who22_freunde,v1_con$v1_whoqol_bref_who22,"v1_whoqol_itm22",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 66 121 308 646 399 246 1786
## [2,] Percent 3.7 6.8 17.2 36.2 22.3 13.8 100
23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v1_whoqol_itm23)
v1_quol_recode(v1_clin$v1_whoqol_bref_who23_wohnbeding,v1_con$v1_whoqol_bref_who23,"v1_whoqol_itm23",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 90 144 230 594 486 242 1786
## [2,] Percent 5 8.1 12.9 33.3 27.2 13.5 100
24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v1_whoqol_itm24)
v1_quol_recode(v1_clin$v1_whoqol_bref_who24_gesundhdiens,v1_con$v1_whoqol_bref_who24,"v1_whoqol_itm24",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 30 44 222 681 567 242 1786
## [2,] Percent 1.7 2.5 12.4 38.1 31.7 13.5 100
25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v1_whoqol_itm25)
v1_quol_recode(v1_clin$v1_whoqol_bref_who25_transport,v1_con$v1_whoqol_bref_who25,"v1_whoqol_itm25",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 35 79 207 628 587 250 1786
## [2,] Percent 2 4.4 11.6 35.2 32.9 14 100
26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v1_whoqol_itm26)
Coding reversed so that higher scores mean symptoms less often.
v1_quol_recode(v1_clin$v1_whoqol_bref_who26_neg_gefuehle,v1_con$v1_whoqol_bref_who26,"v1_whoqol_itm26",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 66 298 366 539 249 268 1786
## [2,] Percent 3.7 16.7 20.5 30.2 13.9 15 100
Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).
Global (continuous [4-20],v1_whoqol_dom_glob)
v1_whoqol_dom_glob_df<-data.frame(as.numeric(v1_whoqol_itm1),as.numeric(v1_whoqol_itm2))
v1_who_glob_no_nas<-rowSums(is.na(v1_whoqol_dom_glob_df))
v1_whoqol_dom_glob<-ifelse((v1_who_glob_no_nas==0) | (v1_who_glob_no_nas==1),
rowMeans(v1_whoqol_dom_glob_df,na.rm=T)*4,NA)
v1_whoqol_dom_glob<-round(v1_whoqol_dom_glob,2)
summary(v1_whoqol_dom_glob)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.0 10.0 14.0 13.9 16.0 20.0 243
Physical Health (continuous [4-20],v1_whoqol_dom_phys)
v1_whoqol_dom_phys_df<-data.frame(as.numeric(v1_whoqol_itm3),as.numeric(v1_whoqol_itm10),as.numeric(v1_whoqol_itm16),as.numeric(v1_whoqol_itm15),as.numeric(v1_whoqol_itm17),as.numeric(v1_whoqol_itm4),as.numeric(v1_whoqol_itm18))
v1_who_phys_no_nas<-rowSums(is.na(v1_whoqol_dom_phys_df))
v1_whoqol_dom_phys<-ifelse((v1_who_phys_no_nas==0) | (v1_who_phys_no_nas==1),
rowMeans(v1_whoqol_dom_phys_df,na.rm=T)*4,NA)
v1_whoqol_dom_phys<-round(v1_whoqol_dom_phys,2)
summary(v1_whoqol_dom_phys)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.14 12.57 15.43 14.92 17.71 20.00 262
Psychological (continuous [4-20],v1_whoqol_dom_psy)
v1_whoqol_dom_psy_df<-data.frame(as.numeric(v1_whoqol_itm5),as.numeric(v1_whoqol_itm7),as.numeric(v1_whoqol_itm19),as.numeric(v1_whoqol_itm11),as.numeric(v1_whoqol_itm26),as.numeric(v1_whoqol_itm6))
v1_who_psy_no_nas<-rowSums(is.na(v1_whoqol_dom_psy_df))
v1_whoqol_dom_psy<-ifelse((v1_who_psy_no_nas==0) | (v1_who_psy_no_nas==1),
rowMeans(v1_whoqol_dom_psy_df,na.rm=T)*4,NA)
v1_whoqol_dom_psy<-round(v1_whoqol_dom_psy,2)
summary(v1_whoqol_dom_psy)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.67 11.33 14.67 14.03 16.67 20.00 266
Social relationships (continuous [4-20],v1_whoqol_dom_soc)
v1_whoqol_dom_soc_df<-data.frame(as.numeric(v1_whoqol_itm20),as.numeric(v1_whoqol_itm22),as.numeric(v1_whoqol_itm21))
v1_who_soc_no_nas<-rowSums(is.na(v1_whoqol_dom_soc_df))
v1_whoqol_dom_soc<-ifelse((v1_who_soc_no_nas==0) | (v1_who_soc_no_nas==1),
rowMeans(v1_whoqol_dom_soc_df,na.rm=T)*4,NA)
v1_whoqol_dom_soc<-round(v1_whoqol_dom_soc,2)
summary(v1_whoqol_dom_soc)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.00 14.67 14.01 16.00 20.00 248
Environment (continuous [4-20],v1_whoqol_dom_env)
v1_whoqol_dom_env_df<-data.frame(as.numeric(v1_whoqol_itm8),as.numeric(v1_whoqol_itm23),as.numeric(v1_whoqol_itm12),as.numeric(v1_whoqol_itm24),as.numeric(v1_whoqol_itm13),as.numeric(v1_whoqol_itm14),as.numeric(v1_whoqol_itm9),as.numeric(v1_whoqol_itm25))
v1_who_env_no_nas<-rowSums(is.na(v1_whoqol_dom_env_df))
v1_whoqol_dom_env<-ifelse((v1_who_env_no_nas==0) | (v1_who_env_no_nas==1),
rowMeans(v1_whoqol_dom_env_df,na.rm=T)*4,NA)
v1_whoqol_dom_env<-round(v1_whoqol_dom_env,2)
summary(v1_whoqol_dom_env)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.50 14.00 16.00 15.67 17.71 20.00 265
Create dataset
v1_whoqol<-data.frame(v1_whoqol_itm1,v1_whoqol_itm2,v1_whoqol_itm3,v1_whoqol_itm4,
v1_whoqol_itm5,v1_whoqol_itm6,v1_whoqol_itm7,v1_whoqol_itm8,
v1_whoqol_itm9,v1_whoqol_itm10,v1_whoqol_itm11,v1_whoqol_itm12,
v1_whoqol_itm13,v1_whoqol_itm14,v1_whoqol_itm15,v1_whoqol_itm16,
v1_whoqol_itm17,v1_whoqol_itm18,v1_whoqol_itm19,v1_whoqol_itm20,
v1_whoqol_itm21,v1_whoqol_itm22,v1_whoqol_itm23,v1_whoqol_itm24,
v1_whoqol_itm25,v1_whoqol_itm26,v1_whoqol_dom_glob,
v1_whoqol_dom_phys,v1_whoqol_dom_psy,v1_whoqol_dom_soc,
v1_whoqol_dom_env)
This is a 10-item questionnaire measuring personality (Rammstedt & John, 2007). It is based on the well-known ‘Big Five’ model of personality. The five dimensions are the following: extraversion, neuroticism, conscientiousness, agreeableness, openness. Instruction: How well do the following statements describe your personality? Each statement starts with: “I see myself as someone who…”. Each item is to be rated on a five point scale (“disagree strongly”-1, “disagree a little”-2,“neither agree nor disagree”-3, “agree a little”-4,“agree strongly”-5). The coding of some items has been reversed so that higher scores on each item mean higher scores on the respective personality dimension. Below, we calculate sumscores for each personality dimension.
1. “…is reserved” (ordinal [1,2,3,4,5], v1_big_five_itm1)
Personality dimension: extraversion, coding reversed.
big_five_recode(v1_clin$v1_bfi_10_bfi1_reserviert,v1_con$v1_bfi_10_bfi1,"v1_big_five_itm1",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 171 528 241 405 195 246 1786
## [2,] Percent 9.6 29.6 13.5 22.7 10.9 13.8 100
2. “… is generally trusting” (ordinal [1,2,3,4,5], v1_big_five_itm2)
Personality dimension: agreeableness.
big_five_recode(v1_clin$v1_bfi_10_bfi2_vertrauen,v1_con$v1_bfi_10_bfi2,"v1_big_five_itm2",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 50 236 258 749 248 245 1786
## [2,] Percent 2.8 13.2 14.4 41.9 13.9 13.7 100
3. “…tends to be lazy” (ordinal [1,2,3,4,5], v1_big_five_itm3)
Personality dimension: conscientiousness, coding reversed.
big_five_recode(v1_clin$v1_bfi_10_bfi3_bequem,v1_con$v1_bfi_10_bfi3,"v1_big_five_itm3",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 89 387 298 471 292 249 1786
## [2,] Percent 5 21.7 16.7 26.4 16.3 13.9 100
4. “…is relaxed, handles stress well” (ordinal [1,2,3,4,5], v1_big_five_itm4)
Personality dimension: neuroticism, coding reversed.
big_five_recode(v1_clin$v1_bfi_10_bfi4_stress,v1_con$v1_bfi_10_bfi4,"v1_big_five_itm4",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 126 484 282 481 167 246 1786
## [2,] Percent 7.1 27.1 15.8 26.9 9.4 13.8 100
5. “… has few artistic interests” (ordinal [1,2,3,4,5], v1_big_five_itm5)
Personality dimension: openness, coding reversed.
big_five_recode(v1_clin$v1_bfi_10_bfi5_kunst,v1_con$v1_bfi_10_bfi5,"v1_big_five_itm5",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 152 303 201 461 417 252 1786
## [2,] Percent 8.5 17 11.3 25.8 23.3 14.1 100
6. “…is outgoing, sociable” (ordinal [1,2,3,4,5], v1_big_five_itm6)
Personality dimension: extraversion.
big_five_recode(v1_clin$v1_bfi_10_bfi6_gesellig,v1_con$v1_bfi_10_bfi6,"v1_big_five_itm6",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 96 336 292 569 247 246 1786
## [2,] Percent 5.4 18.8 16.3 31.9 13.8 13.8 100
7. “…tends to find fault with others” (ordinal [1,2,3,4,5], v1_big_five_itm7)
Personality dimension: agreeableness, coding reversed.
big_five_recode(v1_clin$v1_bfi_10_bfi7_kritik,v1_con$v1_bfi_10_bfi7,"v1_big_five_itm7",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 47 351 410 507 223 248 1786
## [2,] Percent 2.6 19.7 23 28.4 12.5 13.9 100
8. “… does a thorough job” (ordinal [1,2,3,4,5], v1_big_five_itm8)
Personality dimension: conscientiousness.
big_five_recode(v1_clin$v1_bfi_10_bfi8_gruendlich,v1_con$v1_bfi_10_bfi8,"v1_big_five_itm8",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 13 113 176 783 454 247 1786
## [2,] Percent 0.7 6.3 9.9 43.8 25.4 13.8 100
9. “…gets nervous easily” (ordinal [1,2,3,4,5], v1_big_five_itm9)
Personality dimension: neuroticism.
big_five_recode(v1_clin$v1_bfi_10_bfi9_unsicher1,v1_con$v1_bfi_10_bfi9,"v1_big_five_itm9",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 168 446 315 465 150 242 1786
## [2,] Percent 9.4 25 17.6 26 8.4 13.5 100
10. “…habe eine aktive Vorstellungskraft, bin phantasievoll.” (ordinal [1,2,3,4,5], v1_big_five_itm10)
Personality dimension: openness.
big_five_recode(v1_clin$v1_bfi_10_bfi10_phantasie,v1_con$v1_bfi_10_bfi10,"v1_big_five_itm10",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 50 171 222 690 409 244 1786
## [2,] Percent 2.8 9.6 12.4 38.6 22.9 13.7 100
The scoring instructions are described in Rammstedt et al. (2012).
Extraversion (continuous, [1,2,3,4,5], v1_big_five_extra)
v1_big_five_extra<-(as.numeric(v1_big_five_itm1)+as.numeric(v1_big_five_itm6))/2
summary(v1_big_five_extra)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 3.000 3.148 4.000 5.000 253
Neuroticism (continuous, [1,2,3,4,5], v1_big_five_neuro)
v1_big_five_neuro<-(as.numeric(v1_big_five_itm4)+as.numeric(v1_big_five_itm9))/2
summary(v1_big_five_neuro)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.500 3.000 3.019 4.000 5.000 248
Openness (continuous, [1,2,3,4,5], v1_big_five_openn)
v1_big_five_openn<-(as.numeric(v1_big_five_itm5)+as.numeric(v1_big_five_itm10))/2
summary(v1_big_five_openn)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 4.000 3.627 4.500 5.000 257
Conscientiousness (continuous, [1,2,3,4,5], v1_big_five_consc)
v1_big_five_consc<-(as.numeric(v1_big_five_itm3)+as.numeric(v1_big_five_itm8))/2
summary(v1_big_five_consc)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 3.500 3.664 4.500 5.000 257
Agreeableness (continuous, [1,2,3,4,5], v1_big_five_agree)
v1_big_five_agree<-(as.numeric(v1_big_five_itm2)+as.numeric(v1_big_five_itm7))/2
summary(v1_big_five_agree)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 3.000 3.500 3.458 4.000 5.000 253
Create dataset
v1_pers<-data.frame(v1_big_five_itm1,v1_big_five_itm2,v1_big_five_itm3,v1_big_five_itm4,
v1_big_five_itm5,v1_big_five_itm6,v1_big_five_itm7,v1_big_five_itm8,
v1_big_five_itm9,v1_big_five_itm10,v1_big_five_extra,v1_big_five_neuro,
v1_big_five_openn,v1_big_five_consc,v1_big_five_agree)
v1_df<-data.frame(v1_id,
v1_rec,
v1_dem,
v1_eth,
v1_psy_trtmt,
v1_med,
v1_fam_hist,
v1_som_dsrdr,
v1_subst,
v1_scid,
v1_symp_panss,
v1_symp_ids_c,
v1_symp_ymrs,
v1_ill_sev,
v1_nrpsy,
v1_rlgn,
v1_cape,
v1_sf12,
v1_med_adh,
v1_bdi2,
v1_asrm,
v1_mss,
v1_leq,
v1_whoqol,
v1_pers)
## [1] 1323
## [1] 466
v2_clin<-subset(v2_clin, as.character(v2_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v2_clin)[1]
## [1] 1320
v2_con<-subset(v2_con, as.character(v2_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v2_con)[1]
## [1] 466
v2_id<-as.factor(c(as.character(v2_clin$mnppsd),as.character(v2_con$mnppsd)))
v2_interv_date<-c(as.Date(as.character(v2_clin$v2_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v2_con$v2_rekru_visit_rekr_datum), "%Y%m%d"))
v2_age_years_clin<-as.numeric(substr(v2_clin$v2_ausschluss1_rekr_datum,1,4))-
as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))
v2_age_years_con<-as.numeric(substr(v2_con$v2_rekru_visit_rekr_datu,1,4))-
as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))
v2_age_years<-c(v2_age_years_clin,v2_age_years_con)
v2_age<-ifelse(c(as.numeric(substr(v2_clin$v2_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v2_con$v2_rekru_visit_rekr_datu,5,6)))<
c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
v2_age_years-1,v2_age_years)
summary(v2_age)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 18.00 30.00 44.00 42.97 53.00 86.00 710
Create dataset
v2_rec<-data.frame(v2_age,v2_interv_date)
Clinical study participant are asked whether an acute illness episode occurred since the last study visit. Possible answers are “Y”-yes, “N”-no and “C”-chronic symptomatology. The latter category is for people which continually experience symptoms. If the answer was yes, additional questions were asked about the episodes, if not these are omitted. For participants with chronic symptomatology, the participant is asked about the nature of the chronic symptomatology (manic/depressive/mixed/psychotic) and answers are coded in the questions “Did you experience … symptoms during this illness episode?” (first illness episode).
Importantly, if the participant experienced multiple illness episodes since the last study visit, a set of questions (see below) was supposed to be answered for each illness episode. As most interviewers answered these questions only for a maximum of two illness episodes and few participants experienced more than two illness episodes, data are included only for the first two illness episodes.
“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v2_clin_ill_ep_snc_lst)
v2_clin_ill_ep_snc_lst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_ill_ep_snc_lst<-ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==1,"Y",
ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==2,"N",
ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==3,"C",v2_clin_ill_ep_snc_lst)))
v2_clin_ill_ep_snc_lst<-factor(v2_clin_ill_ep_snc_lst)
descT(v2_clin_ill_ep_snc_lst)
## -999 C N Y <NA>
## [1,] No. cases 466 91 452 242 535 1786
## [2,] Percent 26.1 5.1 25.3 13.5 30 100
“If yes, how many illness episodes? (continuous [no. illness episodes], v2_clin_no_ep)”
v2_clin_no_ep<-ifelse(v2_clin_ill_ep_snc_lst=="Y",c(v2_clin$v2_aktu_situat_anzahl_episoden,rep(-999,dim(v2_con)[1])),-999)
descT(v2_clin_no_ep)
## -999 1 2 3 4 5 99 <NA>
## [1,] No. cases 1009 184 32 8 3 3 1 546 1786
## [2,] Percent 56.5 10.3 1.8 0.4 0.2 0.2 0.1 30.6 100
In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).
“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_man)
v2_clin_fst_ill_ep_man<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_man<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y",
-999)
descT(v2_clin_fst_ill_ep_man)
## -999 Y <NA>
## [1,] No. cases 1233 37 516 1786
## [2,] Percent 69 2.1 28.9 100
“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_dep)
v2_clin_fst_ill_ep_dep<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_dep<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y",
-999)
descT(v2_clin_fst_ill_ep_dep)
## -999 Y <NA>
## [1,] No. cases 1119 151 516 1786
## [2,] Percent 62.7 8.5 28.9 100
“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).
v2_clin_fst_ill_ep_mx<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_mx<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y",
-999)
descT(v2_clin_fst_ill_ep_mx)
## -999 Y <NA>
## [1,] No. cases 1257 14 515 1786
## [2,] Percent 70.4 0.8 28.8 100
“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_psy)
v2_clin_fst_ill_ep_psy<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_psy<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y",
-999)
descT(v2_clin_fst_ill_ep_psy)
## -999 Y <NA>
## [1,] No. cases 1207 64 515 1786
## [2,] Percent 67.6 3.6 28.8 100
“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v2_clin_fst_ill_ep_dur)
v2_clin_fst_ill_ep_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_dur<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",
ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
ifelse(v2_clin_ill_ep_snc_lst=="N",-999,v2_clin_fst_ill_ep_dur))))
v2_clin_fst_ill_ep_dur<-ordered(v2_clin_fst_ill_ep_dur,
levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_fst_ill_ep_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 918 39 60 133
## [2,] Percent 51.4 2.2 3.4 7.4
## <NA>
## [1,] 636 1786
## [2,] 35.6 100
“During this episode, were you hospitalized?” (dichotomous, v2_clin_fst_ill_ep_hsp)
v2_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="N",-999,
ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v2_con)[1]))==2,"N",
ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", v2_clin_fst_ill_ep_hsp)))
descT(v2_clin_fst_ill_ep_hsp)
## -999 N Y <NA>
## [1,] No. cases 918 114 122 632 1786
## [2,] Percent 51.4 6.4 6.8 35.4 100
“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v2_clin_fst_ill_ep_hsp_dur)
v2_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_hsp_dur<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",
ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
-999)))
v2_clin_fst_ill_ep_hsp_dur<-ordered(v2_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_fst_ill_ep_hsp_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1123 19 25 72
## [2,] Percent 62.9 1.1 1.4 4
## <NA>
## [1,] 547 1786
## [2,] 30.6 100
The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):
Reason for hospitalization: symptom worsensing (checkbox [Y], v2_clin_fst_ill_ep_symp_wrs)
v2_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_symp_wrs<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y",-999)
descT(v2_clin_fst_ill_ep_symp_wrs)
## -999 Y <NA>
## [1,] No. cases 1169 101 516 1786
## [2,] Percent 65.5 5.7 28.9 100
Reason for hospitalization: self-endangerment (checkbox [Y], v2_clin_fst_ill_ep_slf_end)
v2_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_slf_end<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",
-999)
descT(v2_clin_fst_ill_ep_slf_end)
## -999 Y <NA>
## [1,] No. cases 1257 14 515 1786
## [2,] Percent 70.4 0.8 28.8 100
Reason for hospitalization: suicidality (checkbox [Y], v2_clin_fst_ill_ep_suic)
v2_clin_fst_ill_ep_suic<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_suic<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",
-999)
descT(v2_clin_fst_ill_ep_suic)
## -999 Y <NA>
## [1,] No. cases 1251 20 515 1786
## [2,] Percent 70 1.1 28.8 100
Reason for hospitalization: endangerment of others (checkbox [Y], v2_clin_fst_ill_ep_oth_end)
v2_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_oth_end<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
descT(v2_clin_fst_ill_ep_oth_end)
## -999 Y <NA>
## [1,] No. cases 1267 4 515 1786
## [2,] Percent 70.9 0.2 28.8 100
Reason for hospitalization: medication change (checkbox [Y], v2_clin_fst_ill_ep_med_chg)
v2_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_med_chg<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
descT(v2_clin_fst_ill_ep_med_chg)
## -999 Y <NA>
## [1,] No. cases 1253 17 516 1786
## [2,] Percent 70.2 1 28.9 100
Reason for hospitalization: other (checkbox [Y], v2_clin_fst_ill_ep_othr)
v2_clin_fst_ill_ep_othr<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_othr<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
descT(v2_clin_fst_ill_ep_othr)
## -999 Y <NA>
## [1,] No. cases 1243 28 515 1786
## [2,] Percent 69.6 1.6 28.8 100
“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_man)
v2_clin_sec_ill_ep_man<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_man<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",-999)
descT(v2_clin_sec_ill_ep_man)
## -999 Y <NA>
## [1,] No. cases 1042 5 739 1786
## [2,] Percent 58.3 0.3 41.4 100
“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_dep) #frstill
v2_clin_sec_ill_ep_dep<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_dep<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",
-999)
descT(v2_clin_sec_ill_ep_dep)
## -999 Y <NA>
## [1,] No. cases 1023 24 739 1786
## [2,] Percent 57.3 1.3 41.4 100
“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).
v2_clin_sec_ill_ep_mx<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_mx<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",
-999)
descT(v2_clin_sec_ill_ep_mx)
## -999 Y <NA>
## [1,] No. cases 1044 3 739 1786
## [2,] Percent 58.5 0.2 41.4 100
“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_psy)
v2_clin_sec_ill_ep_psy<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_psy<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",
-999)
descT(v2_clin_sec_ill_ep_psy)
## -999 Y <NA>
## [1,] No. cases 1039 8 739 1786
## [2,] Percent 58.2 0.4 41.4 100
“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v2_clin_sec_ill_ep_dur)
v2_clin_sec_ill_ep_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_dur<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",
ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
ifelse(v2_clin_ill_ep_snc_lst=="N",-999,v2_clin_sec_ill_ep_dur))))
v2_clin_sec_ill_ep_dur<-ordered(v2_clin_sec_ill_ep_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_sec_ill_ep_dur)
## less than two weeks two to four weeks more than four weeks <NA>
## [1,] No. cases 8 14 16 1748
## [2,] Percent 0.4 0.8 0.9 97.9
##
## [1,] 1786
## [2,] 100
“During this episode, were you hospitalized?” (dichotomous, v2_clin_sec_ill_ep_hsp)
v2_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="N",-999,
ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v2_con)[1]))==2,"N",
ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y", v2_clin_sec_ill_ep_hsp)))
v2_clin_sec_ill_ep_hsp<-factor(v2_clin_sec_ill_ep_hsp)
descT(v2_clin_sec_ill_ep_hsp)
## -999 N Y <NA>
## [1,] No. cases 918 19 18 831 1786
## [2,] Percent 51.4 1.1 1 46.5 100
“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v2_clin_sec_ill_ep_hsp_dur)
v2_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_hsp_dur<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",
ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
-999)))
v2_clin_sec_ill_ep_hsp_dur<-ordered(v2_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_sec_ill_ep_hsp_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1028 1 4 9
## [2,] Percent 57.6 0.1 0.2 0.5
## <NA>
## [1,] 744 1786
## [2,] 41.7 100
The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes): Reason for hospitalization: symptom worsensing (checkbox [Y], v2_clin_sec_ill_ep_symp_wrs)
v2_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_symp_wrs<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",-999)
descT(v2_clin_sec_ill_ep_symp_wrs)
## -999 Y <NA>
## [1,] No. cases 1036 11 739 1786
## [2,] Percent 58 0.6 41.4 100
Reason for hospitalization: self-endangerment (checkbox [Y], v2_clin_sec_ill_ep_slf_end)
v2_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_slf_end<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",
-999)
descT(v2_clin_sec_ill_ep_slf_end)
## -999 Y <NA>
## [1,] No. cases 1046 1 739 1786
## [2,] Percent 58.6 0.1 41.4 100
Reason for hospitalization: suicidality (checkbox [Y], v2_clin_sec_ill_ep_suic)
v2_clin_sec_ill_ep_suic<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_suic<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",
-999)
descT(v2_clin_sec_ill_ep_suic)
## -999 Y <NA>
## [1,] No. cases 1044 3 739 1786
## [2,] Percent 58.5 0.2 41.4 100
Reason for hospitalization: endangerment of others (checkbox [Y], v2_clin_sec_ill_ep_oth_end)
v2_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_oth_end<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
descT(v2_clin_sec_ill_ep_oth_end)
## -999 <NA>
## [1,] No. cases 1047 739 1786
## [2,] Percent 58.6 41.4 100
Reason for hospitalization: medication change (checkbox [Y], v2_clin_sec_ill_ep_med_chg)
v2_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_med_chg<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
descT(v2_clin_sec_ill_ep_med_chg)
## -999 Y <NA>
## [1,] No. cases 1046 1 739 1786
## [2,] Percent 58.6 0.1 41.4 100
Reason for hospitalization: other (checkbox [Y], v2_clin_sec_ill_ep_othr)
v2_clin_sec_ill_ep_othr<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_othr<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
descT(v2_clin_sec_ill_ep_othr)
## -999 Y <NA>
## [1,] No. cases 1042 5 739 1786
## [2,] Percent 58.3 0.3 41.4 100
v2_clin_add_oth_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_add_oth_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_aufent,rep(-999,dim(v2_con)[1]))==1,"Y","N")
descT(v2_clin_add_oth_hsp)
## N Y <NA>
## [1,] No. cases 1211 26 549 1786
## [2,] Percent 67.8 1.5 30.7 100
v2_clin_oth_hsp_nmb<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_oth_hsp_nmb<-ifelse(v2_clin_add_oth_hsp=="Y",
c(v2_clin$v2_aktu_situat_aenderung_anzahl,rep(-999,dim(v2_con)[1])),-999)
descT(v2_clin_oth_hsp_nmb)
## -999 1 2 3 <NA>
## [1,] No. cases 1211 21 1 1 552 1786
## [2,] Percent 67.8 1.2 0.1 0.1 30.9 100
v2_clin_oth_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_oth_hsp_dur<-
ifelse(v2_clin_add_oth_hsp=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",
ifelse(v2_clin_add_oth_hsp=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
ifelse(v2_clin_add_oth_hsp=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
ifelse(v2_clin_add_oth_hsp=="N",-999,v2_clin_add_oth_hsp))))
v2_clin_oth_hsp_dur<-ordered(v2_clin_oth_hsp_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_oth_hsp_dur)
## less than two weeks two to four weeks more than four weeks <NA>
## [1,] No. cases 7 7 11 1761
## [2,] Percent 0.4 0.4 0.6 98.6
##
## [1,] 1786
## [2,] 100
v2_clin_othr_psy_med<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_othr_psy_med<-ifelse(v2_clin_add_oth_hsp=="Y" & v2_clin_add_oth_hsp=="Y" &
c(v2_clin$v2_aktu_situat_aenderung_medikament,rep(-999,dim(v2_con)[1]))==1,"Y",
ifelse(v2_clin_add_oth_hsp=="N",-999,v2_clin_othr_psy_med))
descT(v2_clin_othr_psy_med)
## -999 Y <NA>
## [1,] No. cases 1211 2 573 1786
## [2,] Percent 67.8 0.1 32.1 100
This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.
v2_clin_cur_psy_trm<-rep(NA,dim(v2_clin)[1])
v2_con_cur_psy_trm<-rep(NA,dim(v2_con)[1])
v2_clin_cur_psy_trm<-ifelse(v2_clin$v2_aktu_situat_psybehandlung==0,"1",
ifelse(v2_clin$v2_aktu_situat_psybehandlung==3,"2",
ifelse(v2_clin$v2_aktu_situat_psybehandlung==2,"3",
ifelse(v2_clin$v2_aktu_situat_psybehandlung==1,"4",v2_clin_cur_psy_trm))))
v2_con_cur_psy_trm<-ifelse(v2_con$v2_bildung_beruf_psybehandlung==0,"1",
ifelse(v2_con$v2_bildung_beruf_psybehandlung==3,"2",
ifelse(v2_con$v2_bildung_beruf_psybehandlung==2,"3",
ifelse(v2_con$v2_bildung_beruf_psybehandlung==1,"4",v2_con_cur_psy_trm))))
v2_cur_psy_trm<-factor(c(v2_clin_cur_psy_trm,v2_con_cur_psy_trm),ordered=T)
descT(v2_cur_psy_trm)
## 1 2 3 4 <NA>
## [1,] No. cases 333 655 11 39 748 1786
## [2,] Percent 18.6 36.7 0.6 2.2 41.9 100
Create dataset
v2_clin_ill_ep<-data.frame(v2_clin_ill_ep_snc_lst,
v2_clin_no_ep,
v2_clin_fst_ill_ep_man,
v2_clin_fst_ill_ep_dep,
v2_clin_fst_ill_ep_mx,
v2_clin_fst_ill_ep_psy,
v2_clin_fst_ill_ep_dur,
v2_clin_fst_ill_ep_hsp,
v2_clin_fst_ill_ep_hsp_dur,
v2_clin_fst_ill_ep_symp_wrs,
v2_clin_fst_ill_ep_slf_end,
v2_clin_fst_ill_ep_suic,
v2_clin_fst_ill_ep_oth_end,
v2_clin_fst_ill_ep_med_chg,
v2_clin_fst_ill_ep_othr,
v2_clin_sec_ill_ep_man,
v2_clin_sec_ill_ep_dep,
v2_clin_sec_ill_ep_mx,
v2_clin_sec_ill_ep_psy,
v2_clin_sec_ill_ep_dur,
v2_clin_sec_ill_ep_hsp,
v2_clin_sec_ill_ep_hsp_dur,
v2_clin_sec_ill_ep_symp_wrs,
v2_clin_sec_ill_ep_slf_end,
v2_clin_sec_ill_ep_suic,
v2_clin_sec_ill_ep_oth_end,
v2_clin_sec_ill_ep_med_chg,
v2_clin_sec_ill_ep_othr,
v2_clin_add_oth_hsp,
v2_clin_oth_hsp_nmb,
v2_clin_oth_hsp_dur,
v2_clin_othr_psy_med,
v2_cur_psy_trm)
See Visit 1 marital status item for general explanation of the next two items.
Did your marital status change since the last study visit? (dichotomous, v2_cng_mar_stat)
v2_clin_cng_mar_stat<-rep(NA,dim(v2_clin)[1])
v2_clin_cng_mar_stat<-ifelse(v2_clin$v2_aktu_situat_fam_stand==1, "Y",
ifelse(v2_clin$v2_aktu_situat_fam_stand==2, "N", v2_clin_cng_mar_stat))
v2_con_cng_mar_stat<-rep(NA,dim(v2_con)[1])
v2_con_cng_mar_stat<-ifelse(v2_con$v2_famil_wohn_fam_stand==1, "Y",
ifelse(v2_con$v2_famil_wohn_fam_stand==2, "N", v2_con_cng_mar_stat))
v2_cng_mar_stat<-factor(c(v2_clin_cng_mar_stat,v2_con_cng_mar_stat))
v2_clin_marital_stat<-rep(NA,dim(v2_clin)[1])
v2_clin_marital_stat<-ifelse(v2_clin$v2_aktu_situat_fam_familienstand==1,"Married",
ifelse(v2_clin$v2_aktu_situat_fam_familienstand==2,"Married_living_sep",
ifelse(v2_clin$v2_aktu_situat_fam_familienstand==3,"Single",
ifelse(v2_clin$v2_aktu_situat_fam_familienstand==4,"Divorced",
ifelse(v2_clin$v2_aktu_situat_fam_familienstand==5,"Widowed",v2_clin_marital_stat)))))
v2_con_marital_stat<-rep(NA,dim(v2_con)[1])
v2_con_marital_stat<-ifelse(v2_con$v2_famil_wohn_fam_famstand==1,"Married",
ifelse(v2_con$v2_famil_wohn_fam_famstand==2,"Married_living_sep",
ifelse(v2_con$v2_famil_wohn_fam_famstand==3,"Single",
ifelse(v2_con$v2_famil_wohn_fam_famstand==4,"Divorced",
ifelse(v2_con$v2_famil_wohn_fam_famstand==5,"Widowed",v2_con_marital_stat)))))
v2_marital_stat<-factor(c(v2_clin_marital_stat,v2_con_marital_stat))
desc(v2_marital_stat)
## Divorced Married Married_living_sep Single Widowed NA's
## [1,] No. cases 139 260 40 603 20 724 1786
## [2,] Percent 7.8 14.6 2.2 33.8 1.1 40.5 100
v2_clin_partner<-rep(NA,dim(v2_clin)[1])
v2_clin_partner<-ifelse(v2_clin$v2_aktu_situat_fam_fest_partner==1,"Y",
ifelse(v2_clin$v2_aktu_situat_fam_fest_partner==2,"N",v2_clin_partner))
v2_con_partner<-rep(NA,dim(v2_con)[1])
v2_con_partner<-ifelse(v2_con$v2_famil_wohn_fam_partner==1,"Y",
ifelse(v2_con$v2_famil_wohn_fam_partner==2,"N",v2_con_partner))
v2_partner<-factor(c(v2_clin_partner,v2_con_partner))
descT(v2_partner)
## N Y <NA>
## [1,] No. cases 502 540 744 1786
## [2,] Percent 28.1 30.2 41.7 100
v2_no_bio_chld<-c(v2_clin$v2_aktu_situat_fam_kind_gesamt,v2_con$v2_famil_wohn_fam_lkind)
descT(v2_no_bio_chld)
## 0 1 2 3 4 5 <NA>
## [1,] No. cases 650 191 138 60 16 5 726 1786
## [2,] Percent 36.4 10.7 7.7 3.4 0.9 0.3 40.6 100
v2_no_adpt_chld<-c(v2_clin$v2_aktu_situat_fam_adopt_gesamt,v2_con$v2_famil_wohn_fam_adkind)
descT(v2_no_adpt_chld)
## 0 1 2 <NA>
## [1,] No. cases 1037 2 2 745 1786
## [2,] Percent 58.1 0.1 0.1 41.7 100
v2_stp_chld<-c(v2_clin$v2_aktu_situat_fam_stift_gesamt,v2_con$v2_famil_wohn_fam_skind)
descT(v2_stp_chld)
## 0 1 2 3 4 <NA>
## [1,] No. cases 961 50 18 4 3 750 1786
## [2,] Percent 53.8 2.8 1 0.2 0.2 42 100
v2_clin_chg_hsng<-rep(NA,dim(v2_clin)[1])
v2_clin_chg_hsng<-ifelse(v2_clin$v2_wohnsituation_wohn_aenderung==1,"Y",
ifelse(v2_clin$v2_wohnsituation_wohn_aenderung==2,"N",v2_clin_chg_hsng))
v2_con_chg_hsng<-rep(NA,dim(v2_con)[1])
v2_con_chg_hsng<-ifelse(v2_con$v2_famil_wohn_wohn_stand==1,"Y",
ifelse(v2_con$v2_famil_wohn_wohn_stand==2,"N",v2_con_chg_hsng))
v2_chg_hsng<-factor(c(v2_clin_chg_hsng,v2_con_chg_hsng))
descT(v2_chg_hsng)
## N Y <NA>
## [1,] No. cases 925 143 718 1786
## [2,] Percent 51.8 8 40.2 100
v2_clin_liv_aln<-rep(NA,dim(v2_clin)[1])
v2_clin_liv_aln<-ifelse(v2_clin$v2_wohnsituation_wohn_allein==1,"Y",
ifelse(v2_clin$v2_wohnsituation_wohn_allein==0,"N",v2_clin_liv_aln))
v2_con_liv_aln<-rep(NA,dim(v2_con)[1])
v2_con_liv_aln<-ifelse(v2_con$v2_famil_wohn_wohn_allein==1,"Y",
ifelse(v2_con$v2_famil_wohn_wohn_allein==0,"N",v2_con_liv_aln))
v2_liv_aln<-factor(c(v2_clin_liv_aln,v2_con_liv_aln))
descT(v2_liv_aln)
## N Y <NA>
## [1,] No. cases 691 404 691 1786
## [2,] Percent 38.7 22.6 38.7 100
Did your employment situation change since the last study visit?
v2_clin_chg_empl_stat<-rep(NA,dim(v2_clin)[1])
v2_clin_chg_empl_stat<-ifelse(v2_clin$v2_wohnsituation_erwerb_aenderung==1, "Y",
ifelse(v2_clin$v2_wohnsituation_erwerb_aenderung==2, "N",v2_clin_chg_empl_stat))
v2_con_chg_empl_stat<-rep(NA,dim(v2_con)[1])
v2_con_chg_empl_stat<-ifelse(v2_con$v2_bildung_beruf_bild_stand==1, "Y",
ifelse(v2_con$v2_bildung_beruf_bild_stand==2, "N",v2_con_chg_empl_stat))
v2_chg_empl_stat<-factor(c(v2_clin_chg_empl_stat,v2_con_chg_empl_stat))
descT(v2_chg_empl_stat)
## N Y <NA>
## [1,] No. cases 906 155 725 1786
## [2,] Percent 50.7 8.7 40.6 100
Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.
v2_clin_curr_paid_empl<-rep(NA,dim(v2_clin)[1])
v2_clin_curr_paid_empl<-ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==1,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==2,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==3,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==4,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==5,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==6,-999,
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==7,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==8,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==9,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==10,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==11,"N",v2_clin_curr_paid_empl)))))))))))
v2_con_curr_paid_empl<-rep(NA,dim(v2_con)[1])
v2_con_curr_paid_empl<-ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==1,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==2,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==3,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==4,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==5,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==6,-999,
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==7,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==8,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==9,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==10,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==11,"N",v2_con_curr_paid_empl)))))))))))
v2_curr_paid_empl<-factor(c(v2_clin_curr_paid_empl,v2_con_curr_paid_empl))
descT(v2_curr_paid_empl)
## -999 N Y <NA>
## [1,] No. cases 26 492 543 725 1786
## [2,] Percent 1.5 27.5 30.4 40.6 100
NB: Not available (-999) in control participants
v2_clin_disabl_pens<-rep(NA,dim(v2_clin)[1])
v2_clin_disabl_pens<-ifelse(v2_clin$v2_wohnsituation_rente_psych==1,"Y",
ifelse(v2_clin$v2_wohnsituation_rente_psych==2,"N",v2_clin_disabl_pens))
v2_con_disabl_pens<-rep(-999,dim(v2_con)[1])
v2_disabl_pens<-factor(c(v2_clin_disabl_pens,v2_con_disabl_pens))
descT(v2_disabl_pens)
## -999 N Y <NA>
## [1,] No. cases 466 357 279 684 1786
## [2,] Percent 26.1 20 15.6 38.3 100
v2_clin_spec_emp<-rep(NA,dim(v2_clin)[1])
v2_clin_spec_emp<-ifelse(v2_clin$v2_wohnsituation_erwerb_werk==1,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerb_werk==2,"N",v2_clin_spec_emp))
v2_con_spec_emp<-rep(NA,dim(v2_con)[1])
v2_con_spec_emp<-ifelse(v2_con$v2_bildung_beruf_erwerb_wfbm==1,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_wfbm==2,"N",v2_con_spec_emp))
v2_spec_emp<-factor(c(v2_clin_spec_emp,v2_con_spec_emp))
descT(v2_spec_emp)
## N Y <NA>
## [1,] No. cases 413 66 1307 1786
## [2,] Percent 23.1 3.7 73.2 100
Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.
v2_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v2_clin)[1])
v2_clin_wrk_abs_pst_6_mths<-ifelse((v2_clin$v2_wohnsituation_erwerb_unbekannt==1 | v2_clin$v2_wohnsituation_erwerb_rente==1 |
v2_clin$v2_wohnsituation_erwerb_fehlen>26),-999, v2_clin$v2_wohnsituation_erwerb_fehlen)
v2_con_wrk_abs_pst_6_mths<-rep(NA,dim(v2_con)[1])
v2_con_wrk_abs_pst_6_mths<-ifelse((v2_con$v2_bildung_beruf_erwerb_ausfallu==1 | v2_con$v2_bildung_beruf_erwerb_rente==1 |
v2_con$v2_bildung_beruf_erwerb_ausfallm>26),-999, v2_con$v2_bildung_beruf_erwerb_ausfallm)
v2_wrk_abs_pst_6_mths<-c(v2_clin_wrk_abs_pst_6_mths,v2_con_wrk_abs_pst_6_mths)
descT(v2_wrk_abs_pst_6_mths)
## -999 0 1 2 3 4 5 6 7 8 10 12 13 14 15 16
## [1,] No. cases 372 321 10 10 10 14 5 11 1 12 4 8 2 2 1 6
## [2,] Percent 20.8 18 0.6 0.6 0.6 0.8 0.3 0.6 0.1 0.7 0.2 0.4 0.1 0.1 0.1 0.3
## 18 19 20 24 25 26 <NA>
## [1,] 1 1 8 35 4 16 932 1786
## [2,] 0.1 0.1 0.4 2 0.2 0.9 52.2 100
Important: if receiving pension, this question refers to impairments in the household
v2_clin_cur_work_restr<-rep(NA,dim(v2_clin)[1])
v2_clin_cur_work_restr<-ifelse(v2_clin$v2_wohnsituation_erwerb_psysymptom==1,"Y",
ifelse(v2_clin$v2_wohnsituation_erwerb_psysymptom==2,"N",v2_clin_cur_work_restr))
v2_con_cur_work_restr<-rep(NA,dim(v2_con)[1])
v2_con_cur_work_restr<-ifelse(v2_con$v2_bildung_beruf_erwerb_psyeinsch==1,"Y",
ifelse(v2_con$v2_bildung_beruf_erwerb_psyeinsch==2,"N",v2_con_cur_work_restr))
v2_cur_work_restr<-factor(c(v2_clin_cur_work_restr,v2_con_cur_work_restr))
descT(v2_cur_work_restr)
## N Y <NA>
## [1,] No. cases 626 358 802 1786
## [2,] Percent 35.1 20 44.9 100
v2_weight<-c(v2_clin$v2_wohnsituation_erwerb_gewicht,v2_con$v2_bildung_beruf_erwerb_gewicht)
summary(v2_weight)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 43.00 69.00 80.00 83.29 95.00 171.00 744
This item was only recorded in a subset of individuals, because the question was introduced while the study was running.
v2_clin_waist<-v2_clin$v2_wohnsituation_erwerb_tailumf
v2_con_waist<-v2_con$v2_bildung_beruf_erwerb_taille
v2_waist<-c(v2_clin_waist,v2_con_waist)
summary(v2_waist)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 52.00 76.00 86.00 89.12 100.00 164.00 1423
We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.
v2_bmi<-v2_weight/(v1_height/100)^2
summary(v2_bmi)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 16.90 23.15 26.45 27.49 30.74 66.17 749
Create dataset
v2_dem<-data.frame(v2_cng_mar_stat,v2_marital_stat,v2_partner,v2_no_bio_chld,v2_no_adpt_chld,v2_stp_chld,v2_chg_hsng,v2_liv_aln,
v2_chg_empl_stat,v2_curr_paid_empl,v2_disabl_pens,v2_spec_emp,v2_wrk_abs_pst_6_mths,v2_cur_work_restr,
v2_weight,v2_waist,v2_bmi)
The participant is asked the following question: “Before your first illness episode, was there a special event that could have triggered the disease? If yes, please describe.” The following answering alternatives were given: N-“No”, U-“Unclear if trigger, namely”, Y-“yes, namely”.
v2_evnt_prcp_illn<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_evnt_prcp_illn<-ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==1,"N",
ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==2,"U",
ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==3,"Y",
ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==-999,-999,v2_evnt_prcp_illn))))
v2_evnt_prcp_illn<-factor(v2_evnt_prcp_illn)
descT(v2_evnt_prcp_illn)
## -999 N U Y <NA>
## [1,] No. cases 466 176 103 330 711 1786
## [2,] Percent 26.1 9.9 5.8 18.5 39.8 100
If unclear or yes, find the event described in the following item (categorical [text], v2_evnt_prcp_illn_txt) Output masked, as this is very sensitive information.
Create dataset
v2_ev_prc_fst_ep<-data.frame(v2_evnt_prcp_illn,v2_evnt_prcp_illn_txt)
If there occurred one or more illness episodes between study visits, participants were asked whether one of the life events they experienced and coded in the LEQ may have precipitated the episode.
To systematically evaluate this, each life event coded by the participant in the LEQ was afterwards evaluated if it:
A Occurred before the episode B Was, in the opinion of the participant, a precipitating factor for the illness episode C Which ife event, expressed as the corresponding item of the LEQ (“item number…”) she or he experienced
As these items were answered by only a fraction of the patients, these were autmatically recoded using a for loop and descriptive statistics on each item are not given here. The items are, however, included in the dataset as “v2_evnt_prcp_b4_1” to “v2_evnt_prcp_b4_31” (A), “v2_evnt_prcp_it_1” to “v2_evnt_prcp_it_31” (B), and “v2_evnt_prcp_it_1” to “v2_evnt_prcp_it_31” (C).
**Life events: Occurred before illness episode? (dichotomous, v2_evnt_prcp_b4_*)**
for(i in 1:length(grep("v2_ergaenz_leq_leq_zeit_31055_",names(v2_clin)))){
b4_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_zeit_31055_",names(v2_clin))[i]],
paste("v2_evnt_prcp_b4_",i,sep=""))
}
**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v2_evnt_prcp_f_*)**
for(i in 1:length(grep("v2_ergaenz_leq_leq_ausloeser_31055_",names(v2_clin)))){
prcp_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_ausloeser_31055_",names(v2_clin))[i]],
paste("v2_evnt_prcp_f_",i,sep=""))
}
**Life events: LEQ item number (categorical [LEQ item number], v2_evnt_prcp_it_*)**
for(i in 1:length(grep("v2_ergaenz_leq_leq_item_31055_",names(v2_clin)))){
leq_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_item_31055_",names(v2_clin))[i]],
paste("v2_evnt_prcp_it_",i,sep=""))
}
Create dataset
v2_leprcp<-data.frame(v2_evnt_prcp_it_1,v2_evnt_prcp_b4_1,v2_evnt_prcp_f_1,
v2_evnt_prcp_it_2,v2_evnt_prcp_b4_2,v2_evnt_prcp_f_2,
v2_evnt_prcp_it_3,v2_evnt_prcp_b4_3,v2_evnt_prcp_f_3,
v2_evnt_prcp_it_4,v2_evnt_prcp_b4_4,v2_evnt_prcp_f_4,
v2_evnt_prcp_it_5,v2_evnt_prcp_b4_5,v2_evnt_prcp_f_5,
v2_evnt_prcp_it_6,v2_evnt_prcp_b4_6,v2_evnt_prcp_f_6,
v2_evnt_prcp_it_7,v2_evnt_prcp_b4_7,v2_evnt_prcp_f_7,
v2_evnt_prcp_it_8,v2_evnt_prcp_b4_8,v2_evnt_prcp_f_8,
v2_evnt_prcp_it_9,v2_evnt_prcp_b4_9,v2_evnt_prcp_f_9,
v2_evnt_prcp_it_10,v2_evnt_prcp_b4_10,v2_evnt_prcp_f_10,
v2_evnt_prcp_it_11,v2_evnt_prcp_b4_11,v2_evnt_prcp_f_11,
v2_evnt_prcp_it_12,v2_evnt_prcp_b4_12,v2_evnt_prcp_f_12,
v2_evnt_prcp_it_13,v2_evnt_prcp_b4_13,v2_evnt_prcp_f_13,
v2_evnt_prcp_it_14,v2_evnt_prcp_b4_14,v2_evnt_prcp_f_14,
v2_evnt_prcp_it_15,v2_evnt_prcp_b4_15,v2_evnt_prcp_f_15,
v2_evnt_prcp_it_16,v2_evnt_prcp_b4_16,v2_evnt_prcp_f_16,
v2_evnt_prcp_it_17,v2_evnt_prcp_b4_17,v2_evnt_prcp_f_17,
v2_evnt_prcp_it_18,v2_evnt_prcp_b4_18,v2_evnt_prcp_f_18,
v2_evnt_prcp_it_19,v2_evnt_prcp_b4_19,v2_evnt_prcp_f_19,
v2_evnt_prcp_it_20,v2_evnt_prcp_b4_20,v2_evnt_prcp_f_20,
v2_evnt_prcp_it_21,v2_evnt_prcp_b4_21,v2_evnt_prcp_f_21,
v2_evnt_prcp_it_22,v2_evnt_prcp_b4_22,v2_evnt_prcp_f_22,
v2_evnt_prcp_it_23,v2_evnt_prcp_b4_23,v2_evnt_prcp_f_23,
v2_evnt_prcp_it_24,v2_evnt_prcp_b4_24,v2_evnt_prcp_f_24,
v2_evnt_prcp_it_25,v2_evnt_prcp_b4_25,v2_evnt_prcp_f_25,
v2_evnt_prcp_it_26,v2_evnt_prcp_b4_26,v2_evnt_prcp_f_26,
v2_evnt_prcp_it_27,v2_evnt_prcp_b4_27,v2_evnt_prcp_f_27,
v2_evnt_prcp_it_28,v2_evnt_prcp_b4_28,v2_evnt_prcp_f_28,
v2_evnt_prcp_it_29,v2_evnt_prcp_b4_29,v2_evnt_prcp_f_29,
v2_evnt_prcp_it_30,v2_evnt_prcp_b4_30,v2_evnt_prcp_f_30,
v2_evnt_prcp_it_31,v2_evnt_prcp_b4_31,v2_evnt_prcp_f_31)
Please note that all of the following questions refer to suicide attempts and suicidal ideation occurring since the last study visit.
Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.
v2_suic_ide_snc_lst_vst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_suic_ide_snc_lst_vst<-ifelse(c(v2_clin$v2_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v2_con)[1]))==1, "N",
ifelse(c(v2_clin$v2_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v2_con)[1]))==3, "Y", v2_suic_ide_snc_lst_vst))
v2_suic_ide_snc_lst_vst<-factor(v2_suic_ide_snc_lst_vst)
descT(v2_suic_ide_snc_lst_vst)
## -999 N Y <NA>
## [1,] No. cases 466 560 204 556 1786
## [2,] Percent 26.1 31.4 11.4 31.1 100
This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v2_scid_suic_ide<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_scid_suic_ide<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==1, "1",
ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==2, "2",
ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==3, "3",
ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==4, "4",-999))))
v2_scid_suic_ide<-factor(v2_scid_suic_ide,ordered=T)
descT(v2_scid_suic_ide)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1026 116 29 39 18 558 1786
## [2,] Percent 57.4 6.5 1.6 2.2 1 31.2 100
This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.
v2_scid_suic_thght_mth<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_scid_suic_thght_mth<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==1, "1",
ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==2, "2",
ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==3, "3",-999)))
v2_scid_suic_thght_mth<-factor(v2_scid_suic_thght_mth,ordered=T)
descT(v2_scid_suic_thght_mth)
## -999 1 2 3 <NA>
## [1,] No. cases 1026 103 67 31 559 1786
## [2,] Percent 57.4 5.8 3.8 1.7 31.3 100
This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v2_scid_suic_note_thgts<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_scid_suic_note_thgts<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==1, "1",
ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==2, "2",
ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==3, "3",
ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==4, "4",-999))))
v2_scid_suic_note_thgts<-factor(v2_scid_suic_note_thgts,ordered=T)
descT(v2_scid_suic_note_thgts)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1026 179 12 4 5 560 1786
## [2,] Percent 57.4 10 0.7 0.2 0.3 31.4 100
This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.
v2_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_suic_attmpt_snc_lst_vst<-ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==1, "1",
ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==2, "2",
ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==3, "3",-999)))
v2_suic_attmpt_snc_lst_vst<-factor(v2_suic_attmpt_snc_lst_vst,ordered=T)
descT(v2_suic_attmpt_snc_lst_vst)
## -999 1 2 3 <NA>
## [1,] No. cases 466 736 2 14 568 1786
## [2,] Percent 26.1 41.2 0.1 0.8 31.8 100
This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.
v2_no_suic_attmpt<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_no_suic_attmpt<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999, ifelse(v2_suic_attmpt_snc_lst_vst>1, c(v2_clin$v2_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v2_con)[1])),v2_no_suic_attmpt))
v2_no_suic_attmpt<-factor(v2_no_suic_attmpt,ordered=T)
descT(v2_no_suic_attmpt)
## -999 1 3 <NA>
## [1,] No. cases 1202 13 1 570 1786
## [2,] Percent 67.3 0.7 0.1 31.9 100
This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.
v2_prep_suic_attp_ord<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_prep_suic_attp_ord<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999,
ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==1, "1",
ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==2, "2",
ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==3, "3",
ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==4, "4",
v2_prep_suic_attp_ord)))))
v2_prep_suic_attp_ord<-factor(v2_prep_suic_attp_ord,ordered=T)
descT(v2_prep_suic_attp_ord)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1202 5 2 4 4 569 1786
## [2,] Percent 67.3 0.3 0.1 0.2 0.2 31.9 100
This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v2_suic_note_attmpt<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_suic_note_attmpt<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999,
ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==1, "1",
ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==2, "2",
ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==3, "3",
ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==4, "4",
v2_suic_note_attmpt)))))
v2_suic_note_attmpt<-factor(v2_suic_note_attmpt,ordered=T)
descT(v2_suic_note_attmpt)
## -999 1 3 4 <NA>
## [1,] No. cases 1202 8 2 3 571 1786
## [2,] Percent 67.3 0.4 0.1 0.2 32 100
Create dataset
v2_suic<-data.frame(v2_suic_ide_snc_lst_vst,v2_scid_suic_ide,v2_scid_suic_thght_mth,v2_scid_suic_note_thgts,
v2_suic_attmpt_snc_lst_vst,v2_no_suic_attmpt,v2_prep_suic_attp_ord,
v2_suic_note_attmpt)
The code below creates the following variables that summarize the medication of each individual:
Number of antidepressants prescribed (continuous [number], v2_Antidepressants) Number of antipsychotics prescribed (continuous [number], v2_Antipsychotics) Number of mood stabilizers prescribed (continuous [number], v2_Mood_stabilizers) Number of tranquilizers prescribed (continuous [number], v2_Tranquilizers) Number of other psychiatric medications (continuous [number], v2_Other_psychiatric)
#get the following variables from v2_clin
#1. Medication name ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name ["_depot_medi_2"]
#4. Depot category ["_depot_kategorie_2"]
#5. Bedarf name ["_bedarf_medi_1"]
#6. Bedarf category ["_bedarf_kategorie_1"]
v2_clin_medication_variables_1<-as.data.frame(v2_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v2_clin))])
dim(v2_clin_medication_variables_1)
## [1] 1320 61
#recode the variables that are coded as characters/logicals in the "v2_clin_medication_variables_1" as factors
v2_clin_medication_variables_1$v2_medikabehand3_depot_medi_200170_3<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_depot_medi_200170_3)
v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_9<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_9)
v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_9)
v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_10<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_10)
v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_10)
#make the duplicated data frame
v2_clin_medications_duplicated_1<-as.data.frame(t(apply(v2_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v2_clin_medications_duplicated_1)
## [1] 1320 30
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_".
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_clin, not as character
v2_clin_medication_variables_1[,!c(TRUE, FALSE)][v2_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v2_clin_medication_variables_1)
## [1] 1320 61
#bind columns id and medication names, but not categories together
v2_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v2_clin_medication_variables_1[,1], v2_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v2_clin_medication_name_1)
## [1] 1320 31
#get the medication categories from the "_medication_variables_1" dataframe
v2_clin_medication_categories_1<-as.data.frame(v2_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v2_clin_medication_categories_1)
## [1] 1320 31
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_clin, not as character
#Important: v2_clin_medication_name_1=="NA" replaced with is.na(v2_clin_medication_name_1)
v2_clin_medication_categories_1[is.na(v2_clin_medication_name_1)] <- NA
#write.csv(v2_clin_medication_categories_1, file="v2_clin_medication_group_1.csv")
#Make a count table of medications
v2_clin_med_table<-data.frame("mnppsd"=v2_clin$mnppsd)
v2_clin_med_table$v2_Antidepressants<-rowSums(v2_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v2_clin_med_table$v2_Antipsychotics<-rowSums(v2_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v2_clin_med_table$v2_Mood_stabilizers<-rowSums(v2_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v2_clin_med_table$v2_Tranquilizers<-rowSums(v2_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v2_clin_med_table$v2_Other_psychiatric<-rowSums(v2_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
#get the following variables from v2_con
#1. Medication name ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name ["_depot_medi_2"]
#4. Depot category ["_depot_kategorie_2"]
#5. Bedarf name ["_bedarf_medi_2"]
#6. Bedarf category ["_bedarf_kategorie_2"]
v2_con_medication_variables_1<-as.data.frame(v2_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v2_con))])
dim(v2_con_medication_variables_1) #[1] 320 29
## [1] 466 29
#recode the variables that are coded as characters/logicals in the "v2_con_medication_variables_1" as factors
v2_con_medication_variables_1$v2_medikabehand3_med_medi_200705_8<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_med_medi_200705_8)
v2_con_medication_variables_1$v2_medikabehand3_med_kategorie_200705_8<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_med_kategorie_200705_8)
v2_con_medication_variables_1$v2_medikabehand3_depot_medi_201224_2<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_depot_medi_201224_2)
v2_con_medication_variables_1$v2_medikabehand3_depot_kategorie_201224_2<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_depot_kategorie_201224_2)
v2_con_medication_variables_1$v2_medikabehand3_bedarf_medi_201187_4<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_bedarf_medi_201187_4)
v2_con_medication_variables_1$v2_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_bedarf_kategorie_201187_4)
#make the duplicated data frame
v2_con_medications_duplicated_1<-as.data.frame(t(apply(v2_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v2_con_medications_duplicated_1)
## [1] 466 14
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_".
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_con, not as character
v2_con_medication_variables_1[,!c(TRUE, FALSE)][v2_con_medications_duplicated_1=="TRUE"] <- NA
dim(v2_con_medication_variables_1)
## [1] 466 29
#bind columns id and medication names, but not categories together
v2_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v2_con_medication_variables_1[,1], v2_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v2_con_medication_name_1)
## [1] 466 15
#get the medication categories from the "_medication_variables_1" dataframe
v2_con_medication_categories_1<-as.data.frame(v2_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v2_con_medication_categories_1)
## [1] 466 15
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_con, not as character
#Important: v2_con_medication_name_1=="NA" replaced with is.na(v2_con_medication_name_1)
v2_con_medication_categories_1[is.na(v2_con_medication_name_1)] <- NA
#write.csv(v2_con_medication_categories_1, file="v2_con_medication_group_1.csv")
#Make a count table of medications
v2_con_med_table<-data.frame("mnppsd"=v2_con$mnppsd)
v2_con_med_table$v2_Antidepressants<-rowSums(v2_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v2_con_med_table$v2_Antipsychotics<-rowSums(v2_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v2_con_med_table$v2_Mood_stabilizers<-rowSums(v2_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v2_con_med_table$v2_Tranquilizers<-rowSums(v2_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v2_con_med_table$v2_Other_psychiatric<-rowSums(v2_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
Bind v2_clin and v2_con together by rows
v2_drugs<-rbind(v2_clin_med_table,v2_con_med_table)
dim(v2_drugs)
## [1] 1786 6
#check if the id column of v2_drugs and v1_id match
table(droplevels(v2_drugs[,1])==v1_id)
##
## TRUE
## 1786
v2_clin_adv<-ifelse(v2_clin$v2_medikabehand_medi2_nebenwirk==1,"Y","N")
v2_con_adv<-rep("-999",dim(v2_con)[1])
v2_adv<-factor(c(v2_clin_adv,v2_con_adv))
descT(v2_adv)
## -999 N Y <NA>
## [1,] No. cases 466 219 354 747 1786
## [2,] Percent 26.1 12.3 19.8 41.8 100
v2_clin_medchange<-rep(NA,dim(v2_clin)[1])
v2_clin_medchange<-ifelse(v2_clin$v2_medikabehand_medi3_mediaenderung==1,"Y","N")
v2_con_medchange<-rep("-999",dim(v2_con)[1])
v2_medchange<-as.factor(c(v2_clin_medchange,v2_con_medchange))
descT(v2_medchange)
## -999 N Y <NA>
## [1,] No. cases 466 192 381 747 1786
## [2,] Percent 26.1 10.8 21.3 41.8 100
Please see the section in Visit 1 for explanation.
v2_clin_lith<-rep(NA,dim(v2_clin)[1])
v2_clin_lith<-ifelse(v2_clin$v2_medikabehand_med_zusatz_lithium==1,"Y","N")
v2_con_lith<-rep("-999",dim(v2_con)[1])
v2_lith<-as.factor(c(v2_clin_lith,v2_con_lith))
v2_lith<-as.factor(v2_lith)
descT(v2_lith)
## -999 N Y <NA>
## [1,] No. cases 466 215 135 970 1786
## [2,] Percent 26.1 12 7.6 54.3 100
Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.
v2_clin_lith_prd<-rep(NA,dim(v2_clin)[1])
v2_con_lith_prd<-rep(-999,dim(v2_con)[1])
v2_clin_lith_prd<-ifelse(v2_clin_lith=="N", -999, ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==2,1,
ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==1,2,
ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==0,3,NA))))
v2_lith_prd<-factor(c(v2_clin_lith_prd,v2_con_lith_prd))
descT(v2_lith_prd)
## -999 1 2 3 <NA>
## [1,] No. cases 681 54 19 62 970 1786
## [2,] Percent 38.1 3 1.1 3.5 54.3 100
Create dataset
v2_med<-data.frame(v2_drugs[,2:6],v2_adv,v2_medchange,v2_lith,v2_lith_prd)
Here, separate datasets for clinical and control participants are created that contain the raw medication information at visit 2, as specified in the phenotype database.
For each medication that the individual took at visit 2 (including non-psychiatric drugs), the information given below is assessed.
The last character of each variable name always refers to the medication in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.
The medications were not assessed in any specific order, i.e. the order was determined by the individual participant (whatever she or he mentioned first). To classify medications, we used a catalogue, from which the categories and subcategories a medication belongs to were selected (see below).
Below, the variable names of clinical/control participants, respectively, are given in quotes, and the coding is explained in the parentheses.
1.Was the individual treated with any medication? (-1-not assessed, 1-yes, 2-no, 99-unknown)
“v2_medikabehand3_keine_med”/“v2_medikabehand3_keine_med”
Regular medication: Name of the medication (character)
“v2_medikabehand3_med_medi_199998”/“v2_medikabehand3_med_medi_200705”
Regular medication: Category to which the medication belongs (character)
“v2_medikabehand3_med_kategorie_199998”/“v2_medikabehand3_med_kategorie_200705”
Regular medication: Subcategory to which the medication belongs (character)
“v2_medikabehand3_med_kategorie_sub_199998”/“v2_medikabehand3_med_kategorie_sub_200705”
Regular medication: Psychiatric medication? (0-no, 1-yes) “v2_medikabehand3_med_zusatz_199998”/“v2_medikabehand3_med_zusatz_200705”
Regular medication: Dose in the morning (unitless)
“v2_medikabehand3_s_medi1_morgens_199998”/“v2_medikabehand3_s_medi1_morgens_200705”
Regular medication: Dose at midday (unitless)
“v2_medikabehand3_smedi1_mittags_199998”/“v2_medikabehand3_smedi1_mittags_200705”
Regular medication: Dose in the evening (unitless)
“v2_medikabehand3_smedi1_abends_199998”/“v2_medikabehand3_smedi1_abends_200705”
Regular medication: Dose at night (unitless)
“v2_medikabehand3_smedi1_nachts_199998”/“v2_medikabehand3_smedi1_nachts_200705”
Regular medication: Unit of the medication asked in the last four questions (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
“v2_medikabehand3_smedi1_einheit_199998”/“v2_medikabehand3_smedi1_einheit_200705”
Regular medication: Total dose of the medication per day (unitless)
“v2_medikabehand3_smedi1_gesamtdosis_199998”/“v2_medikabehand3_smedi1_gesamtdosis_200705”
Regular medication: Unit of the medication asked in the last question (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
“v2_medikabehand3_smedi1_einheit1_199998”/“v2_medikabehand3_smedi1_einheit1_200705”
Regular medication: Medication name, if not contained in our catalog (character)
“v2_medikabehand3_medikament_text_199998”/“v2_medikabehand3_medikament_text_200705”
Depot medication: Name of the medication (character) “v2_medikabehand3_depot_medi_200170”/"v2_medikabehand3_depot_medi_201224
Depot medication: Category to which the medication belongs (character) “v2_medikabehand3_depot_kategorie_200170”/"v2_medikabehand3_depot_kategorie_201224
Depot medication: Subcategory to which the medication belongs (character)
“v2_medikabehand3_depot_kategorie_sub_200170”/"v2_medikabehand3_depot_kategorie_sub_201224
Depot medication: Psychiatric medication? (0-no, 1-yes) “v2_medikabehand3_depot_zusatz_200170”/“v2_medikabehand3_depot_zusatz_201224”
Depot medication: Total Dose (unitless) “v2_medikabehand3_s_depot_gesamtdosis_200170”/“v2_medikabehand3_s_depot_gesamtdosis_201224”
Depot medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v2_medikabehand3_s_depot_einheit_200170”/ “v2_medikabehand3_s_depot_einheit_201224”
Interval, at which the depot medication is given (days) “v2_medikabehand3_s_depot_tage_200170”/“v2_medikabehand3_s_depot_tage_201224”
Medication name, if not contained in our catalog (character) “v2_medikabehand3_medikament_text_200170”/“v2_medikabehand3_medikament_text_201224”
Pro re nata (PRN) medication: Name of the medication (character) “v2_medikabehand3_bedarf_medi_199584”/“v2_medikabehand3_bedarf_medi_201187”
Pro re nata (PRN) medication: Category to which the medication belongs (character)
“v2_medikabehand3_bedarf_kategorie_199584”/“v2_medikabehand3_bedarf_kategorie_201187”
Pro re nata (PRN) medication: Subcategory to which the medication belongs (character) “v2_medikabehand3_bedarf_kategorie_sub_199584”/“v2_medikabehand3_bedarf_kategorie_sub_201187”
Pro re nata (PRN) medication: Psychiatric medication? (0-no, 1-yes) “v2_medikabehand3_bedarf_zusatz_199584”/“v2_medikabehand3_bedarf_zusatz_201187”
Pro re nata (PRN) medication: Total dose up to (unitless) “v2_medikabehand3_s_bedarf_gesamtdosis_199584”/"v2_medikabehand3_s_bedarf_kommentar_201187
Pro re nata (PRN) medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v2_medikabehand3_s_bedarf_einheit1_199584”/“v2_medikabehand3_s_bedarf_einheit1_201187”
Pro re nata (PRN) medication: Comment (character) “v2_medikabehand3_s_bedarf_kommentar_199584”/“v2_medikabehand3_s_bedarf_kommentar_201187”
Pro re nata (PRN) medication: Medication name, if not contained in our catalog (character) “v2_medikabehand3_medikament_text_199584”/“v2_medikabehand3_medikament_text_201187”
Make datasets containing only information on medication
v2_med_clin_orig<-data.frame(v2_clin$mnppsd,v2_clin[,147:455])
names(v2_med_clin_orig)[1]<-"v1_id"
v2_med_con_orig<-data.frame(v2_con$mnppsd,v2_con[,75:219])
names(v2_med_con_orig)[1]<-"v1_id"
Save raw medication datasets of visit 2
save(v2_med_clin_orig, file="200715_v4.1_psycourse_clin_raw_med_visit2.RData")
save(v2_med_con_orig, file="200715_v4.1_psycourse_con_raw_med_visit2.RData")
Write .csv file
write.table(v2_med_clin_orig,file="200715_v4.1_psycourse_clin_raw_med_visit2.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v2_med_con_orig,file="200715_v4.1_psycourse_con_raw_med_visit2.csv", quote=F, row.names=F, col.names=T, sep="\t")
For more explanation, see Visit 1
This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.
v2_clin_smk_strt_stp<-rep(NA,dim(v2_clin)[1])
v2_clin_smk_strt_stp<-ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==1,"NS",
ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==2,"NN",
ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==3,"YSP",
ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==4,"YST",v2_clin_smk_strt_stp))))
#ATTENTION: answering alternative: e-cigarette only in controls
v2_con_smk_strt_stp<-rep(NA,dim(v2_clin)[1])
v2_con_smk_strt_stp<-ifelse(v2_con$v2_tabalk_folge_tabak1==1 | v2_con$v2_tabalk_folge_tabak1==2,"NS",
ifelse(v2_con$v2_tabalk_folge_tabak1==3,"NN",
ifelse(v2_con$v2_tabalk_folge_tabak1==4,"YSP",
ifelse(v2_con$v2_tabalk_folge_tabak1==5,"YST",v2_con_smk_strt_stp))))
v2_smk_strt_stp<-c(v2_clin_smk_strt_stp,v2_con_smk_strt_stp)
descT(v2_smk_strt_stp)
## NN NS YSP YST <NA>
## [1,] No. cases 374 663 23 15 711 1786
## [2,] Percent 20.9 37.1 1.3 0.8 39.8 100
In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.
Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.
v2_no_cig<-c(rep(NA,dim(v2_clin)[1]),rep(NA,dim(v2_con)[1]))
v2_no_cig<-ifelse((v2_smk_strt_stp=="NN" | v2_smk_strt_stp=="YSP"), -999,
ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") &
c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==1,
c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*365,
ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") &
c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==2,
c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*52,
ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") &
c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==3,
c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*12,
v2_no_cig))))
summary(v2_no_cig[v2_no_cig>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 3650 6022 6172 7300 23725 945
This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.
v2_alc_pst6_mths<-c(v2_clin$v2_tabalk1_ta9_alkkonsum,v2_con$v2_tabalk_folge_alkohol4)
v2_alc_pst6_mths<-factor(v2_alc_pst6_mths, ordered=T)
descT(v2_alc_pst6_mths)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 270 216 111 255 130 46 39 719 1786
## [2,] Percent 15.1 12.1 6.2 14.3 7.3 2.6 2.2 40.3 100
This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.
v2_alc_5orm<-ifelse(v2_alc_pst6_mths<4,-999,
ifelse(is.na(c(v2_clin$v2_tabalk1_ta10_alk_haeufigk_m1,v2_con$v2_tabalk_folge_alkohol5))==T,
c(v2_clin$v2_tabalk1_ta11_alk_haeufigk_f1,v2_con$v2_tabalk_folge_alkohol6),
c(v2_clin$v2_tabalk1_ta10_alk_haeufigk_m1,v2_con$v2_tabalk_folge_alkohol5)))
v2_alc_5orm<-factor(v2_alc_5orm, ordered=T)
descT(v2_alc_5orm)
## -999 1 2 3 4 5 6 7 8 9 <NA>
## [1,] No. cases 597 213 88 59 21 29 33 15 3 7 721 1786
## [2,] Percent 33.4 11.9 4.9 3.3 1.2 1.6 1.8 0.8 0.2 0.4 40.4 100
On follow-up visits, participant were asked whether they had consumed illicit drugs since the last visit. If yes, the following information was collected:
In the PsyCourse dataset, only the information on whether, since the last study visit, the participant consumed any illicit drugs is contained. A separated dataset, containing the raw illicit drug information, is created below.
“During the past six months, did you take ANY illicit drugs?” (dichotomous, v2_pst6_ill_drg)
v2_pst6_ill_drg<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_pst6_ill_drg<-ifelse(c(v2_clin$v2_drogen1_dg1_konsum,v2_con$v2_drogen_folge_drogenkonsum)==2, "Y", "N")
descT(v2_pst6_ill_drg)
## N Y <NA>
## [1,] No. cases 989 81 716 1786
## [2,] Percent 55.4 4.5 40.1 100
Create dataset
v2_subst<-data.frame(v2_smk_strt_stp,
v2_no_cig,
v2_alc_pst6_mths,
v2_alc_5orm,
v2_pst6_ill_drg)
Here, separate datasets for clinical and control participants are created that contain the raw information on illicit drugs at visit 2, exactly as specified in the phenotype database.
For each illicit drug ever taken, the information given below is assessed.
The last character of each variable name always refers to the drug in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.
The drugs are not assessed in any specific order, i.e. the order is determined by the individual participant (whatever she or he mentions first).
Below, the variable names of clinical/control participants are given in quotes, and the coding is explained in the parentheses.
1. Whether the individual consumed illicit drugs since the last visit. (Coding: 1-no, 2-yes) “v2_drogen1_dg1_konsum”/“v2_drogen_folge_drogenkonsum”/
2. The name of the drug: (character) “v2_drogen1_s_dg_droge_28483”/v2_drogen_folge_droge_117794"
The category to which the drug belongs (each item below is a checkbox: 0-not checked, 1-checked):
3. Stimulants: “v2_drogen1_s_dg_drogekt1_28483”/“v2_drogen_folge_droge1_117794
4. Cannabis:”v2_drogen2_s_dg_drogekt1_28483“/”v2_drogen_folge_droge2_117794"
5. Opiates and pain reliefers: “v2_drogen3_s_dg_drogekt1_28483”/“v2_drogen_folge_droge3_117794”
6. Cocaine: “v2_drogen4_s_dg_drogekt1_28483”/“v2_drogen_folge_droge4_117794”
7. Hallucinogens: “v2_drogen5_s_dg_drogekt1_28483”/“v2_drogen_folge_droge5_117794”
8. Inhalants: “v2_drogen6_s_dg_drogekt1_28483”/“v2_drogen_folge_droge6_117794”
9. Tranquilizers: “v2_drogen7_s_dg_drogekt1_28483”/“v2_drogen_folge_droge7_117794”
10. Other: “v2_drogen8_s_dg_drogekt1_28483”/“v2_drogen_folge_droge8_117794”
11. “Referring to the time since the last study visit, how often did you consume it?” “v2_drogen1_s_dga_haeufigk_28483”/“v2_drogen_folge_droge_haeufig_117794”
The coding is given below:
1 - tried 1 time
2 - less than once a month
3 - about once a month
4 - at least 2 times but less than 10 times a month
5 - at least 10 times a month
12. “Referring to the period of time since the last study visit, did you have to take more of the drug to achieve the same effect?” (Coding: 1-no, 2-yes). “v2_drogen1_s_dgf_l6m_dosis_28483”/“v2_drogen_folge_droge_dosis_117794”
Important: There is an error in the original phenotype database, that affects the coding of item 10 (above). In all drugs the exports of the phenotype database do not reflect the input into the graphical user interface. Below, the incorrect variable is replaced with the corrected one
Make datasets containing only information on illicit drugs
v2_drg_clin<-v2_clin[,725:780]
v2_drg_con<-v2_con[,315:392]
Clinical participants
v2_clin_ill_drugs_orig<-data.frame(v2_clin$mnppsd,v2_drg_clin)
names(v2_clin_ill_drugs_orig)[1]<-"v1_id"
#recode wrongly coded item 10
for(i in c(0:4)){
v2_clin_ill_drugs_orig[,12+i*11]<-ifelse(v2_clin_ill_drugs_orig[,12+i*11]==5,1,
ifelse(v2_clin_ill_drugs_orig[,12+i*11]==4,5,
ifelse(v2_clin_ill_drugs_orig[,12+i*11]==3,4,
ifelse(v2_clin_ill_drugs_orig[,12+i*11]==2,3,
ifelse(v2_clin_ill_drugs_orig[,12+i*11]==1,2,NA)))))}
Control participants
v2_con_ill_drugs_orig<-data.frame(v2_con$mnppsd,v2_drg_con)
names(v2_con_ill_drugs_orig)[1]<-"v1_id"
#recode wrongly coded item 10
for(i in c(0:6)){
v2_con_ill_drugs_orig[,12+i*11]<-ifelse(v2_con_ill_drugs_orig[,12+i*11]==5,1,
ifelse(v2_con_ill_drugs_orig[,12+i*11]==4,5,
ifelse(v2_con_ill_drugs_orig[,12+i*11]==3,4,
ifelse(v2_con_ill_drugs_orig[,12+i*11]==2,3,
ifelse(v2_con_ill_drugs_orig[,12+i*11]==1,2,NA)))))}
Save raw illicit drug dataset from visit 2
save(v2_clin_ill_drugs_orig, file="200715_v4.1_psycourse_clin_raw_ill_drg_visit2.RData")
save(v2_con_ill_drugs_orig, file="200715_v4.1_psycourse_con_raw_ill_drg_visit2.RData")
Write .csv file
write.table(v2_clin_ill_drugs_orig,file="200715_v4.1_psycourse_clin_raw_ill_drg_visit2.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v2_con_ill_drugs_orig,file="200715_v4.1_psycourse_con_raw_ill_drg_visit2.csv", quote=F, row.names=F, col.names=T, sep="\t")
For more information on the scale, please see Visit 1
P1 Delusions (ordinal [1,2,3,4,5,6,7], v2_panss_p1)
v2_panss_p1<-c(v2_clin$v2_panss_p_p1_wahnideen,v2_con$v2_panss_p_p1_wahnideen)
v2_panss_p1<-factor(v2_panss_p1, ordered=T)
descT(v2_panss_p1)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 835 55 63 28 17 11 777 1786
## [2,] Percent 46.8 3.1 3.5 1.6 1 0.6 43.5 100
P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v2_panss_p2)
v2_panss_p2<-c(v2_clin$v2_panss_p_p2_form_denkst,v2_con$v2_panss_p_p2_form_denkst)
v2_panss_p2<-factor(v2_panss_p2, ordered=T)
descT(v2_panss_p2)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 750 94 109 45 8 3 777 1786
## [2,] Percent 42 5.3 6.1 2.5 0.4 0.2 43.5 100
P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v2_panss_p3)
v2_panss_p3<-c(v2_clin$v2_panss_p_p3_halluz,v2_con$v2_panss_p_p3_halluz)
v2_panss_p3<-factor(v2_panss_p3, ordered=T)
descT(v2_panss_p3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 900 32 29 30 13 6 776 1786
## [2,] Percent 50.4 1.8 1.6 1.7 0.7 0.3 43.4 100
P4 Excitement (ordinal [1,2,3,4,5,6,7], v2_panss_p4)
v2_panss_p4<-c(v2_clin$v2_panss_p_p4_erregung,v2_con$v2_panss_p_p4_erregung)
v2_panss_p4<-factor(v2_panss_p4, ordered=T)
descT(v2_panss_p4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 789 80 112 21 4 3 777 1786
## [2,] Percent 44.2 4.5 6.3 1.2 0.2 0.2 43.5 100
P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v2_panss_p5)
v2_panss_p5<-c(v2_clin$v2_panss_p_p5_groessenideen,v2_con$v2_panss_p_p5_groessenideen)
v2_panss_p5<-factor(v2_panss_p5, ordered=T)
descT(v2_panss_p5)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 942 27 28 6 4 1 778 1786
## [2,] Percent 52.7 1.5 1.6 0.3 0.2 0.1 43.6 100
P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v2_panss_p6)
v2_panss_p6<-c(v2_clin$v2_panss_p_p6_misstr_verfolg,v2_con$v2_panss_p_p6_misstr_verfolg)
v2_panss_p6<-factor(v2_panss_p6, ordered=T)
descT(v2_panss_p6)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 833 59 77 21 15 4 1 776 1786
## [2,] Percent 46.6 3.3 4.3 1.2 0.8 0.2 0.1 43.4 100
P7 Hostility (ordinal [1,2,3,4,5,6,7], v2_panss_p7)
v2_panss_p7<-c(v2_clin$v2_panss_p_p7_feindseligkeit,v2_con$v2_panss_p_p7_feindseligkeit)
v2_panss_p7<-factor(v2_panss_p7, ordered=T)
descT(v2_panss_p7)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 924 42 34 7 1 1 777 1786
## [2,] Percent 51.7 2.4 1.9 0.4 0.1 0.1 43.5 100
PANSS Positive sum score (continuous [7-49], v2_panss_sum_pos)
v2_panss_sum_pos<-as.numeric.factor(v2_panss_p1)+
as.numeric.factor(v2_panss_p2)+
as.numeric.factor(v2_panss_p3)+
as.numeric.factor(v2_panss_p4)+
as.numeric.factor(v2_panss_p5)+
as.numeric.factor(v2_panss_p6)+
as.numeric.factor(v2_panss_p7)
summary(v2_panss_sum_pos)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.000 7.000 7.000 9.149 10.000 32.000 781
N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v2_panss_n1)
v2_panss_n1<-c(v2_clin$v2_panss_n_n1_affektverflachung,v2_con$v2_panss_n_n1_affektverflachung)
v2_panss_n1<-factor(v2_panss_n1, ordered=T)
descT(v2_panss_n1)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 614 110 131 94 48 8 2 779 1786
## [2,] Percent 34.4 6.2 7.3 5.3 2.7 0.4 0.1 43.6 100
N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v2_panss_n2)
v2_panss_n2<-c(v2_clin$v2_panss_n_n2_emot_rueckzug,v2_con$v2_panss_n_n2_emot_rueckzug)
v2_panss_n2<-factor(v2_panss_n2, ordered=T)
descT(v2_panss_n2)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 697 101 108 77 23 4 776 1786
## [2,] Percent 39 5.7 6 4.3 1.3 0.2 43.4 100
N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v2_panss_n3)
v2_panss_n3<-c(v2_clin$v2_panss_n_n3_mang_aff_rapp,v2_con$v2_panss_n_n3_mang_aff_rapp)
v2_panss_n3<-factor(v2_panss_n3, ordered=T)
descT(v2_panss_n3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 760 88 106 40 11 4 777 1786
## [2,] Percent 42.6 4.9 5.9 2.2 0.6 0.2 43.5 100
N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v2_panss_n4)
v2_panss_n4<-c(v2_clin$v2_panss_n_n4_soz_pass_apath,v2_con$v2_panss_n_n4_soz_pass_apath)
v2_panss_n4<-factor(v2_panss_n4, ordered=T)
descT(v2_panss_n4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 710 79 135 58 25 3 776 1786
## [2,] Percent 39.8 4.4 7.6 3.2 1.4 0.2 43.4 100
N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v2_panss_n5)
v2_panss_n5<-c(v2_clin$v2_panss_n_n5_abstr_denken,v2_con$v2_panss_n_n5_abstr_denken)
v2_panss_n5<-factor(v2_panss_n5, ordered=T)
descT(v2_panss_n5)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 688 100 140 59 14 5 780 1786
## [2,] Percent 38.5 5.6 7.8 3.3 0.8 0.3 43.7 100
N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v2_panss_n6)
v2_panss_n6<-c(v2_clin$v2_panss_n_n6_spon_fl_sprache,v2_con$v2_panss_n_n6_spon_fl_sprache)
v2_panss_n6<-factor(v2_panss_n6, ordered=T)
descT(v2_panss_n6)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 788 75 90 34 16 2 781 1786
## [2,] Percent 44.1 4.2 5 1.9 0.9 0.1 43.7 100
N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v2_panss_n7)
v2_panss_n7<-c(v2_clin$v2_panss_n_n7_stereotyp_ged,v2_con$v2_panss_n_n7_stereotyp_ged)
v2_panss_n7<-factor(v2_panss_n7, ordered=T)
descT(v2_panss_n7)
## 1 2 3 4 5 <NA>
## [1,] No. cases 852 67 62 21 4 780 1786
## [2,] Percent 47.7 3.8 3.5 1.2 0.2 43.7 100
PANSS Negative sum score (continuous [7-49], v2_panss_sum_neg)
v2_panss_sum_neg<-as.numeric.factor(v2_panss_n1)+
as.numeric.factor(v2_panss_n2)+
as.numeric.factor(v2_panss_n3)+
as.numeric.factor(v2_panss_n4)+
as.numeric.factor(v2_panss_n5)+
as.numeric.factor(v2_panss_n6)+
as.numeric.factor(v2_panss_n7)
summary(v2_panss_sum_neg)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.00 7.00 9.00 10.96 13.00 39.00 786
G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v2_panss_g1)
v2_panss_g1<-c(v2_clin$v2_panss_g_g1_sorge_gesundh,v2_con$v2_panss_g_g1_sorge_gesundh)
v2_panss_g1<-factor(v2_panss_g1, ordered=T)
descT(v2_panss_g1)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 722 122 103 49 11 2 1 776 1786
## [2,] Percent 40.4 6.8 5.8 2.7 0.6 0.1 0.1 43.4 100
G2 Anxiety (ordinal [1,2,3,4,5,6,7], v2_panss_g2)
v2_panss_g2<-c(v2_clin$v2_panss_g_g2_angst,v2_con$v2_panss_g_g2_angst)
v2_panss_g2<-factor(v2_panss_g2, ordered=T)
descT(v2_panss_g2)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 654 84 196 50 24 1 1 776 1786
## [2,] Percent 36.6 4.7 11 2.8 1.3 0.1 0.1 43.4 100
G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v2_panss_g3)
v2_panss_g3<-c(v2_clin$v2_panss_g_g3_schuldgefuehle,v2_con$v2_panss_g_g3_schuldgefuehle)
v2_panss_g3<-factor(v2_panss_g3, ordered=T)
descT(v2_panss_g3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 762 79 100 45 16 4 780 1786
## [2,] Percent 42.7 4.4 5.6 2.5 0.9 0.2 43.7 100
G4 Tension (ordinal [1,2,3,4,5,6,7], v2_panss_g4)
v2_panss_g4<-c(v2_clin$v2_panss_g_g4_anspannung,v2_con$v2_panss_g_g4_anspannung)
v2_panss_g4<-factor(v2_panss_g4, ordered=T)
descT(v2_panss_g4)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 666 114 162 52 11 4 1 776 1786
## [2,] Percent 37.3 6.4 9.1 2.9 0.6 0.2 0.1 43.4 100
G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v2_panss_g5)
v2_panss_g5<-c(v2_clin$v2_panss_g_g5_manier_koerperh,v2_con$v2_panss_g_g5_manier_koerperh)
v2_panss_g5<-factor(v2_panss_g5, ordered=T)
descT(v2_panss_g5)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 926 37 32 8 5 1 777 1786
## [2,] Percent 51.8 2.1 1.8 0.4 0.3 0.1 43.5 100
G6 Depression (ordinal [1,2,3,4,5,6,7], v2_panss_g6)
v2_panss_g6<-c(v2_clin$v2_panss_g_g6_depression,v2_con$v2_panss_g_g6_depression)
v2_panss_g6<-factor(v2_panss_g6, ordered=T)
descT(v2_panss_g6)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 574 84 179 100 57 15 777 1786
## [2,] Percent 32.1 4.7 10 5.6 3.2 0.8 43.5 100
G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v2_panss_g7)
v2_panss_g7<-c(v2_clin$v2_panss_g_g7_mot_verlangs,v2_con$v2_panss_g_g7_mot_verlangs)
v2_panss_g7<-factor(v2_panss_g7, ordered=T)
descT(v2_panss_g7)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 706 88 143 63 6 2 778 1786
## [2,] Percent 39.5 4.9 8 3.5 0.3 0.1 43.6 100
G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v2_panss_g8)
v2_panss_g8<-c(v2_clin$v2_panss_g_g8_unkoop_verh,v2_con$v2_panss_g_g8_unkoop_verh)
v2_panss_g8<-factor(v2_panss_g8, ordered=T)
descT(v2_panss_g8)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 940 27 35 4 1 1 778 1786
## [2,] Percent 52.6 1.5 2 0.2 0.1 0.1 43.6 100
G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v2_panss_g9)
v2_panss_g9<-c(v2_clin$v2_panss_g_g9_ungew_denkinh,v2_con$v2_panss_g_g9_ungew_denkinh)
v2_panss_g9<-factor(v2_panss_g9, ordered=T)
descT(v2_panss_g9)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 846 50 76 20 13 4 777 1786
## [2,] Percent 47.4 2.8 4.3 1.1 0.7 0.2 43.5 100
G10 Disorientation (ordinal [1,2,3,4,5,6,7], v2_panss_g10)
v2_panss_g10<-c(v2_clin$v2_panss_g_g10_desorient,v2_con$v2_panss_g_g10_desorient)
v2_panss_g10<-factor(v2_panss_g10, ordered=T)
descT(v2_panss_g10)
## 1 2 3 4 5 <NA>
## [1,] No. cases 951 38 17 2 2 776 1786
## [2,] Percent 53.2 2.1 1 0.1 0.1 43.4 100
G11 Poor attention (ordinal [1,2,3,4,5,6,7], v2_panss_g11)
v2_panss_g11<-c(v2_clin$v2_panss_g_g11_mang_aufmerks,v2_con$v2_panss_g_g11_mang_aufmerks)
v2_panss_g11<-factor(v2_panss_g11, ordered=T)
descT(v2_panss_g11)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 637 101 196 62 8 2 780 1786
## [2,] Percent 35.7 5.7 11 3.5 0.4 0.1 43.7 100
G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v2_panss_g12)
v2_panss_g12<-c(v2_clin$v2_panss_g_g12_mang_urt_einsi,v2_con$v2_panss_g_g12_mang_urt_einsi)
v2_panss_g12<-factor(v2_panss_g12, ordered=T)
descT(v2_panss_g12)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 886 51 50 8 12 2 777 1786
## [2,] Percent 49.6 2.9 2.8 0.4 0.7 0.1 43.5 100
G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v2_panss_g13)
v2_panss_g13<-c(v2_clin$v2_panss_g_g13_willensschwae,v2_con$v2_panss_g_g13_willensschwae)
v2_panss_g13<-factor(v2_panss_g13, ordered=T)
descT(v2_panss_g13)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 890 42 61 14 1 1 777 1786
## [2,] Percent 49.8 2.4 3.4 0.8 0.1 0.1 43.5 100
G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v2_panss_g14)
v2_panss_g14<-c(v2_clin$v2_panss_g_g14_mang_impulsk,v2_con$v2_panss_g_g14_mang_impulsk)
v2_panss_g14<-factor(v2_panss_g14, ordered=T)
descT(v2_panss_g14)
## 1 2 3 4 5 <NA>
## [1,] No. cases 894 32 70 12 1 777 1786
## [2,] Percent 50.1 1.8 3.9 0.7 0.1 43.5 100
G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v2_panss_g15)
v2_panss_g15<-c(v2_clin$v2_panss_g_g15_selbstbezog,v2_con$v2_panss_g_g15_selbstbezog)
v2_panss_g15<-factor(v2_panss_g15, ordered=T)
descT(v2_panss_g15)
## 1 2 3 4 5 <NA>
## [1,] No. cases 906 55 29 16 2 778 1786
## [2,] Percent 50.7 3.1 1.6 0.9 0.1 43.6 100
G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v2_panss_g16)
v2_panss_g16<-c(v2_clin$v2_panss_g_g16_aktsoz_vermeid,v2_con$v2_panss_g_g16_aktsoz_vermeid)
v2_panss_g16<-factor(v2_panss_g16, ordered=T)
descT(v2_panss_g16)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 786 71 101 29 20 2 777 1786
## [2,] Percent 44 4 5.7 1.6 1.1 0.1 43.5 100
PANSS General Psychopathology sum score (continuous [16-112], v2_panss_sum_gen)
v2_panss_sum_gen<-as.numeric.factor(v2_panss_g1)+
as.numeric.factor(v2_panss_g2)+
as.numeric.factor(v2_panss_g3)+
as.numeric.factor(v2_panss_g4)+
as.numeric.factor(v2_panss_g5)+
as.numeric.factor(v2_panss_g6)+
as.numeric.factor(v2_panss_g7)+
as.numeric.factor(v2_panss_g8)+
as.numeric.factor(v2_panss_g9)+
as.numeric.factor(v2_panss_g10)+
as.numeric.factor(v2_panss_g11)+
as.numeric.factor(v2_panss_g12)+
as.numeric.factor(v2_panss_g13)+
as.numeric.factor(v2_panss_g14)+
as.numeric.factor(v2_panss_g15)+
as.numeric.factor(v2_panss_g16)
summary(v2_panss_sum_gen)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 16.00 16.00 20.00 22.66 26.00 68.00 792
Create PANSS Total score (continuous [30-210], v2_panss_sum_tot)
v2_panss_sum_tot<-v2_panss_sum_pos+v2_panss_sum_neg+v2_panss_sum_gen
summary(v2_panss_sum_tot)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 30.00 31.00 38.00 42.75 50.00 137.00 804
Create dataset
v2_symp_panss<-data.frame(v2_panss_p1,v2_panss_p2,v2_panss_p3,v2_panss_p4,v2_panss_p5,v2_panss_p6,v2_panss_p7,
v2_panss_n1,v2_panss_n2,v2_panss_n3,v2_panss_n4,v2_panss_n5,v2_panss_n6,v2_panss_n7,
v2_panss_g1,v2_panss_g2,v2_panss_g3,v2_panss_g4,v2_panss_g5,v2_panss_g6,v2_panss_g7,
v2_panss_g8,v2_panss_g9,v2_panss_g10,v2_panss_g11,v2_panss_g12,v2_panss_g13,v2_panss_g14,
v2_panss_g15,v2_panss_g16,v2_panss_sum_pos,v2_panss_sum_neg,v2_panss_sum_gen,
v2_panss_sum_tot)
For more information on the scale, please see Visit 1
Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v2_idsc_itm1)
v2_idsc_itm1<-c(v2_clin$v2_ids_c_s1_ids1_einschlafschw,v2_con$v2_ids_c_s1_ids1_einschlafschw)
v2_idsc_itm1<-factor(v2_idsc_itm1, ordered=T)
descT(v2_idsc_itm1)
## 0 1 2 3 <NA>
## [1,] No. cases 720 130 85 71 780 1786
## [2,] Percent 40.3 7.3 4.8 4 43.7 100
Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v2_idsc_itm2)
v2_idsc_itm2<-c(v2_clin$v2_ids_c_s1_ids2_naechtl_aufw,v2_con$v2_ids_c_s1_ids2_naechtl_aufw)
v2_idsc_itm2<-factor(v2_idsc_itm2, ordered=T)
descT(v2_idsc_itm2)
## 0 1 2 3 <NA>
## [1,] No. cases 621 152 155 78 780 1786
## [2,] Percent 34.8 8.5 8.7 4.4 43.7 100
Item 3 Early morning insomnia (ordinal [0,1,2,3], v2_idsc_itm3)
v2_idsc_itm3<-c(v2_clin$v2_ids_c_s1_ids3_frueh_aufw,v2_con$v2_ids_c_s1_ids3_frueh_aufw)
v2_idsc_itm3<-factor(v2_idsc_itm3, ordered=T)
descT(v2_idsc_itm3)
## 0 1 2 3 <NA>
## [1,] No. cases 854 66 48 38 780 1786
## [2,] Percent 47.8 3.7 2.7 2.1 43.7 100
Item 4 Hypersomnia (ordinal [0,1,2,3], v2_idsc_itm4)
v2_idsc_itm4<-c(v2_clin$v2_ids_c_s1_ids4_hypersomnie,v2_con$v2_ids_c_s1_ids4_hypersomnie)
v2_idsc_itm4<-factor(v2_idsc_itm4, ordered=T)
descT(v2_idsc_itm4)
## 0 1 2 3 <NA>
## [1,] No. cases 638 251 97 20 780 1786
## [2,] Percent 35.7 14.1 5.4 1.1 43.7 100
Item 5 Mood (sad) (ordinal [0,1,2,3], v2_idsc_itm5)
v2_idsc_itm5<-c(v2_clin$v2_ids_c_s1_ids5_stimmung_trgk,v2_con$v2_ids_c_s1_ids5_stimmung_trgk)
v2_idsc_itm5<-factor(v2_idsc_itm5, ordered=T)
descT(v2_idsc_itm5)
## 0 1 2 3 <NA>
## [1,] No. cases 623 222 104 55 782 1786
## [2,] Percent 34.9 12.4 5.8 3.1 43.8 100
Item 6 Mood (irritable) (ordinal [0,1,2,3], v2_idsc_itm6)
v2_idsc_itm6<-c(v2_clin$v2_ids_c_s1_ids6_stimmung_grzt,v2_con$v2_ids_c_s1_ids6_stimmung_grzt)
v2_idsc_itm6<-factor(v2_idsc_itm6, ordered=T)
descT(v2_idsc_itm6)
## 0 1 2 3 <NA>
## [1,] No. cases 705 224 62 15 780 1786
## [2,] Percent 39.5 12.5 3.5 0.8 43.7 100
Item 7 Mood (anxious) (ordinal [0,1,2,3], v2_idsc_itm7)
v2_idsc_itm7<-c(v2_clin$v2_ids_c_s1_ids7_stimmung_agst,v2_con$v2_ids_c_s1_ids7_stimmung_agst)
v2_idsc_itm7<-factor(v2_idsc_itm7, ordered=T)
descT(v2_idsc_itm7)
## 0 1 2 3 <NA>
## [1,] No. cases 668 214 90 32 782 1786
## [2,] Percent 37.4 12 5 1.8 43.8 100
Item 8 Reactivity of mood (ordinal [0,1,2,3], v2_idsc_itm8)
v2_idsc_itm8<-c(v2_clin$v2_ids_c_s1_ids8_reakt_stimmung,v2_con$v2_ids_c_s1_ids8_reakt_stimmung)
v2_idsc_itm8<-factor(v2_idsc_itm8, ordered=T)
descT(v2_idsc_itm8)
## 0 1 2 3 <NA>
## [1,] No. cases 803 119 56 25 783 1786
## [2,] Percent 45 6.7 3.1 1.4 43.8 100
Item 9 Mood Variation (ordinal [0,1,2,3], v2_idsc_itm9)
v2_idsc_itm9<-c(v2_clin$v2_ids_c_s1_ids9_stimmungsschw,v2_con$v2_ids_c_s1_ids9_stimmungsschw)
v2_idsc_itm9<-factor(v2_idsc_itm9, ordered=T)
descT(v2_idsc_itm9)
## 0 1 2 3 <NA>
## [1,] No. cases 765 87 46 106 782 1786
## [2,] Percent 42.8 4.9 2.6 5.9 43.8 100
Item 9A (categorical [M, A, N], v2_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).
v2_idsc_itm9a_pre<-c(v2_clin$v2_ids_c_s1_ids9a_stimmungsschw,v2_con$v2_ids_c_s1_ids9a_stimmungsschw)
v2_idsc_itm9a<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==1, "M", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==2, "A", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==3, "N", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-factor(v2_idsc_itm9a, ordered=F)
descT(v2_idsc_itm9a)
## -999 A M N <NA>
## [1,] No. cases 765 16 134 37 834 1786
## [2,] Percent 42.8 0.9 7.5 2.1 46.7 100
Item 9B (dichotomous, v2_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.
v2_idsc_itm9b_pre<-c(v2_clin$v2_ids_c_s1_ids9b_stimmungsschw,v2_con$v2_ids_c_s1_ids9b_stimmungsschw)
v2_idsc_itm9b<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm9b<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9b_pre==0, "N", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9b))
v2_idsc_itm9b<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9b_pre==1, "Y", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9b))
v2_idsc_itm9b<-factor(v2_idsc_itm9b, ordered=F)
descT(v2_idsc_itm9b)
## -999 N Y <NA>
## [1,] No. cases 765 89 60 872 1786
## [2,] Percent 42.8 5 3.4 48.8 100
Item 10 Quality of mood (ordinal [0,1,2,3], v2_idsc_itm10)
v2_idsc_itm10<-c(v2_clin$v2_ids_c_s1_ids10_quali_stimmung,v2_con$v2_ids_c_s1_ids10_quali_stimmung)
v2_idsc_itm10<-factor(v2_idsc_itm10, ordered=T)
descT(v2_idsc_itm10)
## 0 1 2 3 <NA>
## [1,] No. cases 840 60 41 59 786 1786
## [2,] Percent 47 3.4 2.3 3.3 44 100
Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.
The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.
Item 11 (ordinal [0,1,2,3], v2_idsc_itm11)
v2_idsc_app_verm<-c(v2_clin$v2_ids_c_s2_ids11_appetit_verm,v2_con$v2_ids_c_s2_ids11_appetit_verm)
v2_idsc_app_gest<-c(v2_clin$v2_ids_c_s2_ids12_appetit_steig,v2_con$v2_ids_c_s2_ids12_appetit_steig)
v2_idsc_itm11<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm11<-ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==T, NA,
ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==F, -999,
ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==T,
v2_idsc_app_verm,
ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &
(v2_idsc_app_verm>v2_idsc_app_gest), v2_idsc_app_verm, ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F & (v2_idsc_app_gest>=v2_idsc_app_verm),-999,v2_idsc_itm11)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v2_idsc_itm11)
## -999 0 1 2 3 <NA>
## [1,] No. cases 300 597 80 25 3 781 1786
## [2,] Percent 16.8 33.4 4.5 1.4 0.2 43.7 100
Item 12 (ordinal [0,1,2,3], v2_idsc_itm12)
v2_idsc_itm12<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm12<-ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==T, NA,
ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==F,
v2_idsc_app_gest,
ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==T,
-999,
ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &
(v2_idsc_app_verm>v2_idsc_app_gest), -999,
ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F & (v2_idsc_app_gest>=v2_idsc_app_verm),
v2_idsc_app_gest,v2_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v2_idsc_itm12)
## -999 0 1 2 3 <NA>
## [1,] No. cases 705 120 111 45 24 781 1786
## [2,] Percent 39.5 6.7 6.2 2.5 1.3 43.7 100
Item 13 (ordinal [0,1,2,3], v2_idsc_itm13)
v2_idsc_gew_abn<-c(v2_clin$v2_ids_c_s2_ids13_gewichtsabn,v2_con$v2_ids_c_s2_ids13_gewichtsabn)
v2_idsc_gew_zun<-c(v2_clin$v2_ids_c_s2_ids14_gewichtszun,v2_con$v2_ids_c_s2_ids14_gewichtszun)
v2_idsc_itm13<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm13<-ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==T, NA,
ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==F, -999,
ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==T,
v2_idsc_gew_abn,
ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F &
(v2_idsc_gew_abn>v2_idsc_gew_zun), v2_idsc_gew_abn, ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F & (v2_idsc_gew_zun >= v2_idsc_gew_abn),-999,v2_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v2_idsc_itm13)
## -999 0 1 2 3 <NA>
## [1,] No. cases 328 571 50 37 21 779 1786
## [2,] Percent 18.4 32 2.8 2.1 1.2 43.6 100
Item 14 (ordinal [0,1,2,3], v2_idsc_itm14)
v2_idsc_itm14<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm14<-ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==T, NA,
ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==F,
v2_idsc_gew_zun,
ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==T,
-999,
ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F &
(v2_idsc_gew_abn>v2_idsc_gew_zun), -999,
ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F & (v2_idsc_gew_zun>=v2_idsc_gew_abn),
v2_idsc_gew_zun,v2_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v2_idsc_itm14)
## -999 0 1 2 3 <NA>
## [1,] No. cases 679 181 72 50 25 779 1786
## [2,] Percent 38 10.1 4 2.8 1.4 43.6 100
Item 15 Concentration/decision making (ordinal [0,1,2,3], v2_idsc_itm15)
v2_idsc_itm15<-c(v2_clin$v2_ids_c_s2_ids15_konz_entscheid,v2_con$v2_ids_c_s2_ids15_konz_entscheid)
v2_idsc_itm15<-factor(v2_idsc_itm15, ordered=T)
descT(v2_idsc_itm15)
## 0 1 2 3 <NA>
## [1,] No. cases 583 258 146 20 779 1786
## [2,] Percent 32.6 14.4 8.2 1.1 43.6 100
Item 16 Outlook (self) (ordinal [0,1,2,3], v2_idsc_itm16)
v2_idsc_itm16<-c(v2_clin$v2_ids_c_s2_ids16_selbstbild,v2_con$v2_ids_c_s2_ids16_selbstbild)
v2_idsc_itm16<-factor(v2_idsc_itm16, ordered=T)
descT(v2_idsc_itm16)
## 0 1 2 3 <NA>
## [1,] No. cases 728 165 60 52 781 1786
## [2,] Percent 40.8 9.2 3.4 2.9 43.7 100
Item 17 Outlook (future) (ordinal [0,1,2,3], v2_idsc_itm17)
v2_idsc_itm17<-c(v2_clin$v2_ids_c_s2_ids17_zukunftssicht,v2_con$v2_ids_c_s2_ids17_zukunftssicht)
v2_idsc_itm17<-factor(v2_idsc_itm17, ordered=T)
descT(v2_idsc_itm17)
## 0 1 2 3 <NA>
## [1,] No. cases 655 245 93 12 781 1786
## [2,] Percent 36.7 13.7 5.2 0.7 43.7 100
Item 18 Suicidal ideation (ordinal [0,1,2,3], v2_idsc_itm18)
v2_idsc_itm18<-c(v2_clin$v2_ids_c_s2_ids18_selbstmordged,v2_con$v2_ids_c_s2_ids18_selbstmordged)
v2_idsc_itm18<-factor(v2_idsc_itm18, ordered=T)
descT(v2_idsc_itm18)
## 0 1 2 3 <NA>
## [1,] No. cases 897 51 53 4 781 1786
## [2,] Percent 50.2 2.9 3 0.2 43.7 100
Item 19 Involvement (ordinal [0,1,2,3], v2_idsc_itm19)
v2_idsc_itm19<-c(v2_clin$v2_ids_c_s2_ids19_interess_aktiv,v2_con$v2_ids_c_s2_ids19_interess_aktiv)
v2_idsc_itm19<-factor(v2_idsc_itm19, ordered=T)
descT(v2_idsc_itm19)
## 0 1 2 3 <NA>
## [1,] No. cases 806 152 31 18 779 1786
## [2,] Percent 45.1 8.5 1.7 1 43.6 100
Item 20 Energy/fatigability (ordinal [0,1,2,3], v2_idsc_itm20)
v2_idsc_itm20<-c(v2_clin$v2_ids_c_s2_ids20_energ_ermued,v2_con$v2_ids_c_s2_ids20_energ_ermued)
v2_idsc_itm20<-factor(v2_idsc_itm20, ordered=T)
descT(v2_idsc_itm20)
## 0 1 2 3 <NA>
## [1,] No. cases 620 249 123 15 779 1786
## [2,] Percent 34.7 13.9 6.9 0.8 43.6 100
Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v2_idsc_itm21)
v2_idsc_itm21<-c(v2_clin$v2_ids_c_s3_ids21_vergn_genuss,v2_con$v2_ids_c_s3_ids21_vergn_genuss)
v2_idsc_itm21<-factor(v2_idsc_itm21, ordered=T)
descT(v2_idsc_itm21)
## 0 1 2 3 <NA>
## [1,] No. cases 820 127 51 8 780 1786
## [2,] Percent 45.9 7.1 2.9 0.4 43.7 100
Item 22 Sexual interest (ordinal [0,1,2,3], v2_idsc_itm22)
v2_idsc_itm22<-c(v2_clin$v2_ids_c_s3_ids22_sex_interesse,v2_con$v2_ids_c_s3_ids22_sex_interesse)
v2_idsc_itm22<-factor(v2_idsc_itm22, ordered=T)
descT(v2_idsc_itm22)
## 0 1 2 3 <NA>
## [1,] No. cases 717 85 111 88 785 1786
## [2,] Percent 40.1 4.8 6.2 4.9 44 100
Item 23 Psychomotor slowing (ordinal [0,1,2,3], v2_idsc_itm23)
v2_idsc_itm23<-c(v2_clin$v2_ids_c_s3_ids23_psymo_hemm,v2_con$v2_ids_c_s3_ids23_psymo_hemm)
v2_idsc_itm23<-factor(v2_idsc_itm23, ordered=T)
descT(v2_idsc_itm23)
## 0 1 2 3 <NA>
## [1,] No. cases 786 171 48 1 780 1786
## [2,] Percent 44 9.6 2.7 0.1 43.7 100
Item 24 Psychomotor agitation (ordinal [0,1,2,3], v2_idsc_itm24)
v2_idsc_itm24<-c(v2_clin$v2_ids_c_s3_ids24_psymo_agitht,v2_con$v2_ids_c_s3_ids24_psymo_agitht)
v2_idsc_itm24<-factor(v2_idsc_itm24, ordered=T)
descT(v2_idsc_itm24)
## 0 1 2 3 <NA>
## [1,] No. cases 834 115 52 5 780 1786
## [2,] Percent 46.7 6.4 2.9 0.3 43.7 100
Item 25 Somatic complaints (ordinal [0,1,2,3], v2_idsc_itm25)
v2_idsc_itm25<-c(v2_clin$v2_ids_c_s3_ids25_som_beschw,v2_con$v2_ids_c_s3_ids25_som_beschw)
v2_idsc_itm25<-factor(v2_idsc_itm25, ordered=T)
descT(v2_idsc_itm25)
## 0 1 2 3 <NA>
## [1,] No. cases 670 263 51 23 779 1786
## [2,] Percent 37.5 14.7 2.9 1.3 43.6 100
Item 26 Sympathetic arousal (ordinal [0,1,2,3], v2_idsc_itm26)
v2_idsc_itm26<-c(v2_clin$v2_ids_c_s3_ids26_veg_erreg,v2_con$v2_ids_c_s3_ids26_veg_erreg)
v2_idsc_itm26<-factor(v2_idsc_itm26, ordered=T)
descT(v2_idsc_itm26)
## 0 1 2 3 <NA>
## [1,] No. cases 718 234 47 7 780 1786
## [2,] Percent 40.2 13.1 2.6 0.4 43.7 100
Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v2_idsc_itm27)
v2_idsc_itm27<-c(v2_clin$v2_ids_c_s3_ids27_panik_phob,v2_con$v2_ids_c_s3_ids27_panik_phob)
v2_idsc_itm27<-factor(v2_idsc_itm27, ordered=T)
descT(v2_idsc_itm27)
## 0 1 2 3 <NA>
## [1,] No. cases 887 78 32 9 780 1786
## [2,] Percent 49.7 4.4 1.8 0.5 43.7 100
Item 28 Gastrointestinal (ordinal [0,1,2,3], v2_idsc_itm28)
v2_idsc_itm28<-c(v2_clin$v2_ids_c_s3_ids28_verdauung,v2_con$v2_ids_c_s3_ids28_verdauung)
v2_idsc_itm28<-factor(v2_idsc_itm28, ordered=T)
descT(v2_idsc_itm28)
## 0 1 2 3 <NA>
## [1,] No. cases 835 103 52 15 781 1786
## [2,] Percent 46.8 5.8 2.9 0.8 43.7 100
Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v2_idsc_itm29)
v2_idsc_itm29<-c(v2_clin$v2_ids_c_s3_ids29_pers_bezieh,v2_con$v2_ids_c_s3_ids29_pers_bezieh)
v2_idsc_itm29<-factor(v2_idsc_itm29, ordered=T)
descT(v2_idsc_itm29)
## 0 1 2 3 <NA>
## [1,] No. cases 815 122 53 17 779 1786
## [2,] Percent 45.6 6.8 3 1 43.6 100
Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v2_idsc_itm30)
v2_idsc_itm30<-c(v2_clin$v2_ids_c_s3_ids30_schwgf_k_energ,v2_con$v2_ids_c_s3_ids30_schwgf_k_energ)
v2_idsc_itm30<-factor(v2_idsc_itm30, ordered=T)
descT(v2_idsc_itm30)
## 0 1 2 3 <NA>
## [1,] No. cases 828 126 36 16 780 1786
## [2,] Percent 46.4 7.1 2 0.9 43.7 100
Create IDS-C30 total score (continuous [0-84], v2_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.
v2_idsc_sum<-as.numeric.factor(v2_idsc_itm1)+
as.numeric.factor(v2_idsc_itm2)+
as.numeric.factor(v2_idsc_itm3)+
as.numeric.factor(v2_idsc_itm4)+
as.numeric.factor(v2_idsc_itm5)+
as.numeric.factor(v2_idsc_itm6)+
as.numeric.factor(v2_idsc_itm7)+
as.numeric.factor(v2_idsc_itm8)+
as.numeric.factor(v2_idsc_itm9)+
as.numeric.factor(v2_idsc_itm10)+
ifelse(is.na(v2_idsc_itm11)==T & is.na(v2_idsc_itm12)==T, NA,
ifelse((v2_idsc_itm11==-999 & v2_idsc_itm12!=-999), v2_idsc_itm12,
ifelse((v2_idsc_itm11!=-999 & v2_idsc_itm12==-999),v2_idsc_itm11, NA)))+
ifelse(is.na(v2_idsc_itm13)==T & is.na(v2_idsc_itm14)==T, NA,
ifelse((v2_idsc_itm13==-999 & v2_idsc_itm14!=-999), v2_idsc_itm14,
ifelse((v2_idsc_itm13!=-999 & v2_idsc_itm14==-999),v2_idsc_itm13, NA)))+
as.numeric.factor(v2_idsc_itm15)+
as.numeric.factor(v2_idsc_itm16)+
as.numeric.factor(v2_idsc_itm17)+
as.numeric.factor(v2_idsc_itm18)+
as.numeric.factor(v2_idsc_itm19)+
as.numeric.factor(v2_idsc_itm20)+
as.numeric.factor(v2_idsc_itm21)+
as.numeric.factor(v2_idsc_itm22)+
as.numeric.factor(v2_idsc_itm23)+
as.numeric.factor(v2_idsc_itm24)+
as.numeric.factor(v2_idsc_itm25)+
as.numeric.factor(v2_idsc_itm26)+
as.numeric.factor(v2_idsc_itm27)+
as.numeric.factor(v2_idsc_itm28)+
as.numeric.factor(v2_idsc_itm29)+
as.numeric.factor(v2_idsc_itm30)
summary(v2_idsc_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 3.00 7.00 10.87 16.00 55.00 844
Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)
v2_idsc_itm11<-factor(v2_idsc_itm11,ordered=T)
v2_idsc_itm12<-factor(v2_idsc_itm12,ordered=T)
v2_idsc_itm13<-factor(v2_idsc_itm13,ordered=T)
v2_idsc_itm14<-factor(v2_idsc_itm14,ordered=T)
Create dataset
v2_symp_ids_c<-data.frame(v2_idsc_itm1,v2_idsc_itm2,v2_idsc_itm3,v2_idsc_itm4,v2_idsc_itm5,v2_idsc_itm6,v2_idsc_itm7,
v2_idsc_itm8,v2_idsc_itm9,v2_idsc_itm9a,v2_idsc_itm9b,v2_idsc_itm10,v2_idsc_itm11,v2_idsc_itm12,
v2_idsc_itm13,v2_idsc_itm14,v2_idsc_itm15,v2_idsc_itm16,v2_idsc_itm17,v2_idsc_itm18,v2_idsc_itm19,
v2_idsc_itm20,v2_idsc_itm21,v2_idsc_itm22,v2_idsc_itm23,v2_idsc_itm24,v2_idsc_itm25,v2_idsc_itm26,
v2_idsc_itm27,v2_idsc_itm28,v2_idsc_itm29,v2_idsc_itm30,v2_idsc_sum)
For more information on the scale, please see Visit 1
Item 1 Elevated mood (ordinal [0,1,2,3,4], v2_ymrs_itm1)
v2_ymrs_itm1<-c(v2_clin$v2_ymrs_ymrs1_gehob_stimm,v2_con$v2_ymrs_ymrs1_gehob_stimm)
v2_ymrs_itm1<-factor(v2_ymrs_itm1, ordered=T)
descT(v2_ymrs_itm1)
## 0 1 2 3 4 <NA>
## [1,] No. cases 844 119 39 4 1 779 1786
## [2,] Percent 47.3 6.7 2.2 0.2 0.1 43.6 100
Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v2_ymrs_itm2)
v2_ymrs_itm2<-c(v2_clin$v2_ymrs_ymrs2_gest_aktiv,v2_con$v2_ymrs_ymrs2_gest_aktiv)
v2_ymrs_itm2<-factor(v2_ymrs_itm2, ordered=T)
descT(v2_ymrs_itm2)
## 0 1 2 3 4 <NA>
## [1,] No. cases 885 84 33 4 1 779 1786
## [2,] Percent 49.6 4.7 1.8 0.2 0.1 43.6 100
Item 3 Sexual interest (ordinal [0,1,2,3,4], v2_ymrs_itm3)
v2_ymrs_itm3<-c(v2_clin$v2_ymrs_ymrs3_sex_interesse,v2_con$v2_ymrs_ymrs3_sex_interesse)
v2_ymrs_itm3<-factor(v2_ymrs_itm3, ordered=T)
descT(v2_ymrs_itm3)
## 0 1 2 3 <NA>
## [1,] No. cases 960 30 15 1 780 1786
## [2,] Percent 53.8 1.7 0.8 0.1 43.7 100
Item 4 Sleep (ordinal [0,1,2,3,4], v2_ymrs_itm4)
v2_ymrs_itm4<-c(v2_clin$v2_ymrs_ymrs4_schlaf,v2_con$v2_ymrs_ymrs4_schlaf)
v2_ymrs_itm4<-factor(v2_ymrs_itm4, ordered=T)
descT(v2_ymrs_itm4)
## 0 1 2 3 <NA>
## [1,] No. cases 932 37 25 13 779 1786
## [2,] Percent 52.2 2.1 1.4 0.7 43.6 100
Item 5 Irritability (ordinal [0,2,4,6,8], v2_ymrs_itm5)
v2_ymrs_itm5<-c(v2_clin$v2_ymrs_ymrs5_reizbarkeit,v2_con$v2_ymrs_ymrs5_reizbarkeit)
v2_ymrs_itm5<-factor(v2_ymrs_itm5, ordered=T)
descT(v2_ymrs_itm5)
## 0 2 4 6 <NA>
## [1,] No. cases 862 123 17 5 779 1786
## [2,] Percent 48.3 6.9 1 0.3 43.6 100
Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v2_ymrs_itm6)
v2_ymrs_itm6<-c(v2_clin$v2_ymrs_ymrs6_sprechweise,v2_con$v2_ymrs_ymrs6_sprechweise)
v2_ymrs_itm6<-factor(v2_ymrs_itm6, ordered=T)
descT(v2_ymrs_itm6)
## 0 2 4 6 <NA>
## [1,] No. cases 874 80 43 9 780 1786
## [2,] Percent 48.9 4.5 2.4 0.5 43.7 100
Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v2_ymrs_itm7)
v2_ymrs_itm7<-c(v2_clin$v2_ymrs_ymrs7_sprachstoer,v2_con$v2_ymrs_ymrs7_sprachstoer)
v2_ymrs_itm7<-factor(v2_ymrs_itm7, ordered=T)
descT(v2_ymrs_itm7)
## 0 1 2 3 <NA>
## [1,] No. cases 894 92 16 4 780 1786
## [2,] Percent 50.1 5.2 0.9 0.2 43.7 100
Item 8 Content (ordinal [0,2,4,6,8], v2_ymrs_itm8)
v2_ymrs_itm8<-c(v2_clin$v2_ymrs_ymrs8_inhalte,v2_con$v2_ymrs_ymrs8_inhalte)
v2_ymrs_itm8<-factor(v2_ymrs_itm8, ordered=T)
descT(v2_ymrs_itm8)
## 0 2 4 6 8 <NA>
## [1,] No. cases 961 26 4 7 8 780 1786
## [2,] Percent 53.8 1.5 0.2 0.4 0.4 43.7 100
Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v2_ymrs_itm9)
v2_ymrs_itm9<-c(v2_clin$v2_ymrs_ymrs9_exp_aggr_verh,v2_con$v2_ymrs_ymrs9_exp_aggr_verh)
v2_ymrs_itm9<-factor(v2_ymrs_itm9, ordered=T)
descT(v2_ymrs_itm9)
## 0 2 4 6 <NA>
## [1,] No. cases 966 30 5 1 784 1786
## [2,] Percent 54.1 1.7 0.3 0.1 43.9 100
Item 10 Appearance (ordinal [0,1,2,3,4], v2_ymrs_itm10)
v2_ymrs_itm10<-c(v2_clin$v2_ymrs_ymrs10_erscheinung,v2_con$v2_ymrs_ymrs10_erscheinung)
v2_ymrs_itm10<-factor(v2_ymrs_itm10, ordered=T)
descT(v2_ymrs_itm10)
## 0 1 2 3 4 <NA>
## [1,] No. cases 909 78 14 2 1 782 1786
## [2,] Percent 50.9 4.4 0.8 0.1 0.1 43.8 100
Item 11 Insight (ordinal [0,1,2,3,4], v2_ymrs_itm11)
v2_ymrs_itm11<-c(v2_clin$v2_ymrs_ymrs11_krkh_einsicht,v2_con$v2_ymrs_ymrs11_krkh_einsicht)
v2_ymrs_itm11<-factor(v2_ymrs_itm11, ordered=T)
descT(v2_ymrs_itm11)
## 0 1 2 3 4 <NA>
## [1,] No. cases 970 15 9 4 4 784 1786
## [2,] Percent 54.3 0.8 0.5 0.2 0.2 43.9 100
Create YMRS total score (continuous [0-60], v2_ymrs_sum)
v2_ymrs_sum<-(as.numeric.factor(v2_ymrs_itm1)+
as.numeric.factor(v2_ymrs_itm2)+
as.numeric.factor(v2_ymrs_itm3)+
as.numeric.factor(v2_ymrs_itm4)+
as.numeric.factor(v2_ymrs_itm5)+
as.numeric.factor(v2_ymrs_itm6)+
as.numeric.factor(v2_ymrs_itm7)+
as.numeric.factor(v2_ymrs_itm8)+
as.numeric.factor(v2_ymrs_itm9)+
as.numeric.factor(v2_ymrs_itm10)+
as.numeric.factor(v2_ymrs_itm11))
summary(v2_ymrs_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 0.000 1.859 2.000 36.000 788
Create dataset
v2_symp_ymrs<-data.frame(v2_ymrs_itm1,
v2_ymrs_itm2,
v2_ymrs_itm3,
v2_ymrs_itm4,
v2_ymrs_itm5,
v2_ymrs_itm6,
v2_ymrs_itm7,
v2_ymrs_itm8,
v2_ymrs_itm9,
v2_ymrs_itm10,
v2_ymrs_itm11,
v2_ymrs_sum)
Please see Visit 1 for more details and explicit rating instructions. A zero on this scale means “not assessable” (both of the following two items) and was coded as “-999”, as were all control participants.
v2_cgi_s<-c(v2_clin$v2_cgi1_cgi1_schweregrad,rep(-999,dim(v2_con)[1]))
v2_cgi_s[v2_cgi_s==0]<- -999
v2_cgi_s<-factor(v2_cgi_s, ordered=T)
descT(v2_cgi_s)
## -999 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 470 30 79 234 252 139 40 1 541 1786
## [2,] Percent 26.3 1.7 4.4 13.1 14.1 7.8 2.2 0.1 30.3 100
During follow-up visits, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. These range from “very much improved”-1 to “very much worse”-7.
v2_cgi_c<-c(v2_clin$v2_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v2_con)[1]))
v2_cgi_c[v2_cgi_c==0]<- -999
v2_cgi_c<-factor(v2_cgi_c, ordered=T)
descT(v2_cgi_c)
## -999 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 486 29 147 201 229 87 20 2 585 1786
## [2,] Percent 27.2 1.6 8.2 11.3 12.8 4.9 1.1 0.1 32.8 100
Please see Visit 1 for more details and explicit rating instructions. A zero on this scale means “not assessable” and was coded as “-999”.
v2_gaf<-c(v2_clin$v2_gaf_gaf_code,v2_con$v2_gaf_gaf_code)
v2_gaf[v2_gaf==0]<- -999
summary(v2_gaf[v2_gaf>0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.00 55.00 68.00 67.48 82.00 100.00 772
Boxplot of GAF scores of both CLINICAL and CONTROL study participants
boxplot(v2_gaf[v2_gaf>0 & v1_stat=="CLINICAL"], v2_gaf[v2_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))
Create dataset
v2_ill_sev<-data.frame(v2_cgi_s,v2_cgi_c,v2_gaf)
There are two differences compared to the test battery assessed in Visit 1:
The “Verbaler Lern- und Merkfähigkeitstest” is added (assesses learning and memory). This test is also implemented in all following visits. Parallel versions of the test are alternately used to avoid recall bias.
The MWT-B is omitted as results of this test are not expected to vary much during the timeframe of the study.
General comments on the testing (character, v2_nrpsy_com) If there were no comments, this item was coded -999.
Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v2_nrpsy_lng)
v2_nrpsy_lng<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_nrpsy_lng<-ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==0, "mother tongue",
ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==1, "good",
ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==2, "sufficient",
ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==3, "not sufficient",v2_nrpsy_lng))))
v2_nrpsy_lng<-factor(v2_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))
descT(v2_nrpsy_lng)
## mother tongue good sufficient not sufficient <NA>
## [1,] No. cases 971 64 6 2 743 1786
## [2,] Percent 54.4 3.6 0.3 0.1 41.6 100
Motivation of the participant (ordinal [“poor”,“average”,“good”], v2_nrpsy_mtv)
v2_nrpsy_mtv_pre<-c(v2_clin$v2_npu1_np_mot,v2_con$v2_npu_folge_np_mot)
v2_nrpsy_mtv<-ifelse(v2_nrpsy_mtv_pre==0, "poor",
ifelse(v2_nrpsy_mtv_pre==1, "average",
ifelse(v2_nrpsy_mtv_pre==2, "good", NA)))
v2_nrpsy_mtv<-factor(v2_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))
descT(v2_nrpsy_mtv)
## poor average good <NA>
## [1,] No. cases 12 85 933 756 1786
## [2,] Percent 0.7 4.8 52.2 42.3 100
The VLMT (Helmstaedter, Lendt, & Lux, 2001) assesses learning and memory. A list of 15 words (list 1) is verbally presented to the participant for five times. After each presentation, the subject is required to recall as many words from the list as he remembers and the interviewer writes those down. After the fifth time, another list of words (list 2; distraction) is presented to the subject, with the same instruction (“recall as many words as possible from the list after I read it to you”). After writing down the recalled words from list 2, the interviewer asks the participant to recall the words from list 1 and writes those down. After a time interval of 25-30 minutes, during which other tests are performed, the interviewer asks the participant again and writes down the recalled words form list 1. Following this free recall phase, the interviewer tests recognition of the words from list 1 by verbally presenting 50 words (from list 1, list 2 and completely new words) and asking the participant whether each word belongs to list 1 or not.
Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.
VLMT_introcheck (categorical [0, 1, 9], v2_nrpsy_vlmt_check) This variable indicates whether a test was:
In contrast to previous versions of the dataset, data are not filtered according to this item but all tests are included.
v2_nrpsy_vlmt_check<-c(v2_clin$v2_vlmt_vlmt_introcheck1,v2_con$v2_npu_folge_np_vlmt)
descT(v2_nrpsy_vlmt_check)
## 0 1 9 <NA>
## [1,] No. cases 75 937 46 728 1786
## [2,] Percent 4.2 52.5 2.6 40.8 100
Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v2_nrpsy_vlmt_corr)
v2_nrpsy_vlmt_corr<-c(v2_clin$v2_vlmt_vlmt3_sw_a5d,v2_con$v2_npu_folge_np_vlmt_gl)
summary(v2_nrpsy_vlmt_corr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 40.00 50.00 49.05 59.00 75.00 802
Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v2_nrpsy_vlmt_lss_d)
v2_nrpsy_vlmt_lss_d<-c(v2_clin$v2_vlmt_vlmt5_aw_ilsd6,v2_con$v2_npu_folge_np_vlmt_vni)
summary(v2_nrpsy_vlmt_lss_d)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -6.000 0.000 2.000 1.734 3.000 9.000 807
Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v2_nrpsy_vlmt_lss_t)
v2_nrpsy_vlmt_lss_t<-c(v2_clin$v2_vlmt_vlmt6_aw_vwd7,v2_con$v2_npu_folge_np_vlmt_vnzv)
summary(v2_nrpsy_vlmt_lss_t)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -5.000 0.000 2.000 1.853 3.000 13.000 820
Recognition performance (corrected for falsely recognized words) (continuous [number of words], v2_nrpsy_vlmt_rec)
v2_nrpsy_vlmt_rec<-c(v2_clin$v2_vlmt_vlmt8_kwl,v2_con$v2_npu_folge_np_vlmt_kw)
summary(v2_nrpsy_vlmt_rec)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -12.00 10.00 13.00 11.72 15.00 15.00 830
For a description of the test, see Visit 1.
TMT Part A, time (continuous [seconds], v2_nrpsy_tmt_A_rt)
v2_nrpsy_tmt_A_rt<-c(v2_clin$v2_npu1_tmt_001,v2_con$v2_npu_folge_np_tmt_001)
summary(v2_nrpsy_tmt_A_rt)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 21.00 28.00 31.88 38.00 142.00 739
TMT Part A, errors (continuous [number of errors], v2_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).
v2_nrpsy_tmt_A_err<-c(v2_clin$v2_npu1_tmt_af_001,v2_con$v2_npu_folge_np_tmtfehler_001)
summary(v2_nrpsy_tmt_A_err)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.1424 0.0000 4.0000 747
TMT Part B, time (continuous [seconds], v2_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.
v2_nrpsy_tmt_B_rt<-c(v2_clin$v2_npu1_tmt_002,v2_con$v2_npu_folge_tmt_002)
v2_nrpsy_tmt_B_rt[v2_nrpsy_tmt_B_rt>300]<-300
summary(v2_nrpsy_tmt_B_rt)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 49.00 64.00 73.65 87.00 300.00 791
TMT Part B, errors (continuous [number of errors], v2_nrpsy_tmt_B_err)
v2_nrpsy_tmt_B_err<-c(v2_clin$v2_npu1_tmt_af_002,v2_con$v2_npu_folge_tmt_af_002)
summary(v2_nrpsy_tmt_B_err)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.4929 1.0000 20.0000 798
For a description of the test, see Visit 1.
Forward (continuous [number of items], v2_nrpsy_dgt_sp_frw)
v2_nrpsy_dgt_sp_frw<-c(v2_clin$v2_npu1_zns_001,v2_con$v2_npu_folge_np_wie_001)
summary(v2_nrpsy_dgt_sp_frw)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 8.000 10.000 9.681 11.000 16.000 747
Backward (continuous [number of items], v2_nrpsy_dgt_sp_bck)
v2_nrpsy_dgt_sp_bck<-c(v2_clin$v2_npu1_zns_002,v2_con$v2_npu_folge_np_wie_002)
summary(v2_nrpsy_dgt_sp_bck)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 5.00 6.00 6.55 8.00 14.00 750
For a description of the test and the coding of incompletet tests, see Visit 1.
v2_introcheck3<-c(v2_clin$v2_npu1_np_introcheck3,v2_con$v2_npu_folge_np_hawier)
v2_nrpsy_dg_sym_pre<-c(v2_clin$v2_npu1_zst_001,v2_con$v2_npu_folge_np_hawier_001)
v2_nrpsy_dg_sym<-ifelse(v2_introcheck3==1, v2_nrpsy_dg_sym_pre,
ifelse(v2_introcheck3==9,-999,
ifelse(v2_introcheck3==0,NA,NA)))
summary(subset(v2_nrpsy_dg_sym,v2_nrpsy_dg_sym>=0))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 52.00 66.00 66.69 82.50 133.00
Create dataset
v2_nrpsy<-data.frame(v2_nrpsy_com,
v2_nrpsy_lng,
v2_nrpsy_mtv,
v2_nrpsy_vlmt_check,
v2_nrpsy_vlmt_corr,
v2_nrpsy_vlmt_lss_d,
v2_nrpsy_vlmt_lss_t,
v2_nrpsy_vlmt_rec,
v2_nrpsy_tmt_A_rt,
v2_nrpsy_tmt_A_err,
v2_nrpsy_tmt_B_rt,
v2_nrpsy_tmt_B_err,
v2_nrpsy_dgt_sp_frw,
v2_nrpsy_dgt_sp_bck,
v2_nrpsy_dg_sym)
All participants were asked to fill out questionnaires on the following topics: current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months (LEQ), and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 1 and 2) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1, all questionnaires were checked on whether they were filled out correctly or not and only those correctly filled-out are included in this dataset.
For explanation, please refer to the section in Visit 1
“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v2_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.
v2_sf12_recode(v2_con$v2_sf12_sf_allgemein,"v2_sf12_itm0")
## -999 1 2 3 4 5 6 7 8 9 10 <NA>
## [1,] No. cases 1320 1 2 6 6 8 10 34 101 72 35 191 1786
## [2,] Percent 73.9 0.1 0.1 0.3 0.3 0.4 0.6 1.9 5.7 4 2 10.7 100
“In general, would you say your health is…” (ordinal [1,2,3,4,5], v2_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.
v2_sf12_recode(v2_con$v2_sf12_sf1,"v2_sf12_itm1")
## -999 1 2 3 4 <NA>
## [1,] No. cases 1320 56 128 91 11 180 1786
## [2,] Percent 73.9 3.1 7.2 5.1 0.6 10.1 100
“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”
“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v2_sf12_itm2)
Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.
v2_sf12_recode(v2_con$v2_sf12_sf2,"v2_sf12_itm2")
## -999 1 2 3 <NA>
## [1,] No. cases 1320 2 33 251 180 1786
## [2,] Percent 73.9 0.1 1.8 14.1 10.1 100
“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v2_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.
v2_sf12_recode(v2_con$v2_sf12_sf3,"v2_sf12_itm3")
## -999 1 2 3 <NA>
## [1,] No. cases 1320 6 36 244 180 1786
## [2,] Percent 73.9 0.3 2 13.7 10.1 100
During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?
“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v2_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.
v2_sf12_recode(v2_con$v2_sf12_sf4,"v2_sf12_itm4")
## -999 1 2 <NA>
## [1,] No. cases 1320 39 244 183 1786
## [2,] Percent 73.9 2.2 13.7 10.2 100
“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v2_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.
v2_sf12_recode(v2_con$v2_sf12_sf5,"v2_sf12_itm5")
## -999 1 2 <NA>
## [1,] No. cases 1320 23 260 183 1786
## [2,] Percent 73.9 1.3 14.6 10.2 100
During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?
“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v2_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.
v2_sf12_recode(v2_con$v2_sf12_sf6,"v2_sf12_itm6")
## -999 1 2 <NA>
## [1,] No. cases 1320 25 260 181 1786
## [2,] Percent 73.9 1.4 14.6 10.1 100
“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v2_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.
v2_sf12_recode(v2_con$v2_sf12_sf7,"v2_sf12_itm7")
## -999 1 2 <NA>
## [1,] No. cases 1320 17 268 181 1786
## [2,] Percent 73.9 1 15 10.1 100
“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v2_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.
v2_sf12_recode(v2_con$v2_sf12_st8,"v2_sf12_itm8")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 149 61 35 27 9 2 183 1786
## [2,] Percent 73.9 8.3 3.4 2 1.5 0.5 0.1 10.2 100
The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS
Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.
“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v2_sf12_itm9)
v2_sf12_recode(v2_con$v2_sf12_st9,"v2_sf12_itm9")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 18 186 55 17 8 1 181 1786
## [2,] Percent 73.9 1 10.4 3.1 1 0.4 0.1 10.1 100
“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v2_sf12_itm10)
v2_sf12_recode(v2_con$v2_sf12_st10,"v2_sf12_itm10")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 13 115 88 55 13 1 181 1786
## [2,] Percent 73.9 0.7 6.4 4.9 3.1 0.7 0.1 10.1 100
“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v2_sf12_itm11)
v2_sf12_recode(v2_con$v2_sf12_st11,"v2_sf12_itm11")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 1 4 9 32 134 104 182 1786
## [2,] Percent 73.9 0.1 0.2 0.5 1.8 7.5 5.8 10.2 100
“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v2_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.
There is an error in the phenotype database regarding this item. The answering alternatives 3, 4, and 5 appear as 4, 5, and 6 in the database exports. These errors are corrected below.
v2_sf12_recode(v2_con$v2_sf12_st12,"v2_sf12_itm12")
## -999 2 4 5 6 <NA>
## [1,] No. cases 1320 3 19 65 198 181 1786
## [2,] Percent 73.9 0.2 1.1 3.6 11.1 10.1 100
#recode error in phenotype database
v2_sf12_itm12[v2_sf12_itm12==4]<-3
v2_sf12_itm12[v2_sf12_itm12==5]<-4
v2_sf12_itm12[v2_sf12_itm12==6]<-5
descT(v2_sf12_itm12)
## -999 2 3 4 5 <NA>
## [1,] No. cases 1320 3 19 65 198 181 1786
## [2,] Percent 73.9 0.2 1.1 3.6 11.1 10.1 100
Create dataset
v2_sf12<-data.frame(v2_sf12_itm0,
v2_sf12_itm1,
v2_sf12_itm2,
v2_sf12_itm3,
v2_sf12_itm4,
v2_sf12_itm5,
v2_sf12_itm6,
v2_sf12_itm7,
v2_sf12_itm8,
v2_sf12_itm9,
v2_sf12_itm10,
v2_sf12_itm11,
v2_sf12_itm12)
For a description of the questionnaire, see Visit 1.
Past seven days (ordinal [1,2,3,4,5,6], v2_med_pst_wk)
v2_med_chk<-c(v2_clin$v2_compl_verwer_fragebogen,rep(1,dim(v2_con)[1]))
v2_med_pst_wk_pre<-c(v2_clin$v2_compl_psychopharm_7_tag,rep(-999,dim(v2_con)[1]))
v2_med_pst_wk<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_med_pst_wk<-ifelse((is.na(v2_med_chk) | v2_med_chk!=2),
v2_med_pst_wk_pre, v2_med_pst_wk)
descT(v2_med_pst_wk)
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 466 615 75 28 3 1 18 580 1786
## [2,] Percent 26.1 34.4 4.2 1.6 0.2 0.1 1 32.5 100
Past six months (ordinal [1,2,3,4,5,6], v2_med_pst_sx_mths)
v2_med_pre<-c(v2_clin$v2_compl_psychopharm_6_mon,rep(-999,dim(v2_con)[1]))
v2_med_pst_sx_mths<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_med_pst_sx_mths<-ifelse((is.na(v2_med_chk) | v2_med_chk!=2),
v2_med_pre, v2_med_pst_sx_mths)
descT(v2_med_pst_sx_mths)
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 466 547 109 63 8 5 10 578 1786
## [2,] Percent 26.1 30.6 6.1 3.5 0.4 0.3 0.6 32.4 100
Create dataset
v2_med_adh<-data.frame(v2_med_pst_wk,v2_med_pst_sx_mths)
For explanation, please refer to the section in Visit 1
1. Sadness (ordinal [0,1,2,3], v2_bdi2_itm1)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi1_traurigkeit,v2_con$v2_bdi2_s1_bdi1,"v2_bdi2_itm1")
## 0 1 2 3 <NA>
## [1,] No. cases 726 260 22 23 755 1786
## [2,] Percent 40.6 14.6 1.2 1.3 42.3 100
2. Pessimism (ordinal [0,1,2,3], v2_bdi2_itm2)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi2_pessimismus,v2_con$v2_bdi2_s1_bdi2,"v2_bdi2_itm2")
## 0 1 2 3 <NA>
## [1,] No. cases 789 135 92 14 756 1786
## [2,] Percent 44.2 7.6 5.2 0.8 42.3 100
3. Past failure (ordinal [0,1,2,3], v2_bdi2_itm3)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi3_versagensgef,v2_con$v2_bdi2_s1_bdi3,"v2_bdi2_itm3")
## 0 1 2 3 <NA>
## [1,] No. cases 690 177 142 20 757 1786
## [2,] Percent 38.6 9.9 8 1.1 42.4 100
4. Loss of pleasure (ordinal [0,1,2,3], v2_bdi2_itm4)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi4_verlust_freude,v2_con$v2_bdi2_s1_bdi4,"v2_bdi2_itm4")
## 0 1 2 3 <NA>
## [1,] No. cases 643 286 72 24 761 1786
## [2,] Percent 36 16 4 1.3 42.6 100
5. Guilty feelings (ordinal [0,1,2,3], v2_bdi2_itm5)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi5_schuldgef,v2_con$v2_bdi2_s1_bdi5,"v2_bdi2_itm5")
## 0 1 2 3 <NA>
## [1,] No. cases 736 251 21 20 758 1786
## [2,] Percent 41.2 14.1 1.2 1.1 42.4 100
6. Punishment feelings (ordinal [0,1,2,3], v2_bdi2_itm6)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi6_bestrafungsgef,v2_con$v2_bdi2_s1_bdi6,"v2_bdi2_itm6")
## 0 1 2 3 <NA>
## [1,] No. cases 857 111 14 48 756 1786
## [2,] Percent 48 6.2 0.8 2.7 42.3 100
7. Self-dislike (ordinal [0,1,2,3], v2_bdi2_itm7)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi7_selbstablehnung,v2_con$v2_bdi2_s1_bdi7,"v2_bdi2_itm7")
## 0 1 2 3 <NA>
## [1,] No. cases 771 148 90 17 760 1786
## [2,] Percent 43.2 8.3 5 1 42.6 100
8. Self-criticalness (ordinal [0,1,2,3], v2_bdi2_itm8)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi8_selbstvorwuerfe,v2_con$v2_bdi2_s1_bdi8,"v2_bdi2_itm8")
## 0 1 2 3 <NA>
## [1,] No. cases 672 253 80 23 758 1786
## [2,] Percent 37.6 14.2 4.5 1.3 42.4 100
9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v2_bdi2_itm9)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi9_selbstmordged,v2_con$v2_bdi2_s1_bdi9,"v2_bdi2_itm9")
## 0 1 2 3 <NA>
## [1,] No. cases 842 175 12 1 756 1786
## [2,] Percent 47.1 9.8 0.7 0.1 42.3 100
10. Crying (ordinal [0,1,2,3], v2_bdi2_itm10)
v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi10_weinen,v2_con$v2_bdi2_s1_bdi10,"v2_bdi2_itm10")
## 0 1 2 3 <NA>
## [1,] No. cases 840 90 22 78 756 1786
## [2,] Percent 47 5 1.2 4.4 42.3 100
11. Agitation (ordinal [0,1,2,3], v2_bdi2_itm11)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi11_unruhe,v2_con$v2_bdi2_s2_bdi11,"v2_bdi2_itm11")
## 0 1 2 3 <NA>
## [1,] No. cases 729 247 28 16 766 1786
## [2,] Percent 40.8 13.8 1.6 0.9 42.9 100
12. Loss of interest (ordinal [0,1,2,3], v2_bdi2_itm12)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi12_interessverl,v2_con$v2_bdi2_s2_bdi12,"v2_bdi2_itm12")
## 0 1 2 3 <NA>
## [1,] No. cases 717 223 50 29 767 1786
## [2,] Percent 40.1 12.5 2.8 1.6 42.9 100
13. Indecisiveness (ordinal [0,1,2,3], v2_bdi2_itm13)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi13_entschlussunf,v2_con$v2_bdi2_s2_bdi13,"v2_bdi2_itm13")
## 0 1 2 3 <NA>
## [1,] No. cases 695 231 55 38 767 1786
## [2,] Percent 38.9 12.9 3.1 2.1 42.9 100
14. Worthlessness (ordinal [0,1,2,3], v2_bdi2_itm14)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi14_wertlosigkeit,v2_con$v2_bdi2_s2_bdi14,"v2_bdi2_itm14")
## 0 1 2 3 <NA>
## [1,] No. cases 796 134 66 24 766 1786
## [2,] Percent 44.6 7.5 3.7 1.3 42.9 100
15. Loss of energy (ordinal [0,1,2,3], v2_bdi2_itm15)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi15_energieverlust,v2_con$v2_bdi2_s2_bdi15,"v2_bdi2_itm15")
## 0 1 2 3 <NA>
## [1,] No. cases 574 335 96 16 765 1786
## [2,] Percent 32.1 18.8 5.4 0.9 42.8 100
16. Changes in sleeping pattern (ordinal [0,1,2,3], v2_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep". There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.
v2_itm_bdi2_chk<-c(v2_clin$v2_bdi2_s1_verwer_fragebogen,v2_con$v2_bdi2_s1_bdi_korrekt)
v2_itm_bdi2_itm16_clin_con<-c(v2_clin$v2_bdi2_s2_bdi16_schlafgewohn,v2_con$v2_bdi2_s2_bdi16)
v2_bdi2_itm16<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_bdi2_itm16<-ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & v2_itm_bdi2_itm16_clin_con==0, 0,
ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) &
(v2_itm_bdi2_itm16_clin_con==1 | v2_itm_bdi2_itm16_clin_con==100), 1,
ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) &
(v2_itm_bdi2_itm16_clin_con==2 | v2_itm_bdi2_itm16_clin_con==200), 2,
ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) &
(v2_itm_bdi2_itm16_clin_con==3 | v2_itm_bdi2_itm16_clin_con==300), 3, v2_bdi2_itm16))))
v2_bdi2_itm16<-factor(v2_bdi2_itm16,ordered=T)
descT(v2_bdi2_itm16)
## 0 1 2 3 <NA>
## [1,] No. cases 538 357 81 43 767 1786
## [2,] Percent 30.1 20 4.5 2.4 42.9 100
17. Irritability (ordinal [0,1,2,3], v2_bdi2_itm17)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi17_reizbarkeit,v2_con$v2_bdi2_s2_bdi17,"v2_bdi2_itm17")
## 0 1 2 3 <NA>
## [1,] No. cases 802 171 36 11 766 1786
## [2,] Percent 44.9 9.6 2 0.6 42.9 100
18. Change in appetite (ordinal [0,1,2,3], v2_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.
v2_itm_bdi2_itm18_clin_con<-c(v2_clin$v2_bdi2_s2_bdi18_appetit,v2_con$v2_bdi2_s2_bdi18)
v2_bdi2_itm18<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_bdi2_itm18<-ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & v2_itm_bdi2_itm18_clin_con==0, 0,
ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) &
(v2_itm_bdi2_itm18_clin_con==1 | v2_itm_bdi2_itm18_clin_con==100), 1,
ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) &
(v2_itm_bdi2_itm18_clin_con==2 | v2_itm_bdi2_itm18_clin_con==200), 2,
ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) &
(v2_itm_bdi2_itm18_clin_con==3 | v2_itm_bdi2_itm18_clin_con==300), 3, v2_bdi2_itm18))))
v2_bdi2_itm18<-factor(v2_bdi2_itm18,ordered=T)
descT(v2_bdi2_itm18)
## 0 1 2 3 <NA>
## [1,] No. cases 680 265 50 25 766 1786
## [2,] Percent 38.1 14.8 2.8 1.4 42.9 100
19. Concentration difficulty (ordinal [0,1,2,3], v2_bdi2_itm19)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi19_konzschw,v2_con$v2_bdi2_s2_bdi19,"v2_bdi2_itm19")
## 0 1 2 3 <NA>
## [1,] No. cases 602 274 133 9 768 1786
## [2,] Percent 33.7 15.3 7.4 0.5 43 100
20. Tiredness or fatigue (ordinal [0,1,2,3], v2_bdi2_itm20)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi20_ermued_ersch,v2_con$v2_bdi2_s2_bdi20,"v2_bdi2_itm20")
## 0 1 2 3 <NA>
## [1,] No. cases 597 320 83 20 766 1786
## [2,] Percent 33.4 17.9 4.6 1.1 42.9 100
21. Loss of interest in sex (ordinal [0,1,2,3], v2_bdi2_itm21)
v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi21_sex_interess,v2_con$v2_bdi2_s2_bdi21,"v2_bdi2_itm21")
## 0 1 2 3 <NA>
## [1,] No. cases 705 180 46 84 771 1786
## [2,] Percent 39.5 10.1 2.6 4.7 43.2 100
BDI-II sum score calculation (continuous [0-63], v2_bdi2_sum)
v2_bdi2_sum<-as.numeric.factor(v2_bdi2_itm1)+
as.numeric.factor(v2_bdi2_itm2)+
as.numeric.factor(v2_bdi2_itm3)+
as.numeric.factor(v2_bdi2_itm4)+
as.numeric.factor(v2_bdi2_itm5)+
as.numeric.factor(v2_bdi2_itm6)+
as.numeric.factor(v2_bdi2_itm7)+
as.numeric.factor(v2_bdi2_itm8)+
as.numeric.factor(v2_bdi2_itm9)+
as.numeric.factor(v2_bdi2_itm10)+
as.numeric.factor(v2_bdi2_itm11)+
as.numeric.factor(v2_bdi2_itm12)+
as.numeric.factor(v2_bdi2_itm13)+
as.numeric.factor(v2_bdi2_itm14)+
as.numeric.factor(v2_bdi2_itm15)+
as.numeric.factor(v2_bdi2_itm16)+
as.numeric.factor(v2_bdi2_itm17)+
as.numeric.factor(v2_bdi2_itm18)+
as.numeric.factor(v2_bdi2_itm19)+
as.numeric.factor(v2_bdi2_itm20)+
as.numeric.factor(v2_bdi2_itm21)
summary(v2_bdi2_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 5.000 8.677 13.000 54.000 803
Create dataset
v2_bdi2<-data.frame(v2_bdi2_itm1,v2_bdi2_itm2,v2_bdi2_itm3,v2_bdi2_itm4,v2_bdi2_itm5,
v2_bdi2_itm6,v2_bdi2_itm7,v2_bdi2_itm8,v2_bdi2_itm9,v2_bdi2_itm10,
v2_bdi2_itm11,v2_bdi2_itm12,v2_bdi2_itm13,v2_bdi2_itm14,
v2_bdi2_itm15,v2_bdi2_itm16,v2_bdi2_itm17,v2_bdi2_itm18,
v2_bdi2_itm19,v2_bdi2_itm20,v2_bdi2_itm21, v2_bdi2_sum)
For explanation, please refer to the section in Visit 1
1. Positive Mood (ordinal [0,1,2,3,4], v2_asrm_itm1)
v2_asrm_recode(v2_clin$v2_asrm_asrm1_gluecklich,v2_con$v2_asrm_asrm1,"v2_asrm_itm1")
## 0 1 2 3 4 <NA>
## [1,] No. cases 722 228 45 23 10 758 1786
## [2,] Percent 40.4 12.8 2.5 1.3 0.6 42.4 100
2 Self-Confidence (ordinal [0,1,2,3,4], v2_asrm_itm2)
v2_asrm_recode(v2_clin$v2_asrm_asrm2_selbstbewusst,v2_con$v2_asrm_asrm2,"v2_asrm_itm2")
## 0 1 2 3 4 <NA>
## [1,] No. cases 763 204 41 14 8 756 1786
## [2,] Percent 42.7 11.4 2.3 0.8 0.4 42.3 100
3. Sleep (ordinal [0,1,2,3,4], v2_asrm_itm3)
v2_asrm_recode(v2_clin$v2_asrm_asrm3_schlaf,v2_con$v2_asrm_asrm3,"v2_asrm_itm3")
## 0 1 2 3 4 <NA>
## [1,] No. cases 864 118 28 10 10 756 1786
## [2,] Percent 48.4 6.6 1.6 0.6 0.6 42.3 100
4. Speech (ordinal [0,1,2,3,4], v2_asrm_itm4)
v2_asrm_recode(v2_clin$v2_asrm_asrm4_reden,v2_con$v2_asrm_asrm4,"v2_asrm_itm4")
## 0 1 2 3 4 <NA>
## [1,] No. cases 798 194 24 11 2 757 1786
## [2,] Percent 44.7 10.9 1.3 0.6 0.1 42.4 100
5. Activity Level (ordinal [0,1,2,3,4], v2_asrm_itm5)
v2_asrm_recode(v2_clin$v2_asrm_asrm5_aktiv,v2_con$v2_asrm_asrm5,"v2_asrm_itm5")
## 0 1 2 3 4 <NA>
## [1,] No. cases 752 209 44 14 10 757 1786
## [2,] Percent 42.1 11.7 2.5 0.8 0.6 42.4 100
Create ASRM sum score (continuous [0-20],v2_asrm_sum)
v2_asrm_sum<-as.numeric.factor(v2_asrm_itm1)+
as.numeric.factor(v2_asrm_itm2)+
as.numeric.factor(v2_asrm_itm3)+
as.numeric.factor(v2_asrm_itm4)+
as.numeric.factor(v2_asrm_itm5)
summary(v2_asrm_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 1.000 1.637 3.000 16.000 760
Create dataset
v2_asrm<-data.frame(v2_asrm_itm1,v2_asrm_itm2,v2_asrm_itm3,v2_asrm_itm4,v2_asrm_itm5,v2_asrm_sum)
For explanation, please refer to the section in Visit 1
1. “I had more energy” (dichotomous, v2_mss_itm1)
v2_mss_recode(v2_clin$v2_mss_s1_mss1_energie,v2_con$v2_mss_s1_mss1,"v2_mss_itm1")
## N Y <NA>
## [1,] No. cases 811 206 769 1786
## [2,] Percent 45.4 11.5 43.1 100
2. “I had trouble sitting still” (dichotomous, v2_mss_itm2)
v2_mss_recode(v2_clin$v2_mss_s1_mss2_ruhig_sitzen,v2_con$v2_mss_s1_mss2,"v2_mss_itm2")
## N Y <NA>
## [1,] No. cases 871 142 773 1786
## [2,] Percent 48.8 8 43.3 100
3. “I drove faster” (dichotomous, v2_mss_itm3)
v2_mss_recode(v2_clin$v2_mss_s1_mss3_auto_fahren,v2_con$v2_mss_s1_mss3,"v2_mss_itm3")
## N Y <NA>
## [1,] No. cases 952 36 798 1786
## [2,] Percent 53.3 2 44.7 100
4. “I drank more alcoholic beverages” (dichotomous, v2_mss_itm4)
v2_mss_recode(v2_clin$v2_mss_s1_mss4_alkohol,v2_con$v2_mss_s1_mss4,"v2_mss_itm4")
## N Y <NA>
## [1,] No. cases 940 74 772 1786
## [2,] Percent 52.6 4.1 43.2 100
5. “I changed clothes several times a day” (dichotomous, v2_mss_itm5)
v2_mss_recode(v2_clin$v2_mss_s1_mss5_umziehen, v2_con$v2_mss_s1_mss5,"v2_mss_itm5")
## N Y <NA>
## [1,] No. cases 949 65 772 1786
## [2,] Percent 53.1 3.6 43.2 100
6. “I wore brighter clothes/make-up” (dichotomous, v2_mss_itm6)
v2_mss_recode(v2_clin$v2_mss_s1_mss6_bunter,v2_con$v2_mss_s1_mss6,"v2_mss_itm6")
## N Y <NA>
## [1,] No. cases 960 53 773 1786
## [2,] Percent 53.8 3 43.3 100
7. “I played music louder” (dichotomous, v2_mss_itm7)
v2_mss_recode(v2_clin$v2_mss_s1_mss7_musik_lauter,v2_con$v2_mss_s1_mss7,"v2_mss_itm7")
## N Y <NA>
## [1,] No. cases 885 132 769 1786
## [2,] Percent 49.6 7.4 43.1 100
8. “I ate faster than usual” (dichotomous, v2_mss_itm8)
v2_mss_recode(v2_clin$v2_mss_s1_mss8_hastiger_essen,v2_con$v2_mss_s1_mss8,"v2_mss_itm8")
## N Y <NA>
## [1,] No. cases 892 122 772 1786
## [2,] Percent 49.9 6.8 43.2 100
9. “I ate more than usual” (dichotomous, v2_mss_itm9)
v2_mss_recode(v2_clin$v2_mss_s1_mss9_mehr_essen,v2_con$v2_mss_s1_mss9,"v2_mss_itm9")
## N Y <NA>
## [1,] No. cases 790 224 772 1786
## [2,] Percent 44.2 12.5 43.2 100
10. “I slept fewer hours than usual” (dichotomous, v2_mss_itm10)
v2_mss_recode(v2_clin$v2_mss_s1_mss10_weniger_schlaf,v2_con$v2_mss_s1_mss10,"v2_mss_itm10")
## N Y <NA>
## [1,] No. cases 909 100 777 1786
## [2,] Percent 50.9 5.6 43.5 100
11. “I started things that I didn’t finish” (dichotomous, v2_mss_itm11)
v2_mss_recode(v2_clin$v2_mss_s1_mss11_unbeendet,v2_con$v2_mss_s1_mss11,"v2_mss_itm11")
## N Y <NA>
## [1,] No. cases 810 206 770 1786
## [2,] Percent 45.4 11.5 43.1 100
12. “I gave away my own possessions” (dichotomous, v2_mss_itm12)
v2_mss_recode(v2_clin$v2_mss_s1_mss12_weggeben,v2_con$v2_mss_s1_mss12,"v2_mss_itm12")
## N Y <NA>
## [1,] No. cases 916 98 772 1786
## [2,] Percent 51.3 5.5 43.2 100
13. “I bought gifts for people” (dichotomous, v2_mss_itm13)
v2_mss_recode(v2_clin$v2_mss_s1_mss13_geschenke,v2_con$v2_mss_s1_mss13,"v2_mss_itm13")
## N Y <NA>
## [1,] No. cases 921 93 772 1786
## [2,] Percent 51.6 5.2 43.2 100
14. “I spent money more freely” (dichotomous, v2_mss_itm14)
v2_mss_recode(v2_clin$v2_mss_s1_mss14_mehr_geld,v2_con$v2_mss_s1_mss14,"v2_mss_itm14")
## N Y <NA>
## [1,] No. cases 794 222 770 1786
## [2,] Percent 44.5 12.4 43.1 100
15. “I accumulated debts” (dichotomous, v2_mss_itm15)
v2_mss_recode(v2_clin$v2_mss_s1_mss15_schulden,v2_con$v2_mss_s1_mss15,"v2_mss_itm15")
## N Y <NA>
## [1,] No. cases 964 52 770 1786
## [2,] Percent 54 2.9 43.1 100
16. “I made unwise business decisions” (dichotomous, v2_mss_itm16)
v2_mss_recode(v2_clin$v2_mss_s1_mss16_unkluge_entsch,v2_con$v2_mss_s1_mss16,"v2_mss_itm16")
## N Y <NA>
## [1,] No. cases 974 37 775 1786
## [2,] Percent 54.5 2.1 43.4 100
17. “I partied more” (dichotomous, v2_mss_itm17)
v2_mss_recode(v2_clin$v2_mss_s1_mss17_parties,v2_con$v2_mss_s1_mss17,"v2_mss_itm17")
## N Y <NA>
## [1,] No. cases 953 60 773 1786
## [2,] Percent 53.4 3.4 43.3 100
18. “I enjoyed flirting” (dichotomous, v2_mss_itm18)
v2_mss_recode(v2_clin$v2_mss_s1_mss18_flirten,v2_con$v2_mss_s1_mss18,"v2_mss_itm18")
## N Y <NA>
## [1,] No. cases 935 79 772 1786
## [2,] Percent 52.4 4.4 43.2 100
19. “I masturbated more often” (dichotomous, v2_mss_itm19)
v2_mss_recode(v2_clin$v2_mss_s2_mss19_selbstbefried,v2_con$v2_mss_s2_mss19,"v2_mss_itm19")
## N Y <NA>
## [1,] No. cases 963 42 781 1786
## [2,] Percent 53.9 2.4 43.7 100
20. “I was more interested in sex than usual” (dichotomous, v2_mss_itm20)
v2_mss_recode(v2_clin$v2_mss_s2_mss20_sex_interess,v2_con$v2_mss_s2_mss20,"v2_mss_itm20")
## N Y <NA>
## [1,] No. cases 917 81 788 1786
## [2,] Percent 51.3 4.5 44.1 100
21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v2_mss_itm21)
v2_mss_recode(v2_clin$v2_mss_s2_mss21_sexpartner,v2_con$v2_mss_s2_mss21,"v2_mss_itm21")
## N Y <NA>
## [1,] No. cases 990 15 781 1786
## [2,] Percent 55.4 0.8 43.7 100
22. “I spent more time on the phone” (dichotomous, v2_mss_itm22)
v2_mss_recode(v2_clin$v2_mss_s2_mss22_mehr_telefon,v2_con$v2_mss_s2_mss22,"v2_mss_itm22")
## N Y <NA>
## [1,] No. cases 890 117 779 1786
## [2,] Percent 49.8 6.6 43.6 100
23. “I spoke louder than usual” (dichotomous, v2_mss_itm23)
v2_mss_recode(v2_clin$v2_mss_s2_mss23_sprache_lauter,v2_con$v2_mss_s2_mss23,"v2_mss_itm23")
## N Y <NA>
## [1,] No. cases 921 80 785 1786
## [2,] Percent 51.6 4.5 44 100
24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v2_mss_itm24)
v2_mss_recode(v2_clin$v2_mss_s2_mss24_spr_schneller,v2_con$v2_mss_s2_mss24,"v2_mss_itm24")
## N Y <NA>
## [1,] No. cases 946 61 779 1786
## [2,] Percent 53 3.4 43.6 100
25. “1 enjoyed punning or rhyming” (dichotomous, v2_mss_itm25)
v2_mss_recode(v2_clin$v2_mss_s2_mss25_witze,v2_con$v2_mss_s2_mss25,"v2_mss_itm25")
## N Y <NA>
## [1,] No. cases 929 78 779 1786
## [2,] Percent 52 4.4 43.6 100
26. “I butted into conversations” (dichotomous, v2_mss_itm26)
v2_mss_recode(v2_clin$v2_mss_s2_mss26_einmischen,v2_con$v2_mss_s2_mss26,"v2_mss_itm26")
## N Y <NA>
## [1,] No. cases 944 65 777 1786
## [2,] Percent 52.9 3.6 43.5 100
27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v2_mss_itm27)
v2_mss_recode(v2_clin$v2_mss_s2_mss27_red_pausenlos,v2_con$v2_mss_s2_mss27,"v2_mss_itm27")
## N Y <NA>
## [1,] No. cases 981 26 779 1786
## [2,] Percent 54.9 1.5 43.6 100
28. “I enjoyed being the centre of attention” (dichotomous, v2_mss_itm28)
v2_mss_recode(v2_clin$v2_mss_s2_mss28_mittelpunkt,v2_con$v2_mss_s2_mss28,"v2_mss_itm28")
## N Y <NA>
## [1,] No. cases 956 52 778 1786
## [2,] Percent 53.5 2.9 43.6 100
29. “I liked to joke and laugh” (dichotomous, v2_mss_itm29)
v2_mss_recode(v2_clin$v2_mss_s2_mss29_herumalbern,v2_con$v2_mss_s2_mss29,"v2_mss_itm29")
## N Y <NA>
## [1,] No. cases 869 136 781 1786
## [2,] Percent 48.7 7.6 43.7 100
30. “People found me entertaining” (dichotomous, v2_mss_itm30)
v2_mss_recode(v2_clin$v2_mss_s2_mss30_unterhaltsamer,v2_con$v2_mss_s2_mss30,"v2_mss_itm30")
## N Y <NA>
## [1,] No. cases 928 77 781 1786
## [2,] Percent 52 4.3 43.7 100
31. “I felt as if I was on top of the world” (dichotomous, v2_mss_itm31)
v2_mss_recode(v2_clin$v2_mss_s2_mss31_obenauf,v2_con$v2_mss_s2_mss31,"v2_mss_itm31")
## N Y <NA>
## [1,] No. cases 921 85 780 1786
## [2,] Percent 51.6 4.8 43.7 100
32. “I was more cheerful than my usual self” (dichotomous, v2_mss_itm32)
v2_mss_recode(v2_clin$v2_mss_s2_mss32_froehlicher,v2_con$v2_mss_s2_mss32,"v2_mss_itm32")
## N Y <NA>
## [1,] No. cases 826 181 779 1786
## [2,] Percent 46.2 10.1 43.6 100
33. “Other people got on my nerves” (dichotomous, v2_mss_itm33)
v2_mss_recode(v2_clin$v2_mss_s2_mss33_ungeduldiger,v2_con$v2_mss_s2_mss33,"v2_mss_itm33")
## N Y <NA>
## [1,] No. cases 792 215 779 1786
## [2,] Percent 44.3 12 43.6 100
34. “I was getting into arguments” (dichotomous, v2_mss_itm34)
v2_mss_recode(v2_clin$v2_mss_s2_mss34_streiten,v2_con$v2_mss_s2_mss34,"v2_mss_itm34")
## N Y <NA>
## [1,] No. cases 932 72 782 1786
## [2,] Percent 52.2 4 43.8 100
35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v2_mss_itm35)
v2_mss_recode(v2_clin$v2_mss_s2_mss35_ideen,v2_con$v2_mss_s2_mss35,"v2_mss_itm35")
## N Y <NA>
## [1,] No. cases 829 178 779 1786
## [2,] Percent 46.4 10 43.6 100
36. “My thoughts raced through my mind” (dichotomous, v2_mss_itm36)
v2_mss_recode(v2_clin$v2_mss_s2_mss36_gedanken,v2_con$v2_mss_s2_mss36,"v2_mss_itm36")
## N Y <NA>
## [1,] No. cases 756 251 779 1786
## [2,] Percent 42.3 14.1 43.6 100
37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v2_mss_itm37)
v2_mss_recode(v2_clin$v2_mss_s2_mss37_konzentration,v2_con$v2_mss_s2_mss37,"v2_mss_itm37")
## N Y <NA>
## [1,] No. cases 866 141 779 1786
## [2,] Percent 48.5 7.9 43.6 100
38. “I thought I was an especially important person” (dichotomous, v2_mss_itm38)
v2_mss_recode(v2_clin$v2_mss_s2_mss38_etw_besonderes,v2_con$v2_mss_s2_mss38,"v2_mss_itm38")
## N Y <NA>
## [1,] No. cases 942 61 783 1786
## [2,] Percent 52.7 3.4 43.8 100
39. “I thought I could change the world” (dichotomous, v2_mss_itm39)
v2_mss_recode(v2_clin$v2_mss_s2_mss39_welt_veraender,v2_con$v2_mss_s2_mss39,"v2_mss_itm39")
## N Y <NA>
## [1,] No. cases 953 55 778 1786
## [2,] Percent 53.4 3.1 43.6 100
40. “I thought I was right most of the time” (dichotomous, v2_mss_itm40)
v2_mss_recode(v2_clin$v2_mss_s2_mss40_recht_haben,v2_con$v2_mss_s2_mss40,"v2_mss_itm40")
## N Y <NA>
## [1,] No. cases 965 44 777 1786
## [2,] Percent 54 2.5 43.5 100
41. “I thought I was superior to others” (dichotomous, v2_mss_itm41)
v2_mss_recode(v2_clin$v2_mss_s3_mss41_ueberlegen,v2_con$v2_mss_s3_mss41,"v2_mss_itm41")
## N Y <NA>
## [1,] No. cases 980 29 777 1786
## [2,] Percent 54.9 1.6 43.5 100
42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v2_mss_itm42)
v2_mss_recode(v2_clin$v2_mss_s3_mss42_uebermut,v2_con$v2_mss_s3_mss42,"v2_mss_itm42")
## N Y <NA>
## [1,] No. cases 940 70 776 1786
## [2,] Percent 52.6 3.9 43.4 100
43. “I thought I knew what other people were thinking” (dichotomous, v2_mss_itm43)
v2_mss_recode(v2_clin$v2_mss_s3_mss43_ged_lesen_akt,v2_con$v2_mss_s3_mss43,"v2_mss_itm43")
## N Y <NA>
## [1,] No. cases 920 89 777 1786
## [2,] Percent 51.5 5 43.5 100
44. “I thought other people knew what I was thinking” (dichotomous, v2_mss_itm44)
v2_mss_recode(v2_clin$v2_mss_s3_mss44_ged_lesen_pas,v2_con$v2_mss_s3_mss44,"v2_mss_itm44")
## N Y <NA>
## [1,] No. cases 950 59 777 1786
## [2,] Percent 53.2 3.3 43.5 100
45. “I thought someone wanted to harm me” (dichotomous, v2_mss_itm45)
v2_mss_recode(v2_clin$v2_mss_s3_mss45_etw_antun,v2_con$v2_mss_s3_mss45,"v2_mss_itm45")
## N Y <NA>
## [1,] No. cases 961 49 776 1786
## [2,] Percent 53.8 2.7 43.4 100
46. “I heard voices when people weren’t there” (dichotomous, v2_mss_itm46)
v2_mss_recode(v2_clin$v2_mss_s3_mss46_stimmen,v2_con$v2_mss_s3_mss46,"v2_mss_itm46")
## N Y <NA>
## [1,] No. cases 946 62 778 1786
## [2,] Percent 53 3.5 43.6 100
47. “I had false beliefs concerning who I was” (dichotomous, v2_mss_itm47)
v2_mss_recode(v2_clin$v2_mss_s3_mss47_jmd_anders,v2_con$v2_mss_s3_mss47,"v2_mss_itm47")
## N Y <NA>
## [1,] No. cases 985 25 776 1786
## [2,] Percent 55.2 1.4 43.4 100
48. “I knew I was getting ill” (dichotomous, v2_mss_itm48)
v2_mss_recode(v2_clin$v2_mss_s3_mss48_krank_einsicht,v2_con$v2_mss_s3_mss48,"v2_mss_itm48")
## N Y <NA>
## [1,] No. cases 884 115 787 1786
## [2,] Percent 49.5 6.4 44.1 100
Create MSS sum score (continuous [0-48],v2_mss_sum)
v2_mss_sum<-ifelse(v2_mss_itm1=="Y",1,0)+
ifelse(v2_mss_itm2=="Y",1,0)+
ifelse(v2_mss_itm3=="Y",1,0)+
ifelse(v2_mss_itm4=="Y",1,0)+
ifelse(v2_mss_itm5=="Y",1,0)+
ifelse(v2_mss_itm6=="Y",1,0)+
ifelse(v2_mss_itm7=="Y",1,0)+
ifelse(v2_mss_itm8=="Y",1,0)+
ifelse(v2_mss_itm9=="Y",1,0)+
ifelse(v2_mss_itm10=="Y",1,0)+
ifelse(v2_mss_itm11=="Y",1,0)+
ifelse(v2_mss_itm12=="Y",1,0)+
ifelse(v2_mss_itm13=="Y",1,0)+
ifelse(v2_mss_itm14=="Y",1,0)+
ifelse(v2_mss_itm15=="Y",1,0)+
ifelse(v2_mss_itm16=="Y",1,0)+
ifelse(v2_mss_itm17=="Y",1,0)+
ifelse(v2_mss_itm18=="Y",1,0)+
ifelse(v2_mss_itm19=="Y",1,0)+
ifelse(v2_mss_itm20=="Y",1,0)+
ifelse(v2_mss_itm21=="Y",1,0)+
ifelse(v2_mss_itm22=="Y",1,0)+
ifelse(v2_mss_itm23=="Y",1,0)+
ifelse(v2_mss_itm24=="Y",1,0)+
ifelse(v2_mss_itm25=="Y",1,0)+
ifelse(v2_mss_itm26=="Y",1,0)+
ifelse(v2_mss_itm27=="Y",1,0)+
ifelse(v2_mss_itm28=="Y",1,0)+
ifelse(v2_mss_itm29=="Y",1,0)+
ifelse(v2_mss_itm30=="Y",1,0)+
ifelse(v2_mss_itm31=="Y",1,0)+
ifelse(v2_mss_itm32=="Y",1,0)+
ifelse(v2_mss_itm33=="Y",1,0)+
ifelse(v2_mss_itm34=="Y",1,0)+
ifelse(v2_mss_itm35=="Y",1,0)+
ifelse(v2_mss_itm36=="Y",1,0)+
ifelse(v2_mss_itm37=="Y",1,0)+
ifelse(v2_mss_itm38=="Y",1,0)+
ifelse(v2_mss_itm39=="Y",1,0)+
ifelse(v2_mss_itm40=="Y",1,0)+
ifelse(v2_mss_itm41=="Y",1,0)+
ifelse(v2_mss_itm42=="Y",1,0)+
ifelse(v2_mss_itm43=="Y",1,0)+
ifelse(v2_mss_itm44=="Y",1,0)+
ifelse(v2_mss_itm45=="Y",1,0)+
ifelse(v2_mss_itm46=="Y",1,0)+
ifelse(v2_mss_itm47=="Y",1,0)+
ifelse(v2_mss_itm48=="Y",1,0)
summary(v2_mss_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 2.000 4.358 6.000 37.000 886
Create dataset
v2_mss<-data.frame(v2_mss_itm1,v2_mss_itm2,v2_mss_itm3,v2_mss_itm4,v2_mss_itm5,v2_mss_itm6,
v2_mss_itm7,v2_mss_itm8,v2_mss_itm9,v2_mss_itm10,v2_mss_itm11,
v2_mss_itm12,v2_mss_itm13,v2_mss_itm14,v2_mss_itm15,v2_mss_itm16,
v2_mss_itm17,v2_mss_itm18,v2_mss_itm19,v2_mss_itm20,v2_mss_itm21,
v2_mss_itm22,v2_mss_itm23,v2_mss_itm24,v2_mss_itm25,v2_mss_itm26,
v2_mss_itm27,v2_mss_itm28,v2_mss_itm29,v2_mss_itm30,v2_mss_itm31,
v2_mss_itm32,v2_mss_itm33,v2_mss_itm34,v2_mss_itm35,v2_mss_itm36,
v2_mss_itm37,v2_mss_itm38,v2_mss_itm39,v2_mss_itm40,v2_mss_itm41,
v2_mss_itm42,v2_mss_itm43,v2_mss_itm44,v2_mss_itm45,v2_mss_itm46,
v2_mss_itm47,v2_mss_itm48,v2_mss_sum)
For explanation, please refer to the section in Visit 1
1. “Major personal illness or injury”
1A Nature (dichotomous [“good”,“bad”], v2_leq_A_1A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq1a_schw_krankh,v2_con$v2_leq_a_leq1a,"v2_leq_A_1A")
## -999 bad good <NA>
## [1,] No. cases 684 253 41 808 1786
## [2,] Percent 38.3 14.2 2.3 45.2 100
1B Impact (ordinal [0,1,2,3], v2_leq_A_1B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq1e_schw_krankh,v2_con$v2_leq_a_leq1e,"v2_leq_A_1B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 682 15 39 77 165 808 1786
## [2,] Percent 38.2 0.8 2.2 4.3 9.2 45.2 100
2. “Major change in eating habits”
2A Nature (dichotomous [“good”,“bad”], v2_leq_A_2A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq2a_ernaehrung,v2_con$v2_leq_a_leq2a,"v2_leq_A_2A")
## -999 bad good <NA>
## [1,] No. cases 689 145 144 808 1786
## [2,] Percent 38.6 8.1 8.1 45.2 100
2B Impact (ordinal [0,1,2,3], v2_leq_A_2B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq2e_ernaehrung,v2_con$v2_leq_a_leq2e,"v2_leq_A_2B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 683 17 68 119 91 808 1786
## [2,] Percent 38.2 1 3.8 6.7 5.1 45.2 100
3. “Major change in sleeping habits”
3A Nature (dichotomous [“good”,“bad”], v2_leq_A_3A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq3a_schlaf,v2_con$v2_leq_a_leq3a,"v2_leq_A_3A")
## -999 bad good <NA>
## [1,] No. cases 694 185 99 808 1786
## [2,] Percent 38.9 10.4 5.5 45.2 100
3B Impact (ordinal [0,1,2,3], v2_leq_A_3B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq3e_schlaf,v2_con$v2_leq_a_leq3e,"v2_leq_A_3B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 690 15 61 112 100 808 1786
## [2,] Percent 38.6 0.8 3.4 6.3 5.6 45.2 100
4. “Major change in usual type and/or amount of recreation”
4A Nature (dichotomous [“good”,“bad”], v2_leq_A_4A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq4a_freizeit,v2_con$v2_leq_a_leq4a,"v2_leq_A_4A")
## -999 bad good <NA>
## [1,] No. cases 631 133 214 808 1786
## [2,] Percent 35.3 7.4 12 45.2 100
4B Impact (ordinal [0,1,2,3], v2_leq_A_4B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq4e_freizeit,v2_con$v2_leq_a_leq4e,"v2_leq_A_4B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 626 19 72 141 120 808 1786
## [2,] Percent 35.1 1.1 4 7.9 6.7 45.2 100
5. “Major dental work”
5A Nature (dichotomous [“good”,“bad”], v2_leq_A_5A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq5a_zahnarzt,v2_con$v2_leq_a_leq5a,"v2_leq_A_5A")
## -999 bad good <NA>
## [1,] No. cases 835 55 88 808 1786
## [2,] Percent 46.8 3.1 4.9 45.2 100
5B Impact (ordinal [0,1,2,3], v2_leq_A_5B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq5e_zahnarzt,v2_con$v2_leq_a_leq5e,"v2_leq_A_5B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 832 30 44 35 37 808 1786
## [2,] Percent 46.6 1.7 2.5 2 2.1 45.2 100
6. “(Female) Pregnancy”
6A Nature (dichotomous [“good”,“bad”], v2_leq_A_6A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq6a_schwanger,v2_con$v2_leq_a_leq6a,"v2_leq_A_6A")
## -999 bad good <NA>
## [1,] No. cases 969 1 8 808 1786
## [2,] Percent 54.3 0.1 0.4 45.2 100
6B Impact (ordinal [0,1,2,3], v2_leq_A_6B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq6e_schwanger,v2_con$v2_leq_a_leq6e,"v2_leq_A_6B")
## -999 0 2 3 <NA>
## [1,] No. cases 969 1 2 6 808 1786
## [2,] Percent 54.3 0.1 0.1 0.3 45.2 100
7. “(Female) Miscarriage or abortion”
7A Nature (dichotomous [“good”,“bad”], v2_leq_A_7A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq7a_fehlg_abtr,v2_con$v2_leq_a_leq7a,"v2_leq_A_7A")
## -999 bad <NA>
## [1,] No. cases 974 4 808 1786
## [2,] Percent 54.5 0.2 45.2 100
7B Impact (ordinal [0,1,2,3], v2_leq_A_7B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq7e_fehlg_abtr,v2_con$v2_leq_a_leq7e,"v2_leq_A_7B")
## -999 0 1 3 <NA>
## [1,] No. cases 973 1 1 3 808 1786
## [2,] Percent 54.5 0.1 0.1 0.2 45.2 100
8. “(Female) Started menopause”
8A Nature (dichotomous [“good”,“bad”], v2_leq_A_8A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq8a_wechseljahre,v2_con$v2_leq_a_leq8a,"v2_leq_A_8A")
## -999 bad good <NA>
## [1,] No. cases 947 26 5 808 1786
## [2,] Percent 53 1.5 0.3 45.2 100
8B Impact (ordinal [0,1,2,3], v2_leq_A_8B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq8e_wechseljahre,v2_con$v2_leq_a_leq8e,"v2_leq_A_8B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 945 1 8 17 7 808 1786
## [2,] Percent 52.9 0.1 0.4 1 0.4 45.2 100
9. “Major difficulties with birth control pills or devices”
9A Nature (dichotomous [“good”,“bad”], v2_leq_A_9A)
v2_leq_a_recode(v2_clin$v2_leq_a_leq9a_verhuetung,v2_con$v2_leq_a_leq9a,"v2_leq_A_9A")
## -999 bad good <NA>
## [1,] No. cases 961 14 3 808 1786
## [2,] Percent 53.8 0.8 0.2 45.2 100
9B Impact (ordinal [0,1,2,3], v2_leq_A_9B)
v2_leq_b_recode(v2_clin$v2_leq_a_leq9e_verhuetung,v2_con$v2_leq_a_leq9e,"v2_leq_A_9B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 960 5 5 1 7 808 1786
## [2,] Percent 53.8 0.3 0.3 0.1 0.4 45.2 100
Create dataset
v2_leq_A<-data.frame(v2_leq_A_1A,v2_leq_A_1B,v2_leq_A_2A,v2_leq_A_2B,v2_leq_A_3A,
v2_leq_A_3B,v2_leq_A_4A,v2_leq_A_4B,v2_leq_A_5A,v2_leq_A_5B,
v2_leq_A_6A,v2_leq_A_6B,v2_leq_A_7A,v2_leq_A_7B,v2_leq_A_8A,
v2_leq_A_8B,v2_leq_A_9A,v2_leq_A_9B)
10. “Difficulty finding a job”
10A Nature (dichotomous [“good”,“bad”], v2_leq_B_10A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq10a_arbeitssuche,v2_con$v2_leq_b_leq10a,"v2_leq_B_10A")
## -999 bad good <NA>
## [1,] No. cases 826 122 30 808 1786
## [2,] Percent 46.2 6.8 1.7 45.2 100
10B Impact (ordinal [0,1,2,3], v2_leq_B_10B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq10e_arbeitssuche,v2_con$v2_leq_b_leq10e,"v2_leq_B_10B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 825 11 36 43 63 808 1786
## [2,] Percent 46.2 0.6 2 2.4 3.5 45.2 100
11. “Beginning work outside the home”
11A Nature (dichotomous [“good”,“bad”], v2_leq_B_11A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq11a_arbeit_aussen,v2_con$v2_leq_b_leq11a,"v2_leq_B_11A")
## -999 bad good <NA>
## [1,] No. cases 835 25 118 808 1786
## [2,] Percent 46.8 1.4 6.6 45.2 100
11B Impact (ordinal [0,1,2,3], v2_leq_B_11B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq11e_arbeit_aussen,v2_con$v2_leq_b_leq11e,"v2_leq_B_11B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 833 8 25 51 61 808 1786
## [2,] Percent 46.6 0.4 1.4 2.9 3.4 45.2 100
12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v2_leq_B_12A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq12a_arbeitswechs,v2_con$v2_leq_b_leq12a,"v2_leq_B_12A")
## -999 bad good <NA>
## [1,] No. cases 849 19 110 808 1786
## [2,] Percent 47.5 1.1 6.2 45.2 100
12B Impact (ordinal [0,1,2,3], v2_leq_B_12B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq12e_arbeitswechs,v2_con$v2_leq_b_leq12e,"v2_leq_B_12B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 847 5 22 38 66 808 1786
## [2,] Percent 47.4 0.3 1.2 2.1 3.7 45.2 100
13. “Changing your work hours or conditions”
13A Nature (dichotomous [“good”,“bad”], v2_leq_B_13A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq13a_veraend_arb,v2_con$v2_leq_b_leq13a,"v2_leq_B_13A")
## -999 bad good <NA>
## [1,] No. cases 795 47 136 808 1786
## [2,] Percent 44.5 2.6 7.6 45.2 100
13B Impact (ordinal [0,1,2,3], v2_leq_B_13B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq13e_veraend_arb,v2_con$v2_leq_b_leq13e,"v2_leq_B_13B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 791 7 50 74 56 808 1786
## [2,] Percent 44.3 0.4 2.8 4.1 3.1 45.2 100
14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v2_leq_B_14A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq14a_veraend_ba,v2_con$v2_leq_b_leq14a,"v2_leq_B_14A")
## -999 bad good <NA>
## [1,] No. cases 804 40 134 808 1786
## [2,] Percent 45 2.2 7.5 45.2 100
14B Impact (ordinal [0,1,2,3], v2_leq_B_14B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq14e_veraend_ba,v2_con$v2_leq_b_leq14e,"v2_leq_B_14B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 803 9 45 66 55 808 1786
## [2,] Percent 45 0.5 2.5 3.7 3.1 45.2 100
15. “Troubles at work with your employer or co-worker”
15A Nature (dichotomous [“good”,“bad”], v2_leq_B_15A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq15a_schw_arbeit,v2_con$v2_leq_b_leq15a,"v2_leq_B_15A")
## -999 bad good <NA>
## [1,] No. cases 861 99 18 808 1786
## [2,] Percent 48.2 5.5 1 45.2 100
15B Impact (ordinal [0,1,2,3], v2_leq_B_15B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq15e_schw_arbeit,v2_con$v2_leq_b_leq15e,"v2_leq_B_15B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 859 10 45 37 27 808 1786
## [2,] Percent 48.1 0.6 2.5 2.1 1.5 45.2 100
16. “Major business readjustment”
16A Nature (dichotomous [“good”,“bad”], v2_leq_B_16A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq16a_betr_reorg,v2_con$v2_leq_b_leq16a,"v2_leq_B_16A")
## -999 bad good <NA>
## [1,] No. cases 935 23 20 808 1786
## [2,] Percent 52.4 1.3 1.1 45.2 100
16B Impact (ordinal [0,1,2,3], v2_leq_B_16B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq16e_betr_reorg,v2_con$v2_leq_b_leq16e,"v2_leq_B_16B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 934 7 12 14 11 808 1786
## [2,] Percent 52.3 0.4 0.7 0.8 0.6 45.2 100
17. “Being fired or laid off from work”
17A Nature (dichotomous [“good”,“bad”], v2_leq_B_17A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq17a_kuendigung,v2_con$v2_leq_b_leq17a,"v2_leq_B_17A")
## -999 bad good <NA>
## [1,] No. cases 921 33 24 808 1786
## [2,] Percent 51.6 1.8 1.3 45.2 100
17B Impact (ordinal [0,1,2,3], v2_leq_B_17B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq17e_kuendigung,v2_con$v2_leq_b_leq17e,"v2_leq_B_17B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 919 6 9 16 28 808 1786
## [2,] Percent 51.5 0.3 0.5 0.9 1.6 45.2 100
18. “Retirement from work”
18A Nature (dichotomous [“good”,“bad”], v2_leq_B_18A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq18a_ende_beruf,v2_con$v2_leq_b_leq18a,"v2_leq_B_18A")
## -999 bad good <NA>
## [1,] No. cases 941 17 20 808 1786
## [2,] Percent 52.7 1 1.1 45.2 100
18B Impact (ordinal [0,1,2,3], v2_leq_B_18B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq18e_ende_beruf,v2_con$v2_leq_b_leq18e,"v2_leq_B_18B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 938 5 5 6 24 808 1786
## [2,] Percent 52.5 0.3 0.3 0.3 1.3 45.2 100
19. “Taking courses by mail or studying at home to help you in your work”
19A Nature (dichotomous [“good”,“bad”], v2_leq_B_19A)
v2_leq_a_recode(v2_clin$v2_leq_b_leq19a_fortbildung,v2_con$v2_leq_b_leq19a,"v2_leq_B_19A")
## -999 bad good <NA>
## [1,] No. cases 917 12 49 808 1786
## [2,] Percent 51.3 0.7 2.7 45.2 100
19B Impact (ordinal [0,1,2,3], v2_leq_B_19B)
v2_leq_b_recode(v2_clin$v2_leq_b_leq19e_fortbildung,v2_con$v2_leq_b_leq19e,"v2_leq_B_19B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 914 4 14 28 18 808 1786
## [2,] Percent 51.2 0.2 0.8 1.6 1 45.2 100
v2_leq_B<-data.frame(v2_leq_B_10A,v2_leq_B_10B,v2_leq_B_11A,v2_leq_B_11B,v2_leq_B_12A,
v2_leq_B_12B,v2_leq_B_13A,v2_leq_B_13B,v2_leq_B_14A,v2_leq_B_14B,
v2_leq_B_15A,v2_leq_B_15B,v2_leq_B_16A,v2_leq_B_16B,v2_leq_B_17A,
v2_leq_B_17B,v2_leq_B_18A,v2_leq_B_18B,v2_leq_B_19A,v2_leq_B_19B)
20. “Beginning or ceasing school, college, or training program”
20A Nature (dichotomous [“good”,“bad”], v2_leq_C_20A)
v2_leq_a_recode(v2_clin$v2_leq_c_d_leq20a_beginn_ende,v2_con$v2_leq_c_d_leq20a,"v2_leq_C_20A")
## -999 bad good <NA>
## [1,] No. cases 914 8 56 808 1786
## [2,] Percent 51.2 0.4 3.1 45.2 100
20B Impact (ordinal [0,1,2,3], v2_leq_C_20B)
v2_leq_b_recode(v2_clin$v2_leq_c_d_leq20e_beginn_ende,v2_con$v2_leq_c_d_leq20e,"v2_leq_C_20B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 913 4 6 25 30 808 1786
## [2,] Percent 51.1 0.2 0.3 1.4 1.7 45.2 100
21. “Change of school, college, or training program”
21A Nature (dichotomous [“good”,“bad”], v2_leq_C_21A)
v2_leq_a_recode(v2_clin$v2_leq_c_d_leq21a_schulwechsel,v2_con$v2_leq_c_d_leq21a,"v2_leq_C_21A")
## -999 bad good <NA>
## [1,] No. cases 966 2 10 808 1786
## [2,] Percent 54.1 0.1 0.6 45.2 100
21B Impact (ordinal [0,1,2,3], v2_leq_C_21B)
v2_leq_b_recode(v2_clin$v2_leq_c_d_leq21e_schulwechsel,v2_con$v2_leq_c_d_leq21e,"v2_leq_C_21B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 965 4 1 7 1 808 1786
## [2,] Percent 54 0.2 0.1 0.4 0.1 45.2 100
22. “Change in career goal or academic major”
A Nature (dichotomous [“good”,“bad”], v2_leq_C_22A)
v2_leq_a_recode(v2_clin$v2_leq_c_d_leq22a_aend_karriere,v2_con$v2_leq_c_d_leq22a,"v2_leq_C_22A")
## -999 bad good <NA>
## [1,] No. cases 940 7 31 808 1786
## [2,] Percent 52.6 0.4 1.7 45.2 100
B Impact (ordinal [0,1,2,3], v2_leq_C_22B)
v2_leq_b_recode(v2_clin$v2_leq_c_d_leq22e_aend_karriere,v2_con$v2_leq_c_d_leq22e,"v2_leq_C_22B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 939 3 7 17 12 808 1786
## [2,] Percent 52.6 0.2 0.4 1 0.7 45.2 100
23. “Problem in school, college, or training program”
23A Nature (dichotomous [“good”,“bad”], v2_leq_C_23A)
v2_leq_a_recode(v2_clin$v2_leq_c_d_leq23a_schulprob,v2_con$v2_leq_c_d_leq23a,"v2_leq_C_23A")
## -999 bad good <NA>
## [1,] No. cases 951 25 2 808 1786
## [2,] Percent 53.2 1.4 0.1 45.2 100
23B Impact (ordinal [0,1,2,3], v2_leq_C_23B)
v2_leq_b_recode(v2_clin$v2_leq_c_d_leq23e_schulprob,v2_con$v2_leq_c_d_leq23e,"v2_leq_C_23B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 950 1 9 10 8 808 1786
## [2,] Percent 53.2 0.1 0.5 0.6 0.4 45.2 100
Create dataset
v2_leq_C<-data.frame(v2_leq_C_20A,v2_leq_C_20B,v2_leq_C_21A,v2_leq_C_21B,v2_leq_C_22A,v2_leq_C_22B,v2_leq_C_23A,v2_leq_C_23B)
24. “Difficulty finding housing”
24A Nature (dichotomous [“good”,“bad”], v2_leq_D_24A)
v2_leq_a_recode(v2_clin$v2_leq_c_d_leq24a_schw_wsuche,v2_con$v2_leq_c_d_leq24a,"v2_leq_D_24A")
## -999 bad good <NA>
## [1,] No. cases 899 61 18 808 1786
## [2,] Percent 50.3 3.4 1 45.2 100
24B Impact (ordinal [0,1,2,3], v2_leq_D_24B)
v2_leq_b_recode(v2_clin$v2_leq_c_d_leq24e_schw_wsuche,v2_con$v2_leq_c_d_leq24e,"v2_leq_D_24B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 898 4 23 16 37 808 1786
## [2,] Percent 50.3 0.2 1.3 0.9 2.1 45.2 100
25. “Changing residence within the same town or city”
A Nature (dichotomous [“good”,“bad”], v2_leq_D_25A)
v2_leq_a_recode(v2_clin$v2_leq_c_d_leq25a_umzug_nah,v2_con$v2_leq_c_d_leq25a,"v2_leq_D_25A")
## -999 bad good <NA>
## [1,] No. cases 912 15 51 808 1786
## [2,] Percent 51.1 0.8 2.9 45.2 100
B Impact (ordinal [0,1,2,3], v2_leq_D_25B)
v2_leq_b_recode(v2_clin$v2_leq_c_d_leq25e_umzug_nah,v2_con$v2_leq_c_d_leq25e,"v2_leq_D_25B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 911 4 10 13 40 808 1786
## [2,] Percent 51 0.2 0.6 0.7 2.2 45.2 100
26. “Moving to a different town, city, state, or country”
26A Nature (dichotomous [“good”,“bad”], v2_leq_D_26A)
v2_leq_a_recode(v2_clin$v2_leq_c_d_leq26a_umzug_fern,v2_con$v2_leq_c_d_leq26a,"v2_leq_D_26A")
## -999 bad good <NA>
## [1,] No. cases 933 8 37 808 1786
## [2,] Percent 52.2 0.4 2.1 45.2 100
26B Impact (ordinal [0,1,2,3], v2_leq_D_26B)
v2_leq_b_recode(v2_clin$v2_leq_c_d_leq26e_umzug_fern,v2_con$v2_leq_c_d_leq26e,"v2_leq_D_26B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 932 3 4 14 25 808 1786
## [2,] Percent 52.2 0.2 0.2 0.8 1.4 45.2 100
27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”
27A Nature (dichotomous [“good”,“bad”], v2_leq_D_27A)
v2_leq_a_recode(v2_clin$v2_leq_c_d_leq27a_veraend_lu,v2_con$v2_leq_c_d_leq27a,"v2_leq_D_27A")
## -999 bad good <NA>
## [1,] No. cases 816 52 110 808 1786
## [2,] Percent 45.7 2.9 6.2 45.2 100
27B Impact (ordinal [0,1,2,3], v2_leq_D_27B)
v2_leq_b_recode(v2_clin$v2_leq_c_d_leq27e_veraend_lu,v2_con$v2_leq_c_d_leq27e,"v2_leq_D_27B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 815 9 35 55 64 808 1786
## [2,] Percent 45.6 0.5 2 3.1 3.6 45.2 100
Create dataset
v2_leq_D<-data.frame(v2_leq_D_24A,v2_leq_D_24B,v2_leq_D_25A,v2_leq_D_25B,v2_leq_D_26A,
v2_leq_D_26B,v2_leq_D_27A,v2_leq_D_27B)
28. “Began a new, close, personal relationship”
28A Nature (dichotomous [“good”,“bad”], v2_leq_E_28A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq28a_neue_bez,v2_con$v2_leq_e_leq28a,"v2_leq_E_28A")
## -999 bad good <NA>
## [1,] No. cases 887 10 81 808 1786
## [2,] Percent 49.7 0.6 4.5 45.2 100
28B Impact (ordinal [0,1,2,3], v2_leq_E_28B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq28e_neue_bez,v2_con$v2_leq_e_leq28e,"v2_leq_E_28B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 886 3 8 32 49 808 1786
## [2,] Percent 49.6 0.2 0.4 1.8 2.7 45.2 100
29. “Became engaged”
29A Nature (dichotomous [“good”,“bad”], v2_leq_E_29A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq29a_verlobung,v2_con$v2_leq_e_leq29a,"v2_leq_E_29A")
## -999 bad good <NA>
## [1,] No. cases 962 3 13 808 1786
## [2,] Percent 53.9 0.2 0.7 45.2 100
29B Impact (ordinal [0,1,2,3], v2_leq_E_29B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq29e_verlobung,v2_con$v2_leq_e_leq29e,"v2_leq_E_29B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 961 5 5 4 3 808 1786
## [2,] Percent 53.8 0.3 0.3 0.2 0.2 45.2 100
30. “Girlfriend or boyfriend problems”
30A Nature (dichotomous [“good”,“bad”], v2_leq_E_30A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq30a_prob_partner,v2_con$v2_leq_e_leq30a,"v2_leq_E_30A")
## -999 bad good <NA>
## [1,] No. cases 879 87 12 808 1786
## [2,] Percent 49.2 4.9 0.7 45.2 100
30B Impact (ordinal [0,1,2,3], v2_leq_E_30B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq30e_prob_partner,v2_con$v2_leq_e_leq30e,"v2_leq_E_30B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 878 4 30 36 30 808 1786
## [2,] Percent 49.2 0.2 1.7 2 1.7 45.2 100
31. “Breaking up with a girlfriend or breaking an engagement”
31A Nature (dichotomous [“good”,“bad”], v2_leq_E_31A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq31a_trennung,v2_con$v2_leq_e_leq31a,"v2_leq_E_31A")
## -999 bad good <NA>
## [1,] No. cases 932 33 13 808 1786
## [2,] Percent 52.2 1.8 0.7 45.2 100
31B Impact (ordinal [0,1,2,3], v2_leq_E_31B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq31e_trennung,v2_con$v2_leq_e_leq31e,"v2_leq_E_31B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 932 2 6 14 24 808 1786
## [2,] Percent 52.2 0.1 0.3 0.8 1.3 45.2 100
32. “(Male) Wife or girlfriend’s pregnancy”
32A Nature (dichotomous [“good”,“bad”], v2_leq_E_32A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq32a_schwanger_p,v2_con$v2_leq_e_leq32a,"v2_leq_E_32A")
## -999 bad good <NA>
## [1,] No. cases 967 2 9 808 1786
## [2,] Percent 54.1 0.1 0.5 45.2 100
32B Impact (ordinal [0,1,2,3], v2_leq_E_32B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq32e_schwanger_p,v2_con$v2_leq_e_leq32e,"v2_leq_E_32B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 967 3 2 2 4 808 1786
## [2,] Percent 54.1 0.2 0.1 0.1 0.2 45.2 100
33. “(Male) Wife or girlfriend having a miscarriage or abortion”
33A Nature (dichotomous [“good”,“bad”], v2_leq_E_33A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq33a_fehlg_abtr_p,v2_con$v2_leq_e_leq33a,"v2_leq_E_33A")
## -999 bad <NA>
## [1,] No. cases 977 1 808 1786
## [2,] Percent 54.7 0.1 45.2 100
33B Impact (ordinal [0,1,2,3], v2_leq_E_33B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq33e_fehlg_abtr_p,v2_con$v2_leq_e_leq33e,"v2_leq_E_33B")
## -999 0 <NA>
## [1,] No. cases 977 1 808 1786
## [2,] Percent 54.7 0.1 45.2 100
34. “Getting married (or beginning to live with someone)”
34A Nature (dichotomous [“good”,“bad”], v2_leq_E_34A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq34a_heirat,v2_con$v2_leq_e_leq34a,"v2_leq_E_34A")
## -999 bad good <NA>
## [1,] No. cases 961 2 15 808 1786
## [2,] Percent 53.8 0.1 0.8 45.2 100
34B Impact (ordinal [0,1,2,3], v2_leq_E_34B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq34e_heirat,v2_con$v2_leq_e_leq34e,"v2_leq_E_34B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 960 2 5 2 9 808 1786
## [2,] Percent 53.8 0.1 0.3 0.1 0.5 45.2 100
35. “A change in closeness with your partner”
35A Nature (dichotomous [“good”,“bad”], v2_leq_E_35A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq35a_veraend_naehe,v2_con$v2_leq_e_leq35a,"v2_leq_E_35A")
## -999 bad good <NA>
## [1,] No. cases 854 50 74 808 1786
## [2,] Percent 47.8 2.8 4.1 45.2 100
35B Impact (ordinal [0,1,2,3], v2_leq_E_35B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq35e_veraend_naehe,v2_con$v2_leq_e_leq35e,"v2_leq_E_35B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 853 3 26 46 50 808 1786
## [2,] Percent 47.8 0.2 1.5 2.6 2.8 45.2 100
36. “Infidelity”
36A Nature (dichotomous [“good”,“bad”], v2_leq_E_36A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq36a_untreue,v2_con$v2_leq_e_leq36a,"v2_leq_E_36A")
## -999 bad good <NA>
## [1,] No. cases 949 21 8 808 1786
## [2,] Percent 53.1 1.2 0.4 45.2 100
36B Impact (ordinal [0,1,2,3], v2_leq_E_36B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq36e_untreue,v2_con$v2_leq_e_leq36e,"v2_leq_E_36B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 948 3 8 6 13 808 1786
## [2,] Percent 53.1 0.2 0.4 0.3 0.7 45.2 100
37. “Trouble with in-laws”
37A Nature (dichotomous [“good”,“bad”], v2_leq_E_37A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq37a_konf_schwiege,v2_con$v2_leq_e_leq37a,"v2_leq_E_37A")
## -999 bad good <NA>
## [1,] No. cases 952 24 2 808 1786
## [2,] Percent 53.3 1.3 0.1 45.2 100
37B Impact (ordinal [0,1,2,3], v2_leq_E_37B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq37e_konf_schwiege,v2_con$v2_leq_e_leq37e,"v2_leq_E_37B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 951 4 10 7 6 808 1786
## [2,] Percent 53.2 0.2 0.6 0.4 0.3 45.2 100
38. “Separation from spouse or partner due to conflict”
38A Nature (dichotomous [“good”,“bad”], v2_leq_E_38A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq38a_trennung_str,v2_con$v2_leq_e_leq38a,"v2_leq_E_38A")
## -999 bad good <NA>
## [1,] No. cases 953 17 8 808 1786
## [2,] Percent 53.4 1 0.4 45.2 100
38B Impact (ordinal [0,1,2,3], v2_leq_E_38B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq38e_trennung_str,v2_con$v2_leq_e_leq38e,"v2_leq_E_38B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 952 4 5 3 14 808 1786
## [2,] Percent 53.3 0.2 0.3 0.2 0.8 45.2 100
39. “Separation from spouse or partner due to work, travel, etc.”
39A Nature (dichotomous [“good”,“bad”], v2_leq_E_39A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq39a_trennung_ber,v2_con$v2_leq_e_leq39a,"v2_leq_E_39A")
## -999 bad good <NA>
## [1,] No. cases 968 9 1 808 1786
## [2,] Percent 54.2 0.5 0.1 45.2 100
39B Impact (ordinal [0,1,2,3], v2_leq_E_39B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq39e_trennung_ber,v2_con$v2_leq_e_leq39e,"v2_leq_E_39B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 967 2 1 3 5 808 1786
## [2,] Percent 54.1 0.1 0.1 0.2 0.3 45.2 100
40. “Reconciliation with spouse or partner”
40A Nature (dichotomous [“good”,“bad”], v2_leq_E_40A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq40a_versoehnung,v2_con$v2_leq_e_leq40a,"v2_leq_E_40A")
## -999 bad good <NA>
## [1,] No. cases 948 2 28 808 1786
## [2,] Percent 53.1 0.1 1.6 45.2 100
40B Impact (ordinal [0,1,2,3], v2_leq_E_40B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq40a_versoehnung,v2_con$v2_leq_e_leq40e,"v2_leq_E_40B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 948 2 26 1 1 808 1786
## [2,] Percent 53.1 0.1 1.5 0.1 0.1 45.2 100
41. “Divorce”
41A Nature (dichotomous [“good”,“bad”], v2_leq_E_41A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq41a_scheidung,v2_con$v2_leq_e_leq41a,"v2_leq_E_41A")
## -999 bad good <NA>
## [1,] No. cases 968 4 6 808 1786
## [2,] Percent 54.2 0.2 0.3 45.2 100
41B Impact (ordinal [0,1,2,3], v2_leq_E_41B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq41e_scheidung,v2_con$v2_leq_e_leq41e,"v2_leq_E_41B")
## -999 0 2 3 <NA>
## [1,] No. cases 966 4 2 6 808 1786
## [2,] Percent 54.1 0.2 0.1 0.3 45.2 100
42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”
42A Nature (dichotomous [“good”,“bad”], v2_leq_E_42A)
v2_leq_a_recode(v2_clin$v2_leq_e_leq42a_veraend_taet,v2_con$v2_leq_e_leq42a,"v2_leq_E_42A")
## -999 bad good <NA>
## [1,] No. cases 934 14 30 808 1786
## [2,] Percent 52.3 0.8 1.7 45.2 100
42B Impact (ordinal [0,1,2,3], v2_leq_E_42B)
v2_leq_b_recode(v2_clin$v2_leq_e_leq42e_veraend_taet,v2_con$v2_leq_e_leq42e,"v2_leq_E_42B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 933 6 16 8 15 808 1786
## [2,] Percent 52.2 0.3 0.9 0.4 0.8 45.2 100
Create dataset
v2_leq_E<-data.frame(v2_leq_E_28A,v2_leq_E_28B,v2_leq_E_29A,v2_leq_E_29B,v2_leq_E_30A,
v2_leq_E_30B,v2_leq_E_31A,v2_leq_E_31B,v2_leq_E_32A,v2_leq_E_32B,
v2_leq_E_33A,v2_leq_E_33B,v2_leq_E_34A,v2_leq_E_34B,v2_leq_E_35A,
v2_leq_E_35B,v2_leq_E_36A,v2_leq_E_36B,v2_leq_E_37A,v2_leq_E_37B,
v2_leq_E_38A,v2_leq_E_38B,v2_leq_E_39A,v2_leq_E_39B,v2_leq_E_40A,
v2_leq_E_40B,v2_leq_E_41A,v2_leq_E_41B,v2_leq_E_42A,v2_leq_E_42B)
43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”
43A Nature (dichotomous [“good”,“bad”], v2_leq_F_43A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq43a_neu_fmitglied,v2_con$v2_leq_f_g_leq43a,"v2_leq_F_43A")
## -999 bad good <NA>
## [1,] No. cases 908 1 69 808 1786
## [2,] Percent 50.8 0.1 3.9 45.2 100
43B Impact (ordinal [0,1,2,3], v2_leq_F_43B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq43e_neu_fmitglied,v2_con$v2_leq_f_g_leq43e,"v2_leq_F_43B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 907 11 15 21 24 808 1786
## [2,] Percent 50.8 0.6 0.8 1.2 1.3 45.2 100
44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”
44A Nature (dichotomous [“good”,“bad”], v2_leq_F_44A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq44a_auszug_fm,v2_con$v2_leq_f_g_leq44a,"v2_leq_F_44A")
## -999 bad good <NA>
## [1,] No. cases 942 20 16 808 1786
## [2,] Percent 52.7 1.1 0.9 45.2 100
44B Impact (ordinal [0,1,2,3], v2_leq_F_44B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq44e_auszug_fm,v2_con$v2_leq_f_g_leq44e,"v2_leq_F_44B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 941 7 8 16 6 808 1786
## [2,] Percent 52.7 0.4 0.4 0.9 0.3 45.2 100
45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”
45A Nature (dichotomous [“good”,“bad”], v2_leq_F_45A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq45a_gz_verh_fm,v2_con$v2_leq_f_g_leq45a,"v2_leq_F_45A")
## -999 bad good <NA>
## [1,] No. cases 829 143 6 808 1786
## [2,] Percent 46.4 8 0.3 45.2 100
45B Impact (ordinal [0,1,2,3], v2_leq_F_45B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq45e_gz_verh_fm,v2_con$v2_leq_f_g_leq45e,"v2_leq_F_45B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 829 5 30 66 48 808 1786
## [2,] Percent 46.4 0.3 1.7 3.7 2.7 45.2 100
46. “Death of spouse or partner”
46A Nature (dichotomous [“good”,“bad”], v2_leq_F_46A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq46a_tod_partner,v2_con$v2_leq_f_g_leq46a,"v2_leq_F_46A")
## -999 bad <NA>
## [1,] No. cases 971 7 808 1786
## [2,] Percent 54.4 0.4 45.2 100
46B Impact (ordinal [0,1,2,3], v2_leq_F_46B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq46e_tod_partner,v2_con$v2_leq_f_g_leq46e,"v2_leq_F_46B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 970 1 1 1 5 808 1786
## [2,] Percent 54.3 0.1 0.1 0.1 0.3 45.2 100
47. “Death of a child”
47A Nature (dichotomous [“good”,“bad”], v2_leq_F_47A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq47a_tod_kind,v2_con$v2_leq_f_g_leq47a,"v2_leq_F_47A")
## -999 bad good <NA>
## [1,] No. cases 972 5 1 808 1786
## [2,] Percent 54.4 0.3 0.1 45.2 100
47B Impact (ordinal [0,1,2,3], v2_leq_F_47B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq47e_tod_kind,v2_con$v2_leq_f_g_leq47e,"v2_leq_F_47B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 971 1 1 1 4 808 1786
## [2,] Percent 54.4 0.1 0.1 0.1 0.2 45.2 100
48. “Death of family member or close friend”
48A Nature (dichotomous [“good”,“bad”], v2_leq_F_48A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq48a_tod_fm_ef,v2_con$v2_leq_f_g_leq48a,"v2_leq_F_48A")
## -999 bad good <NA>
## [1,] No. cases 903 67 8 808 1786
## [2,] Percent 50.6 3.8 0.4 45.2 100
48B Impact (ordinal [0,1,2,3], v2_leq_F_48B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq48e_tod_fm_ef,v2_con$v2_leq_f_g_leq48e,"v2_leq_F_48B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 901 5 26 21 25 808 1786
## [2,] Percent 50.4 0.3 1.5 1.2 1.4 45.2 100
49. “Birth of a grandchild”
49A Nature (dichotomous [“good”,“bad”], v2_leq_F_49A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq49a_geb_enkel,v2_con$v2_leq_f_g_leq49a,"v2_leq_F_49A")
## -999 bad good <NA>
## [1,] No. cases 951 3 24 808 1786
## [2,] Percent 53.2 0.2 1.3 45.2 100
49B Impact (ordinal [0,1,2,3], v2_leq_F_49B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq49e_geb_enkel,v2_con$v2_leq_f_g_leq49e,"v2_leq_F_49B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 950 4 5 7 12 808 1786
## [2,] Percent 53.2 0.2 0.3 0.4 0.7 45.2 100
50. “Change in marital status of your parents”
50A Nature (dichotomous [“good”,“bad”], v2_leq_F_50A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq50a_fstand_eltern,v2_con$v2_leq_f_g_leq50a,"v2_leq_F_50A")
## -999 bad good <NA>
## [1,] No. cases 966 5 7 808 1786
## [2,] Percent 54.1 0.3 0.4 45.2 100
50B Impact (ordinal [0,1,2,3], v2_leq_F_50B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq50e_fstand_eltern,v2_con$v2_leq_f_g_leq50e,"v2_leq_F_50B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 965 1 6 2 4 808 1786
## [2,] Percent 54 0.1 0.3 0.1 0.2 45.2 100
Create dataset
v2_leq_F<-data.frame(v2_leq_F_43A,v2_leq_F_43B,v2_leq_F_44A,v2_leq_F_44B,v2_leq_F_45A,
v2_leq_F_45B,v2_leq_F_46A,v2_leq_F_46B,v2_leq_F_47A,v2_leq_F_47B,
v2_leq_F_48A,v2_leq_F_48B,v2_leq_F_49A,v2_leq_F_49B,v2_leq_F_50A,
v2_leq_F_50B)
51. “Change in child care arrangements”
51A Nature (dichotomous [“good”,“bad”], v2_leq_G_51A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq51a_kindbetr,v2_con$v2_leq_f_g_leq51a,"v2_leq_G_51A")
## -999 bad good <NA>
## [1,] No. cases 950 8 20 808 1786
## [2,] Percent 53.2 0.4 1.1 45.2 100
51B Impact (ordinal [0,1,2,3], v2_leq_G_51B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq51e_kindbetr,v2_con$v2_leq_f_g_leq51e,"v2_leq_G_51B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 949 2 6 10 11 808 1786
## [2,] Percent 53.1 0.1 0.3 0.6 0.6 45.2 100
52. “Conflicts with spouse or partner about parenting”
52A Nature (dichotomous [“good”,“bad”], v2_leq_G_52A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq52a_konf_eschaft,v2_con$v2_leq_f_g_leq52a,"v2_leq_G_52A")
## -999 bad good <NA>
## [1,] No. cases 952 19 7 808 1786
## [2,] Percent 53.3 1.1 0.4 45.2 100
52B Impact (ordinal [0,1,2,3], v2_leq_G_52B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq52e_konf_eschaft,v2_con$v2_leq_f_g_leq52e,"v2_leq_G_52B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 951 3 5 9 10 808 1786
## [2,] Percent 53.2 0.2 0.3 0.5 0.6 45.2 100
53. “Conflicts with child’s grandparents (or other important person) about parenting”
53A Nature (dichotomous [“good”,“bad”], v2_leq_G_53A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq53a_konf_geltern,v2_con$v2_leq_f_g_leq53a,"v2_leq_G_53A")
## -999 bad good <NA>
## [1,] No. cases 970 6 2 808 1786
## [2,] Percent 54.3 0.3 0.1 45.2 100
53B Impact (ordinal [0,1,2,3], v2_leq_G_53B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq53e_konf_geltern,v2_con$v2_leq_f_g_leq53e,"v2_leq_G_53B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 969 3 3 2 1 808 1786
## [2,] Percent 54.3 0.2 0.2 0.1 0.1 45.2 100
54. “Taking on full responsibility for parenting as a single parent”
54A Nature (dichotomous [“good”,“bad”], v2_leq_G_54A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq54a_alleinerz,v2_con$v2_leq_f_g_leq54a,"v2_leq_G_54A")
## -999 bad good <NA>
## [1,] No. cases 971 4 3 808 1786
## [2,] Percent 54.4 0.2 0.2 45.2 100
54B Impact (ordinal [0,1,2,3], v2_leq_G_54B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq54e_alleinerz,v2_con$v2_leq_f_g_leq54e,"v2_leq_G_54B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 970 2 1 3 2 808 1786
## [2,] Percent 54.3 0.1 0.1 0.2 0.1 45.2 100
55. “Custody battles with former spouse or partner”
55A Nature (dichotomous [“good”,“bad”], v2_leq_G_55A)
v2_leq_a_recode(v2_clin$v2_leq_f_g_leq55a_sorgerecht,v2_con$v2_leq_f_g_leq55a,"v2_leq_G_55A")
## -999 bad good <NA>
## [1,] No. cases 964 9 5 808 1786
## [2,] Percent 54 0.5 0.3 45.2 100
55B Impact (ordinal [0,1,2,3], v2_leq_G_55B)
v2_leq_b_recode(v2_clin$v2_leq_f_g_leq55e_sorgerecht,v2_con$v2_leq_f_g_leq55e,"v2_leq_G_55B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 963 3 1 6 5 808 1786
## [2,] Percent 53.9 0.2 0.1 0.3 0.3 45.2 100
Create dataset
v2_leq_G<-data.frame(v2_leq_G_51A,v2_leq_G_51B,v2_leq_G_52A,v2_leq_G_52B,v2_leq_G_53A,
v2_leq_G_53B,v2_leq_G_54A,v2_leq_G_54B,v2_leq_G_55A,v2_leq_G_55B)
69. “Major change in finances (increased or decreased income)”
69A Nature (dichotomous [“good”,“bad”], v2_leq_I_69A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq69a_finanz_sit,v2_con$v2_leq_i_j_k_leq69a,"v2_leq_I_69A")
## -999 bad good <NA>
## [1,] No. cases 708 131 139 808 1786
## [2,] Percent 39.6 7.3 7.8 45.2 100
69B Impact (ordinal [0,1,2,3], v2_leq_I_69B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq69e_finanz_sit,v2_con$v2_leq_i_j_k_leq69e,"v2_leq_I_69B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 705 8 73 94 98 808 1786
## [2,] Percent 39.5 0.4 4.1 5.3 5.5 45.2 100
70. “Took on a moderate purchase, such as TV, car, freezer, etc.”
70A Nature (dichotomous [“good”,“bad”], v2_leq_I_70A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq70a_finanz_verpfl,v2_con$v2_leq_i_j_k_leq70a,"v2_leq_I_70A")
## -999 bad good <NA>
## [1,] No. cases 894 24 60 808 1786
## [2,] Percent 50.1 1.3 3.4 45.2 100
70B Impact (ordinal [0,1,2,3], v2_leq_I_70B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq70e_finanz_verpfl,v2_con$v2_leq_i_j_k_leq70e,"v2_leq_I_70B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 892 13 33 30 10 808 1786
## [2,] Percent 49.9 0.7 1.8 1.7 0.6 45.2 100
71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”
71A Nature (dichotomous [“good”,“bad”], v2_leq_I_71A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq71a_hypothek,v2_con$v2_leq_i_j_k_leq71a,"v2_leq_I_71A")
## -999 bad good <NA>
## [1,] No. cases 955 12 11 808 1786
## [2,] Percent 53.5 0.7 0.6 45.2 100
71B Impact (ordinal [0,1,2,3], v2_leq_I_71B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq71e_hypothek,v2_con$v2_leq_i_j_k_leq71e,"v2_leq_I_71B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 954 3 8 7 6 808 1786
## [2,] Percent 53.4 0.2 0.4 0.4 0.3 45.2 100
72. “Experienced a foreclosure on a mortgage or loan”
72A Nature (dichotomous [“good”,“bad”], v2_leq_I_72A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq72a_hypoth_kuend,v2_con$v2_leq_i_j_k_leq72a,"v2_leq_I_72A")
## -999 bad good <NA>
## [1,] No. cases 967 2 9 808 1786
## [2,] Percent 54.1 0.1 0.5 45.2 100
72B Impact (ordinal [0,1,2,3], v2_leq_I_72B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq72e_hypoth_kuend,v2_con$v2_leq_i_j_k_leq72e,"v2_leq_I_72B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 966 3 6 1 2 808 1786
## [2,] Percent 54.1 0.2 0.3 0.1 0.1 45.2 100
73. “Credit rating difficulties”
73A Nature (dichotomous [“good”,“bad”], v2_leq_I_73A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq73a_kreditwuerdk,v2_con$v2_leq_i_j_k_leq73a,"v2_leq_I_73A")
## -999 bad <NA>
## [1,] No. cases 947 31 808 1786
## [2,] Percent 53 1.7 45.2 100
73B Impact (ordinal [0,1,2,3], v2_leq_I_73B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq73e_kreditwuerdk,v2_con$v2_leq_i_j_k_leq73e,"v2_leq_I_73B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 947 3 12 8 8 808 1786
## [2,] Percent 53 0.2 0.7 0.4 0.4 45.2 100
Create dataset
v2_leq_I<-data.frame(v2_leq_I_69A,v2_leq_I_69B,v2_leq_I_70A,v2_leq_I_70B,v2_leq_I_71A,
v2_leq_I_71B,v2_leq_I_72A,v2_leq_I_72B,v2_leq_I_73A,v2_leq_I_73B)
74. “Being robbed or victim of identity theft”
74A Nature (dichotomous [“good”,“bad”], v2_leq_J_74A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq74a_opf_diebstahl,v2_con$v2_leq_i_j_k_leq74a,"v2_leq_J_74A")
## -999 bad good <NA>
## [1,] No. cases 945 30 3 808 1786
## [2,] Percent 52.9 1.7 0.2 45.2 100
74B Impact (ordinal [0,1,2,3], v2_leq_J_74B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq74e_opf_diebstahl,v2_con$v2_leq_i_j_k_leq74e,"v2_leq_J_74B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 944 2 13 13 6 808 1786
## [2,] Percent 52.9 0.1 0.7 0.7 0.3 45.2 100
75. “Being a victim of a violent act (rape, assault, etc.)”
75A Nature (dichotomous [“good”,“bad”], v2_leq_J_75A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq75a_opf_gewalttat,v2_con$v2_leq_i_j_k_leq75a,"v2_leq_J_75A")
## -999 bad <NA>
## [1,] No. cases 967 11 808 1786
## [2,] Percent 54.1 0.6 45.2 100
75B Impact (ordinal [0,1,2,3], v2_leq_J_75B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq75e_opf_gewalttat,v2_con$v2_leq_i_j_k_leq75e,"v2_leq_J_75B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 966 2 2 3 5 808 1786
## [2,] Percent 54.1 0.1 0.1 0.2 0.3 45.2 100
76. “Involved in an accident”
76A Nature (dichotomous [“good”,“bad”], v2_leq_J_76A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq76a_unfall,v2_con$v2_leq_i_j_k_leq76a,"v2_leq_J_76A")
## -999 bad good <NA>
## [1,] No. cases 932 43 3 808 1786
## [2,] Percent 52.2 2.4 0.2 45.2 100
76B Impact (ordinal [0,1,2,3], v2_leq_J_76B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq76e_unfall,v2_con$v2_leq_i_j_k_leq76e,"v2_leq_J_76B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 931 7 20 14 6 808 1786
## [2,] Percent 52.1 0.4 1.1 0.8 0.3 45.2 100
77. “Involved in a law suit”
77A Nature (dichotomous [“good”,“bad”], v2_leq_J_77A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq77a_rechtsstreit,v2_con$v2_leq_i_j_k_leq77a,"v2_leq_J_77A")
## -999 bad good <NA>
## [1,] No. cases 916 45 17 808 1786
## [2,] Percent 51.3 2.5 1 45.2 100
77B Impact (ordinal [0,1,2,3], v2_leq_J_77B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq77e_rechtsstreit,v2_con$v2_leq_i_j_k_leq77e,"v2_leq_J_77B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 915 6 19 18 20 808 1786
## [2,] Percent 51.2 0.3 1.1 1 1.1 45.2 100
78. “Involved in a minor violation of the law (traffic tickets, disturbing the peace, etc)”
78A Nature (dichotomous [“good”,“bad”], v2_leq_J_78A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq78a_owi,v2_con$v2_leq_i_j_k_leq78a,"v2_leq_J_78A")
## -999 bad good <NA>
## [1,] No. cases 937 38 3 808 1786
## [2,] Percent 52.5 2.1 0.2 45.2 100
78B Impact (ordinal [0,1,2,3], v2_leq_J_78B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq78e_owi,v2_con$v2_leq_i_j_k_leq78e,"v2_leq_J_78B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 935 10 20 10 3 808 1786
## [2,] Percent 52.4 0.6 1.1 0.6 0.2 45.2 100
79. “Legal troubles resulting in your being arrested or held in jail”
79A Nature (dichotomous [“good”,“bad”], v2_leq_J_79A)
v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq79a_konf_gesetz,v2_con$v2_leq_i_j_k_leq79a,"v2_leq_J_79A")
## -999 bad <NA>
## [1,] No. cases 973 5 808 1786
## [2,] Percent 54.5 0.3 45.2 100
79B Impact (ordinal [0,1,2,3], v2_leq_J_79B)
v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq79e_konf_gesetz,v2_con$v2_leq_i_j_k_leq79e,"v2_leq_J_79B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 972 1 1 2 2 808 1786
## [2,] Percent 54.4 0.1 0.1 0.1 0.1 45.2 100
Create dataset
v2_leq_J<-data.frame(v2_leq_J_74A,v2_leq_J_74B,v2_leq_J_75A,v2_leq_J_75B,v2_leq_J_76A,
v2_leq_J_76B,v2_leq_J_77A,v2_leq_J_77B,v2_leq_J_78A,v2_leq_J_78B,
v2_leq_J_79A,v2_leq_J_79B)
Create LEQ dataset
v2_leq<-data.frame(v2_leq_A,v2_leq_B,v2_leq_C,v2_leq_D,v2_leq_E,v2_leq_F,v2_leq_G,
v2_leq_H,v2_leq_I,v2_leq_J)
For explanation, please refer to the section in Visit 1
1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v2_whoqol_itm1)
v2_quol_recode(v2_clin$v2_whoqol_bref_who1_lebensqualitaet,v2_con$v2_whoqol_bref_who1,"v2_whoqol_itm1",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 26 67 280 439 205 769 1786
## [2,] Percent 1.5 3.8 15.7 24.6 11.5 43.1 100
2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v2_whoqol_itm2)”
v2_quol_recode(v2_clin$v2_whoqol_bref_who2_gesundheit,v2_con$v2_whoqol_bref_who2,"v2_whoqol_itm2",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 49 188 218 418 144 769 1786
## [2,] Percent 2.7 10.5 12.2 23.4 8.1 43.1 100
3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v2_whoqol_itm3)
Coding reversed so that higher scores mean less impairment by pain.
v2_quol_recode(v2_clin$v2_whoqol_bref_who3_schmerzen,v2_con$v2_whoqol_bref_who3,"v2_whoqol_itm3",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 12 56 93 210 634 781 1786
## [2,] Percent 0.7 3.1 5.2 11.8 35.5 43.7 100
4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v2_whoqol_itm4)”
Coding reversed so that higher scores mean less dependence on medical treatment.
v2_quol_recode(v2_clin$v2_whoqol_bref_who4_med_behand,v2_con$v2_whoqol_bref_who4,"v2_whoqol_itm4",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 92 194 138 186 394 782 1786
## [2,] Percent 5.2 10.9 7.7 10.4 22.1 43.8 100
5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v2_whoqol_itm5)
v2_quol_recode(v2_clin$v2_whoqol_bref_who5_lebensgenuss,v2_con$v2_whoqol_bref_who5,"v2_whoqol_itm5",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 50 121 260 422 150 783 1786
## [2,] Percent 2.8 6.8 14.6 23.6 8.4 43.8 100
6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v2_whoqol_itm6)
v2_quol_recode(v2_clin$v2_whoqol_bref_who6_lebenssinn,v2_con$v2_whoqol_bref_who6,"v2_whoqol_itm6",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 62 123 178 368 262 793 1786
## [2,] Percent 3.5 6.9 10 20.6 14.7 44.4 100
7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v2_whoqol_itm7)
v2_quol_recode(v2_clin$v2_whoqol_bref_who7_konzentration,v2_con$v2_whoqol_bref_who7,"v2_whoqol_itm7",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 20 134 388 397 66 781 1786
## [2,] Percent 1.1 7.5 21.7 22.2 3.7 43.7 100
8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v2_whoqol_itm8)
v2_quol_recode(v2_clin$v2_whoqol_bref_who8_sicherheit,v2_con$v2_whoqol_bref_who8,"v2_whoqol_itm8",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 25 71 249 450 210 781 1786
## [2,] Percent 1.4 4 13.9 25.2 11.8 43.7 100
9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v2_whoqol_itm9)
v2_quol_recode(v2_clin$v2_whoqol_bref_who9_umweltbed,v2_con$v2_whoqol_bref_who9,"v2_whoqol_itm9",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 15 36 212 490 249 784 1786
## [2,] Percent 0.8 2 11.9 27.4 13.9 43.9 100
10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v2_whoqol_itm10)
v2_quol_recode(v2_clin$v2_whoqol_bref_who10_energie,v2_con$v2_whoqol_bref_who10,"v2_whoqol_itm10",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 18 100 229 432 228 779 1786
## [2,] Percent 1 5.6 12.8 24.2 12.8 43.6 100
11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v2_whoqol_itm11)
v2_quol_recode(v2_clin$v2_whoqol_bref_who11_aussehen,v2_con$v2_whoqol_bref_who11,"v2_whoqol_itm11",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 20 76 227 431 251 781 1786
## [2,] Percent 1.1 4.3 12.7 24.1 14.1 43.7 100
12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v2_whoqol_itm12)
v2_quol_recode(v2_clin$v2_whoqol_bref_who12_genug_geld,v2_con$v2_whoqol_bref_who12,"v2_whoqol_itm12",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 53 126 238 338 251 780 1786
## [2,] Percent 3 7.1 13.3 18.9 14.1 43.7 100
13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v2_whoqol_itm13)
v2_quol_recode(v2_clin$v2_whoqol_bref_who13_infozugang,v2_con$v2_whoqol_bref_who13,"v2_whoqol_itm13",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 8 20 111 358 509 780 1786
## [2,] Percent 0.4 1.1 6.2 20 28.5 43.7 100
14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v2_whoqol_itm14)
v2_quol_recode(v2_clin$v2_whoqol_bref_who14_freizeitaktiv,v2_con$v2_whoqol_bref_who14,"v2_whoqol_itm14",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 11 56 202 380 355 782 1786
## [2,] Percent 0.6 3.1 11.3 21.3 19.9 43.8 100
15. “How well are you able to get around? (ordinal [1,2,3,4,5], v2_whoqol_itm15)”
v2_quol_recode(v2_clin$v2_whoqol_bref_who15_fortbewegung,v2_con$v2_whoqol_bref_who15,"v2_whoqol_itm15",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 4 38 149 353 461 781 1786
## [2,] Percent 0.2 2.1 8.3 19.8 25.8 43.7 100
16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v2_whoqol_itm16)
v2_quol_recode(v2_clin$v2_whoqol_bref_who16_schlaf,v2_con$v2_whoqol_bref_who16,"v2_whoqol_itm16",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 46 158 177 436 201 768 1786
## [2,] Percent 2.6 8.8 9.9 24.4 11.3 43 100
17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v2_whoqol_itm17)
v2_quol_recode(v2_clin$v2_whoqol_bref_who17_alltag,v2_con$v2_whoqol_bref_who17,"v2_whoqol_itm17",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 33 115 191 443 234 770 1786
## [2,] Percent 1.8 6.4 10.7 24.8 13.1 43.1 100
18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v2_whoqol_itm18)
v2_quol_recode(v2_clin$v2_whoqol_bref_who18_arbeitsfhgk,v2_con$v2_whoqol_bref_who18,"v2_whoqol_itm18",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 71 199 181 350 206 779 1786
## [2,] Percent 4 11.1 10.1 19.6 11.5 43.6 100
19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v2_whoqol_itm19)
v2_quol_recode(v2_clin$v2_whoqol_bref_who19_selbstzufried,v2_con$v2_whoqol_bref_who19,"v2_whoqol_itm19",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 48 137 227 454 151 769 1786
## [2,] Percent 2.7 7.7 12.7 25.4 8.5 43.1 100
20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v2_whoqol_itm20)
v2_quol_recode(v2_clin$v2_whoqol_bref_who20_pers_bezieh,v2_con$v2_whoqol_bref_who20,"v2_whoqol_itm20",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 33 107 228 457 187 774 1786
## [2,] Percent 1.8 6 12.8 25.6 10.5 43.3 100
21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v2_whoqol_itm21)
v2_quol_recode(v2_clin$v2_whoqol_bref_who21_sexualleben,v2_con$v2_whoqol_bref_who21,"v2_whoqol_itm21",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 98 179 284 307 129 789 1786
## [2,] Percent 5.5 10 15.9 17.2 7.2 44.2 100
22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v2_whoqol_itm22)
v2_quol_recode(v2_clin$v2_whoqol_bref_who22_freunde,v2_con$v2_whoqol_bref_who22,"v2_whoqol_itm22",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 38 61 221 453 237 776 1786
## [2,] Percent 2.1 3.4 12.4 25.4 13.3 43.4 100
23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v2_whoqol_itm23)
v2_quol_recode(v2_clin$v2_whoqol_bref_who23_wohnbeding,v2_con$v2_whoqol_bref_who23,"v2_whoqol_itm23",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 48 63 149 450 308 768 1786
## [2,] Percent 2.7 3.5 8.3 25.2 17.2 43 100
24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v2_whoqol_itm24)
v2_quol_recode(v2_clin$v2_whoqol_bref_who24_gesundhdiens,v2_con$v2_whoqol_bref_who24,"v2_whoqol_itm24",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 17 20 117 470 391 771 1786
## [2,] Percent 1 1.1 6.6 26.3 21.9 43.2 100
25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v2_whoqol_itm25)
v2_quol_recode(v2_clin$v2_whoqol_bref_who25_transport,v2_con$v2_whoqol_bref_who25,"v2_whoqol_itm25",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 18 44 115 429 409 771 1786
## [2,] Percent 1 2.5 6.4 24 22.9 43.2 100
26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v2_whoqol_itm26)
Coding reversed so that higher scores mean symptoms less often.
v2_quol_recode(v2_clin$v2_whoqol_bref_who26_neg_gefuehle,v2_con$v2_whoqol_bref_who26,"v2_whoqol_itm26",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 28 149 241 373 215 780 1786
## [2,] Percent 1.6 8.3 13.5 20.9 12 43.7 100
Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).
Global (continuous [4-20],v2_whoqol_dom_glob)
v2_whoqol_dom_glob_df<-data.frame(as.numeric(v2_whoqol_itm1),as.numeric(v2_whoqol_itm2))
v2_who_glob_no_nas<-rowSums(is.na(v2_whoqol_dom_glob_df))
v2_whoqol_dom_glob<-ifelse((v2_who_glob_no_nas==0) | (v2_who_glob_no_nas==1),
rowMeans(v2_whoqol_dom_glob_df,na.rm=T)*4,NA)
v2_whoqol_dom_glob<-round(v2_whoqol_dom_glob,2)
summary(v2_whoqol_dom_glob)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.00 14.00 14.26 16.00 20.00 768
Physical Health (continuous [4-20],v2_whoqol_dom_phys)
v2_whoqol_dom_phys_df<-data.frame(as.numeric(v2_whoqol_itm3),as.numeric(v2_whoqol_itm10),as.numeric(v2_whoqol_itm16),as.numeric(v2_whoqol_itm15),as.numeric(v2_whoqol_itm17),as.numeric(v2_whoqol_itm4),as.numeric(v2_whoqol_itm18))
v2_who_phys_no_nas<-rowSums(is.na(v2_whoqol_dom_phys_df))
v2_whoqol_dom_phys<-ifelse((v2_who_phys_no_nas==0) | (v2_who_phys_no_nas==1),
rowMeans(v2_whoqol_dom_phys_df,na.rm=T)*4,NA)
v2_whoqol_dom_phys<-round(v2_whoqol_dom_phys,2)
summary(v2_whoqol_dom_phys)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.14 13.14 15.43 15.24 17.71 20.00 783
Psychological (continuous [4-20],v2_whoqol_dom_psy)
v2_whoqol_dom_psy_df<-data.frame(as.numeric(v2_whoqol_itm5),as.numeric(v2_whoqol_itm7),as.numeric(v2_whoqol_itm19),as.numeric(v2_whoqol_itm11),as.numeric(v2_whoqol_itm26),as.numeric(v2_whoqol_itm6))
v2_who_psy_no_nas<-rowSums(is.na(v2_whoqol_dom_psy_df))
v2_whoqol_dom_psy<-ifelse((v2_who_psy_no_nas==0) | (v2_who_psy_no_nas==1),
rowMeans(v2_whoqol_dom_psy_df,na.rm=T)*4,NA)
v2_whoqol_dom_psy<-round(v2_whoqol_dom_psy,2)
summary(v2_whoqol_dom_psy)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.00 14.67 14.29 16.67 20.00 784
Social relationships (continuous [4-20],v2_whoqol_dom_soc)
v2_whoqol_dom_soc_df<-data.frame(as.numeric(v2_whoqol_itm20),as.numeric(v2_whoqol_itm22),as.numeric(v2_whoqol_itm21))
v2_who_soc_no_nas<-rowSums(is.na(v2_whoqol_dom_soc_df))
v2_whoqol_dom_soc<-ifelse((v2_who_soc_no_nas==0) | (v2_who_soc_no_nas==1),
rowMeans(v2_whoqol_dom_soc_df,na.rm=T)*4,NA)
v2_whoqol_dom_soc<-round(v2_whoqol_dom_soc,2)
summary(v2_whoqol_dom_soc)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.00 14.67 14.17 16.00 20.00 769
Environment (continuous [4-20],v2_whoqol_dom_env)
v2_whoqol_dom_env_df<-data.frame(as.numeric(v2_whoqol_itm8),as.numeric(v2_whoqol_itm23),as.numeric(v2_whoqol_itm12),as.numeric(v2_whoqol_itm24),as.numeric(v2_whoqol_itm13),as.numeric(v2_whoqol_itm14),as.numeric(v2_whoqol_itm9),as.numeric(v2_whoqol_itm25))
v2_who_env_no_nas<-rowSums(is.na(v2_whoqol_dom_env_df))
v2_whoqol_dom_env<-ifelse((v2_who_env_no_nas==0) | (v2_who_env_no_nas==1),
rowMeans(v2_whoqol_dom_env_df,na.rm=T)*4,NA)
v2_whoqol_dom_env<-round(v2_whoqol_dom_env,2)
summary(v2_whoqol_dom_env)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 6.00 14.50 16.00 15.92 18.00 20.00 781
Create dataset
v2_whoqol<-data.frame(v2_whoqol_itm1,v2_whoqol_itm2,v2_whoqol_itm3,v2_whoqol_itm4,
v2_whoqol_itm5,v2_whoqol_itm6,v2_whoqol_itm7,v2_whoqol_itm8,
v2_whoqol_itm9,v2_whoqol_itm10,v2_whoqol_itm11,v2_whoqol_itm12,
v2_whoqol_itm13,v2_whoqol_itm14,v2_whoqol_itm15,v2_whoqol_itm16,
v2_whoqol_itm17,v2_whoqol_itm18,v2_whoqol_itm19,v2_whoqol_itm20,
v2_whoqol_itm21,v2_whoqol_itm22,v2_whoqol_itm23,v2_whoqol_itm24,
v2_whoqol_itm25,v2_whoqol_itm26,v2_whoqol_dom_glob,
v2_whoqol_dom_phys,v2_whoqol_dom_psy,v2_whoqol_dom_soc,
v2_whoqol_dom_env)
v2_df<-data.frame(v2_id,
v2_rec,
v2_clin_ill_ep,
v2_con_problems,
v2_dem,
v2_ev_prc_fst_ep,
v2_suic,
v2_leprcp,
v2_med,
v2_subst,
v2_symp_panss,
v2_symp_ids_c,
v2_symp_ymrs,
v2_ill_sev,
v2_nrpsy,
v2_sf12,
v2_med_adh,
v2_bdi2,
v2_asrm,
v2_mss,
v2_leq,
v2_whoqol)
## [1] 1323
## [1] 466
v3_clin<-subset(v3_clin, as.character(v3_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v3_clin)[1]
## [1] 1320
v3_con<-subset(v3_con, as.character(v3_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v3_con)[1]
## [1] 466
v3_id<-as.factor(c(as.character(v3_clin$mnppsd),as.character(v3_con$mnppsd)))
##Create separation column
v3_sep<-rep(as.factor("XXXXX"),(dim(v3_clin)[1]+dim(v3_con)[1]))
v3_interv_date<-c(as.Date(as.character(v3_clin$v3_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v3_con$v3_rekru_visit_rekr_datum), "%Y%m%d"))
v3_age_years_clin<-as.numeric(substr(v3_clin$v3_ausschluss1_rekr_datum,1,4))-
as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))
v3_age_years_con<-as.numeric(substr(v3_con$v3_rekru_visit_rekr_datu,1,4))-
as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))
v3_age_years<-c(v3_age_years_clin,v3_age_years_con)
v3_age<-ifelse(c(as.numeric(substr(v3_clin$v3_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v3_con$v3_rekru_visit_rekr_datu,5,6)))<
c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
v3_age_years-1,v3_age_years)
summary(v3_age)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 19.00 30.00 45.00 43.34 54.00 87.00 845
Create dataset
v3_rec<-data.frame(v3_age,v3_interv_date)
Please see Visit 2 for explanation.
“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v3_clin_ill_ep_snc_lst)
v3_clin_ill_ep_snc_lst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_ill_ep_snc_lst<-ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==1,"Y",
ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==2,"N",
ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==3,"C",v3_clin_ill_ep_snc_lst)))
v3_clin_ill_ep_snc_lst<-factor(v3_clin_ill_ep_snc_lst)
descT(v3_clin_ill_ep_snc_lst)
## -999 C N Y <NA>
## [1,] No. cases 466 91 386 181 662 1786
## [2,] Percent 26.1 5.1 21.6 10.1 37.1 100
“If yes, how many illness episodes? (continuous [no. illness episodes], v3_clin_no_ep)”
v3_clin_no_ep<-ifelse(v3_clin_ill_ep_snc_lst=="Y",c(v3_clin$v3_aktu_situat_anzahl_episoden,rep(-999,dim(v3_con)[1])),-999)
descT(v3_clin_no_ep)
## -999 1 2 3 4 <NA>
## [1,] No. cases 943 136 36 2 1 668 1786
## [2,] Percent 52.8 7.6 2 0.1 0.1 37.4 100
In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).
“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_man)
v3_clin_fst_ill_ep_man<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_man<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y",
-999)
descT(v3_clin_fst_ill_ep_man)
## -999 Y <NA>
## [1,] No. cases 1100 36 650 1786
## [2,] Percent 61.6 2 36.4 100
“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_dep)
v3_clin_fst_ill_ep_dep<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_dep<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y",
-999)
descT(v3_clin_fst_ill_ep_dep)
## -999 Y <NA>
## [1,] No. cases 1035 101 650 1786
## [2,] Percent 58 5.7 36.4 100
“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).
v3_clin_fst_ill_ep_mx<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_mx<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y",
-999)
descT(v3_clin_fst_ill_ep_mx)
## -999 Y <NA>
## [1,] No. cases 1124 12 650 1786
## [2,] Percent 62.9 0.7 36.4 100
“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_psy)
v3_clin_fst_ill_ep_psy<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_psy<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y",
-999)
descT(v3_clin_fst_ill_ep_psy)
## -999 Y <NA>
## [1,] No. cases 1089 47 650 1786
## [2,] Percent 61 2.6 36.4 100
“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v3_clin_fst_ill_ep_dur)
v3_clin_fst_ill_ep_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_dur<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",
ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
ifelse(v3_clin_ill_ep_snc_lst=="N",-999,v3_clin_fst_ill_ep_dur))))
v3_clin_fst_ill_ep_dur<-ordered(v3_clin_fst_ill_ep_dur,
levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_fst_ill_ep_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 852 32 44 100
## [2,] Percent 47.7 1.8 2.5 5.6
## <NA>
## [1,] 758 1786
## [2,] 42.4 100
“During this episode, were you hospitalized?” (dichotomous, v3_clin_fst_ill_ep_hsp)
v3_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="N",-999,
ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v3_con)[1]))==2,"N",
ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", v3_clin_fst_ill_ep_hsp)))
descT(v3_clin_fst_ill_ep_hsp)
## -999 N Y <NA>
## [1,] No. cases 852 94 84 756 1786
## [2,] Percent 47.7 5.3 4.7 42.3 100
“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v3_clin_fst_ill_ep_hsp_dur)
v3_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_hsp_dur<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",
ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
-999)))
v3_clin_fst_ill_ep_hsp_dur<-ordered(v3_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_fst_ill_ep_hsp_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 1037 14 19 44
## [2,] Percent 58.1 0.8 1.1 2.5
## <NA>
## [1,] 672 1786
## [2,] 37.6 100
The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):
Reason for hospitalization: symptom worsensing (checkbox [Y], v3_clin_fst_ill_ep_symp_wrs)
v3_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_symp_wrs<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y",-999)
descT(v3_clin_fst_ill_ep_symp_wrs)
## -999 Y <NA>
## [1,] No. cases 1067 68 651 1786
## [2,] Percent 59.7 3.8 36.5 100
Reason for hospitalization: self-endangerment (checkbox [Y], v3_clin_fst_ill_ep_slf_end)
v3_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_slf_end<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",
-999)
descT(v3_clin_fst_ill_ep_slf_end)
## -999 Y <NA>
## [1,] No. cases 1126 10 650 1786
## [2,] Percent 63 0.6 36.4 100
Reason for hospitalization: suicidality (checkbox [Y], v3_clin_fst_ill_ep_suic)
v3_clin_fst_ill_ep_suic<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_suic<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",
-999)
descT(v3_clin_fst_ill_ep_suic)
## -999 Y <NA>
## [1,] No. cases 1125 11 650 1786
## [2,] Percent 63 0.6 36.4 100
Reason for hospitalization: endangerment of others (checkbox [Y], v3_clin_fst_ill_ep_oth_end)
v3_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_oth_end<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
descT(v3_clin_fst_ill_ep_oth_end)
## -999 Y <NA>
## [1,] No. cases 1133 3 650 1786
## [2,] Percent 63.4 0.2 36.4 100
Reason for hospitalization: medication change (checkbox [Y], v3_clin_fst_ill_ep_med_chg)
v3_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_med_chg<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
descT(v3_clin_fst_ill_ep_med_chg)
## -999 Y <NA>
## [1,] No. cases 1125 11 650 1786
## [2,] Percent 63 0.6 36.4 100
Reason for hospitalization: other (checkbox [Y], v3_clin_fst_ill_ep_othr)
v3_clin_fst_ill_ep_othr<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_othr<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
descT(v3_clin_fst_ill_ep_othr)
## -999 Y <NA>
## [1,] No. cases 1126 10 650 1786
## [2,] Percent 63 0.6 36.4 100
“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_man)
v3_clin_sec_ill_ep_man<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_man<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",-999)
descT(v3_clin_sec_ill_ep_man)
## -999 Y <NA>
## [1,] No. cases 965 6 815 1786
## [2,] Percent 54 0.3 45.6 100
“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_dep) #frstill
v3_clin_sec_ill_ep_dep<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_dep<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",
-999)
descT(v3_clin_sec_ill_ep_dep)
## -999 Y <NA>
## [1,] No. cases 952 19 815 1786
## [2,] Percent 53.3 1.1 45.6 100
“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).
v3_clin_sec_ill_ep_mx<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_mx<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",
-999)
descT(v3_clin_sec_ill_ep_mx)
## -999 Y <NA>
## [1,] No. cases 970 1 815 1786
## [2,] Percent 54.3 0.1 45.6 100
“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_psy)
v3_clin_sec_ill_ep_psy<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_psy<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",
-999)
descT(v3_clin_sec_ill_ep_psy)
## -999 Y <NA>
## [1,] No. cases 968 3 815 1786
## [2,] Percent 54.2 0.2 45.6 100
“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v3_clin_sec_ill_ep_dur)
v3_clin_sec_ill_ep_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_dur<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",
ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
ifelse(v3_clin_ill_ep_snc_lst=="N",-999,v3_clin_sec_ill_ep_dur))))
v3_clin_sec_ill_ep_dur<-ordered(v3_clin_sec_ill_ep_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_sec_ill_ep_dur)
## less than two weeks two to four weeks more than four weeks <NA>
## [1,] No. cases 7 7 12 1760
## [2,] Percent 0.4 0.4 0.7 98.5
##
## [1,] 1786
## [2,] 100
“During this episode, were you hospitalized?” (dichotomous, v3_clin_sec_ill_ep_hsp)
v3_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="N",-999,
ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v3_con)[1]))==2,"N",
ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y", v3_clin_sec_ill_ep_hsp)))
descT(v3_clin_sec_ill_ep_hsp)
## -999 N Y <NA>
## [1,] No. cases 852 14 11 909 1786
## [2,] Percent 47.7 0.8 0.6 50.9 100
“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v3_clin_sec_ill_ep_hsp_dur)
v3_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_hsp_dur<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",
ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
-999)))
v3_clin_sec_ill_ep_hsp_dur<-ordered(v3_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_sec_ill_ep_hsp_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 957 3 4 3
## [2,] Percent 53.6 0.2 0.2 0.2
## <NA>
## [1,] 819 1786
## [2,] 45.9 100
The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes): Reason for hospitalization: symptom worsensing (checkbox [Y], v3_clin_sec_ill_ep_symp_wrs)
v3_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_symp_wrs<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",-999)
descT(v3_clin_sec_ill_ep_symp_wrs)
## -999 Y <NA>
## [1,] No. cases 964 7 815 1786
## [2,] Percent 54 0.4 45.6 100
Reason for hospitalization: self-endangerment (checkbox [Y], v3_clin_sec_ill_ep_slf_end)
v3_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_slf_end<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",
-999)
descT(v3_clin_sec_ill_ep_slf_end)
## -999 Y <NA>
## [1,] No. cases 970 1 815 1786
## [2,] Percent 54.3 0.1 45.6 100
Reason for hospitalization: suicidality (checkbox [Y], v3_clin_sec_ill_ep_suic)
v3_clin_sec_ill_ep_suic<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_suic<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",
-999)
descT(v3_clin_sec_ill_ep_suic)
## -999 Y <NA>
## [1,] No. cases 969 2 815 1786
## [2,] Percent 54.3 0.1 45.6 100
Reason for hospitalization: endangerment of others (checkbox [Y], v3_clin_sec_ill_ep_oth_end)
v3_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_oth_end<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
descT(v3_clin_sec_ill_ep_oth_end)
## -999 <NA>
## [1,] No. cases 971 815 1786
## [2,] Percent 54.4 45.6 100
Reason for hospitalization: medication change (checkbox [Y], v3_clin_sec_ill_ep_med_chg)
v3_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_med_chg<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
descT(v3_clin_sec_ill_ep_med_chg)
## -999 Y <NA>
## [1,] No. cases 968 3 815 1786
## [2,] Percent 54.2 0.2 45.6 100
Reason for hospitalization: other (checkbox [Y], v3_clin_sec_ill_ep_othr)
v3_clin_sec_ill_ep_othr<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_othr<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
descT(v3_clin_sec_ill_ep_othr)
## -999 <NA>
## [1,] No. cases 971 815 1786
## [2,] Percent 54.4 45.6 100
v3_clin_add_oth_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_add_oth_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_aufent,rep(-999,dim(v3_con)[1]))==1,"Y","N")
descT(v3_clin_add_oth_hsp)
## N Y <NA>
## [1,] No. cases 1102 18 666 1786
## [2,] Percent 61.7 1 37.3 100
v3_clin_oth_hsp_nmb<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_oth_hsp_nmb<-ifelse(v3_clin_add_oth_hsp=="Y",
c(v3_clin$v3_aktu_situat_aenderung_anzahl,rep(-999,dim(v3_con)[1])),-999)
descT(v3_clin_oth_hsp_nmb)
## -999 1 2 <NA>
## [1,] No. cases 1102 10 2 672 1786
## [2,] Percent 61.7 0.6 0.1 37.6 100
v3_clin_oth_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_oth_hsp_dur<-
ifelse(v3_clin_add_oth_hsp=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",
ifelse(v3_clin_add_oth_hsp=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
ifelse(v3_clin_add_oth_hsp=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
ifelse(v3_clin_add_oth_hsp=="N",-999,v3_clin_add_oth_hsp))))
v3_clin_oth_hsp_dur<-ordered(v3_clin_oth_hsp_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_oth_hsp_dur)
## less than two weeks two to four weeks more than four weeks <NA>
## [1,] No. cases 3 8 5 1770
## [2,] Percent 0.2 0.4 0.3 99.1
##
## [1,] 1786
## [2,] 100
v3_clin_othr_psy_med<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_othr_psy_med<-ifelse(v3_clin_add_oth_hsp=="Y" & v3_clin_add_oth_hsp=="Y" &
c(v3_clin$v3_aktu_situat_aenderung_medikament,rep(-999,dim(v3_con)[1]))==1,"Y",
ifelse(v3_clin_add_oth_hsp=="N",-999,v3_clin_othr_psy_med))
descT(v3_clin_othr_psy_med)
## -999 Y <NA>
## [1,] No. cases 1102 4 680 1786
## [2,] Percent 61.7 0.2 38.1 100
This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.
v3_clin_cur_psy_trm<-rep(NA,dim(v3_clin)[1])
v3_con_cur_psy_trm<-rep(NA,dim(v3_con)[1])
v3_clin_cur_psy_trm<-ifelse(v3_clin$v3_aktu_situat_psybehandlung==0,"1",
ifelse(v3_clin$v3_aktu_situat_psybehandlung==3,"2",
ifelse(v3_clin$v3_aktu_situat_psybehandlung==2,"3",
ifelse(v3_clin$v3_aktu_situat_psybehandlung==1,"4",v3_clin_cur_psy_trm))))
v3_con_cur_psy_trm<-ifelse(v3_con$v3_bildung_beruf_psybehandlung==0,"1",
ifelse(v3_con$v3_bildung_beruf_psybehandlung==3,"2",
ifelse(v3_con$v3_bildung_beruf_psybehandlung==2,"3",
ifelse(v3_con$v3_bildung_beruf_psybehandlung==1,"4",v3_con_cur_psy_trm))))
v3_cur_psy_trm<-factor(c(v3_clin_cur_psy_trm,v3_con_cur_psy_trm),ordered=T)
descT(v3_cur_psy_trm)
## 1 2 3 4 <NA>
## [1,] No. cases 304 559 7 37 879 1786
## [2,] Percent 17 31.3 0.4 2.1 49.2 100
Create dataset
v3_clin_ill_ep<-data.frame(v3_clin_ill_ep_snc_lst,
v3_clin_no_ep,
v3_clin_fst_ill_ep_man,
v3_clin_fst_ill_ep_dep,
v3_clin_fst_ill_ep_mx,
v3_clin_fst_ill_ep_psy,
v3_clin_fst_ill_ep_dur,
v3_clin_fst_ill_ep_hsp,
v3_clin_fst_ill_ep_hsp_dur,
v3_clin_fst_ill_ep_symp_wrs,
v3_clin_fst_ill_ep_slf_end,
v3_clin_fst_ill_ep_suic,
v3_clin_fst_ill_ep_oth_end,
v3_clin_fst_ill_ep_med_chg,
v3_clin_fst_ill_ep_othr,
v3_clin_sec_ill_ep_man,
v3_clin_sec_ill_ep_dep,
v3_clin_sec_ill_ep_mx,
v3_clin_sec_ill_ep_psy,
v3_clin_sec_ill_ep_dur,
v3_clin_sec_ill_ep_hsp,
v3_clin_sec_ill_ep_hsp_dur,
v3_clin_sec_ill_ep_symp_wrs,
v3_clin_sec_ill_ep_slf_end,
v3_clin_sec_ill_ep_suic,
v3_clin_sec_ill_ep_oth_end,
v3_clin_sec_ill_ep_med_chg,
v3_clin_sec_ill_ep_othr,
v3_clin_add_oth_hsp,
v3_clin_oth_hsp_nmb,
v3_clin_oth_hsp_dur,
v3_clin_othr_psy_med,
v3_cur_psy_trm)
See Visit 1 marital status item for general explanation of the next two items.
Did your marital status change since the last study visit? (dichotomous, v3_cng_mar_stat)
v3_clin_cng_mar_stat<-rep(NA,dim(v3_clin)[1])
v3_clin_cng_mar_stat<-ifelse(v3_clin$v3_aktu_situat_fam_stand==1, "Y",
ifelse(v3_clin$v3_aktu_situat_fam_stand==2, "N", v3_clin_cng_mar_stat))
v3_con_cng_mar_stat<-rep(NA,dim(v3_con)[1])
v3_con_cng_mar_stat<-ifelse(v3_con$v3_famil_wohn_fam_stand==1, "Y",
ifelse(v3_con$v3_famil_wohn_fam_stand==2, "N", v3_con_cng_mar_stat))
v3_cng_mar_stat<-factor(c(v3_clin_cng_mar_stat,v3_con_cng_mar_stat))
v3_clin_marital_stat<-rep(NA,dim(v3_clin)[1])
v3_clin_marital_stat<-ifelse(v3_clin$v3_aktu_situat_fam_familienstand==1,"Married",
ifelse(v3_clin$v3_aktu_situat_fam_familienstand==2,"Married_living_sep",
ifelse(v3_clin$v3_aktu_situat_fam_familienstand==3,"Single",
ifelse(v3_clin$v3_aktu_situat_fam_familienstand==4,"Divorced",
ifelse(v3_clin$v3_aktu_situat_fam_familienstand==5,"Widowed",v3_clin_marital_stat)))))
v3_con_marital_stat<-rep(NA,dim(v3_con)[1])
v3_con_marital_stat<-ifelse(v3_con$v3_famil_wohn_fam_famstand==1,"Married",
ifelse(v3_con$v3_famil_wohn_fam_famstand==2,"Married_living_sep",
ifelse(v3_con$v3_famil_wohn_fam_famstand==3,"Single",
ifelse(v3_con$v3_famil_wohn_fam_famstand==4,"Divorced",
ifelse(v3_con$v3_famil_wohn_fam_famstand==5,"Widowed",v3_con_marital_stat)))))
v3_marital_stat<-factor(c(v3_clin_marital_stat,v3_con_marital_stat))
desc(v3_marital_stat)
## Divorced Married Married_living_sep Single Widowed NA's
## [1,] No. cases 130 227 38 523 12 856 1786
## [2,] Percent 7.3 12.7 2.1 29.3 0.7 47.9 100
v3_clin_partner<-rep(NA,dim(v3_clin)[1])
v3_clin_partner<-ifelse(v3_clin$v3_aktu_situat_fam_fest_partner==1,"Y",
ifelse(v3_clin$v3_aktu_situat_fam_fest_partner==2,"N",v3_clin_partner))
v3_con_partner<-rep(NA,dim(v3_con)[1])
v3_con_partner<-ifelse(v3_con$v3_famil_wohn_fam_partner==1,"Y",
ifelse(v3_con$v3_famil_wohn_fam_partner==2,"N",v3_con_partner))
v3_partner<-factor(c(v3_clin_partner,v3_con_partner))
descT(v3_partner)
## N Y <NA>
## [1,] No. cases 449 470 867 1786
## [2,] Percent 25.1 26.3 48.5 100
v3_no_bio_chld<-c(v3_clin$v3_aktu_situat_fam_kind_gesamt,v3_con$v3_famil_wohn_fam_lkind)
descT(v3_no_bio_chld)
## 0 1 2 3 4 5 <NA>
## [1,] No. cases 582 158 113 64 11 2 856 1786
## [2,] Percent 32.6 8.8 6.3 3.6 0.6 0.1 47.9 100
v3_no_adpt_chld<-c(v3_clin$v3_aktu_situat_fam_adopt_gesamt,v3_con$v3_famil_wohn_fam_adkind)
descT(v3_no_adpt_chld)
## 0 1 2 <NA>
## [1,] No. cases 921 2 1 862 1786
## [2,] Percent 51.6 0.1 0.1 48.3 100
v3_stp_chld<-c(v3_clin$v3_aktu_situat_fam_stift_gesamt,v3_con$v3_famil_wohn_fam_skind)
descT(v3_stp_chld)
## 0 1 2 3 4 <NA>
## [1,] No. cases 837 45 19 4 2 879 1786
## [2,] Percent 46.9 2.5 1.1 0.2 0.1 49.2 100
v3_clin_chg_hsng<-rep(NA,dim(v3_clin)[1])
v3_clin_chg_hsng<-ifelse(v3_clin$v3_wohnsituation_wohn_aenderung==1,"Y",
ifelse(v3_clin$v3_wohnsituation_wohn_aenderung==2,"N",v3_clin_chg_hsng))
v3_con_chg_hsng<-rep(NA,dim(v3_con)[1])
v3_con_chg_hsng<-ifelse(v3_con$v3_famil_wohn_wohn_stand==1,"Y",
ifelse(v3_con$v3_famil_wohn_wohn_stand==2,"N",v3_con_chg_hsng))
v3_chg_hsng<-factor(c(v3_clin_chg_hsng,v3_con_chg_hsng))
descT(v3_chg_hsng)
## N Y <NA>
## [1,] No. cases 839 98 849 1786
## [2,] Percent 47 5.5 47.5 100
v3_clin_liv_aln<-rep(NA,dim(v3_clin)[1])
v3_clin_liv_aln<-ifelse(v3_clin$v3_wohnsituation_wohn_allein==1,"Y",
ifelse(v3_clin$v3_wohnsituation_wohn_allein==0,"N",v3_clin_liv_aln))
v3_con_liv_aln<-rep(NA,dim(v3_con)[1])
v3_con_liv_aln<-ifelse(v3_con$v3_famil_wohn_wohn_allein==1,"Y",
ifelse(v3_con$v3_famil_wohn_wohn_allein==0,"N",v3_con_liv_aln))
v3_liv_aln<-factor(c(v3_clin_liv_aln,v3_con_liv_aln))
descT(v3_liv_aln)
## N Y <NA>
## [1,] No. cases 584 365 837 1786
## [2,] Percent 32.7 20.4 46.9 100
Did your employment situation change since the last study visit?
v3_clin_chg_empl_stat<-rep(NA,dim(v3_clin)[1])
v3_clin_chg_empl_stat<-ifelse(v3_clin$v3_wohnsituation_erwerb_aenderung==1, "Y",
ifelse(v3_clin$v3_wohnsituation_erwerb_aenderung==2, "N",v3_clin_chg_empl_stat))
v3_con_chg_empl_stat<-rep(NA,dim(v3_con)[1])
v3_con_chg_empl_stat<-ifelse(v3_con$v3_bildung_beruf_bild_stand==1, "Y",
ifelse(v3_con$v3_bildung_beruf_bild_stand==2, "N",v3_con_chg_empl_stat))
v3_chg_empl_stat<-factor(c(v3_clin_chg_empl_stat,v3_con_chg_empl_stat))
descT(v3_chg_empl_stat)
## N Y <NA>
## [1,] No. cases 788 131 867 1786
## [2,] Percent 44.1 7.3 48.5 100
Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.
v3_clin_curr_paid_empl<-rep(NA,dim(v3_clin)[1])
v3_clin_curr_paid_empl<-ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==1,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==2,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==3,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==4,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==5,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==6,-999,
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==7,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==8,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==9,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==10,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==11,"N",v3_clin_curr_paid_empl)))))))))))
v3_con_curr_paid_empl<-rep(NA,dim(v3_con)[1])
v3_con_curr_paid_empl<-ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==1,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==2,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==3,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==4,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==5,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==6,-999,
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==7,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==8,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==9,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==10,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==11,"N",v3_con_curr_paid_empl)))))))))))
v3_curr_paid_empl<-factor(c(v3_clin_curr_paid_empl,v3_con_curr_paid_empl))
descT(v3_curr_paid_empl)
## -999 N Y <NA>
## [1,] No. cases 19 435 473 859 1786
## [2,] Percent 1.1 24.4 26.5 48.1 100
NB: Not available (-999) in control participants
v3_clin_disabl_pens<-rep(NA,dim(v3_clin)[1])
v3_clin_disabl_pens<-ifelse(v3_clin$v3_wohnsituation_rente_psych==1,"Y",
ifelse(v3_clin$v3_wohnsituation_rente_psych==2,"N",v3_clin_disabl_pens))
v3_con_disabl_pens<-rep(-999,dim(v3_con)[1])
v3_disabl_pens<-factor(c(v3_clin_disabl_pens,v3_con_disabl_pens))
descT(v3_disabl_pens)
## -999 N Y <NA>
## [1,] No. cases 466 282 266 772 1786
## [2,] Percent 26.1 15.8 14.9 43.2 100
v3_clin_spec_emp<-rep(NA,dim(v3_clin)[1])
v3_clin_spec_emp<-ifelse(v3_clin$v3_wohnsituation_erwerb_werk==1,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerb_werk==2,"N",v3_clin_spec_emp))
v3_con_spec_emp<-rep(NA,dim(v3_con)[1])
v3_con_spec_emp<-ifelse(v3_con$v3_bildung_beruf_erwerb_wfbm==1,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_wfbm==2,"N",v3_con_spec_emp))
v3_spec_emp<-factor(c(v3_clin_spec_emp,v3_con_spec_emp))
descT(v3_spec_emp)
## N Y <NA>
## [1,] No. cases 399 62 1325 1786
## [2,] Percent 22.3 3.5 74.2 100
Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.
v3_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v3_clin)[1])
v3_clin_wrk_abs_pst_6_mths<-ifelse((v3_clin$v3_wohnsituation_erwerb_unbekannt==1 | v3_clin$v3_wohnsituation_erwerb_rente==1 |
v3_clin$v3_wohnsituation_erwerb_fehlen>26),-999, v3_clin$v3_wohnsituation_erwerb_fehlen)
v3_con_wrk_abs_pst_6_mths<-rep(NA,dim(v3_con)[1])
v3_con_wrk_abs_pst_6_mths<-ifelse((v3_con$v3_bildung_beruf_erwerb_ausfallu==1 | v3_con$v3_bildung_beruf_erwerb_rente==1 |
v3_con$v3_bildung_beruf_erwerb_ausfallm>26),-999, v3_con$v3_bildung_beruf_erwerb_ausfallm)
v3_wrk_abs_pst_6_mths<-c(v3_clin_wrk_abs_pst_6_mths,v3_con_wrk_abs_pst_6_mths)
descT(v3_wrk_abs_pst_6_mths)
## -999 0 1 2 3 4 6 7 8 9 10 11 12 13 14
## [1,] No. cases 337 265 12 15 9 6 3 2 9 1 2 1 4 1 1
## [2,] Percent 18.9 14.8 0.7 0.8 0.5 0.3 0.2 0.1 0.5 0.1 0.1 0.1 0.2 0.1 0.1
## 15 16 17 18 24 26 <NA>
## [1,] 1 2 1 1 15 4 1094 1786
## [2,] 0.1 0.1 0.1 0.1 0.8 0.2 61.3 100
Important: if receiving pension, this question refers to impairments in the household
v3_clin_cur_work_restr<-rep(NA,dim(v3_clin)[1])
v3_clin_cur_work_restr<-ifelse(v3_clin$v3_wohnsituation_erwerb_psysymptom==1,"Y",
ifelse(v3_clin$v3_wohnsituation_erwerb_psysymptom==2,"N",v3_clin_cur_work_restr))
v3_con_cur_work_restr<-rep(NA,dim(v3_con)[1])
v3_con_cur_work_restr<-ifelse(v3_con$v3_bildung_beruf_erwerb_psyeinsch==1,"Y",
ifelse(v3_con$v3_bildung_beruf_erwerb_psyeinsch==2,"N",v3_con_cur_work_restr))
v3_cur_work_restr<-factor(c(v3_clin_cur_work_restr,v3_con_cur_work_restr))
descT(v3_cur_work_restr)
## N Y <NA>
## [1,] No. cases 577 284 925 1786
## [2,] Percent 32.3 15.9 51.8 100
v3_weight<-c(v3_clin$v3_wohnsituation_erwerb_gewicht,v3_con$v3_bildung_beruf_erwerb_gewicht)
summary(v3_weight)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 41 68 80 83 95 175 862
This item was only recorded in a subset of individuals, because the question was introduced while the study was running.
v3_clin_waist<-v3_clin$v3_wohnsituation_erwerb_tailumf
v3_con_waist<-v3_con$v3_bildung_beruf_erwerb_taille
v3_waist<-c(v3_clin_waist,v3_con_waist)
summary(v3_waist)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 60.00 76.00 85.00 89.04 101.00 175.00 1404
We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.
v3_bmi<-v3_weight/(v1_height/100)^2
summary(v3_bmi)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 16.63 22.78 26.33 27.43 30.83 50.78 864
Create dataset
v3_dem<-data.frame(v3_cng_mar_stat,v3_marital_stat,v3_partner,v3_no_bio_chld,v3_no_adpt_chld,v3_stp_chld,v3_chg_hsng,v3_liv_aln,
v3_chg_empl_stat,v3_curr_paid_empl,v3_disabl_pens,v3_spec_emp,v3_wrk_abs_pst_6_mths,v3_cur_work_restr,
v3_weight,v3_bmi,v3_waist)
Please see Visit 2 for explanation.
**Life events: Occurred before illness episode? (dichotomous, v3_evnt_prcp_b4_*)**
for(i in 1:length(grep("v3_ergaenz_leq_leq_zeit_31055_",names(v3_clin)))){
b4_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_zeit_31055_",names(v3_clin))[i]],
paste("v3_evnt_prcp_b4_",i,sep=""))
}
**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v3_evnt_prcp_f_*)**
for(i in 1:length(grep("v3_ergaenz_leq_leq_ausloeser_31055_",names(v3_clin)))){
prcp_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_ausloeser_31055_",names(v3_clin))[i]],
paste("v3_evnt_prcp_f_",i,sep=""))
}
**Life events: LEQ item number (categorical [LEQ item number], v3_evnt_prcp_it_*)**
for(i in 1:length(grep("v3_ergaenz_leq_leq_item_31055_",names(v3_clin)))){
leq_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_item_31055_",names(v3_clin))[i]],
paste("v3_evnt_prcp_it_",i,sep=""))
}
Create dataset
v3_leprcp<-data.frame(v3_evnt_prcp_it_1,v3_evnt_prcp_b4_1,v3_evnt_prcp_f_1,
v3_evnt_prcp_it_2,v3_evnt_prcp_b4_2,v3_evnt_prcp_f_2,
v3_evnt_prcp_it_3,v3_evnt_prcp_b4_3,v3_evnt_prcp_f_3,
v3_evnt_prcp_it_4,v3_evnt_prcp_b4_4,v3_evnt_prcp_f_4,
v3_evnt_prcp_it_5,v3_evnt_prcp_b4_5,v3_evnt_prcp_f_5,
v3_evnt_prcp_it_6,v3_evnt_prcp_b4_6,v3_evnt_prcp_f_6,
v3_evnt_prcp_it_7,v3_evnt_prcp_b4_7,v3_evnt_prcp_f_7,
v3_evnt_prcp_it_8,v3_evnt_prcp_b4_8,v3_evnt_prcp_f_8,
v3_evnt_prcp_it_9,v3_evnt_prcp_b4_9,v3_evnt_prcp_f_9,
v3_evnt_prcp_it_10,v3_evnt_prcp_b4_10,v3_evnt_prcp_f_10,
v3_evnt_prcp_it_11,v3_evnt_prcp_b4_11,v3_evnt_prcp_f_11,
v3_evnt_prcp_it_12,v3_evnt_prcp_b4_12,v3_evnt_prcp_f_12,
v3_evnt_prcp_it_13,v3_evnt_prcp_b4_13,v3_evnt_prcp_f_13,
v3_evnt_prcp_it_14,v3_evnt_prcp_b4_14,v3_evnt_prcp_f_14,
v3_evnt_prcp_it_15,v3_evnt_prcp_b4_15,v3_evnt_prcp_f_15,
v3_evnt_prcp_it_16,v3_evnt_prcp_b4_16,v3_evnt_prcp_f_16,
v3_evnt_prcp_it_17,v3_evnt_prcp_b4_17,v3_evnt_prcp_f_17,
v3_evnt_prcp_it_18,v3_evnt_prcp_b4_18,v3_evnt_prcp_f_18,
v3_evnt_prcp_it_19,v3_evnt_prcp_b4_19,v3_evnt_prcp_f_19,
v3_evnt_prcp_it_20,v3_evnt_prcp_b4_20,v3_evnt_prcp_f_20,
v3_evnt_prcp_it_21,v3_evnt_prcp_b4_21,v3_evnt_prcp_f_21,
v3_evnt_prcp_it_22,v3_evnt_prcp_b4_22,v3_evnt_prcp_f_22,
v3_evnt_prcp_it_23,v3_evnt_prcp_b4_23,v3_evnt_prcp_f_23,
v3_evnt_prcp_it_24,v3_evnt_prcp_b4_24,v3_evnt_prcp_f_24,
v3_evnt_prcp_it_25,v3_evnt_prcp_b4_25,v3_evnt_prcp_f_25,
v3_evnt_prcp_it_26,v3_evnt_prcp_b4_26,v3_evnt_prcp_f_26,
v3_evnt_prcp_it_27,v3_evnt_prcp_b4_27,v3_evnt_prcp_f_27,
v3_evnt_prcp_it_28,v3_evnt_prcp_b4_28,v3_evnt_prcp_f_28,
v3_evnt_prcp_it_29,v3_evnt_prcp_b4_29,v3_evnt_prcp_f_29,
v3_evnt_prcp_it_30,v3_evnt_prcp_b4_30,v3_evnt_prcp_f_30,
v3_evnt_prcp_it_31,v3_evnt_prcp_b4_31,v3_evnt_prcp_f_31)
Please note that all of the following questions refer to suicide attempts and suicidal ideation occurring since the last study visit.
Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.
v3_suic_ide_snc_lst_vst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_suic_ide_snc_lst_vst<-ifelse(c(v3_clin$v3_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v3_con)[1]))==1, "N",
ifelse(c(v3_clin$v3_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v3_con)[1]))==3, "Y", v3_suic_ide_snc_lst_vst))
v3_suic_ide_snc_lst_vst<-factor(v3_suic_ide_snc_lst_vst)
descT(v3_suic_ide_snc_lst_vst)
## -999 N Y <NA>
## [1,] No. cases 466 485 169 666 1786
## [2,] Percent 26.1 27.2 9.5 37.3 100
This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v3_scid_suic_ide<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_scid_suic_ide<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==1, "1",
ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==2, "2",
ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==3, "3",
ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==4, "4",-999))))
v3_scid_suic_ide<-factor(v3_scid_suic_ide,ordered=T)
descT(v3_scid_suic_ide)
## -999 1 2 3 4 <NA>
## [1,] No. cases 951 89 21 28 31 666 1786
## [2,] Percent 53.2 5 1.2 1.6 1.7 37.3 100
This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.
v3_scid_suic_thght_mth<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_scid_suic_thght_mth<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==1, "1",
ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==2, "2",
ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==3, "3",-999)))
v3_scid_suic_thght_mth<-factor(v3_scid_suic_thght_mth,ordered=T)
descT(v3_scid_suic_thght_mth)
## -999 1 2 3 <NA>
## [1,] No. cases 951 69 54 39 673 1786
## [2,] Percent 53.2 3.9 3 2.2 37.7 100
This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v3_scid_suic_note_thgts<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_scid_suic_note_thgts<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==1, "1",
ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==2, "2",
ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==3, "3",
ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==4, "4",-999))))
v3_scid_suic_note_thgts<-factor(v3_scid_suic_note_thgts,ordered=T)
descT(v3_scid_suic_note_thgts)
## -999 1 2 3 4 <NA>
## [1,] No. cases 951 149 5 2 6 673 1786
## [2,] Percent 53.2 8.3 0.3 0.1 0.3 37.7 100
This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.
v3_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_suic_attmpt_snc_lst_vst<-ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==1, "1",
ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==2, "2",
ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==3, "3",-999)))
v3_suic_attmpt_snc_lst_vst<-factor(v3_suic_attmpt_snc_lst_vst,ordered=T)
descT(v3_suic_attmpt_snc_lst_vst)
## -999 1 2 3 <NA>
## [1,] No. cases 466 629 2 13 676 1786
## [2,] Percent 26.1 35.2 0.1 0.7 37.8 100
This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.
v3_no_suic_attmpt<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_no_suic_attmpt<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999, ifelse(v3_suic_attmpt_snc_lst_vst>1, c(v3_clin$v3_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v3_con)[1])),v3_no_suic_attmpt))
v3_no_suic_attmpt<-factor(v3_no_suic_attmpt,ordered=T)
descT(v3_no_suic_attmpt)
## -999 1 2 <NA>
## [1,] No. cases 1095 13 2 676 1786
## [2,] Percent 61.3 0.7 0.1 37.8 100
This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.
v3_prep_suic_attp_ord<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_prep_suic_attp_ord<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999,
ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==1, "1",
ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==2, "2",
ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==3, "3",
ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==4, "4",
v3_prep_suic_attp_ord)))))
v3_prep_suic_attp_ord<-factor(v3_prep_suic_attp_ord,ordered=T)
descT(v3_prep_suic_attp_ord)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1095 7 3 2 3 676 1786
## [2,] Percent 61.3 0.4 0.2 0.1 0.2 37.8 100
This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v3_suic_note_attmpt<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_suic_note_attmpt<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999,
ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==1, "1",
ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==2, "2",
ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==3, "3",
ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==4, "4",
v3_suic_note_attmpt)))))
v3_suic_note_attmpt<-factor(v3_suic_note_attmpt,ordered=T)
descT(v3_suic_note_attmpt)
## -999 1 3 4 <NA>
## [1,] No. cases 1095 8 1 3 679 1786
## [2,] Percent 61.3 0.4 0.1 0.2 38 100
Create dataset
v3_suic<-data.frame(v3_suic_ide_snc_lst_vst,v3_scid_suic_ide,v3_scid_suic_thght_mth,v3_scid_suic_note_thgts,
v3_suic_attmpt_snc_lst_vst,v3_no_suic_attmpt,v3_prep_suic_attp_ord,
v3_suic_note_attmpt)
The code below creates the following variables that summarize the medication of each individual:
Number of antidepressants prescribed (continuous [number], v3_Antidepressants) Number of antipsychotics prescribed (continuous [number], v3_Antipsychotics) Number of mood stabilizers prescribed (continuous [number], v3_Mood_stabilizers) Number of tranquilizers prescribed (continuous [number], v3_Tranquilizers) Number of other psychiatric medications (continuous [number], v3_Other_psychiatric)
#get the following variables from v3_clin
#1. Medication name ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name ["_depot_medi_2"]
#4. Depot category ["_depot_kategorie_2"]
#5. Bedarf name ["_bedarf_medi_1"]
#6. Bedarf category ["_bedarf_kategorie_1"]
v3_clin_medication_variables_1<-as.data.frame(v3_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v3_clin))])
dim(v3_clin_medication_variables_1)
## [1] 1320 61
#recode the variables that are coded as characters/logicals in the "v3_clin_medication_variables_1" as factors
v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_15<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_15)
v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_15<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_15)
v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_16<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_16)
v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_16<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_16)
v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_17<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_17)
v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_17<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_17)
v3_clin_medication_variables_1$v3_medikabehand3_depot_medi_200170_3<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_depot_medi_200170_3)
v3_clin_medication_variables_1$v3_medikabehand3_depot_kategorie_200170_3<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_depot_kategorie_200170_3)
v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_8<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_8)
v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_8<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_8)
v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_9<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_9)
v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_9)
v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_10<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_10)
v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_10)
#make the duplicated data frame
v3_clin_medications_duplicated_1<-as.data.frame(t(apply(v3_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v3_clin_medications_duplicated_1)
## [1] 1320 30
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_".
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_clin, not as character
v3_clin_medication_variables_1[,!c(TRUE, FALSE)][v3_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v3_clin_medication_variables_1)
## [1] 1320 61
#bind columns id and medication names, but not categories together
v3_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v3_clin_medication_variables_1[,1], v3_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v3_clin_medication_name_1)
## [1] 1320 31
#get the medication categories from the "_medication_variables_1" dataframe
v3_clin_medication_categories_1<-as.data.frame(v3_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v3_clin_medication_categories_1)
## [1] 1320 31
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_clin, not as character
#Important: v3_clin_medication_name_1=="NA" replaced with is.na(v3_clin_medication_name_1)
v3_clin_medication_categories_1[is.na(v3_clin_medication_name_1)] <- NA
#write.csv(v3_clin_medication_categories_1, file="v3_clin_medication_group_1.csv")
#Make a count table of medications
v3_clin_med_table<-data.frame("mnppsd"=v3_clin$mnppsd)
v3_clin_med_table$v3_Antidepressants<-rowSums(v3_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v3_clin_med_table$v3_Antipsychotics<-rowSums(v3_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v3_clin_med_table$v3_Mood_stabilizers<-rowSums(v3_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v3_clin_med_table$v3_Tranquilizers<-rowSums(v3_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v3_clin_med_table$v3_Other_psychiatric<-rowSums(v3_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
#get the following variables from v3_con
#1. Medication name ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name ["_depot_medi_2"]
#4. Depot category ["_depot_kategorie_2"]
#5. Bedarf name ["_bedarf_medi_2"]
#6. Bedarf category ["_bedarf_kategorie_2"]
v3_con_medication_variables_1<-as.data.frame(v3_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v3_con))])
dim(v3_con_medication_variables_1) #[1] 320 29
## [1] 466 29
#recode the variables that are coded as characters/logicals in the "v3_con_medication_variables_1" as factors
v3_con_medication_variables_1$v3_medikabehand3_med_medi_200705_8<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_med_medi_200705_8)
v3_con_medication_variables_1$v3_medikabehand3_med_kategorie_200705_8<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_med_kategorie_200705_8)
v3_con_medication_variables_1$v3_medikabehand3_bedarf_medi_201187_4<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_bedarf_medi_201187_4)
v3_con_medication_variables_1$v3_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_bedarf_kategorie_201187_4)
#make the duplicated data frame
v3_con_medications_duplicated_1<-as.data.frame(t(apply(v3_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v3_con_medications_duplicated_1)
## [1] 466 14
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_".
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_con, not as character
v3_con_medication_variables_1[,!c(TRUE, FALSE)][v3_con_medications_duplicated_1=="TRUE"] <- NA
dim(v3_con_medication_variables_1)
## [1] 466 29
#bind columns id and medication names, but not categories together
v3_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v3_con_medication_variables_1[,1], v3_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v3_con_medication_name_1)
## [1] 466 15
#get the medication categories from the "_medication_variables_1" dataframe
v3_con_medication_categories_1<-as.data.frame(v3_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v3_con_medication_categories_1)
## [1] 466 15
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_con, not as character
#Important: v3_con_medication_name_1=="NA" replaced with is.na(v3_con_medication_name_1)
v3_con_medication_categories_1[is.na(v3_con_medication_name_1)] <- NA
#write.csv(v3_con_medication_categories_1, file="v3_con_medication_group_1.csv")
#Make a count table of medications
v3_con_med_table<-data.frame("mnppsd"=v3_con$mnppsd)
v3_con_med_table$v3_Antidepressants<-rowSums(v3_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v3_con_med_table$v3_Antipsychotics<-rowSums(v3_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v3_con_med_table$v3_Mood_stabilizers<-rowSums(v3_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v3_con_med_table$v3_Tranquilizers<-rowSums(v3_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v3_con_med_table$v3_Other_psychiatric<-rowSums(v3_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
Bind v3_clin and v3_con together by rows
v3_drugs<-rbind(v3_clin_med_table,v3_con_med_table)
dim(v3_drugs)
## [1] 1786 6
#check if the id column of v3_drugs and v1_id match
table(droplevels(v3_drugs[,1])==v1_id)
##
## TRUE
## 1786
v3_clin_adv<-ifelse(v3_clin$v3_medikabehand_medi2_nebenwirk==1,"Y","N")
v3_con_adv<-rep("-999",dim(v3_con)[1])
v3_adv<-factor(c(v3_clin_adv,v3_con_adv))
descT(v3_adv)
## -999 N Y <NA>
## [1,] No. cases 466 160 273 887 1786
## [2,] Percent 26.1 9 15.3 49.7 100
v3_clin_medchange<-rep(NA,dim(v3_clin)[1])
v3_clin_medchange<-ifelse(v3_clin$v3_medikabehand_medi3_mediaenderung==1,"Y","N")
v3_con_medchange<-rep("-999",dim(v3_con)[1])
v3_medchange<-as.factor(c(v3_clin_medchange,v3_con_medchange))
descT(v3_medchange)
## -999 N Y <NA>
## [1,] No. cases 466 187 250 883 1786
## [2,] Percent 26.1 10.5 14 49.4 100
Please see the section in Visit 1 for explanation.
v3_clin_lith<-rep(NA,dim(v3_clin)[1])
v3_clin_lith<-ifelse(v3_clin$v3_medikabehand_med_zusatz_lithium==1,"Y","N")
v3_con_lith<-rep("-999",dim(v3_con)[1])
v3_lith<-as.factor(c(v3_clin_lith,v3_con_lith))
v3_lith<-as.factor(v3_lith)
descT(v3_lith)
## -999 N Y <NA>
## [1,] No. cases 466 150 104 1066 1786
## [2,] Percent 26.1 8.4 5.8 59.7 100
Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.
v3_clin_lith_prd<-rep(NA,dim(v3_clin)[1])
v3_con_lith_prd<-rep(-999,dim(v3_con)[1])
v3_clin_lith_prd<-ifelse(v3_clin_lith=="N", -999, ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==2,1,
ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==1,2,
ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==0,3,NA))))
v3_lith_prd<-factor(c(v3_clin_lith_prd,v3_con_lith_prd))
descT(v3_lith_prd)
## -999 1 2 3 <NA>
## [1,] No. cases 616 31 24 49 1066 1786
## [2,] Percent 34.5 1.7 1.3 2.7 59.7 100
Create dataset
v3_med<-data.frame(v3_drugs[,2:6],v3_adv,v3_medchange,v3_lith,v3_lith_prd)
Here, separate datasets for clinical and control participants are created that contain the raw medication information at visit 3, as specified in the phenotype database.
For each medication that the individual took at visit 3 (including non-psychiatric drugs), the information given below is assessed.
The last character of each variable name always refers to the medication in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.
The medications were not assessed in any specific order, i.e. the order was determined by the individual participant (whatever she or he mentioned first). To classify medications, we used a catalogue, from which the categories and subcategories a medication belongs to were selected (see below).
Below, the variable names of clinical/control participants, respectively, are given in quotes, and the coding is explained in the parentheses.
1.Was the individual treated with any medication? (-1-not assessed, 1-yes, 2-no, 99-unknown)
“v3_medikabehand3_keine_med”/“v3_medikabehand3_keine_med”
Regular medication: Name of the medication (character)
“v3_medikabehand3_med_medi_199998”/“v3_medikabehand3_med_medi_200705”
Regular medication: Category to which the medication belongs (character)
“v3_medikabehand3_med_kategorie_199998”/“v3_medikabehand3_med_kategorie_200705”
Regular medication: Subcategory to which the medication belongs (character)
“v3_medikabehand3_med_kategorie_sub_199998”/“v3_medikabehand3_med_kategorie_sub_200705”
Regular medication: Psychiatric medication? (0-no, 1-yes) “v3_medikabehand3_med_zusatz_199998”/“v3_medikabehand3_med_zusatz_200705”
Regular medication: Dose in the morning (unitless)
“v3_medikabehand3_s_medi1_morgens_199998”/“v3_medikabehand3_s_medi1_morgens_200705”
Regular medication: Dose at midday (unitless)
“v3_medikabehand3_smedi1_mittags_199998”/“v3_medikabehand3_smedi1_mittags_200705”
Regular medication: Dose in the evening (unitless)
“v3_medikabehand3_smedi1_abends_199998”/“v3_medikabehand3_smedi1_abends_200705”
Regular medication: Dose at night (unitless)
“v3_medikabehand3_smedi1_nachts_199998”/“v3_medikabehand3_smedi1_nachts_200705”
Regular medication: Unit of the medication asked in the last four questions (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
“v3_medikabehand3_smedi1_einheit_199998”/“v3_medikabehand3_smedi1_einheit_200705”
Regular medication: Total dose of the medication per day (unitless)
“v3_medikabehand3_smedi1_gesamtdosis_199998”/“v3_medikabehand3_smedi1_gesamtdosis_200705”
Regular medication: Unit of the medication asked in the last question (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
“v3_medikabehand3_smedi1_einheit1_199998”/“v3_medikabehand3_smedi1_einheit1_200705”
Regular medication: Medication name, if not contained in our catalog (character)
“v3_medikabehand3_medikament_text_199998”/“v3_medikabehand3_medikament_text_200705”
Depot medication: Name of the medication (character) “v3_medikabehand3_depot_medi_200170”/"v3_medikabehand3_depot_medi_201224
Depot medication: Category to which the medication belongs (character) “v3_medikabehand3_depot_kategorie_200170”/"v3_medikabehand3_depot_kategorie_201224
Depot medication: Subcategory to which the medication belongs (character)
“v3_medikabehand3_depot_kategorie_sub_200170”/"v3_medikabehand3_depot_kategorie_sub_201224
Depot medication: Psychiatric medication? (0-no, 1-yes) “v3_medikabehand3_depot_zusatz_200170”/“v3_medikabehand3_depot_zusatz_201224”
Depot medication: Total Dose (unitless) “v3_medikabehand3_s_depot_gesamtdosis_200170”/“v3_medikabehand3_s_depot_gesamtdosis_201224”
Depot medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v3_medikabehand3_s_depot_einheit_200170”/ “v3_medikabehand3_s_depot_einheit_201224”
Interval, at which the depot medication is given (days) “v3_medikabehand3_s_depot_tage_200170”/“v3_medikabehand3_s_depot_tage_201224”
Medication name, if not contained in our catalog (character) “v3_medikabehand3_medikament_text_200170”/“v3_medikabehand3_medikament_text_201224”
Pro re nata (PRN) medication: Name of the medication (character) “v3_medikabehand3_bedarf_medi_199584”/“v3_medikabehand3_bedarf_medi_201187”
Pro re nata (PRN) medication: Category to which the medication belongs (character)
“v3_medikabehand3_bedarf_kategorie_199584”/“v3_medikabehand3_bedarf_kategorie_201187”
Pro re nata (PRN) medication: Subcategory to which the medication belongs (character) “v3_medikabehand3_bedarf_kategorie_sub_199584”/“v3_medikabehand3_bedarf_kategorie_sub_201187”
Pro re nata (PRN) medication: Psychiatric medication? (0-no, 1-yes) “v3_medikabehand3_bedarf_zusatz_199584”/“v3_medikabehand3_bedarf_zusatz_201187”
Pro re nata (PRN) medication: Total dose up to (unitless) “v3_medikabehand3_s_bedarf_gesamtdosis_199584”/"v3_medikabehand3_s_bedarf_kommentar_201187
Pro re nata (PRN) medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v3_medikabehand3_s_bedarf_einheit1_199584”/“v3_medikabehand3_s_bedarf_einheit1_201187”
Pro re nata (PRN) medication: Comment (character) “v3_medikabehand3_s_bedarf_kommentar_199584”/“v3_medikabehand3_s_bedarf_kommentar_201187”
Pro re nata (PRN) medication: Medication name, if not contained in our catalog (character) “v3_medikabehand3_medikament_text_199584”/“v3_medikabehand3_medikament_text_201187”
Make datasets containing only information on medication
v3_med_clin_orig<-data.frame(v3_clin$mnppsd,v3_clin[,147:455])
names(v3_med_clin_orig)[1]<-"v1_id"
v3_med_con_orig<-data.frame(v3_con$mnppsd,v3_con[,75:219])
names(v3_med_con_orig)[1]<-"v1_id"
Save raw medication datasets of visit 3
save(v3_med_clin_orig, file="200715_v4.1_psycourse_clin_raw_med_visit3.RData")
save(v3_med_con_orig, file="200715_v4.1_psycourse_con_raw_med_visit3.RData")
Write .csv file
write.table(v3_med_clin_orig,file="200715_v4.1_psycourse_clin_raw_med_visit3.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v3_med_con_orig,file="200715_v4.1_psycourse_con_raw_med_visit3.csv", quote=F, row.names=F, col.names=T, sep="\t")
For more explanation, see Visit 1
This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.
v3_clin_smk_strt_stp<-rep(NA,dim(v3_clin)[1])
v3_clin_smk_strt_stp<-ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==1,"NS",
ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==2,"NN",
ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==3,"YSP",
ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==4,"YST",v3_clin_smk_strt_stp))))
#ATTENTION: answering alternative: e-cigarette only in controls
v3_con_smk_strt_stp<-rep(NA,dim(v3_clin)[1])
v3_con_smk_strt_stp<-ifelse(v3_con$v3_tabalk_folge_tabak1==1 | v3_con$v3_tabalk_folge_tabak1==2,"NS",
ifelse(v3_con$v3_tabalk_folge_tabak1==3,"NN",
ifelse(v3_con$v3_tabalk_folge_tabak1==4,"YSP",
ifelse(v3_con$v3_tabalk_folge_tabak1==5,"YST",v3_con_smk_strt_stp))))
v3_smk_strt_stp<-c(v3_clin_smk_strt_stp,v3_con_smk_strt_stp)
descT(v3_smk_strt_stp)
## NN NS YSP YST <NA>
## [1,] No. cases 318 577 30 10 851 1786
## [2,] Percent 17.8 32.3 1.7 0.6 47.6 100
In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.
Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.
v3_no_cig<-c(rep(NA,dim(v3_clin)[1]),rep(NA,dim(v3_con)[1]))
v3_no_cig<-ifelse((v3_smk_strt_stp=="NN" | v3_smk_strt_stp=="YSP"), -999,
ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") &
c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==1,
c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*365,
ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") &
c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==2,
c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*52,
ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") &
c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==3,
c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*12,
v3_no_cig))))
summary(v3_no_cig[v3_no_cig>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 3650 5475 5990 7300 73000 1085
This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.
v3_alc_pst6_mths<-c(v3_clin$v3_tabalk1_ta9_alkkonsum,v3_con$v3_tabalk_folge_alkohol4)
v3_alc_pst6_mths<-factor(v3_alc_pst6_mths, ordered=T)
descT(v3_alc_pst6_mths)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 210 183 100 223 130 47 43 850 1786
## [2,] Percent 11.8 10.2 5.6 12.5 7.3 2.6 2.4 47.6 100
This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.
v3_alc_5orm<-ifelse(v3_alc_pst6_mths<4,-999,
ifelse(is.na(c(v3_clin$v3_tabalk1_ta10_alk_haeufigk_m1,v3_con$v3_tabalk_folge_alkohol5))==T,
c(v3_clin$v3_tabalk1_ta11_alk_haeufigk_f1,v3_con$v3_tabalk_folge_alkohol6),
c(v3_clin$v3_tabalk1_ta10_alk_haeufigk_m1,v3_con$v3_tabalk_folge_alkohol5)))
v3_alc_5orm<-factor(v3_alc_5orm, ordered=T)
descT(v3_alc_5orm)
## -999 1 2 3 4 5 6 7 8 9 <NA>
## [1,] No. cases 493 175 76 62 34 20 40 18 4 9 855 1786
## [2,] Percent 27.6 9.8 4.3 3.5 1.9 1.1 2.2 1 0.2 0.5 47.9 100
For more information see visit 2.
“During the past six months, did you take ANY illicit drugs?” (dichotomous, v3_pst6_ill_drg)
v3_pst6_ill_drg<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_pst6_ill_drg<-ifelse(c(v3_clin$v3_drogen1_dg1_konsum,v3_con$v3_drogen_folge_drogenkonsum)==2, "Y", "N")
descT(v3_pst6_ill_drg)
## N Y <NA>
## [1,] No. cases 857 77 852 1786
## [2,] Percent 48 4.3 47.7 100
Create dataset
v3_subst<-data.frame(v3_smk_strt_stp,
v3_no_cig,
v3_alc_pst6_mths,
v3_alc_5orm,
v3_pst6_ill_drg)
Here, separate datasets for clinical and control participants are created that contain the raw information on illicit drugs at visit 3, exactly as specified in the phenotype database.
For each illicit drug ever taken, the information given below is assessed.
The last character of each variable name always refers to the drug in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.
The drugs are not assessed in any specific order, i.e. the order is determined by the individual participant (whatever she or he mentions first).
Below, the variable names of clinical/control participants are given in quotes, and the coding is explained in the parentheses.
1. Whether the individual consumed illicit drugs since the last visit. (Coding: 1-no, 2-yes) “v3_drogen1_dg1_konsum”/“v3_drogen_folge_drogenkonsum”
2. The name of the drug: (character) “v3_drogen1_s_dg_droge_28483”/“v3_drogen_folge_droge_117794”
The category to which the drug belongs (each item below is a checkbox: 0-not checked, 1-checked):
3. Stimulants: “v3_drogen1_s_dg_drogekt1_28483”/“v3_drogen_folge_droge1_117794”
4. Cannabis: “v3_drogen2_s_dg_drogekt1_28483”/“v3_drogen_folge_droge2_117794” 5. Opiates and pain reliefers: “v3_drogen3_s_dg_drogekt1_28483”/“v3_drogen_folge_droge3_117794”
6. Cocaine: “v3_drogen4_s_dg_drogekt1_28483”/“v3_drogen_folge_droge4_117794”
7. Hallucinogens: “v3_drogen5_s_dg_drogekt1_28483”/“v3_drogen_folge_droge5_117794”
8. Inhalants: “v3_drogen6_s_dg_drogekt1_28483”/“v3_drogen_folge_droge6_117794”
9. Tranquilizers: “v3_drogen7_s_dg_drogekt1_28483”/“v3_drogen_folge_droge7_117794”
10. Other: “v3_drogen8_s_dg_drogekt1_28483”/“v3_drogen_folge_droge8_117794”
11. “Referring to the time since the last study visit, how often did you consume it?” “v3_drogen1_s_dga_haeufigk_28483”/“v3_drogen_folge_droge_haeufig_117794”
The coding is given below:
1 - tried 1 time
2 - less than once a month
3 - about once a month
4 - at least 2 times but less than 10 times a month
5 - at least 10 times a month
12. “Referring to the period of time since the last study visit, did you have to take more of the drug to achieve the same effect?” (Coding: 1-no, 2-yes) “v3_drogen1_s_dgf_l6m_dosis_28483”/“v3_drogen_folge_droge_dosis_117794”
Important: There is an error in the original phenotype database, that affects the coding of item 10 (above). In all drugs the exports of the phenotype database do not reflect the input into the graphical user interface. Below, the incorrect variable is replaced with the corrected one
Make datasets containing only information on illicit drugs
v3_drg_clin<-v3_clin[,725:780]
v3_drg_con<-v3_con[,315:392]
Clinical participants
v3_clin_ill_drugs_orig<-data.frame(v3_clin$mnppsd,v3_drg_clin)
names(v3_clin_ill_drugs_orig)[1]<-"v1_id"
#recode wrongly coded item 10
for(i in c(0:4)){
v3_clin_ill_drugs_orig[,12+i*11]<-ifelse(v3_clin_ill_drugs_orig[,12+i*11]==5,1,
ifelse(v3_clin_ill_drugs_orig[,12+i*11]==4,5,
ifelse(v3_clin_ill_drugs_orig[,12+i*11]==3,4,
ifelse(v3_clin_ill_drugs_orig[,12+i*11]==2,3,
ifelse(v3_clin_ill_drugs_orig[,12+i*11]==1,2,NA)))))}
Control participants
v3_con_ill_drugs_orig<-data.frame(v3_con$mnppsd,v3_drg_con)
names(v3_con_ill_drugs_orig)[1]<-"v1_id"
#recode wrongly coded item 10
for(i in c(0:6)){
v3_con_ill_drugs_orig[,12+i*11]<-ifelse(v3_con_ill_drugs_orig[,12+i*11]==5,1,
ifelse(v3_con_ill_drugs_orig[,12+i*11]==4,5,
ifelse(v3_con_ill_drugs_orig[,12+i*11]==3,4,
ifelse(v3_con_ill_drugs_orig[,12+i*11]==2,3,
ifelse(v3_con_ill_drugs_orig[,12+i*11]==1,2,NA)))))}
Save raw illicit drug dataset from visit 3
save(v3_clin_ill_drugs_orig, file="200715_v4.1_psycourse_clin_raw_ill_drg_visit3.RData")
save(v3_con_ill_drugs_orig, file="200715_v4.1_psycourse_con_raw_ill_drg_visit3.RData")
Write .csv file
write.table(v3_clin_ill_drugs_orig,file="200715_v4.1_psycourse_clin_raw_ill_drg_visit3.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v3_con_ill_drugs_orig,file="200715_v4.1_psycourse_con_raw_ill_drg_visit3.csv", quote=F, row.names=F, col.names=T, sep="\t")
For more information on the scale, please see Visit 1
P1 Delusions (ordinal [1,2,3,4,5,6,7], v3_panss_p1)
v3_panss_p1<-c(v3_clin$v3_panss_p_p1_wahnideen,v3_con$v3_panss_p_p1_wahnideen)
v3_panss_p1<-factor(v3_panss_p1, ordered=T)
descT(v3_panss_p1)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 716 40 64 29 14 6 1 916 1786
## [2,] Percent 40.1 2.2 3.6 1.6 0.8 0.3 0.1 51.3 100
P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v3_panss_p2)
v3_panss_p2<-c(v3_clin$v3_panss_p_p2_form_denkst,v3_con$v3_panss_p_p2_form_denkst)
v3_panss_p2<-factor(v3_panss_p2, ordered=T)
descT(v3_panss_p2)
## 1 2 3 4 5 7 <NA>
## [1,] No. cases 660 66 89 45 10 1 915 1786
## [2,] Percent 37 3.7 5 2.5 0.6 0.1 51.2 100
P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v3_panss_p3)
v3_panss_p3<-c(v3_clin$v3_panss_p_p3_halluz,v3_con$v3_panss_p_p3_halluz)
v3_panss_p3<-factor(v3_panss_p3, ordered=T)
descT(v3_panss_p3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 784 25 28 20 11 3 915 1786
## [2,] Percent 43.9 1.4 1.6 1.1 0.6 0.2 51.2 100
P4 Excitement (ordinal [1,2,3,4,5,6,7], v3_panss_p4)
v3_panss_p4<-c(v3_clin$v3_panss_p_p4_erregung,v3_con$v3_panss_p_p4_erregung)
v3_panss_p4<-factor(v3_panss_p4, ordered=T)
descT(v3_panss_p4)
## 1 2 3 4 5 <NA>
## [1,] No. cases 676 56 112 24 3 915 1786
## [2,] Percent 37.8 3.1 6.3 1.3 0.2 51.2 100
P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v3_panss_p5)
v3_panss_p5<-c(v3_clin$v3_panss_p_p5_groessenideen,v3_con$v3_panss_p_p5_groessenideen)
v3_panss_p5<-factor(v3_panss_p5, ordered=T)
descT(v3_panss_p5)
## 1 2 3 4 5 <NA>
## [1,] No. cases 806 21 29 11 3 916 1786
## [2,] Percent 45.1 1.2 1.6 0.6 0.2 51.3 100
P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v3_panss_p6)
v3_panss_p6<-c(v3_clin$v3_panss_p_p6_misstr_verfolg,v3_con$v3_panss_p_p6_misstr_verfolg)
v3_panss_p6<-factor(v3_panss_p6, ordered=T)
descT(v3_panss_p6)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 710 48 72 25 13 3 915 1786
## [2,] Percent 39.8 2.7 4 1.4 0.7 0.2 51.2 100
P7 Hostility (ordinal [1,2,3,4,5,6,7], v3_panss_p7)
v3_panss_p7<-c(v3_clin$v3_panss_p_p7_feindseligkeit,v3_con$v3_panss_p_p7_feindseligkeit)
v3_panss_p7<-factor(v3_panss_p7, ordered=T)
descT(v3_panss_p7)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 800 35 28 6 1 1 915 1786
## [2,] Percent 44.8 2 1.6 0.3 0.1 0.1 51.2 100
PANSS Positive sum score (continuous [7-49], v3_panss_sum_pos)
v3_panss_sum_pos<-as.numeric.factor(v3_panss_p1)+
as.numeric.factor(v3_panss_p2)+
as.numeric.factor(v3_panss_p3)+
as.numeric.factor(v3_panss_p4)+
as.numeric.factor(v3_panss_p5)+
as.numeric.factor(v3_panss_p6)+
as.numeric.factor(v3_panss_p7)
summary(v3_panss_sum_pos)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.0 7.0 7.0 9.2 10.0 30.0 917
N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v3_panss_n1)
v3_panss_n1<-c(v3_clin$v3_panss_n_n1_affektverflachung,v3_con$v3_panss_n_n1_affektverflachung)
v3_panss_n1<-factor(v3_panss_n1, ordered=T)
descT(v3_panss_n1)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 567 83 108 64 41 3 920 1786
## [2,] Percent 31.7 4.6 6 3.6 2.3 0.2 51.5 100
N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v3_panss_n2)
v3_panss_n2<-c(v3_clin$v3_panss_n_n2_emot_rueckzug,v3_con$v3_panss_n_n2_emot_rueckzug)
v3_panss_n2<-factor(v3_panss_n2, ordered=T)
descT(v3_panss_n2)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 624 75 88 67 15 2 915 1786
## [2,] Percent 34.9 4.2 4.9 3.8 0.8 0.1 51.2 100
N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v3_panss_n3)
v3_panss_n3<-c(v3_clin$v3_panss_n_n3_mang_aff_rapp,v3_con$v3_panss_n_n3_mang_aff_rapp)
v3_panss_n3<-factor(v3_panss_n3, ordered=T)
descT(v3_panss_n3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 673 70 87 33 7 1 915 1786
## [2,] Percent 37.7 3.9 4.9 1.8 0.4 0.1 51.2 100
N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v3_panss_n4)
v3_panss_n4<-c(v3_clin$v3_panss_n_n4_soz_pass_apath,v3_con$v3_panss_n_n4_soz_pass_apath)
v3_panss_n4<-factor(v3_panss_n4, ordered=T)
descT(v3_panss_n4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 626 67 116 39 16 7 915 1786
## [2,] Percent 35.1 3.8 6.5 2.2 0.9 0.4 51.2 100
N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v3_panss_n5)
v3_panss_n5<-c(v3_clin$v3_panss_n_n5_abstr_denken,v3_con$v3_panss_n_n5_abstr_denken)
v3_panss_n5<-factor(v3_panss_n5, ordered=T)
descT(v3_panss_n5)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 583 87 125 56 10 4 921 1786
## [2,] Percent 32.6 4.9 7 3.1 0.6 0.2 51.6 100
N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v3_panss_n6)
v3_panss_n6<-c(v3_clin$v3_panss_n_n6_spon_fl_sprache,v3_con$v3_panss_n_n6_spon_fl_sprache)
v3_panss_n6<-factor(v3_panss_n6, ordered=T)
descT(v3_panss_n6)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 721 44 66 28 10 2 915 1786
## [2,] Percent 40.4 2.5 3.7 1.6 0.6 0.1 51.2 100
N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v3_panss_n7)
v3_panss_n7<-c(v3_clin$v3_panss_n_n7_stereotyp_ged,v3_con$v3_panss_n_n7_stereotyp_ged)
v3_panss_n7<-factor(v3_panss_n7, ordered=T)
descT(v3_panss_n7)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 734 51 66 13 3 1 918 1786
## [2,] Percent 41.1 2.9 3.7 0.7 0.2 0.1 51.4 100
PANSS Negative sum score (continuous [7-49], v3_panss_sum_neg)
v3_panss_sum_neg<-as.numeric.factor(v3_panss_n1)+
as.numeric.factor(v3_panss_n2)+
as.numeric.factor(v3_panss_n3)+
as.numeric.factor(v3_panss_n4)+
as.numeric.factor(v3_panss_n5)+
as.numeric.factor(v3_panss_n6)+
as.numeric.factor(v3_panss_n7)
summary(v3_panss_sum_neg)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.00 7.00 9.00 10.65 12.00 34.00 929
G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v3_panss_g1)
v3_panss_g1<-c(v3_clin$v3_panss_g_g1_sorge_gesundh,v3_con$v3_panss_g_g1_sorge_gesundh)
v3_panss_g1<-factor(v3_panss_g1, ordered=T)
descT(v3_panss_g1)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 649 90 88 32 7 2 918 1786
## [2,] Percent 36.3 5 4.9 1.8 0.4 0.1 51.4 100
G2 Anxiety (ordinal [1,2,3,4,5,6,7], v3_panss_g2)
v3_panss_g2<-c(v3_clin$v3_panss_g_g2_angst,v3_con$v3_panss_g_g2_angst)
v3_panss_g2<-factor(v3_panss_g2, ordered=T)
descT(v3_panss_g2)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 603 53 153 41 19 1 1 915 1786
## [2,] Percent 33.8 3 8.6 2.3 1.1 0.1 0.1 51.2 100
G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v3_panss_g3)
v3_panss_g3<-c(v3_clin$v3_panss_g_g3_schuldgefuehle,v3_con$v3_panss_g_g3_schuldgefuehle)
v3_panss_g3<-factor(v3_panss_g3, ordered=T)
descT(v3_panss_g3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 692 36 88 39 14 1 916 1786
## [2,] Percent 38.7 2 4.9 2.2 0.8 0.1 51.3 100
G4 Tension (ordinal [1,2,3,4,5,6,7], v3_panss_g4)
v3_panss_g4<-c(v3_clin$v3_panss_g_g4_anspannung,v3_con$v3_panss_g_g4_anspannung)
v3_panss_g4<-factor(v3_panss_g4, ordered=T)
descT(v3_panss_g4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 610 84 112 52 10 2 916 1786
## [2,] Percent 34.2 4.7 6.3 2.9 0.6 0.1 51.3 100
G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v3_panss_g5)
v3_panss_g5<-c(v3_clin$v3_panss_g_g5_manier_koerperh,v3_con$v3_panss_g_g5_manier_koerperh)
v3_panss_g5<-factor(v3_panss_g5, ordered=T)
descT(v3_panss_g5)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 803 35 21 5 3 2 917 1786
## [2,] Percent 45 2 1.2 0.3 0.2 0.1 51.3 100
G6 Depression (ordinal [1,2,3,4,5,6,7], v3_panss_g6)
v3_panss_g6<-c(v3_clin$v3_panss_g_g6_depression,v3_con$v3_panss_g_g6_depression)
v3_panss_g6<-factor(v3_panss_g6, ordered=T)
descT(v3_panss_g6)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 541 59 142 84 38 5 1 916 1786
## [2,] Percent 30.3 3.3 8 4.7 2.1 0.3 0.1 51.3 100
G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v3_panss_g7)
v3_panss_g7<-c(v3_clin$v3_panss_g_g7_mot_verlangs,v3_con$v3_panss_g_g7_mot_verlangs)
v3_panss_g7<-factor(v3_panss_g7, ordered=T)
descT(v3_panss_g7)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 652 63 103 48 4 1 915 1786
## [2,] Percent 36.5 3.5 5.8 2.7 0.2 0.1 51.2 100
G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v3_panss_g8)
v3_panss_g8<-c(v3_clin$v3_panss_g_g8_unkoop_verh,v3_con$v3_panss_g_g8_unkoop_verh)
v3_panss_g8<-factor(v3_panss_g8, ordered=T)
descT(v3_panss_g8)
## 1 2 3 4 6 <NA>
## [1,] No. cases 818 22 27 2 1 916 1786
## [2,] Percent 45.8 1.2 1.5 0.1 0.1 51.3 100
G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v3_panss_g9)
v3_panss_g9<-c(v3_clin$v3_panss_g_g9_ungew_denkinh,v3_con$v3_panss_g_g9_ungew_denkinh)
v3_panss_g9<-factor(v3_panss_g9, ordered=T)
descT(v3_panss_g9)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 720 49 72 21 6 2 1 915 1786
## [2,] Percent 40.3 2.7 4 1.2 0.3 0.1 0.1 51.2 100
G10 Disorientation (ordinal [1,2,3,4,5,6,7], v3_panss_g10)
v3_panss_g10<-c(v3_clin$v3_panss_g_g10_desorient,v3_con$v3_panss_g_g10_desorient)
v3_panss_g10<-factor(v3_panss_g10, ordered=T)
descT(v3_panss_g10)
## 1 2 3 4 <NA>
## [1,] No. cases 821 27 19 1 918 1786
## [2,] Percent 46 1.5 1.1 0.1 51.4 100
G11 Poor attention (ordinal [1,2,3,4,5,6,7], v3_panss_g11)
v3_panss_g11<-c(v3_clin$v3_panss_g_g11_mang_aufmerks,v3_con$v3_panss_g_g11_mang_aufmerks)
v3_panss_g11<-factor(v3_panss_g11, ordered=T)
descT(v3_panss_g11)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 559 78 161 66 4 1 917 1786
## [2,] Percent 31.3 4.4 9 3.7 0.2 0.1 51.3 100
G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v3_panss_g12)
v3_panss_g12<-c(v3_clin$v3_panss_g_g12_mang_urt_einsi,v3_con$v3_panss_g_g12_mang_urt_einsi)
v3_panss_g12<-factor(v3_panss_g12, ordered=T)
descT(v3_panss_g12)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 749 41 50 22 4 3 1 916 1786
## [2,] Percent 41.9 2.3 2.8 1.2 0.2 0.2 0.1 51.3 100
G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v3_panss_g13)
v3_panss_g13<-c(v3_clin$v3_panss_g_g13_willensschwae,v3_con$v3_panss_g_g13_willensschwae)
v3_panss_g13<-factor(v3_panss_g13, ordered=T)
descT(v3_panss_g13)
## 1 2 3 4 <NA>
## [1,] No. cases 772 23 51 24 916 1786
## [2,] Percent 43.2 1.3 2.9 1.3 51.3 100
G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v3_panss_g14)
v3_panss_g14<-c(v3_clin$v3_panss_g_g14_mang_impulsk,v3_con$v3_panss_g_g14_mang_impulsk)
v3_panss_g14<-factor(v3_panss_g14, ordered=T)
descT(v3_panss_g14)
## 1 2 3 4 <NA>
## [1,] No. cases 758 29 72 11 916 1786
## [2,] Percent 42.4 1.6 4 0.6 51.3 100
G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v3_panss_g15)
v3_panss_g15<-c(v3_clin$v3_panss_g_g15_selbstbezog,v3_con$v3_panss_g_g15_selbstbezog)
v3_panss_g15<-factor(v3_panss_g15, ordered=T)
descT(v3_panss_g15)
## 1 2 3 4 5 <NA>
## [1,] No. cases 784 45 29 10 3 915 1786
## [2,] Percent 43.9 2.5 1.6 0.6 0.2 51.2 100
G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v3_panss_g16)
v3_panss_g16<-c(v3_clin$v3_panss_g_g16_aktsoz_vermeid,v3_con$v3_panss_g_g16_aktsoz_vermeid)
v3_panss_g16<-factor(v3_panss_g16, ordered=T)
descT(v3_panss_g16)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 716 44 74 20 15 1 916 1786
## [2,] Percent 40.1 2.5 4.1 1.1 0.8 0.1 51.3 100
PANSS General Psychopathology sum score (continuous [16-112], v3_panss_sum_gen)
v3_panss_sum_gen<-as.numeric.factor(v3_panss_g1)+
as.numeric.factor(v3_panss_g2)+
as.numeric.factor(v3_panss_g3)+
as.numeric.factor(v3_panss_g4)+
as.numeric.factor(v3_panss_g5)+
as.numeric.factor(v3_panss_g6)+
as.numeric.factor(v3_panss_g7)+
as.numeric.factor(v3_panss_g8)+
as.numeric.factor(v3_panss_g9)+
as.numeric.factor(v3_panss_g10)+
as.numeric.factor(v3_panss_g11)+
as.numeric.factor(v3_panss_g12)+
as.numeric.factor(v3_panss_g13)+
as.numeric.factor(v3_panss_g14)+
as.numeric.factor(v3_panss_g15)+
as.numeric.factor(v3_panss_g16)
summary(v3_panss_sum_gen)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 16.00 16.00 20.00 22.18 26.00 56.00 933
Create PANSS Total score (continuous [30-210], v3_panss_sum_tot)
v3_panss_sum_tot<-v3_panss_sum_pos+v3_panss_sum_neg+v3_panss_sum_gen
summary(v3_panss_sum_tot)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 30.00 31.00 37.00 41.94 49.00 112.00 947
Create dataset
v3_symp_panss<-data.frame(v3_panss_p1,v3_panss_p2,v3_panss_p3,v3_panss_p4,v3_panss_p5,v3_panss_p6,v3_panss_p7,
v3_panss_n1,v3_panss_n2,v3_panss_n3,v3_panss_n4,v3_panss_n5,v3_panss_n6,v3_panss_n7,
v3_panss_g1,v3_panss_g2,v3_panss_g3,v3_panss_g4,v3_panss_g5,v3_panss_g6,v3_panss_g7,
v3_panss_g8,v3_panss_g9,v3_panss_g10,v3_panss_g11,v3_panss_g12,v3_panss_g13,v3_panss_g14,
v3_panss_g15,v3_panss_g16,v3_panss_sum_pos,v3_panss_sum_neg,v3_panss_sum_gen,
v3_panss_sum_tot)
For more information on the scale, please see Visit 1
Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v3_idsc_itm1)
v3_idsc_itm1<-c(v3_clin$v3_ids_c_s1_ids1_einschlafschw,v3_con$v3_ids_c_s1_ids1_einschlafschw)
v3_idsc_itm1<-factor(v3_idsc_itm1, ordered=T)
descT(v3_idsc_itm1)
## 0 1 2 3 <NA>
## [1,] No. cases 617 112 75 60 922 1786
## [2,] Percent 34.5 6.3 4.2 3.4 51.6 100
Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v3_idsc_itm2)
v3_idsc_itm2<-c(v3_clin$v3_ids_c_s1_ids2_naechtl_aufw,v3_con$v3_ids_c_s1_ids2_naechtl_aufw)
v3_idsc_itm2<-factor(v3_idsc_itm2, ordered=T)
descT(v3_idsc_itm2)
## 0 1 2 3 <NA>
## [1,] No. cases 551 143 98 74 920 1786
## [2,] Percent 30.9 8 5.5 4.1 51.5 100
Item 3 Early morning insomnia (ordinal [0,1,2,3], v3_idsc_itm3)
v3_idsc_itm3<-c(v3_clin$v3_ids_c_s1_ids3_frueh_aufw,v3_con$v3_ids_c_s1_ids3_frueh_aufw)
v3_idsc_itm3<-factor(v3_idsc_itm3, ordered=T)
descT(v3_idsc_itm3)
## 0 1 2 3 <NA>
## [1,] No. cases 724 62 39 40 921 1786
## [2,] Percent 40.5 3.5 2.2 2.2 51.6 100
Item 4 Hypersomnia (ordinal [0,1,2,3], v3_idsc_itm4)
v3_idsc_itm4<-c(v3_clin$v3_ids_c_s1_ids4_hypersomnie,v3_con$v3_ids_c_s1_ids4_hypersomnie)
v3_idsc_itm4<-factor(v3_idsc_itm4, ordered=T)
descT(v3_idsc_itm4)
## 0 1 2 3 <NA>
## [1,] No. cases 592 191 66 18 919 1786
## [2,] Percent 33.1 10.7 3.7 1 51.5 100
Item 5 Mood (sad) (ordinal [0,1,2,3], v3_idsc_itm5)
v3_idsc_itm5<-c(v3_clin$v3_ids_c_s1_ids5_stimmung_trgk,v3_con$v3_ids_c_s1_ids5_stimmung_trgk)
v3_idsc_itm5<-factor(v3_idsc_itm5, ordered=T)
descT(v3_idsc_itm5)
## 0 1 2 3 <NA>
## [1,] No. cases 558 191 76 40 921 1786
## [2,] Percent 31.2 10.7 4.3 2.2 51.6 100
Item 6 Mood (irritable) (ordinal [0,1,2,3], v3_idsc_itm6)
v3_idsc_itm6<-c(v3_clin$v3_ids_c_s1_ids6_stimmung_grzt,v3_con$v3_ids_c_s1_ids6_stimmung_grzt)
v3_idsc_itm6<-factor(v3_idsc_itm6, ordered=T)
descT(v3_idsc_itm6)
## 0 1 2 3 <NA>
## [1,] No. cases 579 214 53 18 922 1786
## [2,] Percent 32.4 12 3 1 51.6 100
Item 7 Mood (anxious) (ordinal [0,1,2,3], v3_idsc_itm7)
v3_idsc_itm7<-c(v3_clin$v3_ids_c_s1_ids7_stimmung_agst,v3_con$v3_ids_c_s1_ids7_stimmung_agst)
v3_idsc_itm7<-factor(v3_idsc_itm7, ordered=T)
descT(v3_idsc_itm7)
## 0 1 2 3 <NA>
## [1,] No. cases 605 159 72 31 919 1786
## [2,] Percent 33.9 8.9 4 1.7 51.5 100
Item 8 Reactivity of mood (ordinal [0,1,2,3], v3_idsc_itm8)
v3_idsc_itm8<-c(v3_clin$v3_ids_c_s1_ids8_reakt_stimmung,v3_con$v3_ids_c_s1_ids8_reakt_stimmung)
v3_idsc_itm8<-factor(v3_idsc_itm8, ordered=T)
descT(v3_idsc_itm8)
## 0 1 2 3 <NA>
## [1,] No. cases 709 101 29 26 921 1786
## [2,] Percent 39.7 5.7 1.6 1.5 51.6 100
Item 9 Mood Variation (ordinal [0,1,2,3], v3_idsc_itm9)
v3_idsc_itm9<-c(v3_clin$v3_ids_c_s1_ids9_stimmungsschw,v3_con$v3_ids_c_s1_ids9_stimmungsschw)
v3_idsc_itm9<-factor(v3_idsc_itm9, ordered=T)
descT(v3_idsc_itm9)
## 0 1 2 3 <NA>
## [1,] No. cases 687 77 28 74 920 1786
## [2,] Percent 38.5 4.3 1.6 4.1 51.5 100
Item 9A (categorical [M, A, N], v3_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).
v3_idsc_itm9a_pre<-c(v3_clin$v3_ids_c_s1_ids9a_stimmungsschw,v3_con$v3_ids_c_s1_ids9a_stimmungsschw)
v3_idsc_itm9a<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==1, "M", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==2, "A", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==3, "N", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-factor(v3_idsc_itm9a, ordered=F)
descT(v3_idsc_itm9a)
## -999 A M N <NA>
## [1,] No. cases 687 18 92 38 951 1786
## [2,] Percent 38.5 1 5.2 2.1 53.2 100
Item 9B (dichotomous, v3_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.
v3_idsc_itm9b_pre<-c(v3_clin$v3_ids_c_s1_ids9b_stimmungsschw,v3_con$v3_ids_c_s1_ids9b_stimmungsschw)
v3_idsc_itm9b<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm9b<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9b_pre==0, "N", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9b))
v3_idsc_itm9b<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9b_pre==1, "Y", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9b))
v3_idsc_itm9b<-factor(v3_idsc_itm9b, ordered=F)
descT(v3_idsc_itm9b)
## -999 N Y <NA>
## [1,] No. cases 687 54 74 971 1786
## [2,] Percent 38.5 3 4.1 54.4 100
Item 10 Quality of mood (ordinal [0,1,2,3], v3_idsc_itm10)
v3_idsc_itm10<-c(v3_clin$v3_ids_c_s1_ids10_quali_stimmung,v3_con$v3_ids_c_s1_ids10_quali_stimmung)
v3_idsc_itm10<-factor(v3_idsc_itm10, ordered=T)
descT(v3_idsc_itm10)
## 0 1 2 3 <NA>
## [1,] No. cases 731 54 25 47 929 1786
## [2,] Percent 40.9 3 1.4 2.6 52 100
Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.
The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.
Item 11 (ordinal [0,1,2,3], v3_idsc_itm11)
v3_idsc_app_verm<-c(v3_clin$v3_ids_c_s2_ids11_appetit_verm,v3_con$v3_ids_c_s2_ids11_appetit_verm)
v3_idsc_app_gest<-c(v3_clin$v3_ids_c_s2_ids12_appetit_steig,v3_con$v3_ids_c_s2_ids12_appetit_steig)
v3_idsc_itm11<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm11<-ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==T, NA,
ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==F, -999,
ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==T,
v3_idsc_app_verm,
ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &
(v3_idsc_app_verm>v3_idsc_app_gest), v3_idsc_app_verm, ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F & (v3_idsc_app_gest>=v3_idsc_app_verm),-999,v3_idsc_itm11)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v3_idsc_itm11)
## -999 0 1 2 3 <NA>
## [1,] No. cases 262 503 74 23 5 919 1786
## [2,] Percent 14.7 28.2 4.1 1.3 0.3 51.5 100
Item 12 (ordinal [0,1,2,3], v3_idsc_itm12)
v3_idsc_itm12<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm12<-ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==T, NA,
ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==F,
v3_idsc_app_gest,
ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==T,
-999,
ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &
(v3_idsc_app_verm>v3_idsc_app_gest), -999,
ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F & (v3_idsc_app_gest>=v3_idsc_app_verm),
v3_idsc_app_gest,v3_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v3_idsc_itm12)
## -999 0 1 2 3 <NA>
## [1,] No. cases 605 137 81 25 19 919 1786
## [2,] Percent 33.9 7.7 4.5 1.4 1.1 51.5 100
Item 13 (ordinal [0,1,2,3], v3_idsc_itm13)
v3_idsc_gew_abn<-c(v3_clin$v3_ids_c_s2_ids13_gewichtsabn,v3_con$v3_ids_c_s2_ids13_gewichtsabn)
v3_idsc_gew_zun<-c(v3_clin$v3_ids_c_s2_ids14_gewichtszun,v3_con$v3_ids_c_s2_ids14_gewichtszun)
v3_idsc_itm13<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm13<-ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==T, NA,
ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==F, -999,
ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==T,
v3_idsc_gew_abn,
ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F &
(v3_idsc_gew_abn>v3_idsc_gew_zun), v3_idsc_gew_abn, ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F & (v3_idsc_gew_zun >= v3_idsc_gew_abn),-999,v3_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v3_idsc_itm13)
## -999 0 1 2 3 <NA>
## [1,] No. cases 291 465 48 42 21 919 1786
## [2,] Percent 16.3 26 2.7 2.4 1.2 51.5 100
Item 14 (ordinal [0,1,2,3], v3_idsc_itm14)
v3_idsc_itm14<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm14<-ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==T, NA,
ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==F,
v3_idsc_gew_zun,
ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==T,
-999,
ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F &
(v3_idsc_gew_abn>v3_idsc_gew_zun), -999,
ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F & (v3_idsc_gew_zun>=v3_idsc_gew_abn),
v3_idsc_gew_zun,v3_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v3_idsc_itm14)
## -999 0 1 2 3 <NA>
## [1,] No. cases 576 171 66 31 23 919 1786
## [2,] Percent 32.3 9.6 3.7 1.7 1.3 51.5 100
Item 15 Concentration/decision making (ordinal [0,1,2,3], v3_idsc_itm15)
v3_idsc_itm15<-c(v3_clin$v3_ids_c_s2_ids15_konz_entscheid,v3_con$v3_ids_c_s2_ids15_konz_entscheid)
v3_idsc_itm15<-factor(v3_idsc_itm15, ordered=T)
descT(v3_idsc_itm15)
## 0 1 2 3 <NA>
## [1,] No. cases 511 222 117 17 919 1786
## [2,] Percent 28.6 12.4 6.6 1 51.5 100
Item 16 Outlook (self) (ordinal [0,1,2,3], v3_idsc_itm16)
v3_idsc_itm16<-c(v3_clin$v3_ids_c_s2_ids16_selbstbild,v3_con$v3_ids_c_s2_ids16_selbstbild)
v3_idsc_itm16<-factor(v3_idsc_itm16, ordered=T)
descT(v3_idsc_itm16)
## 0 1 2 3 <NA>
## [1,] No. cases 652 137 39 41 917 1786
## [2,] Percent 36.5 7.7 2.2 2.3 51.3 100
Item 17 Outlook (future) (ordinal [0,1,2,3], v3_idsc_itm17)
v3_idsc_itm17<-c(v3_clin$v3_ids_c_s2_ids17_zukunftssicht,v3_con$v3_ids_c_s2_ids17_zukunftssicht)
v3_idsc_itm17<-factor(v3_idsc_itm17, ordered=T)
descT(v3_idsc_itm17)
## 0 1 2 3 <NA>
## [1,] No. cases 587 202 63 14 920 1786
## [2,] Percent 32.9 11.3 3.5 0.8 51.5 100
Item 18 Suicidal ideation (ordinal [0,1,2,3], v3_idsc_itm18)
v3_idsc_itm18<-c(v3_clin$v3_ids_c_s2_ids18_selbstmordged,v3_con$v3_ids_c_s2_ids18_selbstmordged)
v3_idsc_itm18<-factor(v3_idsc_itm18, ordered=T)
descT(v3_idsc_itm18)
## 0 1 2 3 <NA>
## [1,] No. cases 786 42 37 2 919 1786
## [2,] Percent 44 2.4 2.1 0.1 51.5 100
Item 19 Involvement (ordinal [0,1,2,3], v3_idsc_itm19)
v3_idsc_itm19<-c(v3_clin$v3_ids_c_s2_ids19_interess_aktiv,v3_con$v3_ids_c_s2_ids19_interess_aktiv)
v3_idsc_itm19<-factor(v3_idsc_itm19, ordered=T)
descT(v3_idsc_itm19)
## 0 1 2 3 <NA>
## [1,] No. cases 720 104 25 16 921 1786
## [2,] Percent 40.3 5.8 1.4 0.9 51.6 100
Item 20 Energy/fatigability (ordinal [0,1,2,3], v3_idsc_itm20)
v3_idsc_itm20<-c(v3_clin$v3_ids_c_s2_ids20_energ_ermued,v3_con$v3_ids_c_s2_ids20_energ_ermued)
v3_idsc_itm20<-factor(v3_idsc_itm20, ordered=T)
descT(v3_idsc_itm20)
## 0 1 2 3 <NA>
## [1,] No. cases 586 175 95 13 917 1786
## [2,] Percent 32.8 9.8 5.3 0.7 51.3 100
Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v3_idsc_itm21)
v3_idsc_itm21<-c(v3_clin$v3_ids_c_s3_ids21_vergn_genuss,v3_con$v3_ids_c_s3_ids21_vergn_genuss)
v3_idsc_itm21<-factor(v3_idsc_itm21, ordered=T)
descT(v3_idsc_itm21)
## 0 1 2 3 <NA>
## [1,] No. cases 730 95 30 12 919 1786
## [2,] Percent 40.9 5.3 1.7 0.7 51.5 100
Item 22 Sexual interest (ordinal [0,1,2,3], v3_idsc_itm22)
v3_idsc_itm22<-c(v3_clin$v3_ids_c_s3_ids22_sex_interesse,v3_con$v3_ids_c_s3_ids22_sex_interesse)
v3_idsc_itm22<-factor(v3_idsc_itm22, ordered=T)
descT(v3_idsc_itm22)
## 0 1 2 3 <NA>
## [1,] No. cases 638 64 85 75 924 1786
## [2,] Percent 35.7 3.6 4.8 4.2 51.7 100
Item 23 Psychomotor slowing (ordinal [0,1,2,3], v3_idsc_itm23)
v3_idsc_itm23<-c(v3_clin$v3_ids_c_s3_ids23_psymo_hemm,v3_con$v3_ids_c_s3_ids23_psymo_hemm)
v3_idsc_itm23<-factor(v3_idsc_itm23, ordered=T)
descT(v3_idsc_itm23)
## 0 1 2 3 <NA>
## [1,] No. cases 712 129 24 3 918 1786
## [2,] Percent 39.9 7.2 1.3 0.2 51.4 100
Item 24 Psychomotor agitation (ordinal [0,1,2,3], v3_idsc_itm24)
v3_idsc_itm24<-c(v3_clin$v3_ids_c_s3_ids24_psymo_agitht,v3_con$v3_ids_c_s3_ids24_psymo_agitht)
v3_idsc_itm24<-factor(v3_idsc_itm24, ordered=T)
descT(v3_idsc_itm24)
## 0 1 2 3 <NA>
## [1,] No. cases 700 109 50 4 923 1786
## [2,] Percent 39.2 6.1 2.8 0.2 51.7 100
Item 25 Somatic complaints (ordinal [0,1,2,3], v3_idsc_itm25)
v3_idsc_itm25<-c(v3_clin$v3_ids_c_s3_ids25_som_beschw,v3_con$v3_ids_c_s3_ids25_som_beschw)
v3_idsc_itm25<-factor(v3_idsc_itm25, ordered=T)
descT(v3_idsc_itm25)
## 0 1 2 3 <NA>
## [1,] No. cases 584 214 52 19 917 1786
## [2,] Percent 32.7 12 2.9 1.1 51.3 100
Item 26 Sympathetic arousal (ordinal [0,1,2,3], v3_idsc_itm26)
v3_idsc_itm26<-c(v3_clin$v3_ids_c_s3_ids26_veg_erreg,v3_con$v3_ids_c_s3_ids26_veg_erreg)
v3_idsc_itm26<-factor(v3_idsc_itm26, ordered=T)
descT(v3_idsc_itm26)
## 0 1 2 3 <NA>
## [1,] No. cases 640 166 45 15 920 1786
## [2,] Percent 35.8 9.3 2.5 0.8 51.5 100
Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v3_idsc_itm27)
v3_idsc_itm27<-c(v3_clin$v3_ids_c_s3_ids27_panik_phob,v3_con$v3_ids_c_s3_ids27_panik_phob)
v3_idsc_itm27<-factor(v3_idsc_itm27, ordered=T)
descT(v3_idsc_itm27)
## 0 1 2 3 <NA>
## [1,] No. cases 775 49 28 14 920 1786
## [2,] Percent 43.4 2.7 1.6 0.8 51.5 100
Item 28 Gastrointestinal (ordinal [0,1,2,3], v3_idsc_itm28)
v3_idsc_itm28<-c(v3_clin$v3_ids_c_s3_ids28_verdauung,v3_con$v3_ids_c_s3_ids28_verdauung)
v3_idsc_itm28<-factor(v3_idsc_itm28, ordered=T)
descT(v3_idsc_itm28)
## 0 1 2 3 <NA>
## [1,] No. cases 734 82 40 12 918 1786
## [2,] Percent 41.1 4.6 2.2 0.7 51.4 100
Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v3_idsc_itm29)
v3_idsc_itm29<-c(v3_clin$v3_ids_c_s3_ids29_pers_bezieh,v3_con$v3_ids_c_s3_ids29_pers_bezieh)
v3_idsc_itm29<-factor(v3_idsc_itm29, ordered=T)
descT(v3_idsc_itm29)
## 0 1 2 3 <NA>
## [1,] No. cases 700 106 43 15 922 1786
## [2,] Percent 39.2 5.9 2.4 0.8 51.6 100
Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v3_idsc_itm30)
v3_idsc_itm30<-c(v3_clin$v3_ids_c_s3_ids30_schwgf_k_energ,v3_con$v3_ids_c_s3_ids30_schwgf_k_energ)
v3_idsc_itm30<-factor(v3_idsc_itm30, ordered=T)
descT(v3_idsc_itm30)
## 0 1 2 3 <NA>
## [1,] No. cases 714 103 38 12 919 1786
## [2,] Percent 40 5.8 2.1 0.7 51.5 100
Create IDS-C30 total score (continuous [0-84], v3_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.
v3_idsc_sum<-as.numeric.factor(v3_idsc_itm1)+
as.numeric.factor(v3_idsc_itm2)+
as.numeric.factor(v3_idsc_itm3)+
as.numeric.factor(v3_idsc_itm4)+
as.numeric.factor(v3_idsc_itm5)+
as.numeric.factor(v3_idsc_itm6)+
as.numeric.factor(v3_idsc_itm7)+
as.numeric.factor(v3_idsc_itm8)+
as.numeric.factor(v3_idsc_itm9)+
as.numeric.factor(v3_idsc_itm10)+
ifelse(is.na(v3_idsc_itm11)==T & is.na(v3_idsc_itm12)==T, NA,
ifelse((v3_idsc_itm11==-999 & v3_idsc_itm12!=-999), v3_idsc_itm12,
ifelse((v3_idsc_itm11!=-999 & v3_idsc_itm12==-999),v3_idsc_itm11, NA)))+
ifelse(is.na(v3_idsc_itm13)==T & is.na(v3_idsc_itm14)==T, NA,
ifelse((v3_idsc_itm13==-999 & v3_idsc_itm14!=-999), v3_idsc_itm14,
ifelse((v3_idsc_itm13!=-999 & v3_idsc_itm14==-999),v3_idsc_itm13, NA)))+
as.numeric.factor(v3_idsc_itm15)+
as.numeric.factor(v3_idsc_itm16)+
as.numeric.factor(v3_idsc_itm17)+
as.numeric.factor(v3_idsc_itm18)+
as.numeric.factor(v3_idsc_itm19)+
as.numeric.factor(v3_idsc_itm20)+
as.numeric.factor(v3_idsc_itm21)+
as.numeric.factor(v3_idsc_itm22)+
as.numeric.factor(v3_idsc_itm23)+
as.numeric.factor(v3_idsc_itm24)+
as.numeric.factor(v3_idsc_itm25)+
as.numeric.factor(v3_idsc_itm26)+
as.numeric.factor(v3_idsc_itm27)+
as.numeric.factor(v3_idsc_itm28)+
as.numeric.factor(v3_idsc_itm29)+
as.numeric.factor(v3_idsc_itm30)
summary(v3_idsc_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 3.0 7.0 10.3 15.0 71.0 980
Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)
v3_idsc_itm11<-factor(v3_idsc_itm11,ordered=T)
v3_idsc_itm12<-factor(v3_idsc_itm12,ordered=T)
v3_idsc_itm13<-factor(v3_idsc_itm13,ordered=T)
v3_idsc_itm14<-factor(v3_idsc_itm14,ordered=T)
Create dataset
v3_symp_ids_c<-data.frame(v3_idsc_itm1,v3_idsc_itm2,v3_idsc_itm3,v3_idsc_itm4,v3_idsc_itm5,v3_idsc_itm6,v3_idsc_itm7,
v3_idsc_itm8,v3_idsc_itm9,v3_idsc_itm9a,v3_idsc_itm9b,v3_idsc_itm10,v3_idsc_itm11,v3_idsc_itm12,
v3_idsc_itm13,v3_idsc_itm14,v3_idsc_itm15,v3_idsc_itm16,v3_idsc_itm17,v3_idsc_itm18,v3_idsc_itm19,
v3_idsc_itm20,v3_idsc_itm21,v3_idsc_itm22,v3_idsc_itm23,v3_idsc_itm24,v3_idsc_itm25,v3_idsc_itm26,
v3_idsc_itm27,v3_idsc_itm28,v3_idsc_itm29,v3_idsc_itm30,v3_idsc_sum)
For more information on the scale, please see Visit 1
Item 1 Elevated mood (ordinal [0,1,2,3,4], v3_ymrs_itm1)
v3_ymrs_itm1<-c(v3_clin$v3_ymrs_ymrs1_gehob_stimm,v3_con$v3_ymrs_ymrs1_gehob_stimm)
v3_ymrs_itm1<-factor(v3_ymrs_itm1, ordered=T)
descT(v3_ymrs_itm1)
## 0 1 2 3 4 <NA>
## [1,] No. cases 719 101 44 2 1 919 1786
## [2,] Percent 40.3 5.7 2.5 0.1 0.1 51.5 100
Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v3_ymrs_itm2)
v3_ymrs_itm2<-c(v3_clin$v3_ymrs_ymrs2_gest_aktiv,v3_con$v3_ymrs_ymrs2_gest_aktiv)
v3_ymrs_itm2<-factor(v3_ymrs_itm2, ordered=T)
descT(v3_ymrs_itm2)
## 0 1 2 3 <NA>
## [1,] No. cases 750 81 29 9 917 1786
## [2,] Percent 42 4.5 1.6 0.5 51.3 100
Item 3 Sexual interest (ordinal [0,1,2,3,4], v3_ymrs_itm3)
v3_ymrs_itm3<-c(v3_clin$v3_ymrs_ymrs3_sex_interesse,v3_con$v3_ymrs_ymrs3_sex_interesse)
v3_ymrs_itm3<-factor(v3_ymrs_itm3, ordered=T)
descT(v3_ymrs_itm3)
## 0 1 2 3 <NA>
## [1,] No. cases 820 21 25 2 918 1786
## [2,] Percent 45.9 1.2 1.4 0.1 51.4 100
Item 4 Sleep (ordinal [0,1,2,3,4], v3_ymrs_itm4)
v3_ymrs_itm4<-c(v3_clin$v3_ymrs_ymrs4_schlaf,v3_con$v3_ymrs_ymrs4_schlaf)
v3_ymrs_itm4<-factor(v3_ymrs_itm4, ordered=T)
descT(v3_ymrs_itm4)
## 0 1 2 3 <NA>
## [1,] No. cases 792 41 22 12 919 1786
## [2,] Percent 44.3 2.3 1.2 0.7 51.5 100
Item 5 Irritability (ordinal [0,2,4,6,8], v3_ymrs_itm5)
v3_ymrs_itm5<-c(v3_clin$v3_ymrs_ymrs5_reizbarkeit,v3_con$v3_ymrs_ymrs5_reizbarkeit)
v3_ymrs_itm5<-factor(v3_ymrs_itm5, ordered=T)
descT(v3_ymrs_itm5)
## 0 2 4 6 <NA>
## [1,] No. cases 722 125 21 1 917 1786
## [2,] Percent 40.4 7 1.2 0.1 51.3 100
Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v3_ymrs_itm6)
v3_ymrs_itm6<-c(v3_clin$v3_ymrs_ymrs6_sprechweise,v3_con$v3_ymrs_ymrs6_sprechweise)
v3_ymrs_itm6<-factor(v3_ymrs_itm6, ordered=T)
descT(v3_ymrs_itm6)
## 0 2 4 6 8 <NA>
## [1,] No. cases 739 56 57 14 1 919 1786
## [2,] Percent 41.4 3.1 3.2 0.8 0.1 51.5 100
Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v3_ymrs_itm7)
v3_ymrs_itm7<-c(v3_clin$v3_ymrs_ymrs7_sprachstoer,v3_con$v3_ymrs_ymrs7_sprachstoer)
v3_ymrs_itm7<-factor(v3_ymrs_itm7, ordered=T)
descT(v3_ymrs_itm7)
## 0 1 2 3 <NA>
## [1,] No. cases 771 70 22 4 919 1786
## [2,] Percent 43.2 3.9 1.2 0.2 51.5 100
Item 8 Content (ordinal [0,2,4,6,8], v3_ymrs_itm8)
v3_ymrs_itm8<-c(v3_clin$v3_ymrs_ymrs8_inhalte,v3_con$v3_ymrs_ymrs8_inhalte)
v3_ymrs_itm8<-factor(v3_ymrs_itm8, ordered=T)
descT(v3_ymrs_itm8)
## 0 2 4 6 8 <NA>
## [1,] No. cases 818 23 4 9 12 920 1786
## [2,] Percent 45.8 1.3 0.2 0.5 0.7 51.5 100
Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v3_ymrs_itm9)
v3_ymrs_itm9<-c(v3_clin$v3_ymrs_ymrs9_exp_aggr_verh,v3_con$v3_ymrs_ymrs9_exp_aggr_verh)
v3_ymrs_itm9<-factor(v3_ymrs_itm9, ordered=T)
descT(v3_ymrs_itm9)
## 0 2 4 <NA>
## [1,] No. cases 837 29 2 918 1786
## [2,] Percent 46.9 1.6 0.1 51.4 100
Item 10 Appearance (ordinal [0,1,2,3,4], v3_ymrs_itm10)
v3_ymrs_itm10<-c(v3_clin$v3_ymrs_ymrs10_erscheinung,v3_con$v3_ymrs_ymrs10_erscheinung)
v3_ymrs_itm10<-factor(v3_ymrs_itm10, ordered=T)
descT(v3_ymrs_itm10)
## 0 1 2 3 4 <NA>
## [1,] No. cases 783 65 15 3 1 919 1786
## [2,] Percent 43.8 3.6 0.8 0.2 0.1 51.5 100
Item 11 Insight (ordinal [0,1,2,3,4], v3_ymrs_itm11)
v3_ymrs_itm11<-c(v3_clin$v3_ymrs_ymrs11_krkh_einsicht,v3_con$v3_ymrs_ymrs11_krkh_einsicht)
v3_ymrs_itm11<-factor(v3_ymrs_itm11, ordered=T)
descT(v3_ymrs_itm11)
## 0 1 2 3 4 <NA>
## [1,] No. cases 825 15 17 4 2 923 1786
## [2,] Percent 46.2 0.8 1 0.2 0.1 51.7 100
Create YMRS total score (continuous [0-60], v3_ymrs_sum)
v3_ymrs_sum<-(as.numeric.factor(v3_ymrs_itm1)+
as.numeric.factor(v3_ymrs_itm2)+
as.numeric.factor(v3_ymrs_itm3)+
as.numeric.factor(v3_ymrs_itm4)+
as.numeric.factor(v3_ymrs_itm5)+
as.numeric.factor(v3_ymrs_itm6)+
as.numeric.factor(v3_ymrs_itm7)+
as.numeric.factor(v3_ymrs_itm8)+
as.numeric.factor(v3_ymrs_itm9)+
as.numeric.factor(v3_ymrs_itm10)+
as.numeric.factor(v3_ymrs_itm11))
summary(v3_ymrs_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 0.000 2.205 2.000 30.000 936
Create dataset
v3_symp_ymrs<-data.frame(v3_ymrs_itm1,
v3_ymrs_itm2,
v3_ymrs_itm3,
v3_ymrs_itm4,
v3_ymrs_itm5,
v3_ymrs_itm6,
v3_ymrs_itm7,
v3_ymrs_itm8,
v3_ymrs_itm9,
v3_ymrs_itm10,
v3_ymrs_itm11,
v3_ymrs_sum)
Please see Visit 1 for more details and explicit rating instructions.
v3_cgi_s<-c(v3_clin$v3_cgi1_cgi1_schweregrad,rep(-999,dim(v3_con)[1]))
v3_cgi_s[v3_cgi_s==0]<- -999
v3_cgi_s<-factor(v3_cgi_s, ordered=T)
descT(v3_cgi_s)
## -999 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 466 16 58 215 193 134 32 1 671 1786
## [2,] Percent 26.1 0.9 3.2 12 10.8 7.5 1.8 0.1 37.6 100
Here, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “very much improved”-1 to “very much worse”-7.
v3_cgi_c<-c(v3_clin$v3_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v3_con)[1]))
v3_cgi_c[v3_cgi_c==0]<- -999
v3_cgi_c<-factor(v3_cgi_c, ordered=T)
descT(v3_cgi_c)
## -999 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 482 12 89 129 256 107 16 2 693 1786
## [2,] Percent 27 0.7 5 7.2 14.3 6 0.9 0.1 38.8 100
Please see Visit 1 for more details and explicit rating instructions.
v3_gaf<-c(v3_clin$v3_gaf_gaf_code,v3_con$v3_gaf_gaf_code)
v3_gaf[v3_gaf==0]<- -999
summary(v3_gaf[v3_gaf>0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 25.00 55.00 68.00 67.69 81.00 100.00 913
boxplot(v3_gaf[v3_gaf>0 & v1_stat=="CLINICAL"], v3_gaf[v3_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))
Create dataset
v3_ill_sev<-data.frame(v3_cgi_s,v3_cgi_c,v3_gaf)
There are no differences compared to the test battery assessed in Visit 2.
General comments on the testing (character, v3_nrpsy_com) If there were no comments, this item was coded -999.
Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v3_nrpsy_lng)
v3_nrpsy_lng<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_nrpsy_lng<-ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==0, "mother tongue",
ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==1, "good",
ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==2, "sufficient",
ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==3, "not sufficient",v3_nrpsy_lng))))
v3_nrpsy_lng<-factor(v3_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))
descT(v3_nrpsy_lng)
## mother tongue good sufficient not sufficient <NA>
## [1,] No. cases 863 44 4 1 874 1786
## [2,] Percent 48.3 2.5 0.2 0.1 48.9 100
Motivation of the participant (ordinal [“poor”,“average”,“good”], v3_nrpsy_mtv)
v3_nrpsy_mtv_pre<-c(v3_clin$v3_npu1_np_mot,v3_con$v3_npu_folge_np_mot)
v3_nrpsy_mtv<-ifelse(v3_nrpsy_mtv_pre==0, "poor",
ifelse(v3_nrpsy_mtv_pre==1, "average",
ifelse(v3_nrpsy_mtv_pre==2, "good", NA)))
v3_nrpsy_mtv<-factor(v3_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))
descT(v3_nrpsy_mtv)
## poor average good <NA>
## [1,] No. cases 13 65 820 888 1786
## [2,] Percent 0.7 3.6 45.9 49.7 100
For a description of the test and the variables, see Visit 2.
Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.
VLMT_introcheck (categorical [0, 1, 9], v3_nrpsy_vlmt_check)
v3_nrpsy_vlmt_check<-c(v3_clin$v3_vlmt_vlmt_introcheck1,v3_con$v3_npu_folge_np_vlmt)
descT(v3_nrpsy_vlmt_check)
## 0 1 9 <NA>
## [1,] No. cases 68 834 27 857 1786
## [2,] Percent 3.8 46.7 1.5 48 100
Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v3_nrpsy_vlmt_corr)
v3_nrpsy_vlmt_corr<-c(v3_clin$v3_vlmt_vlmt3_sw_a5d,v3_con$v3_npu_folge_np_vlmt_gl)
summary(v3_nrpsy_vlmt_corr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 42.00 52.00 51.08 61.00 75.00 916
Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v3_nrpsy_vlmt_lss_d)
v3_nrpsy_vlmt_lss_d<-c(v3_clin$v3_vlmt_vlmt5_aw_ilsd6,v3_con$v3_npu_folge_np_vlmt_vni)
summary(v3_nrpsy_vlmt_lss_d)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -5.000 0.000 1.000 1.707 3.000 14.000 926
Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v3_nrpsy_vlmt_lss_t)
v3_nrpsy_vlmt_lss_t<-c(v3_clin$v3_vlmt_vlmt6_aw_vwd7,v3_con$v3_npu_folge_np_vlmt_vnzv)
summary(v3_nrpsy_vlmt_lss_t)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -5.000 0.000 2.000 1.947 3.000 15.000 935
Recognition performance (corrected for falsely recognized words) (continuous [number of words], v3_nrpsy_vlmt_rec)
v3_nrpsy_vlmt_rec<-c(v3_clin$v3_vlmt_vlmt8_kwl,v3_con$v3_npu_folge_np_vlmt_kw)
summary(v3_nrpsy_vlmt_rec)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -19.00 10.00 13.00 11.63 15.00 15.00 940
For a description of the test, see Visit 1.
TMT Part A, time (continuous [seconds], v3_nrpsy_tmt_A_rt)
v3_nrpsy_tmt_A_rt<-c(v3_clin$v3_npu1_tmt_001,v3_con$v3_npu_folge_np_tmt_001)
summary(v3_nrpsy_tmt_A_rt)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 9.00 20.00 27.00 30.79 37.00 179.00 875
TMT Part A, errors (continuous [number of errors], v3_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).
v3_nrpsy_tmt_A_err<-c(v3_clin$v3_npu1_tmt_af_001,v3_con$v3_npu_folge_np_tmtfehler_001)
summary(v3_nrpsy_tmt_A_err)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.0905 0.0000 6.0000 880
TMT Part B, time (continuous [seconds], v3_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.
v3_nrpsy_tmt_B_rt<-c(v3_clin$v3_npu1_tmt_002,v3_con$v3_npu_folge_tmt_002)
v3_nrpsy_tmt_B_rt[v3_nrpsy_tmt_B_rt>300]<-300
summary(v3_nrpsy_tmt_B_rt)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 23.00 47.00 63.00 73.24 85.00 300.00 894
TMT Part B, errors (continuous [number of errors], v3_nrpsy_tmt_B_err)
v3_nrpsy_tmt_B_err<-c(v3_clin$v3_npu1_tmt_af_002,v3_con$v3_npu_folge_tmt_af_002)
summary(v3_nrpsy_tmt_B_err)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.5124 1.0000 18.0000 898
For a description of the test, see Visit 1.
Forward (continuous [number of items], v3_nrpsy_dgt_sp_frw)
v3_nrpsy_dgt_sp_frw<-c(v3_clin$v3_npu1_zns_001,v3_con$v3_npu_folge_np_wie_001)
summary(v3_nrpsy_dgt_sp_frw)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.000 8.000 10.000 9.762 11.000 16.000 885
Backward (continuous [number of items], v3_nrpsy_dgt_sp_bck)
v3_nrpsy_dgt_sp_bck<-c(v3_clin$v3_npu1_zns_002,v3_con$v3_npu_folge_np_wie_002)
summary(v3_nrpsy_dgt_sp_bck)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 5.000 6.000 6.627 8.000 14.000 887
For a description of the test, see Visit 1.
v3_introcheck3<-c(v3_clin$v3_npu1_np_introcheck3,v3_con$v3_npu_folge_np_hawier)
v3_nrpsy_dg_sym_pre<-c(v3_clin$v3_npu1_zst_001,v3_con$v3_npu_folge_np_hawier_001)
v3_nrpsy_dg_sym<-ifelse(v3_introcheck3==1, v3_nrpsy_dg_sym_pre,
ifelse(v3_introcheck3==9,-999,
ifelse(v3_introcheck3==0,NA,NA)))
summary(subset(v3_nrpsy_dg_sym,v3_nrpsy_dg_sym>=0))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 15.00 53.50 70.00 69.87 86.00 133.00
Create dataset
v3_nrpsy<-data.frame(v3_nrpsy_com,
v3_nrpsy_lng,
v3_nrpsy_mtv,
v3_nrpsy_vlmt_check,
v3_nrpsy_vlmt_corr,
v3_nrpsy_vlmt_lss_d,
v3_nrpsy_vlmt_lss_t,
v3_nrpsy_vlmt_rec,
v3_nrpsy_tmt_A_rt,
v3_nrpsy_tmt_A_err,
v3_nrpsy_tmt_B_rt,
v3_nrpsy_tmt_B_err,
v3_nrpsy_dgt_sp_frw,
v3_nrpsy_dgt_sp_bck,
v3_nrpsy_dg_sym)
Participants were asked to fill out questionnaires on the following topics: childhood trauma/early life stress (CTS), current medication adherence (compliance), current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 2 and 3) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1 and 2, questionnaires that were not filled out correctly were excluded from the dataset.
For explanation, please refer to the section in Visit 1
“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v3_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.
v3_sf12_recode(v3_con$v3_sf12_sf_allgemein,"v3_sf12_itm0")
## -999 2 3 4 5 6 7 8 9 10 <NA>
## [1,] No. cases 1320 2 6 7 7 10 32 78 80 37 207 1786
## [2,] Percent 73.9 0.1 0.3 0.4 0.4 0.6 1.8 4.4 4.5 2.1 11.6 100
“In general, would you say your health is…” (ordinal [1,2,3,4,5], v3_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.
v3_sf12_recode(v3_con$v3_sf12_sf1,"v3_sf12_itm1")
## -999 1 2 3 4 5 <NA>
## [1,] No. cases 1320 60 128 72 11 3 192 1786
## [2,] Percent 73.9 3.4 7.2 4 0.6 0.2 10.8 100
“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”
“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v3_sf12_itm2) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.
v3_sf12_recode(v3_con$v3_sf12_sf2,"v3_sf12_itm2")
## -999 1 2 3 <NA>
## [1,] No. cases 1320 2 20 252 192 1786
## [2,] Percent 73.9 0.1 1.1 14.1 10.8 100
“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v3_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.
v3_sf12_recode(v3_con$v3_sf12_sf3,"v3_sf12_itm3")
## -999 1 2 3 <NA>
## [1,] No. cases 1320 1 30 243 192 1786
## [2,] Percent 73.9 0.1 1.7 13.6 10.8 100
During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?
“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v3_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.
v3_sf12_recode(v3_con$v3_sf12_sf4,"v3_sf12_itm4")
## -999 1 2 <NA>
## [1,] No. cases 1320 36 238 192 1786
## [2,] Percent 73.9 2 13.3 10.8 100
“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v3_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.
v3_sf12_recode(v3_con$v3_sf12_sf5,"v3_sf12_itm5")
## -999 1 2 <NA>
## [1,] No. cases 1320 24 248 194 1786
## [2,] Percent 73.9 1.3 13.9 10.9 100
During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?
“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v3_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.
v3_sf12_recode(v3_con$v3_sf12_sf6,"v3_sf12_itm6")
## -999 1 2 <NA>
## [1,] No. cases 1320 24 250 192 1786
## [2,] Percent 73.9 1.3 14 10.8 100
“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v3_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.
v3_sf12_recode(v3_con$v3_sf12_sf7,"v3_sf12_itm7")
## -999 1 2 <NA>
## [1,] No. cases 1320 18 256 192 1786
## [2,] Percent 73.9 1 14.3 10.8 100
“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v3_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.
v3_sf12_recode(v3_con$v3_sf12_st8,"v3_sf12_itm8")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 157 61 28 20 7 1 192 1786
## [2,] Percent 73.9 8.8 3.4 1.6 1.1 0.4 0.1 10.8 100
The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS
Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.
“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v3_sf12_itm9)
v3_sf12_recode(v3_con$v3_sf12_st9,"v3_sf12_itm9")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 28 156 63 24 2 1 192 1786
## [2,] Percent 73.9 1.6 8.7 3.5 1.3 0.1 0.1 10.8 100
“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v3_sf12_itm10)
v3_sf12_recode(v3_con$v3_sf12_st10,"v3_sf12_itm10")
## -999 1 2 3 4 5 <NA>
## [1,] No. cases 1320 20 103 75 61 15 192 1786
## [2,] Percent 73.9 1.1 5.8 4.2 3.4 0.8 10.8 100
“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v3_sf12_itm11)
v3_sf12_recode(v3_con$v3_sf12_st11,"v3_sf12_itm11")
## -999 2 3 4 5 6 <NA>
## [1,] No. cases 1320 5 12 34 113 110 192 1786
## [2,] Percent 73.9 0.3 0.7 1.9 6.3 6.2 10.8 100
“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v3_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.
There is an error in the phenotype database regarding this item. The answering alternatives 3, 4, and 5 appear as 4, 5, and 6 in the database exports. These errors are corrected below.
v3_sf12_recode(v3_con$v3_sf12_st12,"v3_sf12_itm12")
## -999 2 4 5 6 <NA>
## [1,] No. cases 1320 3 15 53 194 201 1786
## [2,] Percent 73.9 0.2 0.8 3 10.9 11.3 100
#recode error in phenotype database
v3_sf12_itm12[v3_sf12_itm12==4]<-3
v3_sf12_itm12[v3_sf12_itm12==5]<-4
v3_sf12_itm12[v3_sf12_itm12==6]<-5
descT(v3_sf12_itm12)
## -999 2 3 4 5 <NA>
## [1,] No. cases 1320 3 15 53 194 201 1786
## [2,] Percent 73.9 0.2 0.8 3 10.9 11.3 100
Create dataset
v3_sf12<-data.frame(v3_sf12_itm0,
v3_sf12_itm1,
v3_sf12_itm2,
v3_sf12_itm3,
v3_sf12_itm4,
v3_sf12_itm5,
v3_sf12_itm6,
v3_sf12_itm7,
v3_sf12_itm8,
v3_sf12_itm9,
v3_sf12_itm10,
v3_sf12_itm11,
v3_sf12_itm12)
The CTS (Bernstein et al., 2003) used here is a German short version (Grabe et al., 2012) of the CTQ (Bernstein et al., 1994). It is used as a screening instrument to assess childhood trauma/early life stress. Validated threshold values are available (Glaesmer et al., 2013) to transform these values into a dichotomous scale (childhood trauma/early life stress: yes/no; see below). Each of the five questions is on a five-point scale.
Important: analogous to other questionnaires, we have, as specified in the test manual, reversed the encoding so that, in the present dataset, higher scores on every item indicate a higher level of childhood trauma/early life stress. Do not reverse encoding. Each questions starts with “When I grew up”
1. “…I had the feeling to be loved” (ordinal [1,2,3,4,5], v3_cts_1)
Encoding reversed so that higher scores on each item indicate a higher level of childhood trauma/early life stress. This item measures emotional neglect.
cts_recode(v3_clin$v3_chidlhood_childhood_1,v3_con$v3_chidlhood_childhood_1,"v3_cts_1",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 273 330 136 113 35 899 1786
## [2,] Percent 15.3 18.5 7.6 6.3 2 50.3 100
descT(v3_cts_1)
## 1 2 3 4 5 <NA>
## [1,] No. cases 273 330 136 113 35 899 1786
## [2,] Percent 15.3 18.5 7.6 6.3 2 50.3 100
2. “…persons in my family hit me so hard that I bruised” (ordinal [1,2,3,4,5], v3_cts_2)
This item measures physical abuse.
cts_recode(v3_clin$v3_chidlhood_childhood_2,v3_con$v3_chidlhood_childhood_2,"v3_cts_2",0)
## 0 1 2 3 4 5 NA's
## [1,] No. cases 1 624 110 73 40 22 916 1786
## [2,] Percent 0.1 34.9 6.2 4.1 2.2 1.2 51.3 100
3. “…I had the feeling someone in my family hated me” (ordinal [1,2,3,4,5], v3_cts_3)
This item measures emotional abuse.
cts_recode(v3_clin$v3_chidlhood_childhood_3,v3_con$v3_chidlhood_childhood_3,"v3_cts_3",0)
## 0 1 2 3 4 5 NA's
## [1,] No. cases 1 561 126 81 63 40 914 1786
## [2,] Percent 0.1 31.4 7.1 4.5 3.5 2.2 51.2 100
4. “…someone harassed me sexually” (ordinal [1,2,3,4,5], v3_cts_4)
This items measures sexual abuse.
cts_recode(v3_clin$v3_chidlhood_childhood_4,v3_con$v3_chidlhood_childhood_4,"v3_cts_4",0)
## 0 1 2 3 4 5 NA's
## [1,] No. cases 1 723 64 43 21 10 924 1786
## [2,] Percent 0.1 40.5 3.6 2.4 1.2 0.6 51.7 100
5. “…there was someone who took me to the doctor when I needed it” (ordinal [1,2,3,4,5], v3_cts_5) Encoding reversed so that higher scores on each item indicate a higher level of childhood trauma/early life stress. This item measures physical neglect.
cts_recode(v3_clin$v3_chidlhood_childhood_5,v3_con$v3_chidlhood_childhood_5,"v3_cts_5",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 452 211 125 55 41 902 1786
## [2,] Percent 25.3 11.8 7 3.1 2.3 50.5 100
This assessment indicates whether a participant suffered from childhood trauma/early life stress or not (see description above). More specifically, if any of the five items exceeded the threshold given by (Glaesmer et al., 2013), an individual was determined to have experienced childhood trauma/early life stress. If individuals filled out the questionnaire incompletely but one item nevertheless passed the threshold, these individuals are included in the present dataset. Only those individuals in which all items are completet and below the threshold are set to “N”.
v3_cts_els_dic<-c(rep(NA,dim(v3_clin)[1]),rep(NA,dim(v3_clin)[1]))
v3_cts_els_dic<-ifelse(v3_cts_1>3 | v3_cts_2>2 | v3_cts_3>2 | v3_cts_4>1 | v3_cts_5>3, "Y",
ifelse((is.na(v3_cts_1)==F & is.na(v3_cts_2)==F & is.na(v3_cts_3)==F &
is.na(v3_cts_4)==F & is.na(v3_cts_5)==F),"N", v3_cts_els_dic))
descT(v3_cts_els_dic)
## N Y <NA>
## [1,] No. cases 500 367 919 1786
## [2,] Percent 28 20.5 51.5 100
1. “…I had the feeling to be loved” (dichotomous, v3_cts_1_dic)
v3_cts_1_dic<-ifelse(v3_cts_1>3, "Y","N")
descT(v3_cts_1_dic)
## N Y <NA>
## [1,] No. cases 739 148 899 1786
## [2,] Percent 41.4 8.3 50.3 100
2. “…persons in my family hit me so hard that I bruised” (dichotomous, v3_cts_2_dic)
v3_cts_2_dic<-ifelse(v3_cts_2>2, "Y","N")
descT(v3_cts_2_dic)
## N Y <NA>
## [1,] No. cases 735 135 916 1786
## [2,] Percent 41.2 7.6 51.3 100
3. “…I had the feeling someone in my family hated me” (dichotomous, v3_cts_3_dic)
v3_cts_3_dic<-ifelse(v3_cts_3>2, "Y","N")
descT(v3_cts_3_dic)
## N Y <NA>
## [1,] No. cases 688 184 914 1786
## [2,] Percent 38.5 10.3 51.2 100
4. “…someone harassed me sexually” (dichotomous, v3_cts_4_dic)
v3_cts_4_dic<-ifelse(v3_cts_4>1, "Y","N")
descT(v3_cts_4_dic)
## N Y <NA>
## [1,] No. cases 724 138 924 1786
## [2,] Percent 40.5 7.7 51.7 100
5. “…there was someone who took me to the doctor when I needed it” (dichotomous, v3_cts_5_dic)
v3_cts_5_dic<-ifelse(v3_cts_5>3, "Y","N")
descT(v3_cts_5_dic)
## N Y <NA>
## [1,] No. cases 788 96 902 1786
## [2,] Percent 44.1 5.4 50.5 100
Create dataset
v3_cts<-data.frame(v3_cts_1,v3_cts_2,v3_cts_3,v3_cts_4,v3_cts_5,v3_cts_els_dic,
v3_cts_1_dic,v3_cts_2_dic,v3_cts_3_dic,v3_cts_4_dic,v3_cts_5_dic)
For a description of the questionnaire, see Visit 1.
Past seven days (ordinal [1,2,3,4,5,6], v3_med_pst_wk)
v3_med_chk<-c(v3_clin$v3_compl_verwer_fragebogen,rep(1,dim(v3_con)[1]))
v3_med_pst_wk_pre<-c(v3_clin$v3_compl_psychopharm_7_tag,rep(-999,dim(v3_con)[1]))
v3_med_pst_wk<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_med_pst_wk<-ifelse((is.na(v3_med_chk) | v3_med_chk!=2),
v3_med_pst_wk_pre, v3_med_pst_wk)
descT(v3_med_pst_wk)
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 466 509 62 19 4 3 13 710 1786
## [2,] Percent 26.1 28.5 3.5 1.1 0.2 0.2 0.7 39.8 100
Past six months (ordinal [1,2,3,4,5,6], v3_med_pst_sx_mths)
v3_med_pre<-c(v3_clin$v3_compl_psychopharm_6_mon,rep(-999,dim(v3_con)[1]))
v3_med_pst_sx_mths<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_med_pst_sx_mths<-ifelse((is.na(v3_med_chk) | v3_med_chk!=2),
v3_med_pre, v3_med_pst_sx_mths)
descT(v3_med_pst_sx_mths)
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 466 460 88 40 12 4 10 706 1786
## [2,] Percent 26.1 25.8 4.9 2.2 0.7 0.2 0.6 39.5 100
Create dataset
v3_med_adh<-data.frame(v3_med_pst_wk,v3_med_pst_sx_mths)
For explanation, please refer to the section in Visit 1
1. Sadness (ordinal [0,1,2,3], v3_bdi2_itm1)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi1_traurigkeit,v3_con$v3_bdi2_s1_bdi1,"v3_bdi2_itm1")
## 0 1 2 3 <NA>
## [1,] No. cases 668 212 21 4 881 1786
## [2,] Percent 37.4 11.9 1.2 0.2 49.3 100
2. Pessimism (ordinal [0,1,2,3], v3_bdi2_itm2)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi2_pessimismus,v3_con$v3_bdi2_s1_bdi2,"v3_bdi2_itm2")
## 0 1 2 3 <NA>
## [1,] No. cases 701 144 48 10 883 1786
## [2,] Percent 39.2 8.1 2.7 0.6 49.4 100
3. Past failure (ordinal [0,1,2,3], v3_bdi2_itm3)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi3_versagensgef,v3_con$v3_bdi2_s1_bdi3,"v3_bdi2_itm3")
## 0 1 2 3 <NA>
## [1,] No. cases 646 139 106 14 881 1786
## [2,] Percent 36.2 7.8 5.9 0.8 49.3 100
4. Loss of pleasure (ordinal [0,1,2,3], v3_bdi2_itm4)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi4_verlust_freude,v3_con$v3_bdi2_s1_bdi4,"v3_bdi2_itm4")
## 0 1 2 3 <NA>
## [1,] No. cases 592 247 45 19 883 1786
## [2,] Percent 33.1 13.8 2.5 1.1 49.4 100
5. Guilty feelings (ordinal [0,1,2,3], v3_bdi2_itm5)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi5_schuldgef,v3_con$v3_bdi2_s1_bdi5,"v3_bdi2_itm5")
## 0 1 2 3 <NA>
## [1,] No. cases 677 196 20 10 883 1786
## [2,] Percent 37.9 11 1.1 0.6 49.4 100
6. Punishment feelings (ordinal [0,1,2,3], v3_bdi2_itm6)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi6_bestrafungsgef,v3_con$v3_bdi2_s1_bdi6,"v3_bdi2_itm6")
## 0 1 2 3 <NA>
## [1,] No. cases 740 113 11 37 885 1786
## [2,] Percent 41.4 6.3 0.6 2.1 49.6 100
7. Self-dislike (ordinal [0,1,2,3], v3_bdi2_itm7)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi7_selbstablehnung,v3_con$v3_bdi2_s1_bdi7,"v3_bdi2_itm7")
## 0 1 2 3 <NA>
## [1,] No. cases 721 112 56 13 884 1786
## [2,] Percent 40.4 6.3 3.1 0.7 49.5 100
8. Self-criticalness (ordinal [0,1,2,3], v3_bdi2_itm8)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi8_selbstvorwuerfe,v3_con$v3_bdi2_s1_bdi8,"v3_bdi2_itm8")
## 0 1 2 3 <NA>
## [1,] No. cases 617 217 54 16 882 1786
## [2,] Percent 34.5 12.2 3 0.9 49.4 100
9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v3_bdi2_itm9)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi9_selbstmordged,v3_con$v3_bdi2_s1_bdi9,"v3_bdi2_itm9")
## 0 1 2 3 <NA>
## [1,] No. cases 779 114 7 6 880 1786
## [2,] Percent 43.6 6.4 0.4 0.3 49.3 100
10. Crying (ordinal [0,1,2,3], v3_bdi2_itm10)
v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi10_weinen,v3_con$v3_bdi2_s1_bdi10,"v3_bdi2_itm10")
## 0 1 2 3 <NA>
## [1,] No. cases 745 75 20 63 883 1786
## [2,] Percent 41.7 4.2 1.1 3.5 49.4 100
11. Agitation (ordinal [0,1,2,3], v3_bdi2_itm11)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi11_unruhe,v3_con$v3_bdi2_s2_bdi11,"v3_bdi2_itm11")
## 0 1 2 3 <NA>
## [1,] No. cases 672 188 26 10 890 1786
## [2,] Percent 37.6 10.5 1.5 0.6 49.8 100
12. Loss of interest (ordinal [0,1,2,3], v3_bdi2_itm12)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi12_interessverl,v3_con$v3_bdi2_s2_bdi12,"v3_bdi2_itm12")
## 0 1 2 3 <NA>
## [1,] No. cases 683 154 34 23 892 1786
## [2,] Percent 38.2 8.6 1.9 1.3 49.9 100
13. Indecisiveness (ordinal [0,1,2,3], v3_bdi2_itm13)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi13_entschlussunf,v3_con$v3_bdi2_s2_bdi13,"v3_bdi2_itm13")
## 0 1 2 3 <NA>
## [1,] No. cases 619 198 43 34 892 1786
## [2,] Percent 34.7 11.1 2.4 1.9 49.9 100
14. Worthlessness (ordinal [0,1,2,3], v3_bdi2_itm14)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi14_wertlosigkeit,v3_con$v3_bdi2_s2_bdi14,"v3_bdi2_itm14")
## 0 1 2 3 <NA>
## [1,] No. cases 693 119 68 13 893 1786
## [2,] Percent 38.8 6.7 3.8 0.7 50 100
15. Loss of energy (ordinal [0,1,2,3], v3_bdi2_itm15)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi15_energieverlust,v3_con$v3_bdi2_s2_bdi15,"v3_bdi2_itm15")
## 0 1 2 3 <NA>
## [1,] No. cases 527 291 66 6 896 1786
## [2,] Percent 29.5 16.3 3.7 0.3 50.2 100
16. Changes in sleeping pattern (ordinal [0,1,2,3], v3_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep". There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.
v3_itm_bdi2_chk<-c(v3_clin$v3_bdi2_s1_verwer_fragebogen,v3_con$v3_bdi2_s1_bdi_korrekt)
v3_itm_bdi2_itm16_clin_con<-c(v3_clin$v3_bdi2_s2_bdi16_schlafgewohn,v3_con$v3_bdi2_s2_bdi16)
v3_bdi2_itm16<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_bdi2_itm16<-ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & v3_itm_bdi2_itm16_clin_con==0, 0,
ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) &
(v3_itm_bdi2_itm16_clin_con==1 | v3_itm_bdi2_itm16_clin_con==100), 1,
ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) &
(v3_itm_bdi2_itm16_clin_con==2 | v3_itm_bdi2_itm16_clin_con==200), 2,
ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) &
(v3_itm_bdi2_itm16_clin_con==3 | v3_itm_bdi2_itm16_clin_con==300), 3, v3_bdi2_itm16))))
v3_bdi2_itm16<-factor(v3_bdi2_itm16,ordered=T)
descT(v3_bdi2_itm16)
## 0 1 2 3 <NA>
## [1,] No. cases 506 287 67 35 891 1786
## [2,] Percent 28.3 16.1 3.8 2 49.9 100
17. Irritability (ordinal [0,1,2,3], v3_bdi2_itm17)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi17_reizbarkeit,v3_con$v3_bdi2_s2_bdi17,"v3_bdi2_itm17")
## 0 1 2 3 <NA>
## [1,] No. cases 686 171 27 9 893 1786
## [2,] Percent 38.4 9.6 1.5 0.5 50 100
18. Change in appetite (ordinal [0,1,2,3], v3_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.
v3_itm_bdi2_itm18_clin_con<-c(v3_clin$v3_bdi2_s2_bdi18_appetit,v3_con$v3_bdi2_s2_bdi18)
v3_bdi2_itm18<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_bdi2_itm18<-ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & v3_itm_bdi2_itm18_clin_con==0, 0,
ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) &
(v3_itm_bdi2_itm18_clin_con==1 | v3_itm_bdi2_itm18_clin_con==100), 1,
ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) &
(v3_itm_bdi2_itm18_clin_con==2 | v3_itm_bdi2_itm18_clin_con==200), 2,
ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) &
(v3_itm_bdi2_itm18_clin_con==3 | v3_itm_bdi2_itm18_clin_con==300), 3, v3_bdi2_itm18))))
v3_bdi2_itm18<-factor(v3_bdi2_itm18,ordered=T)
descT(v3_bdi2_itm18)
## 0 1 2 3 <NA>
## [1,] No. cases 625 211 40 17 893 1786
## [2,] Percent 35 11.8 2.2 1 50 100
19. Concentration difficulty (ordinal [0,1,2,3], v3_bdi2_itm19)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi19_konzschw,v3_con$v3_bdi2_s2_bdi19,"v3_bdi2_itm19")
## 0 1 2 3 <NA>
## [1,] No. cases 547 231 111 7 890 1786
## [2,] Percent 30.6 12.9 6.2 0.4 49.8 100
20. Tiredness or fatigue (ordinal [0,1,2,3], v3_bdi2_itm20)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi20_ermued_ersch,v3_con$v3_bdi2_s2_bdi20,"v3_bdi2_itm20")
## 0 1 2 3 <NA>
## [1,] No. cases 541 283 54 17 891 1786
## [2,] Percent 30.3 15.8 3 1 49.9 100
21. Loss of interest in sex (ordinal [0,1,2,3], v3_bdi2_itm21)
v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi21_sex_interess,v3_con$v3_bdi2_s2_bdi21,"v3_bdi2_itm21")
## 0 1 2 3 <NA>
## [1,] No. cases 651 127 41 73 894 1786
## [2,] Percent 36.5 7.1 2.3 4.1 50.1 100
BDI-II sum score calculation (continuous [0-63], v3_bdi2_sum)
v3_bdi2_sum<-as.numeric.factor(v3_bdi2_itm1)+
as.numeric.factor(v3_bdi2_itm2)+
as.numeric.factor(v3_bdi2_itm3)+
as.numeric.factor(v3_bdi2_itm4)+
as.numeric.factor(v3_bdi2_itm5)+
as.numeric.factor(v3_bdi2_itm6)+
as.numeric.factor(v3_bdi2_itm7)+
as.numeric.factor(v3_bdi2_itm8)+
as.numeric.factor(v3_bdi2_itm9)+
as.numeric.factor(v3_bdi2_itm10)+
as.numeric.factor(v3_bdi2_itm11)+
as.numeric.factor(v3_bdi2_itm12)+
as.numeric.factor(v3_bdi2_itm13)+
as.numeric.factor(v3_bdi2_itm14)+
as.numeric.factor(v3_bdi2_itm15)+
as.numeric.factor(v3_bdi2_itm16)+
as.numeric.factor(v3_bdi2_itm17)+
as.numeric.factor(v3_bdi2_itm18)+
as.numeric.factor(v3_bdi2_itm19)+
as.numeric.factor(v3_bdi2_itm20)+
as.numeric.factor(v3_bdi2_itm21)
summary(v3_bdi2_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 4.000 7.707 11.000 53.000 925
Create dataset
v3_bdi2<-data.frame(v3_bdi2_itm1,v3_bdi2_itm2,v3_bdi2_itm3,v3_bdi2_itm4,v3_bdi2_itm5,
v3_bdi2_itm6,v3_bdi2_itm7,v3_bdi2_itm8,v3_bdi2_itm9,v3_bdi2_itm10,
v3_bdi2_itm11,v3_bdi2_itm12,v3_bdi2_itm13,v3_bdi2_itm14,
v3_bdi2_itm15,v3_bdi2_itm16,v3_bdi2_itm17,v3_bdi2_itm18,
v3_bdi2_itm19,v3_bdi2_itm20,v3_bdi2_itm21, v3_bdi2_sum)
For explanation, please refer to the section in Visit 1
1. Positive Mood (ordinal [0,1,2,3,4], v3_asrm_itm1)
v3_asrm_recode(v3_clin$v3_asrm_asrm1_gluecklich,v3_con$v3_asrm_asrm1,"v3_asrm_itm1")
## 0 1 2 3 4 <NA>
## [1,] No. cases 611 201 56 24 5 889 1786
## [2,] Percent 34.2 11.3 3.1 1.3 0.3 49.8 100
2 Self-Confidence (ordinal [0,1,2,3,4], v3_asrm_itm2)
v3_asrm_recode(v3_clin$v3_asrm_asrm2_selbstbewusst,v3_con$v3_asrm_asrm2,"v3_asrm_itm2")
## 0 1 2 3 4 <NA>
## [1,] No. cases 659 171 42 21 3 890 1786
## [2,] Percent 36.9 9.6 2.4 1.2 0.2 49.8 100
3. Sleep (ordinal [0,1,2,3,4], v3_asrm_itm3)
v3_asrm_recode(v3_clin$v3_asrm_asrm3_schlaf,v3_con$v3_asrm_asrm3,"v3_asrm_itm3")
## 0 1 2 3 4 <NA>
## [1,] No. cases 748 96 29 16 8 889 1786
## [2,] Percent 41.9 5.4 1.6 0.9 0.4 49.8 100
4. Speech (ordinal [0,1,2,3,4], v3_asrm_itm4)
v3_asrm_recode(v3_clin$v3_asrm_asrm4_reden,v3_con$v3_asrm_asrm4,"v3_asrm_itm4")
## 0 1 2 3 4 <NA>
## [1,] No. cases 703 159 18 13 3 890 1786
## [2,] Percent 39.4 8.9 1 0.7 0.2 49.8 100
5. Activity Level (ordinal [0,1,2,3,4], v3_asrm_itm5)
v3_asrm_recode(v3_clin$v3_asrm_asrm5_aktiv,v3_con$v3_asrm_asrm5,"v3_asrm_itm5")
## 0 1 2 3 4 <NA>
## [1,] No. cases 653 181 41 9 11 891 1786
## [2,] Percent 36.6 10.1 2.3 0.5 0.6 49.9 100
Create ASRM sum score (continuous [0-20],v3_asrm_sum)
v3_asrm_sum<-as.numeric.factor(v3_asrm_itm1)+
as.numeric.factor(v3_asrm_itm2)+
as.numeric.factor(v3_asrm_itm3)+
as.numeric.factor(v3_asrm_itm4)+
as.numeric.factor(v3_asrm_itm5)
summary(v3_asrm_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 1.000 1.712 2.000 18.000 892
Create dataset
v3_asrm<-data.frame(v3_asrm_itm1,v3_asrm_itm2,v3_asrm_itm3,v3_asrm_itm4,v3_asrm_itm5,v3_asrm_sum)
For explanation, please refer to the section in Visit 1
1. “I had more energy” (dichotomous, v3_mss_itm1)
v3_mss_recode(v3_clin$v3_mss_s1_mss1_energie,v3_con$v3_mss_s1_mss1,"v3_mss_itm1")
## N Y <NA>
## [1,] No. cases 735 162 889 1786
## [2,] Percent 41.2 9.1 49.8 100
2. “I had trouble sitting still” (dichotomous, v3_mss_itm2)
v3_mss_recode(v3_clin$v3_mss_s1_mss2_ruhig_sitzen,v3_con$v3_mss_s1_mss2,"v3_mss_itm2")
## N Y <NA>
## [1,] No. cases 783 113 890 1786
## [2,] Percent 43.8 6.3 49.8 100
3. “I drove faster” (dichotomous, v3_mss_itm3)
v3_mss_recode(v3_clin$v3_mss_s1_mss3_auto_fahren,v3_con$v3_mss_s1_mss3,"v3_mss_itm3")
## N Y <NA>
## [1,] No. cases 837 30 919 1786
## [2,] Percent 46.9 1.7 51.5 100
4. “I drank more alcoholic beverages” (dichotomous, v3_mss_itm4)
v3_mss_recode(v3_clin$v3_mss_s1_mss4_alkohol,v3_con$v3_mss_s1_mss4,"v3_mss_itm4")
## N Y <NA>
## [1,] No. cases 813 76 897 1786
## [2,] Percent 45.5 4.3 50.2 100
5. “I changed clothes several times a day” (dichotomous, v3_mss_itm5)
v3_mss_recode(v3_clin$v3_mss_s1_mss5_umziehen, v3_con$v3_mss_s1_mss5,"v3_mss_itm5")
## N Y <NA>
## [1,] No. cases 821 70 895 1786
## [2,] Percent 46 3.9 50.1 100
6. “I wore brighter clothes/make-up” (dichotomous, v3_mss_itm6)
v3_mss_recode(v3_clin$v3_mss_s1_mss6_bunter,v3_con$v3_mss_s1_mss6,"v3_mss_itm6")
## N Y <NA>
## [1,] No. cases 841 55 890 1786
## [2,] Percent 47.1 3.1 49.8 100
7. “I played music louder” (dichotomous, v3_mss_itm7)
v3_mss_recode(v3_clin$v3_mss_s1_mss7_musik_lauter,v3_con$v3_mss_s1_mss7,"v3_mss_itm7")
## N Y <NA>
## [1,] No. cases 778 118 890 1786
## [2,] Percent 43.6 6.6 49.8 100
8. “I ate faster than usual” (dichotomous, v3_mss_itm8)
v3_mss_recode(v3_clin$v3_mss_s1_mss8_hastiger_essen,v3_con$v3_mss_s1_mss8,"v3_mss_itm8")
## N Y <NA>
## [1,] No. cases 806 89 891 1786
## [2,] Percent 45.1 5 49.9 100
9. “I ate more than usual” (dichotomous, v3_mss_itm9)
v3_mss_recode(v3_clin$v3_mss_s1_mss9_mehr_essen,v3_con$v3_mss_s1_mss9,"v3_mss_itm9")
## N Y <NA>
## [1,] No. cases 742 152 892 1786
## [2,] Percent 41.5 8.5 49.9 100
10. “I slept fewer hours than usual” (dichotomous, v3_mss_itm10)
v3_mss_recode(v3_clin$v3_mss_s1_mss10_weniger_schlaf,v3_con$v3_mss_s1_mss10,"v3_mss_itm10")
## N Y <NA>
## [1,] No. cases 788 107 891 1786
## [2,] Percent 44.1 6 49.9 100
11. “I started things that I didn’t finish” (dichotomous, v3_mss_itm11)
v3_mss_recode(v3_clin$v3_mss_s1_mss11_unbeendet,v3_con$v3_mss_s1_mss11,"v3_mss_itm11")
## N Y <NA>
## [1,] No. cases 727 169 890 1786
## [2,] Percent 40.7 9.5 49.8 100
12. “I gave away my own possessions” (dichotomous, v3_mss_itm12)
v3_mss_recode(v3_clin$v3_mss_s1_mss12_weggeben,v3_con$v3_mss_s1_mss12,"v3_mss_itm12")
## N Y <NA>
## [1,] No. cases 817 77 892 1786
## [2,] Percent 45.7 4.3 49.9 100
13. “I bought gifts for people” (dichotomous, v3_mss_itm13)
v3_mss_recode(v3_clin$v3_mss_s1_mss13_geschenke,v3_con$v3_mss_s1_mss13,"v3_mss_itm13")
## N Y <NA>
## [1,] No. cases 810 85 891 1786
## [2,] Percent 45.4 4.8 49.9 100
14. “I spent money more freely” (dichotomous, v3_mss_itm14)
v3_mss_recode(v3_clin$v3_mss_s1_mss14_mehr_geld,v3_con$v3_mss_s1_mss14,"v3_mss_itm14")
## N Y <NA>
## [1,] No. cases 688 208 890 1786
## [2,] Percent 38.5 11.6 49.8 100
15. “I accumulated debts” (dichotomous, v3_mss_itm15)
v3_mss_recode(v3_clin$v3_mss_s1_mss15_schulden,v3_con$v3_mss_s1_mss15,"v3_mss_itm15")
## N Y <NA>
## [1,] No. cases 844 52 890 1786
## [2,] Percent 47.3 2.9 49.8 100
16. “I made unwise business decisions” (dichotomous, v3_mss_itm16)
v3_mss_recode(v3_clin$v3_mss_s1_mss16_unkluge_entsch,v3_con$v3_mss_s1_mss16,"v3_mss_itm16")
## N Y <NA>
## [1,] No. cases 866 30 890 1786
## [2,] Percent 48.5 1.7 49.8 100
17. “I partied more” (dichotomous, v3_mss_itm17)
v3_mss_recode(v3_clin$v3_mss_s1_mss17_parties,v3_con$v3_mss_s1_mss17,"v3_mss_itm17")
## N Y <NA>
## [1,] No. cases 853 44 889 1786
## [2,] Percent 47.8 2.5 49.8 100
18. “I enjoyed flirting” (dichotomous, v3_mss_itm18)
v3_mss_recode(v3_clin$v3_mss_s1_mss18_flirten,v3_con$v3_mss_s1_mss18,"v3_mss_itm18")
## N Y <NA>
## [1,] No. cases 828 65 893 1786
## [2,] Percent 46.4 3.6 50 100
19. “I masturbated more often” (dichotomous, v3_mss_itm19)
v3_mss_recode(v3_clin$v3_mss_s2_mss19_selbstbefried,v3_con$v3_mss_s2_mss19,"v3_mss_itm19")
## N Y <NA>
## [1,] No. cases 841 42 903 1786
## [2,] Percent 47.1 2.4 50.6 100
20. “I was more interested in sex than usual” (dichotomous, v3_mss_itm20)
v3_mss_recode(v3_clin$v3_mss_s2_mss20_sex_interess,v3_con$v3_mss_s2_mss20,"v3_mss_itm20")
## N Y <NA>
## [1,] No. cases 797 89 900 1786
## [2,] Percent 44.6 5 50.4 100
21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v3_mss_itm21)
v3_mss_recode(v3_clin$v3_mss_s2_mss21_sexpartner,v3_con$v3_mss_s2_mss21,"v3_mss_itm21")
## N Y <NA>
## [1,] No. cases 870 15 901 1786
## [2,] Percent 48.7 0.8 50.4 100
22. “I spent more time on the phone” (dichotomous, v3_mss_itm22)
v3_mss_recode(v3_clin$v3_mss_s2_mss22_mehr_telefon,v3_con$v3_mss_s2_mss22,"v3_mss_itm22")
## N Y <NA>
## [1,] No. cases 782 107 897 1786
## [2,] Percent 43.8 6 50.2 100
23. “I spoke louder than usual” (dichotomous, v3_mss_itm23)
v3_mss_recode(v3_clin$v3_mss_s2_mss23_sprache_lauter,v3_con$v3_mss_s2_mss23,"v3_mss_itm23")
## N Y <NA>
## [1,] No. cases 828 59 899 1786
## [2,] Percent 46.4 3.3 50.3 100
24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v3_mss_itm24)
v3_mss_recode(v3_clin$v3_mss_s2_mss24_spr_schneller,v3_con$v3_mss_s2_mss24,"v3_mss_itm24")
## N Y <NA>
## [1,] No. cases 837 49 900 1786
## [2,] Percent 46.9 2.7 50.4 100
25. “1 enjoyed punning or rhyming” (dichotomous, v3_mss_itm25)
v3_mss_recode(v3_clin$v3_mss_s2_mss25_witze,v3_con$v3_mss_s2_mss25,"v3_mss_itm25")
## N Y <NA>
## [1,] No. cases 804 84 898 1786
## [2,] Percent 45 4.7 50.3 100
26. “I butted into conversations” (dichotomous, v3_mss_itm26)
v3_mss_recode(v3_clin$v3_mss_s2_mss26_einmischen,v3_con$v3_mss_s2_mss26,"v3_mss_itm26")
## N Y <NA>
## [1,] No. cases 838 52 896 1786
## [2,] Percent 46.9 2.9 50.2 100
27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v3_mss_itm27)
v3_mss_recode(v3_clin$v3_mss_s2_mss27_red_pausenlos,v3_con$v3_mss_s2_mss27,"v3_mss_itm27")
## N Y <NA>
## [1,] No. cases 864 26 896 1786
## [2,] Percent 48.4 1.5 50.2 100
28. “I enjoyed being the centre of attention” (dichotomous, v3_mss_itm28)
v3_mss_recode(v3_clin$v3_mss_s2_mss28_mittelpunkt,v3_con$v3_mss_s2_mss28,"v3_mss_itm28")
## N Y <NA>
## [1,] No. cases 831 57 898 1786
## [2,] Percent 46.5 3.2 50.3 100
29. “I liked to joke and laugh” (dichotomous, v3_mss_itm29)
v3_mss_recode(v3_clin$v3_mss_s2_mss29_herumalbern,v3_con$v3_mss_s2_mss29,"v3_mss_itm29")
## N Y <NA>
## [1,] No. cases 766 122 898 1786
## [2,] Percent 42.9 6.8 50.3 100
30. “People found me entertaining” (dichotomous, v3_mss_itm30)
v3_mss_recode(v3_clin$v3_mss_s2_mss30_unterhaltsamer,v3_con$v3_mss_s2_mss30,"v3_mss_itm30")
## N Y <NA>
## [1,] No. cases 807 82 897 1786
## [2,] Percent 45.2 4.6 50.2 100
31. “I felt as if I was on top of the world” (dichotomous, v3_mss_itm31)
v3_mss_recode(v3_clin$v3_mss_s2_mss31_obenauf,v3_con$v3_mss_s2_mss31,"v3_mss_itm31")
## N Y <NA>
## [1,] No. cases 802 86 898 1786
## [2,] Percent 44.9 4.8 50.3 100
32. “I was more cheerful than my usual self” (dichotomous, v3_mss_itm32)
v3_mss_recode(v3_clin$v3_mss_s2_mss32_froehlicher,v3_con$v3_mss_s2_mss32,"v3_mss_itm32")
## N Y <NA>
## [1,] No. cases 733 155 898 1786
## [2,] Percent 41 8.7 50.3 100
33. “Other people got on my nerves” (dichotomous, v3_mss_itm33)
v3_mss_recode(v3_clin$v3_mss_s2_mss33_ungeduldiger,v3_con$v3_mss_s2_mss33,"v3_mss_itm33")
## N Y <NA>
## [1,] No. cases 693 195 898 1786
## [2,] Percent 38.8 10.9 50.3 100
34. “I was getting into arguments” (dichotomous, v3_mss_itm34)
v3_mss_recode(v3_clin$v3_mss_s2_mss34_streiten,v3_con$v3_mss_s2_mss34,"v3_mss_itm34")
## N Y <NA>
## [1,] No. cases 823 64 899 1786
## [2,] Percent 46.1 3.6 50.3 100
35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v3_mss_itm35)
v3_mss_recode(v3_clin$v3_mss_s2_mss35_ideen,v3_con$v3_mss_s2_mss35,"v3_mss_itm35")
## N Y <NA>
## [1,] No. cases 758 131 897 1786
## [2,] Percent 42.4 7.3 50.2 100
36. “My thoughts raced through my mind” (dichotomous, v3_mss_itm36)
v3_mss_recode(v3_clin$v3_mss_s2_mss36_gedanken,v3_con$v3_mss_s2_mss36,"v3_mss_itm36")
## N Y <NA>
## [1,] No. cases 681 207 898 1786
## [2,] Percent 38.1 11.6 50.3 100
37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v3_mss_itm37)
v3_mss_recode(v3_clin$v3_mss_s2_mss37_konzentration,v3_con$v3_mss_s2_mss37,"v3_mss_itm37")
## N Y <NA>
## [1,] No. cases 764 124 898 1786
## [2,] Percent 42.8 6.9 50.3 100
38. “I thought I was an especially important person” (dichotomous, v3_mss_itm38)
v3_mss_recode(v3_clin$v3_mss_s2_mss38_etw_besonderes,v3_con$v3_mss_s2_mss38,"v3_mss_itm38")
## N Y <NA>
## [1,] No. cases 832 55 899 1786
## [2,] Percent 46.6 3.1 50.3 100
39. “I thought I could change the world” (dichotomous, v3_mss_itm39)
v3_mss_recode(v3_clin$v3_mss_s2_mss39_welt_veraender,v3_con$v3_mss_s2_mss39,"v3_mss_itm39")
## N Y <NA>
## [1,] No. cases 833 55 898 1786
## [2,] Percent 46.6 3.1 50.3 100
40. “I thought I was right most of the time” (dichotomous, v3_mss_itm40)
v3_mss_recode(v3_clin$v3_mss_s2_mss40_recht_haben,v3_con$v3_mss_s2_mss40,"v3_mss_itm40")
## N Y <NA>
## [1,] No. cases 845 42 899 1786
## [2,] Percent 47.3 2.4 50.3 100
41. “I thought I was superior to others” (dichotomous, v3_mss_itm41)
v3_mss_recode(v3_clin$v3_mss_s3_mss41_ueberlegen,v3_con$v3_mss_s3_mss41,"v3_mss_itm41")
## N Y <NA>
## [1,] No. cases 864 29 893 1786
## [2,] Percent 48.4 1.6 50 100
42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v3_mss_itm42)
v3_mss_recode(v3_clin$v3_mss_s3_mss42_uebermut,v3_con$v3_mss_s3_mss42,"v3_mss_itm42")
## N Y <NA>
## [1,] No. cases 830 63 893 1786
## [2,] Percent 46.5 3.5 50 100
43. “I thought I knew what other people were thinking” (dichotomous, v3_mss_itm43)
v3_mss_recode(v3_clin$v3_mss_s3_mss43_ged_lesen_akt,v3_con$v3_mss_s3_mss43,"v3_mss_itm43")
## N Y <NA>
## [1,] No. cases 828 66 892 1786
## [2,] Percent 46.4 3.7 49.9 100
44. “I thought other people knew what I was thinking” (dichotomous, v3_mss_itm44)
v3_mss_recode(v3_clin$v3_mss_s3_mss44_ged_lesen_pas,v3_con$v3_mss_s3_mss44,"v3_mss_itm44")
## N Y <NA>
## [1,] No. cases 848 42 896 1786
## [2,] Percent 47.5 2.4 50.2 100
45. “I thought someone wanted to harm me” (dichotomous, v3_mss_itm45)
v3_mss_recode(v3_clin$v3_mss_s3_mss45_etw_antun,v3_con$v3_mss_s3_mss45,"v3_mss_itm45")
## N Y <NA>
## [1,] No. cases 855 38 893 1786
## [2,] Percent 47.9 2.1 50 100
46. “I heard voices when people weren’t there” (dichotomous, v3_mss_itm46)
v3_mss_recode(v3_clin$v3_mss_s3_mss46_stimmen,v3_con$v3_mss_s3_mss46,"v3_mss_itm46")
## N Y <NA>
## [1,] No. cases 842 52 892 1786
## [2,] Percent 47.1 2.9 49.9 100
47. “I had false beliefs concerning who I was” (dichotomous, v3_mss_itm47)
v3_mss_recode(v3_clin$v3_mss_s3_mss47_jmd_anders,v3_con$v3_mss_s3_mss47,"v3_mss_itm47")
## N Y <NA>
## [1,] No. cases 869 24 893 1786
## [2,] Percent 48.7 1.3 50 100
48. “I knew I was getting ill” (dichotomous, v3_mss_itm48)
v3_mss_recode(v3_clin$v3_mss_s3_mss48_krank_einsicht,v3_con$v3_mss_s3_mss48,"v3_mss_itm48")
## N Y <NA>
## [1,] No. cases 803 83 900 1786
## [2,] Percent 45 4.6 50.4 100
Create MSS sum score (continuous [0-48],v3_mss_sum)
v3_mss_sum<-ifelse(v3_mss_itm1=="Y",1,0)+
ifelse(v3_mss_itm2=="Y",1,0)+
ifelse(v3_mss_itm3=="Y",1,0)+
ifelse(v3_mss_itm4=="Y",1,0)+
ifelse(v3_mss_itm5=="Y",1,0)+
ifelse(v3_mss_itm6=="Y",1,0)+
ifelse(v3_mss_itm7=="Y",1,0)+
ifelse(v3_mss_itm8=="Y",1,0)+
ifelse(v3_mss_itm9=="Y",1,0)+
ifelse(v3_mss_itm10=="Y",1,0)+
ifelse(v3_mss_itm11=="Y",1,0)+
ifelse(v3_mss_itm12=="Y",1,0)+
ifelse(v3_mss_itm13=="Y",1,0)+
ifelse(v3_mss_itm14=="Y",1,0)+
ifelse(v3_mss_itm15=="Y",1,0)+
ifelse(v3_mss_itm16=="Y",1,0)+
ifelse(v3_mss_itm17=="Y",1,0)+
ifelse(v3_mss_itm18=="Y",1,0)+
ifelse(v3_mss_itm19=="Y",1,0)+
ifelse(v3_mss_itm20=="Y",1,0)+
ifelse(v3_mss_itm21=="Y",1,0)+
ifelse(v3_mss_itm22=="Y",1,0)+
ifelse(v3_mss_itm23=="Y",1,0)+
ifelse(v3_mss_itm24=="Y",1,0)+
ifelse(v3_mss_itm25=="Y",1,0)+
ifelse(v3_mss_itm26=="Y",1,0)+
ifelse(v3_mss_itm27=="Y",1,0)+
ifelse(v3_mss_itm28=="Y",1,0)+
ifelse(v3_mss_itm29=="Y",1,0)+
ifelse(v3_mss_itm30=="Y",1,0)+
ifelse(v3_mss_itm31=="Y",1,0)+
ifelse(v3_mss_itm32=="Y",1,0)+
ifelse(v3_mss_itm33=="Y",1,0)+
ifelse(v3_mss_itm34=="Y",1,0)+
ifelse(v3_mss_itm35=="Y",1,0)+
ifelse(v3_mss_itm36=="Y",1,0)+
ifelse(v3_mss_itm37=="Y",1,0)+
ifelse(v3_mss_itm38=="Y",1,0)+
ifelse(v3_mss_itm39=="Y",1,0)+
ifelse(v3_mss_itm40=="Y",1,0)+
ifelse(v3_mss_itm41=="Y",1,0)+
ifelse(v3_mss_itm42=="Y",1,0)+
ifelse(v3_mss_itm43=="Y",1,0)+
ifelse(v3_mss_itm44=="Y",1,0)+
ifelse(v3_mss_itm45=="Y",1,0)+
ifelse(v3_mss_itm46=="Y",1,0)+
ifelse(v3_mss_itm47=="Y",1,0)+
ifelse(v3_mss_itm48=="Y",1,0)
summary(v3_mss_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 2.000 4.175 6.000 48.000 981
Create dataset
v3_mss<-data.frame(v3_mss_itm1,v3_mss_itm2,v3_mss_itm3,v3_mss_itm4,v3_mss_itm5,v3_mss_itm6,
v3_mss_itm7,v3_mss_itm8,v3_mss_itm9,v3_mss_itm10,v3_mss_itm11,
v3_mss_itm12,v3_mss_itm13,v3_mss_itm14,v3_mss_itm15,v3_mss_itm16,
v3_mss_itm17,v3_mss_itm18,v3_mss_itm19,v3_mss_itm20,v3_mss_itm21,
v3_mss_itm22,v3_mss_itm23,v3_mss_itm24,v3_mss_itm25,v3_mss_itm26,
v3_mss_itm27,v3_mss_itm28,v3_mss_itm29,v3_mss_itm30,v3_mss_itm31,
v3_mss_itm32,v3_mss_itm33,v3_mss_itm34,v3_mss_itm35,v3_mss_itm36,
v3_mss_itm37,v3_mss_itm38,v3_mss_itm39,v3_mss_itm40,v3_mss_itm41,
v3_mss_itm42,v3_mss_itm43,v3_mss_itm44,v3_mss_itm45,v3_mss_itm46,
v3_mss_itm47,v3_mss_itm48, v3_mss_sum)
For explanation, please refer to the section in Visit 1
1. “Major personal illness or injury”
1A Nature (dichotomous [“good”,“bad”], v3_leq_A_1A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq1a_schw_krankh,v3_con$v3_leq_a_leq1a,"v3_leq_A_1A")
## -999 bad good <NA>
## [1,] No. cases 631 193 29 933 1786
## [2,] Percent 35.3 10.8 1.6 52.2 100
1B Impact (ordinal [0,1,2,3], v3_leq_A_1B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq1e_schw_krankh,v3_con$v3_leq_a_leq1e,"v3_leq_A_1B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 627 14 33 61 118 933 1786
## [2,] Percent 35.1 0.8 1.8 3.4 6.6 52.2 100
2. “Major change in eating habits”
2A Nature (dichotomous [“good”,“bad”], v3_leq_A_2A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq2a_ernaehrung,v3_con$v3_leq_a_leq2a,"v3_leq_A_2A")
## -999 bad good <NA>
## [1,] No. cases 650 88 115 933 1786
## [2,] Percent 36.4 4.9 6.4 52.2 100
2B Impact (ordinal [0,1,2,3], v3_leq_A_2B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq2e_ernaehrung,v3_con$v3_leq_a_leq2e,"v3_leq_A_2B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 644 17 53 66 73 933 1786
## [2,] Percent 36.1 1 3 3.7 4.1 52.2 100
3. “Major change in sleeping habits”
3A Nature (dichotomous [“good”,“bad”], v3_leq_A_3A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq3a_schlaf,v3_con$v3_leq_a_leq3a,"v3_leq_A_3A")
## -999 bad good <NA>
## [1,] No. cases 647 137 69 933 1786
## [2,] Percent 36.2 7.7 3.9 52.2 100
3B Impact (ordinal [0,1,2,3], v3_leq_A_3B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq3e_schlaf,v3_con$v3_leq_a_leq3e,"v3_leq_A_3B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 645 18 35 73 82 933 1786
## [2,] Percent 36.1 1 2 4.1 4.6 52.2 100
4. “Major change in usual type and/or amount of recreation”
4A Nature (dichotomous [“good”,“bad”], v3_leq_A_4A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq4a_freizeit,v3_con$v3_leq_a_leq4a,"v3_leq_A_4A")
## -999 bad good <NA>
## [1,] No. cases 591 87 175 933 1786
## [2,] Percent 33.1 4.9 9.8 52.2 100
4B Impact (ordinal [0,1,2,3], v3_leq_A_4B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq4e_freizeit,v3_con$v3_leq_a_leq4e,"v3_leq_A_4B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 584 8 59 109 93 933 1786
## [2,] Percent 32.7 0.4 3.3 6.1 5.2 52.2 100
5. “Major dental work”
5A Nature (dichotomous [“good”,“bad”], v3_leq_A_5A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq5a_zahnarzt,v3_con$v3_leq_a_leq5a,"v3_leq_A_5A")
## -999 bad good <NA>
## [1,] No. cases 743 45 65 933 1786
## [2,] Percent 41.6 2.5 3.6 52.2 100
5B Impact (ordinal [0,1,2,3], v3_leq_A_5B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq5e_zahnarzt,v3_con$v3_leq_a_leq5e,"v3_leq_A_5B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 739 24 29 31 30 933 1786
## [2,] Percent 41.4 1.3 1.6 1.7 1.7 52.2 100
6. “(Female) Pregnancy”
6A Nature (dichotomous [“good”,“bad”], v3_leq_A_6A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq6a_schwanger,v3_con$v3_leq_a_leq6a,"v3_leq_A_6A")
## -999 bad good <NA>
## [1,] No. cases 846 1 6 933 1786
## [2,] Percent 47.4 0.1 0.3 52.2 100
6B Impact (ordinal [0,1,2,3], v3_leq_A_6B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq6e_schwanger,v3_con$v3_leq_a_leq6e,"v3_leq_A_6B")
## -999 0 1 3 <NA>
## [1,] No. cases 846 1 1 5 933 1786
## [2,] Percent 47.4 0.1 0.1 0.3 52.2 100
7. “(Female) Miscarriage or abortion”
7A Nature (dichotomous [“good”,“bad”], v3_leq_A_7A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq7a_fehlg_abtr,v3_con$v3_leq_a_leq7a,"v3_leq_A_7A")
## -999 bad <NA>
## [1,] No. cases 852 1 933 1786
## [2,] Percent 47.7 0.1 52.2 100
7B Impact (ordinal [0,1,2,3], v3_leq_A_7B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq7e_fehlg_abtr,v3_con$v3_leq_a_leq7e,"v3_leq_A_7B")
## -999 1 <NA>
## [1,] No. cases 852 1 933 1786
## [2,] Percent 47.7 0.1 52.2 100
8. “(Female) Started menopause”
8A Nature (dichotomous [“good”,“bad”], v3_leq_A_8A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq8a_wechseljahre,v3_con$v3_leq_a_leq8a,"v3_leq_A_8A")
## -999 bad good <NA>
## [1,] No. cases 823 23 7 933 1786
## [2,] Percent 46.1 1.3 0.4 52.2 100
8B Impact (ordinal [0,1,2,3], v3_leq_A_8B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq8e_wechseljahre,v3_con$v3_leq_a_leq8e,"v3_leq_A_8B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 822 4 6 14 7 933 1786
## [2,] Percent 46 0.2 0.3 0.8 0.4 52.2 100
9. “Major difficulties with birth control pills or devices”
9A Nature (dichotomous [“good”,“bad”], v3_leq_A_9A)
v3_leq_a_recode(v3_clin$v3_leq_a_leq9a_verhuetung,v3_con$v3_leq_a_leq9a,"v3_leq_A_9A")
## -999 bad good <NA>
## [1,] No. cases 836 14 3 933 1786
## [2,] Percent 46.8 0.8 0.2 52.2 100
9B Impact (ordinal [0,1,2,3], v3_leq_A_9B)
v3_leq_b_recode(v3_clin$v3_leq_a_leq9e_verhuetung,v3_con$v3_leq_a_leq9e,"v3_leq_A_9B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 836 2 4 6 5 933 1786
## [2,] Percent 46.8 0.1 0.2 0.3 0.3 52.2 100
Create dataset
v3_leq_A<-data.frame(v3_leq_A_1A,v3_leq_A_1B,v3_leq_A_2A,v3_leq_A_2B,v3_leq_A_3A,
v3_leq_A_3B,v3_leq_A_4A,v3_leq_A_4B,v3_leq_A_5A,v3_leq_A_5B,
v3_leq_A_6A,v3_leq_A_6B,v3_leq_A_7A,v3_leq_A_7B,v3_leq_A_8A,
v3_leq_A_8B,v3_leq_A_9A,v3_leq_A_9B)
10. “Difficulty finding a job”
10A Nature (dichotomous [“good”,“bad”], v3_leq_B_10A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq10a_arbeitssuche,v3_con$v3_leq_b_leq10a,"v3_leq_B_10A")
## -999 bad good <NA>
## [1,] No. cases 740 88 25 933 1786
## [2,] Percent 41.4 4.9 1.4 52.2 100
10B Impact (ordinal [0,1,2,3], v3_leq_B_10B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq10e_arbeitssuche,v3_con$v3_leq_b_leq10e,"v3_leq_B_10B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 739 7 29 43 35 933 1786
## [2,] Percent 41.4 0.4 1.6 2.4 2 52.2 100
11. “Beginning work outside the home”
11A Nature (dichotomous [“good”,“bad”], v3_leq_B_11A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq11a_arbeit_aussen,v3_con$v3_leq_b_leq11a,"v3_leq_B_11A")
## -999 bad good <NA>
## [1,] No. cases 749 16 88 933 1786
## [2,] Percent 41.9 0.9 4.9 52.2 100
11B Impact (ordinal [0,1,2,3], v3_leq_B_11B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq11e_arbeit_aussen,v3_con$v3_leq_b_leq11e,"v3_leq_B_11B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 746 6 17 38 46 933 1786
## [2,] Percent 41.8 0.3 1 2.1 2.6 52.2 100
12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v3_leq_B_12A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq12a_arbeitswechs,v3_con$v3_leq_b_leq12a,"v3_leq_B_12A")
## -999 bad good <NA>
## [1,] No. cases 743 12 98 933 1786
## [2,] Percent 41.6 0.7 5.5 52.2 100
12B Impact (ordinal [0,1,2,3], v3_leq_B_12B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq12e_arbeitswechs,v3_con$v3_leq_b_leq12e,"v3_leq_B_12B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 741 7 16 34 55 933 1786
## [2,] Percent 41.5 0.4 0.9 1.9 3.1 52.2 100
13. “Changing your work hours or conditions”
13A Nature (dichotomous [“good”,“bad”], v3_leq_B_13A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq13a_veraend_arb,v3_con$v3_leq_b_leq13a,"v3_leq_B_13A")
## -999 bad good <NA>
## [1,] No. cases 689 36 128 933 1786
## [2,] Percent 38.6 2 7.2 52.2 100
13B Impact (ordinal [0,1,2,3], v3_leq_B_13B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq13e_veraend_arb,v3_con$v3_leq_b_leq13e,"v3_leq_B_13B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 688 7 47 61 50 933 1786
## [2,] Percent 38.5 0.4 2.6 3.4 2.8 52.2 100
14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v3_leq_B_14A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq14a_veraend_ba,v3_con$v3_leq_b_leq14a,"v3_leq_B_14A")
## -999 bad good <NA>
## [1,] No. cases 689 27 137 933 1786
## [2,] Percent 38.6 1.5 7.7 52.2 100
14B Impact (ordinal [0,1,2,3], v3_leq_B_14B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq14e_veraend_ba,v3_con$v3_leq_b_leq14e,"v3_leq_B_14B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 686 8 39 65 55 933 1786
## [2,] Percent 38.4 0.4 2.2 3.6 3.1 52.2 100
15. “Troubles at work with your employer or co-worker”
15A Nature (dichotomous [“good”,“bad”], v3_leq_B_15A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq15a_schw_arbeit,v3_con$v3_leq_b_leq15a,"v3_leq_B_15A")
## -999 bad good <NA>
## [1,] No. cases 752 81 20 933 1786
## [2,] Percent 42.1 4.5 1.1 52.2 100
15B Impact (ordinal [0,1,2,3], v3_leq_B_15B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq15e_schw_arbeit,v3_con$v3_leq_b_leq15e,"v3_leq_B_15B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 751 13 39 28 22 933 1786
## [2,] Percent 42 0.7 2.2 1.6 1.2 52.2 100
16. “Major business readjustment”
16A Nature (dichotomous [“good”,“bad”], v3_leq_B_16A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq16a_betr_reorg,v3_con$v3_leq_b_leq16a,"v3_leq_B_16A")
## -999 bad good <NA>
## [1,] No. cases 814 20 19 933 1786
## [2,] Percent 45.6 1.1 1.1 52.2 100
16B Impact (ordinal [0,1,2,3], v3_leq_B_16B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq16e_betr_reorg,v3_con$v3_leq_b_leq16e,"v3_leq_B_16B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 813 8 9 8 15 933 1786
## [2,] Percent 45.5 0.4 0.5 0.4 0.8 52.2 100
17. “Being fired or laid off from work”
17A Nature (dichotomous [“good”,“bad”], v3_leq_B_17A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq17a_kuendigung,v3_con$v3_leq_b_leq17a,"v3_leq_B_17A")
## -999 bad good <NA>
## [1,] No. cases 802 28 23 933 1786
## [2,] Percent 44.9 1.6 1.3 52.2 100
17B Impact (ordinal [0,1,2,3], v3_leq_B_17B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq17e_kuendigung,v3_con$v3_leq_b_leq17e,"v3_leq_B_17B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 800 4 10 16 23 933 1786
## [2,] Percent 44.8 0.2 0.6 0.9 1.3 52.2 100
18. “Retirement from work”
18A Nature (dichotomous [“good”,“bad”], v3_leq_B_18A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq18a_ende_beruf,v3_con$v3_leq_b_leq18a,"v3_leq_B_18A")
## -999 bad good <NA>
## [1,] No. cases 829 8 16 933 1786
## [2,] Percent 46.4 0.4 0.9 52.2 100
18B Impact (ordinal [0,1,2,3], v3_leq_B_18B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq18e_ende_beruf,v3_con$v3_leq_b_leq18e,"v3_leq_B_18B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 829 1 5 5 13 933 1786
## [2,] Percent 46.4 0.1 0.3 0.3 0.7 52.2 100
19. “Taking courses by mail or studying at home to help you in your work”
19A Nature (dichotomous [“good”,“bad”], v3_leq_B_19A)
v3_leq_a_recode(v3_clin$v3_leq_b_leq19a_fortbildung,v3_con$v3_leq_b_leq19a,"v3_leq_B_19A")
## -999 bad good <NA>
## [1,] No. cases 808 5 40 933 1786
## [2,] Percent 45.2 0.3 2.2 52.2 100
19B Impact (ordinal [0,1,2,3], v3_leq_B_19B)
v3_leq_b_recode(v3_clin$v3_leq_b_leq19e_fortbildung,v3_con$v3_leq_b_leq19e,"v3_leq_B_19B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 808 5 12 14 14 933 1786
## [2,] Percent 45.2 0.3 0.7 0.8 0.8 52.2 100
v3_leq_B<-data.frame(v3_leq_B_10A,v3_leq_B_10B,v3_leq_B_11A,v3_leq_B_11B,v3_leq_B_12A,
v3_leq_B_12B,v3_leq_B_13A,v3_leq_B_13B,v3_leq_B_14A,v3_leq_B_14B,
v3_leq_B_15A,v3_leq_B_15B,v3_leq_B_16A,v3_leq_B_16B,v3_leq_B_17A,
v3_leq_B_17B,v3_leq_B_18A,v3_leq_B_18B,v3_leq_B_19A,v3_leq_B_19B)
20. “Beginning or ceasing school, college, or training program”
20A Nature (dichotomous [“good”,“bad”], v3_leq_C_20A)
v3_leq_a_recode(v3_clin$v3_leq_c_d_leq20a_beginn_ende,v3_con$v3_leq_c_d_leq20a,"v3_leq_C_20A")
## -999 bad good <NA>
## [1,] No. cases 797 7 49 933 1786
## [2,] Percent 44.6 0.4 2.7 52.2 100
20B Impact (ordinal [0,1,2,3], v3_leq_C_20B)
v3_leq_b_recode(v3_clin$v3_leq_c_d_leq20e_beginn_ende,v3_con$v3_leq_c_d_leq20e,"v3_leq_C_20B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 795 4 10 14 30 933 1786
## [2,] Percent 44.5 0.2 0.6 0.8 1.7 52.2 100
21. “Change of school, college, or training program”
21A Nature (dichotomous [“good”,“bad”], v3_leq_C_21A)
v3_leq_a_recode(v3_clin$v3_leq_c_d_leq21a_schulwechsel,v3_con$v3_leq_c_d_leq21a,"v3_leq_C_21A")
## -999 bad good <NA>
## [1,] No. cases 847 3 3 933 1786
## [2,] Percent 47.4 0.2 0.2 52.2 100
21B Impact (ordinal [0,1,2,3], v3_leq_C_21B)
v3_leq_b_recode(v3_clin$v3_leq_c_d_leq21e_schulwechsel,v3_con$v3_leq_c_d_leq21e,"v3_leq_C_21B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 846 1 1 2 3 933 1786
## [2,] Percent 47.4 0.1 0.1 0.1 0.2 52.2 100
22. “Change in career goal or academic major”
A Nature (dichotomous [“good”,“bad”], v3_leq_C_22A)
v3_leq_a_recode(v3_clin$v3_leq_c_d_leq22a_aend_karriere,v3_con$v3_leq_c_d_leq22a,"v3_leq_C_22A")
## -999 bad good <NA>
## [1,] No. cases 813 6 34 933 1786
## [2,] Percent 45.5 0.3 1.9 52.2 100
B Impact (ordinal [0,1,2,3], v3_leq_C_22B)
v3_leq_b_recode(v3_clin$v3_leq_c_d_leq22e_aend_karriere,v3_con$v3_leq_c_d_leq22e,"v3_leq_C_22B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 812 2 8 12 19 933 1786
## [2,] Percent 45.5 0.1 0.4 0.7 1.1 52.2 100
23. “Problem in school, college, or training program”
23A Nature (dichotomous [“good”,“bad”], v3_leq_C_23A)
v3_leq_a_recode(v3_clin$v3_leq_c_d_leq23a_schulprob,v3_con$v3_leq_c_d_leq23a,"v3_leq_C_23A")
## -999 bad <NA>
## [1,] No. cases 836 17 933 1786
## [2,] Percent 46.8 1 52.2 100
23B Impact (ordinal [0,1,2,3], v3_leq_C_23B)
v3_leq_b_recode(v3_clin$v3_leq_c_d_leq23e_schulprob,v3_con$v3_leq_c_d_leq23e,"v3_leq_C_23B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 835 2 3 8 5 933 1786
## [2,] Percent 46.8 0.1 0.2 0.4 0.3 52.2 100
Create dataset
v3_leq_C<-data.frame(v3_leq_C_20A,v3_leq_C_20B,v3_leq_C_21A,v3_leq_C_21B,v3_leq_C_22A,v3_leq_C_22B,v3_leq_C_23A,v3_leq_C_23B)
24. “Difficulty finding housing”
24A Nature (dichotomous [“good”,“bad”], v3_leq_D_24A)
v3_leq_a_recode(v3_clin$v3_leq_c_d_leq24a_schw_wsuche,v3_con$v3_leq_c_d_leq24a,"v3_leq_D_24A")
## -999 bad good <NA>
## [1,] No. cases 801 43 9 933 1786
## [2,] Percent 44.8 2.4 0.5 52.2 100
24B Impact (ordinal [0,1,2,3], v3_leq_D_24B)
v3_leq_b_recode(v3_clin$v3_leq_c_d_leq24e_schw_wsuche,v3_con$v3_leq_c_d_leq24e,"v3_leq_D_24B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 801 2 11 18 21 933 1786
## [2,] Percent 44.8 0.1 0.6 1 1.2 52.2 100
25. “Changing residence within the same town or city”
A Nature (dichotomous [“good”,“bad”], v3_leq_D_25A)
v3_leq_a_recode(v3_clin$v3_leq_c_d_leq25a_umzug_nah,v3_con$v3_leq_c_d_leq25a,"v3_leq_D_25A")
## -999 bad good <NA>
## [1,] No. cases 809 6 38 933 1786
## [2,] Percent 45.3 0.3 2.1 52.2 100
B Impact (ordinal [0,1,2,3], v3_leq_D_25B)
v3_leq_b_recode(v3_clin$v3_leq_c_d_leq25e_umzug_nah,v3_con$v3_leq_c_d_leq25e,"v3_leq_D_25B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 809 4 3 12 25 933 1786
## [2,] Percent 45.3 0.2 0.2 0.7 1.4 52.2 100
26. “Moving to a different town, city, state, or country”
26A Nature (dichotomous [“good”,“bad”], v3_leq_D_26A)
v3_leq_a_recode(v3_clin$v3_leq_c_d_leq26a_umzug_fern,v3_con$v3_leq_c_d_leq26a,"v3_leq_D_26A")
## -999 bad good <NA>
## [1,] No. cases 821 4 28 933 1786
## [2,] Percent 46 0.2 1.6 52.2 100
26B Impact (ordinal [0,1,2,3], v3_leq_D_26B)
v3_leq_b_recode(v3_clin$v3_leq_c_d_leq26e_umzug_fern,v3_con$v3_leq_c_d_leq26e,"v3_leq_D_26B")
## -999 1 2 3 <NA>
## [1,] No. cases 821 3 5 24 933 1786
## [2,] Percent 46 0.2 0.3 1.3 52.2 100
27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”
27A Nature (dichotomous [“good”,“bad”], v3_leq_D_27A)
v3_leq_a_recode(v3_clin$v3_leq_c_d_leq27a_veraend_lu,v3_con$v3_leq_c_d_leq27a,"v3_leq_D_27A")
## -999 bad good <NA>
## [1,] No. cases 735 44 74 933 1786
## [2,] Percent 41.2 2.5 4.1 52.2 100
27B Impact (ordinal [0,1,2,3], v3_leq_D_27B)
v3_leq_b_recode(v3_clin$v3_leq_c_d_leq27e_veraend_lu,v3_con$v3_leq_c_d_leq27e,"v3_leq_D_27B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 734 4 23 51 41 933 1786
## [2,] Percent 41.1 0.2 1.3 2.9 2.3 52.2 100
Create dataset
v3_leq_D<-data.frame(v3_leq_D_24A,v3_leq_D_24B,v3_leq_D_25A,v3_leq_D_25B,v3_leq_D_26A,
v3_leq_D_26B,v3_leq_D_27A,v3_leq_D_27B)
28. “Began a new, close, personal relationship”
28A Nature (dichotomous [“good”,“bad”], v3_leq_E_28A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq28a_neue_bez,v3_con$v3_leq_e_leq28a,"v3_leq_E_28A")
## -999 bad good <NA>
## [1,] No. cases 772 4 77 933 1786
## [2,] Percent 43.2 0.2 4.3 52.2 100
28B Impact (ordinal [0,1,2,3], v3_leq_E_28B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq28e_neue_bez,v3_con$v3_leq_e_leq28e,"v3_leq_E_28B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 771 4 7 31 40 933 1786
## [2,] Percent 43.2 0.2 0.4 1.7 2.2 52.2 100
29. “Became engaged”
29A Nature (dichotomous [“good”,“bad”], v3_leq_E_29A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq29a_verlobung,v3_con$v3_leq_e_leq29a,"v3_leq_E_29A")
## -999 good <NA>
## [1,] No. cases 845 8 933 1786
## [2,] Percent 47.3 0.4 52.2 100
29B Impact (ordinal [0,1,2,3], v3_leq_E_29B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq29e_verlobung,v3_con$v3_leq_e_leq29e,"v3_leq_E_29B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 844 1 1 2 5 933 1786
## [2,] Percent 47.3 0.1 0.1 0.1 0.3 52.2 100
30. “Girlfriend or boyfriend problems”
30A Nature (dichotomous [“good”,“bad”], v3_leq_E_30A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq30a_prob_partner,v3_con$v3_leq_e_leq30a,"v3_leq_E_30A")
## -999 bad good <NA>
## [1,] No. cases 741 106 6 933 1786
## [2,] Percent 41.5 5.9 0.3 52.2 100
30B Impact (ordinal [0,1,2,3], v3_leq_E_30B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq30e_prob_partner,v3_con$v3_leq_e_leq30e,"v3_leq_E_30B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 740 5 28 43 37 933 1786
## [2,] Percent 41.4 0.3 1.6 2.4 2.1 52.2 100
31. “Breaking up with a girlfriend or breaking an engagement”
31A Nature (dichotomous [“good”,“bad”], v3_leq_E_31A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq31a_trennung,v3_con$v3_leq_e_leq31a,"v3_leq_E_31A")
## -999 bad good <NA>
## [1,] No. cases 794 36 23 933 1786
## [2,] Percent 44.5 2 1.3 52.2 100
31B Impact (ordinal [0,1,2,3], v3_leq_E_31B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq31e_trennung,v3_con$v3_leq_e_leq31e,"v3_leq_E_31B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 794 2 8 18 31 933 1786
## [2,] Percent 44.5 0.1 0.4 1 1.7 52.2 100
32. “(Male) Wife or girlfriend’s pregnancy”
32A Nature (dichotomous [“good”,“bad”], v3_leq_E_32A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq32a_schwanger_p,v3_con$v3_leq_e_leq32a,"v3_leq_E_32A")
## -999 good <NA>
## [1,] No. cases 849 4 933 1786
## [2,] Percent 47.5 0.2 52.2 100
32B Impact (ordinal [0,1,2,3], v3_leq_E_32B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq32e_schwanger_p,v3_con$v3_leq_e_leq32e,"v3_leq_E_32B")
## -999 0 3 <NA>
## [1,] No. cases 849 1 3 933 1786
## [2,] Percent 47.5 0.1 0.2 52.2 100
33. “(Male) Wife or girlfriend having a miscarriage or abortion”
33A Nature (dichotomous [“good”,“bad”], v3_leq_E_33A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq33a_fehlg_abtr_p,v3_con$v3_leq_e_leq33a,"v3_leq_E_33A")
## -999 bad <NA>
## [1,] No. cases 851 2 933 1786
## [2,] Percent 47.6 0.1 52.2 100
33B Impact (ordinal [0,1,2,3], v3_leq_E_33B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq33e_fehlg_abtr_p,v3_con$v3_leq_e_leq33e,"v3_leq_E_33B")
## -999 2 3 <NA>
## [1,] No. cases 851 1 1 933 1786
## [2,] Percent 47.6 0.1 0.1 52.2 100
34. “Getting married (or beginning to live with someone)”
34A Nature (dichotomous [“good”,“bad”], v3_leq_E_34A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq34a_heirat,v3_con$v3_leq_e_leq34a,"v3_leq_E_34A")
## -999 bad good <NA>
## [1,] No. cases 839 2 12 933 1786
## [2,] Percent 47 0.1 0.7 52.2 100
34B Impact (ordinal [0,1,2,3], v3_leq_E_34B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq34e_heirat,v3_con$v3_leq_e_leq34e,"v3_leq_E_34B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 839 2 4 5 3 933 1786
## [2,] Percent 47 0.1 0.2 0.3 0.2 52.2 100
35. “A change in closeness with your partner”
35A Nature (dichotomous [“good”,“bad”], v3_leq_E_35A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq35a_veraend_naehe,v3_con$v3_leq_e_leq35a,"v3_leq_E_35A")
## -999 bad good <NA>
## [1,] No. cases 758 43 52 933 1786
## [2,] Percent 42.4 2.4 2.9 52.2 100
35B Impact (ordinal [0,1,2,3], v3_leq_E_35B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq35e_veraend_naehe,v3_con$v3_leq_e_leq35e,"v3_leq_E_35B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 757 3 15 35 43 933 1786
## [2,] Percent 42.4 0.2 0.8 2 2.4 52.2 100
36. “Infidelity”
36A Nature (dichotomous [“good”,“bad”], v3_leq_E_36A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq36a_untreue,v3_con$v3_leq_e_leq36a,"v3_leq_E_36A")
## -999 bad good <NA>
## [1,] No. cases 834 17 2 933 1786
## [2,] Percent 46.7 1 0.1 52.2 100
36B Impact (ordinal [0,1,2,3], v3_leq_E_36B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq36e_untreue,v3_con$v3_leq_e_leq36e,"v3_leq_E_36B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 833 1 7 4 8 933 1786
## [2,] Percent 46.6 0.1 0.4 0.2 0.4 52.2 100
37. “Trouble with in-laws”
37A Nature (dichotomous [“good”,“bad”], v3_leq_E_37A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq37a_konf_schwiege,v3_con$v3_leq_e_leq37a,"v3_leq_E_37A")
## -999 bad good <NA>
## [1,] No. cases 823 27 3 933 1786
## [2,] Percent 46.1 1.5 0.2 52.2 100
37B Impact (ordinal [0,1,2,3], v3_leq_E_37B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq37e_konf_schwiege,v3_con$v3_leq_e_leq37e,"v3_leq_E_37B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 822 2 13 8 8 933 1786
## [2,] Percent 46 0.1 0.7 0.4 0.4 52.2 100
38. “Separation from spouse or partner due to conflict”
38A Nature (dichotomous [“good”,“bad”], v3_leq_E_38A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq38a_trennung_str,v3_con$v3_leq_e_leq38a,"v3_leq_E_38A")
## -999 bad good <NA>
## [1,] No. cases 838 10 5 933 1786
## [2,] Percent 46.9 0.6 0.3 52.2 100
38B Impact (ordinal [0,1,2,3], v3_leq_E_38B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq38e_trennung_str,v3_con$v3_leq_e_leq38e,"v3_leq_E_38B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 836 2 2 4 9 933 1786
## [2,] Percent 46.8 0.1 0.1 0.2 0.5 52.2 100
39. “Separation from spouse or partner due to work, travel, etc.”
39A Nature (dichotomous [“good”,“bad”], v3_leq_E_39A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq39a_trennung_ber,v3_con$v3_leq_e_leq39a,"v3_leq_E_39A")
## -999 bad good <NA>
## [1,] No. cases 848 4 1 933 1786
## [2,] Percent 47.5 0.2 0.1 52.2 100
39B Impact (ordinal [0,1,2,3], v3_leq_E_39B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq39e_trennung_ber,v3_con$v3_leq_e_leq39e,"v3_leq_E_39B")
## -999 0 2 3 <NA>
## [1,] No. cases 847 2 2 2 933 1786
## [2,] Percent 47.4 0.1 0.1 0.1 52.2 100
40. “Reconciliation with spouse or partner”
40A Nature (dichotomous [“good”,“bad”], v3_leq_E_40A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq40a_versoehnung,v3_con$v3_leq_e_leq40a,"v3_leq_E_40A")
## -999 good <NA>
## [1,] No. cases 831 22 933 1786
## [2,] Percent 46.5 1.2 52.2 100
40B Impact (ordinal [0,1,2,3], v3_leq_E_40B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq40a_versoehnung,v3_con$v3_leq_e_leq40e,"v3_leq_E_40B")
## -999 1 2 3 <NA>
## [1,] No. cases 831 18 2 2 933 1786
## [2,] Percent 46.5 1 0.1 0.1 52.2 100
41. “Divorce”
41A Nature (dichotomous [“good”,“bad”], v3_leq_E_41A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq41a_scheidung,v3_con$v3_leq_e_leq41a,"v3_leq_E_41A")
## -999 bad good <NA>
## [1,] No. cases 844 5 4 933 1786
## [2,] Percent 47.3 0.3 0.2 52.2 100
41B Impact (ordinal [0,1,2,3], v3_leq_E_41B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq41e_scheidung,v3_con$v3_leq_e_leq41e,"v3_leq_E_41B")
## -999 0 2 3 <NA>
## [1,] No. cases 843 3 2 5 933 1786
## [2,] Percent 47.2 0.2 0.1 0.3 52.2 100
42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”
42A Nature (dichotomous [“good”,“bad”], v3_leq_E_42A)
v3_leq_a_recode(v3_clin$v3_leq_e_leq42a_veraend_taet,v3_con$v3_leq_e_leq42a,"v3_leq_E_42A")
## -999 bad good <NA>
## [1,] No. cases 818 10 25 933 1786
## [2,] Percent 45.8 0.6 1.4 52.2 100
42B Impact (ordinal [0,1,2,3], v3_leq_E_42B)
v3_leq_b_recode(v3_clin$v3_leq_e_leq42e_veraend_taet,v3_con$v3_leq_e_leq42e,"v3_leq_E_42B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 817 2 11 12 11 933 1786
## [2,] Percent 45.7 0.1 0.6 0.7 0.6 52.2 100
Create dataset
v3_leq_E<-data.frame(v3_leq_E_28A,v3_leq_E_28B,v3_leq_E_29A,v3_leq_E_29B,v3_leq_E_30A,
v3_leq_E_30B,v3_leq_E_31A,v3_leq_E_31B,v3_leq_E_32A,v3_leq_E_32B,
v3_leq_E_33A,v3_leq_E_33B,v3_leq_E_34A,v3_leq_E_34B,v3_leq_E_35A,
v3_leq_E_35B,v3_leq_E_36A,v3_leq_E_36B,v3_leq_E_37A,v3_leq_E_37B,
v3_leq_E_38A,v3_leq_E_38B,v3_leq_E_39A,v3_leq_E_39B,v3_leq_E_40A,
v3_leq_E_40B,v3_leq_E_41A,v3_leq_E_41B,v3_leq_E_42A,v3_leq_E_42B)
43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”
43A Nature (dichotomous [“good”,“bad”], v3_leq_F_43A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq43a_neu_fmitglied,v3_con$v3_leq_f_g_leq43a,"v3_leq_F_43A")
## -999 bad good <NA>
## [1,] No. cases 805 4 44 933 1786
## [2,] Percent 45.1 0.2 2.5 52.2 100
43B Impact (ordinal [0,1,2,3], v3_leq_F_43B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq43e_neu_fmitglied,v3_con$v3_leq_f_g_leq43e,"v3_leq_F_43B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 804 6 12 15 16 933 1786
## [2,] Percent 45 0.3 0.7 0.8 0.9 52.2 100
44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”
44A Nature (dichotomous [“good”,“bad”], v3_leq_F_44A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq44a_auszug_fm,v3_con$v3_leq_f_g_leq44a,"v3_leq_F_44A")
## -999 bad good <NA>
## [1,] No. cases 830 9 14 933 1786
## [2,] Percent 46.5 0.5 0.8 52.2 100
44B Impact (ordinal [0,1,2,3], v3_leq_F_44B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq44e_auszug_fm,v3_con$v3_leq_f_g_leq44e,"v3_leq_F_44B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 829 3 5 5 11 933 1786
## [2,] Percent 46.4 0.2 0.3 0.3 0.6 52.2 100
45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”
45A Nature (dichotomous [“good”,“bad”], v3_leq_F_45A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq45a_gz_verh_fm,v3_con$v3_leq_f_g_leq45a,"v3_leq_F_45A")
## -999 bad good <NA>
## [1,] No. cases 714 126 13 933 1786
## [2,] Percent 40 7.1 0.7 52.2 100
45B Impact (ordinal [0,1,2,3], v3_leq_F_45B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq45e_gz_verh_fm,v3_con$v3_leq_f_g_leq45e,"v3_leq_F_45B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 713 8 31 54 47 933 1786
## [2,] Percent 39.9 0.4 1.7 3 2.6 52.2 100
46. “Death of spouse or partner”
46A Nature (dichotomous [“good”,“bad”], v3_leq_F_46A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq46a_tod_partner,v3_con$v3_leq_f_g_leq46a,"v3_leq_F_46A")
## -999 bad <NA>
## [1,] No. cases 851 2 933 1786
## [2,] Percent 47.6 0.1 52.2 100
46B Impact (ordinal [0,1,2,3], v3_leq_F_46B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq46e_tod_partner,v3_con$v3_leq_f_g_leq46e,"v3_leq_F_46B")
## -999 0 2 3 <NA>
## [1,] No. cases 849 2 1 1 933 1786
## [2,] Percent 47.5 0.1 0.1 0.1 52.2 100
47. “Death of a child”
47A Nature (dichotomous [“good”,“bad”], v3_leq_F_47A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq47a_tod_kind,v3_con$v3_leq_f_g_leq47a,"v3_leq_F_47A")
## -999 bad <NA>
## [1,] No. cases 850 3 933 1786
## [2,] Percent 47.6 0.2 52.2 100
47B Impact (ordinal [0,1,2,3], v3_leq_F_47B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq47e_tod_kind,v3_con$v3_leq_f_g_leq47e,"v3_leq_F_47B")
## -999 0 3 <NA>
## [1,] No. cases 848 2 3 933 1786
## [2,] Percent 47.5 0.1 0.2 52.2 100
48. “Death of family member or close friend”
48A Nature (dichotomous [“good”,“bad”], v3_leq_F_48A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq48a_tod_fm_ef,v3_con$v3_leq_f_g_leq48a,"v3_leq_F_48A")
## -999 bad good <NA>
## [1,] No. cases 779 69 5 933 1786
## [2,] Percent 43.6 3.9 0.3 52.2 100
48B Impact (ordinal [0,1,2,3], v3_leq_F_48B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq48e_tod_fm_ef,v3_con$v3_leq_f_g_leq48e,"v3_leq_F_48B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 777 13 18 17 28 933 1786
## [2,] Percent 43.5 0.7 1 1 1.6 52.2 100
49. “Birth of a grandchild”
49A Nature (dichotomous [“good”,“bad”], v3_leq_F_49A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq49a_geb_enkel,v3_con$v3_leq_f_g_leq49a,"v3_leq_F_49A")
## -999 good <NA>
## [1,] No. cases 835 18 933 1786
## [2,] Percent 46.8 1 52.2 100
49B Impact (ordinal [0,1,2,3], v3_leq_F_49B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq49e_geb_enkel,v3_con$v3_leq_f_g_leq49e,"v3_leq_F_49B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 834 1 4 2 12 933 1786
## [2,] Percent 46.7 0.1 0.2 0.1 0.7 52.2 100
50. “Change in marital status of your parents”
50A Nature (dichotomous [“good”,“bad”], v3_leq_F_50A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq50a_fstand_eltern,v3_con$v3_leq_f_g_leq50a,"v3_leq_F_50A")
## -999 bad good <NA>
## [1,] No. cases 848 3 2 933 1786
## [2,] Percent 47.5 0.2 0.1 52.2 100
50B Impact (ordinal [0,1,2,3], v3_leq_F_50B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq50e_fstand_eltern,v3_con$v3_leq_f_g_leq50e,"v3_leq_F_50B")
## -999 0 1 3 <NA>
## [1,] No. cases 847 1 2 3 933 1786
## [2,] Percent 47.4 0.1 0.1 0.2 52.2 100
Create dataset
v3_leq_F<-data.frame(v3_leq_F_43A,v3_leq_F_43B,v3_leq_F_44A,v3_leq_F_44B,v3_leq_F_45A,
v3_leq_F_45B,v3_leq_F_46A,v3_leq_F_46B,v3_leq_F_47A,v3_leq_F_47B,
v3_leq_F_48A,v3_leq_F_48B,v3_leq_F_49A,v3_leq_F_49B,v3_leq_F_50A,
v3_leq_F_50B)
51. “Change in child care arrangements”
51A Nature (dichotomous [“good”,“bad”], v3_leq_G_51A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq51a_kindbetr,v3_con$v3_leq_f_g_leq51a,"v3_leq_G_51A")
## -999 bad good <NA>
## [1,] No. cases 834 4 15 933 1786
## [2,] Percent 46.7 0.2 0.8 52.2 100
51B Impact (ordinal [0,1,2,3], v3_leq_G_51B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq51e_kindbetr,v3_con$v3_leq_f_g_leq51e,"v3_leq_G_51B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 833 1 5 5 9 933 1786
## [2,] Percent 46.6 0.1 0.3 0.3 0.5 52.2 100
52. “Conflicts with spouse or partner about parenting”
52A Nature (dichotomous [“good”,“bad”], v3_leq_G_52A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq52a_konf_eschaft,v3_con$v3_leq_f_g_leq52a,"v3_leq_G_52A")
## -999 bad good <NA>
## [1,] No. cases 840 11 2 933 1786
## [2,] Percent 47 0.6 0.1 52.2 100
52B Impact (ordinal [0,1,2,3], v3_leq_G_52B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq52e_konf_eschaft,v3_con$v3_leq_f_g_leq52e,"v3_leq_G_52B")
## -999 1 2 3 <NA>
## [1,] No. cases 839 6 6 2 933 1786
## [2,] Percent 47 0.3 0.3 0.1 52.2 100
53. “Conflicts with child’s grandparents (or other important person) about parenting”
53A Nature (dichotomous [“good”,“bad”], v3_leq_G_53A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq53a_konf_geltern,v3_con$v3_leq_f_g_leq53a,"v3_leq_G_53A")
## -999 bad good <NA>
## [1,] No. cases 844 8 1 933 1786
## [2,] Percent 47.3 0.4 0.1 52.2 100
53B Impact (ordinal [0,1,2,3], v3_leq_G_53B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq53e_konf_geltern,v3_con$v3_leq_f_g_leq53e,"v3_leq_G_53B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 843 1 1 4 4 933 1786
## [2,] Percent 47.2 0.1 0.1 0.2 0.2 52.2 100
54. “Taking on full responsibility for parenting as a single parent”
54A Nature (dichotomous [“good”,“bad”], v3_leq_G_54A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq54a_alleinerz,v3_con$v3_leq_f_g_leq54a,"v3_leq_G_54A")
## -999 bad good <NA>
## [1,] No. cases 845 1 7 933 1786
## [2,] Percent 47.3 0.1 0.4 52.2 100
54B Impact (ordinal [0,1,2,3], v3_leq_G_54B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq54e_alleinerz,v3_con$v3_leq_f_g_leq54e,"v3_leq_G_54B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 844 1 1 2 5 933 1786
## [2,] Percent 47.3 0.1 0.1 0.1 0.3 52.2 100
55. “Custody battles with former spouse or partner”
55A Nature (dichotomous [“good”,“bad”], v3_leq_G_55A)
v3_leq_a_recode(v3_clin$v3_leq_f_g_leq55a_sorgerecht,v3_con$v3_leq_f_g_leq55a,"v3_leq_G_55A")
## -999 bad good <NA>
## [1,] No. cases 845 7 1 933 1786
## [2,] Percent 47.3 0.4 0.1 52.2 100
55B Impact (ordinal [0,1,2,3], v3_leq_G_55B)
v3_leq_b_recode(v3_clin$v3_leq_f_g_leq55e_sorgerecht,v3_con$v3_leq_f_g_leq55e,"v3_leq_G_55B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 844 2 2 1 4 933 1786
## [2,] Percent 47.3 0.1 0.1 0.1 0.2 52.2 100
Create dataset
v3_leq_G<-data.frame(v3_leq_G_51A,v3_leq_G_51B,v3_leq_G_52A,v3_leq_G_52B,v3_leq_G_53A,
v3_leq_G_53B,v3_leq_G_54A,v3_leq_G_54B,v3_leq_G_55A,v3_leq_G_55B)
69. “Major change in finances (increased or decreased income)”
69A Nature (dichotomous [“good”,“bad”], v3_leq_I_69A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq69a_finanz_sit,v3_con$v3_leq_i_j_k_leq69a,"v3_leq_I_69A")
## -999 bad good <NA>
## [1,] No. cases 661 77 115 933 1786
## [2,] Percent 37 4.3 6.4 52.2 100
69B Impact (ordinal [0,1,2,3], v3_leq_I_69B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq69e_finanz_sit,v3_con$v3_leq_i_j_k_leq69e,"v3_leq_I_69B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 656 8 45 73 71 933 1786
## [2,] Percent 36.7 0.4 2.5 4.1 4 52.2 100
70. “Took on a moderate purchase, such as TV, car, freezer, etc.”
70A Nature (dichotomous [“good”,“bad”], v3_leq_I_70A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq70a_finanz_verpfl,v3_con$v3_leq_i_j_k_leq70a,"v3_leq_I_70A")
## -999 bad good <NA>
## [1,] No. cases 798 18 37 933 1786
## [2,] Percent 44.7 1 2.1 52.2 100
70B Impact (ordinal [0,1,2,3], v3_leq_I_70B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq70e_finanz_verpfl,v3_con$v3_leq_i_j_k_leq70e,"v3_leq_I_70B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 797 4 22 20 10 933 1786
## [2,] Percent 44.6 0.2 1.2 1.1 0.6 52.2 100
71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”
71A Nature (dichotomous [“good”,“bad”], v3_leq_I_71A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq71a_hypothek,v3_con$v3_leq_i_j_k_leq71a,"v3_leq_I_71A")
## -999 bad good <NA>
## [1,] No. cases 841 5 7 933 1786
## [2,] Percent 47.1 0.3 0.4 52.2 100
71B Impact (ordinal [0,1,2,3], v3_leq_I_71B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq71e_hypothek,v3_con$v3_leq_i_j_k_leq71e,"v3_leq_I_71B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 840 3 1 4 5 933 1786
## [2,] Percent 47 0.2 0.1 0.2 0.3 52.2 100
72. “Experienced a foreclosure on a mortgage or loan”
72A Nature (dichotomous [“good”,“bad”], v3_leq_I_72A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq72a_hypoth_kuend,v3_con$v3_leq_i_j_k_leq72a,"v3_leq_I_72A")
## -999 bad good <NA>
## [1,] No. cases 843 1 9 933 1786
## [2,] Percent 47.2 0.1 0.5 52.2 100
72B Impact (ordinal [0,1,2,3], v3_leq_I_72B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq72e_hypoth_kuend,v3_con$v3_leq_i_j_k_leq72e,"v3_leq_I_72B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 842 2 1 4 4 933 1786
## [2,] Percent 47.1 0.1 0.1 0.2 0.2 52.2 100
73. “Credit rating difficulties”
73A Nature (dichotomous [“good”,“bad”], v3_leq_I_73A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq73a_kreditwuerdk,v3_con$v3_leq_i_j_k_leq73a,"v3_leq_I_73A")
## -999 bad good <NA>
## [1,] No. cases 829 23 1 933 1786
## [2,] Percent 46.4 1.3 0.1 52.2 100
73B Impact (ordinal [0,1,2,3], v3_leq_I_73B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq73e_kreditwuerdk,v3_con$v3_leq_i_j_k_leq73e,"v3_leq_I_73B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 827 5 3 6 12 933 1786
## [2,] Percent 46.3 0.3 0.2 0.3 0.7 52.2 100
Create dataset
v3_leq_I<-data.frame(v3_leq_I_69A,v3_leq_I_69B,v3_leq_I_70A,v3_leq_I_70B,v3_leq_I_71A,
v3_leq_I_71B,v3_leq_I_72A,v3_leq_I_72B,v3_leq_I_73A,v3_leq_I_73B)
74. “Being robbed or victim of identity theft”
74A Nature (dichotomous [“good”,“bad”], v3_leq_J_74A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq74a_opf_diebstahl,v3_con$v3_leq_i_j_k_leq74a,"v3_leq_J_74A")
## -999 bad good <NA>
## [1,] No. cases 821 31 1 933 1786
## [2,] Percent 46 1.7 0.1 52.2 100
74B Impact (ordinal [0,1,2,3], v3_leq_J_74B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq74e_opf_diebstahl,v3_con$v3_leq_i_j_k_leq74e,"v3_leq_J_74B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 819 6 13 9 6 933 1786
## [2,] Percent 45.9 0.3 0.7 0.5 0.3 52.2 100
75. “Being a victim of a violent act (rape, assault, etc.)”
75A Nature (dichotomous [“good”,“bad”], v3_leq_J_75A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq75a_opf_gewalttat,v3_con$v3_leq_i_j_k_leq75a,"v3_leq_J_75A")
## -999 bad <NA>
## [1,] No. cases 845 8 933 1786
## [2,] Percent 47.3 0.4 52.2 100
75B Impact (ordinal [0,1,2,3], v3_leq_J_75B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq75e_opf_gewalttat,v3_con$v3_leq_i_j_k_leq75e,"v3_leq_J_75B")
## -999 0 2 3 <NA>
## [1,] No. cases 843 3 3 4 933 1786
## [2,] Percent 47.2 0.2 0.2 0.2 52.2 100
76. “Involved in an accident”
76A Nature (dichotomous [“good”,“bad”], v3_leq_J_76A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq76a_unfall,v3_con$v3_leq_i_j_k_leq76a,"v3_leq_J_76A")
## -999 bad <NA>
## [1,] No. cases 832 21 933 1786
## [2,] Percent 46.6 1.2 52.2 100
76B Impact (ordinal [0,1,2,3], v3_leq_J_76B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq76e_unfall,v3_con$v3_leq_i_j_k_leq76e,"v3_leq_J_76B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 830 7 9 3 4 933 1786
## [2,] Percent 46.5 0.4 0.5 0.2 0.2 52.2 100
77. “Involved in a law suit”
77A Nature (dichotomous [“good”,“bad”], v3_leq_J_77A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq77a_rechtsstreit,v3_con$v3_leq_i_j_k_leq77a,"v3_leq_J_77A")
## -999 bad good <NA>
## [1,] No. cases 809 32 12 933 1786
## [2,] Percent 45.3 1.8 0.7 52.2 100
77B Impact (ordinal [0,1,2,3], v3_leq_J_77B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq77e_rechtsstreit,v3_con$v3_leq_i_j_k_leq77e,"v3_leq_J_77B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 807 3 16 11 16 933 1786
## [2,] Percent 45.2 0.2 0.9 0.6 0.9 52.2 100
78. “Involved in a minor violation of the law (traffic tickets, disturbing the peace, etc)”
78A Nature (dichotomous [“good”,“bad”], v3_leq_J_78A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq78a_owi,v3_con$v3_leq_i_j_k_leq78a,"v3_leq_J_78A")
## -999 bad good <NA>
## [1,] No. cases 812 40 1 933 1786
## [2,] Percent 45.5 2.2 0.1 52.2 100
78B Impact (ordinal [0,1,2,3], v3_leq_J_78B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq78e_owi,v3_con$v3_leq_i_j_k_leq78e,"v3_leq_J_78B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 810 16 15 8 4 933 1786
## [2,] Percent 45.4 0.9 0.8 0.4 0.2 52.2 100
79. “Legal troubles resulting in your being arrested or held in jail”
79A Nature (dichotomous [“good”,“bad”], v3_leq_J_79A)
v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq79a_konf_gesetz,v3_con$v3_leq_i_j_k_leq79a,"v3_leq_J_79A")
## -999 bad <NA>
## [1,] No. cases 849 4 933 1786
## [2,] Percent 47.5 0.2 52.2 100
79B Impact (ordinal [0,1,2,3], v3_leq_J_79B)
v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq79e_konf_gesetz,v3_con$v3_leq_i_j_k_leq79e,"v3_leq_J_79B")
## -999 0 2 3 <NA>
## [1,] No. cases 848 2 1 2 933 1786
## [2,] Percent 47.5 0.1 0.1 0.1 52.2 100
Create dataset
v3_leq_J<-data.frame(v3_leq_J_74A,v3_leq_J_74B,v3_leq_J_75A,v3_leq_J_75B,v3_leq_J_76A,
v3_leq_J_76B,v3_leq_J_77A,v3_leq_J_77B,v3_leq_J_78A,v3_leq_J_78B,
v3_leq_J_79A,v3_leq_J_79B)
Create LEQ dataset
v3_leq<-data.frame(v3_leq_A,v3_leq_B,v3_leq_C,v3_leq_D,v3_leq_E,v3_leq_F,v3_leq_G,
v3_leq_H,v3_leq_I,v3_leq_J)
For explanation, please refer to the section in Visit 1
1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v3_whoqol_itm1)
v3_quol_recode(v3_clin$v3_whoqol_bref_who1_lebensqualitaet,v3_con$v3_whoqol_bref_who1,"v3_whoqol_itm1",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 15 56 216 403 201 895 1786
## [2,] Percent 0.8 3.1 12.1 22.6 11.3 50.1 100
2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v3_whoqol_itm2)”
v3_quol_recode(v3_clin$v3_whoqol_bref_who2_gesundheit,v3_con$v3_whoqol_bref_who2,"v3_whoqol_itm2",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 36 149 182 365 160 894 1786
## [2,] Percent 2 8.3 10.2 20.4 9 50.1 100
3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v3_whoqol_itm3)
Coding reversed so that higher scores mean less impairment by pain.
v3_quol_recode(v3_clin$v3_whoqol_bref_who3_schmerzen,v3_con$v3_whoqol_bref_who3,"v3_whoqol_itm3",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 7 43 80 173 583 900 1786
## [2,] Percent 0.4 2.4 4.5 9.7 32.6 50.4 100
4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v3_whoqol_itm4)”
Coding reversed so that higher scores mean less dependence on medical treatment.
v3_quol_recode(v3_clin$v3_whoqol_bref_who4_med_behand,v3_con$v3_whoqol_bref_who4,"v3_whoqol_itm4",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 90 171 107 160 359 899 1786
## [2,] Percent 5 9.6 6 9 20.1 50.3 100
5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v3_whoqol_itm5)
v3_quol_recode(v3_clin$v3_whoqol_bref_who5_lebensgenuss,v3_con$v3_whoqol_bref_who5,"v3_whoqol_itm5",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 25 88 234 389 155 895 1786
## [2,] Percent 1.4 4.9 13.1 21.8 8.7 50.1 100
6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v3_whoqol_itm6)
v3_quol_recode(v3_clin$v3_whoqol_bref_who6_lebenssinn,v3_con$v3_whoqol_bref_who6,"v3_whoqol_itm6",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 46 89 169 320 264 898 1786
## [2,] Percent 2.6 5 9.5 17.9 14.8 50.3 100
7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v3_whoqol_itm7)
v3_quol_recode(v3_clin$v3_whoqol_bref_who7_konzentration,v3_con$v3_whoqol_bref_who7,"v3_whoqol_itm7",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 14 126 313 369 72 892 1786
## [2,] Percent 0.8 7.1 17.5 20.7 4 49.9 100
8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v3_whoqol_itm8)
v3_quol_recode(v3_clin$v3_whoqol_bref_who8_sicherheit,v3_con$v3_whoqol_bref_who8,"v3_whoqol_itm8",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 14 54 194 417 214 893 1786
## [2,] Percent 0.8 3 10.9 23.3 12 50 100
9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v3_whoqol_itm9)
v3_quol_recode(v3_clin$v3_whoqol_bref_who9_umweltbed,v3_con$v3_whoqol_bref_who9,"v3_whoqol_itm9",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 12 32 180 425 242 895 1786
## [2,] Percent 0.7 1.8 10.1 23.8 13.5 50.1 100
10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v3_whoqol_itm10)
v3_quol_recode(v3_clin$v3_whoqol_bref_who10_energie,v3_con$v3_whoqol_bref_who10,"v3_whoqol_itm10",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 13 66 203 362 250 892 1786
## [2,] Percent 0.7 3.7 11.4 20.3 14 49.9 100
11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v3_whoqol_itm11)
v3_quol_recode(v3_clin$v3_whoqol_bref_who11_aussehen,v3_con$v3_whoqol_bref_who11,"v3_whoqol_itm11",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 21 53 171 400 247 894 1786
## [2,] Percent 1.2 3 9.6 22.4 13.8 50.1 100
12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v3_whoqol_itm12)
v3_quol_recode(v3_clin$v3_whoqol_bref_who12_genug_geld,v3_con$v3_whoqol_bref_who12,"v3_whoqol_itm12",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 34 117 194 297 248 896 1786
## [2,] Percent 1.9 6.6 10.9 16.6 13.9 50.2 100
13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v3_whoqol_itm13)
v3_quol_recode(v3_clin$v3_whoqol_bref_who13_infozugang,v3_con$v3_whoqol_bref_who13,"v3_whoqol_itm13",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 3 17 78 321 472 895 1786
## [2,] Percent 0.2 1 4.4 18 26.4 50.1 100
14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v3_whoqol_itm14)
v3_quol_recode(v3_clin$v3_whoqol_bref_who14_freizeitaktiv,v3_con$v3_whoqol_bref_who14,"v3_whoqol_itm14",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 9 56 178 302 348 893 1786
## [2,] Percent 0.5 3.1 10 16.9 19.5 50 100
15. “How well are you able to get around? (ordinal [1,2,3,4,5], v3_whoqol_itm15)”
v3_quol_recode(v3_clin$v3_whoqol_bref_who15_fortbewegung,v3_con$v3_whoqol_bref_who15,"v3_whoqol_itm15",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 7 31 109 323 418 898 1786
## [2,] Percent 0.4 1.7 6.1 18.1 23.4 50.3 100
16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v3_whoqol_itm16)
v3_quol_recode(v3_clin$v3_whoqol_bref_who16_schlaf,v3_con$v3_whoqol_bref_who16,"v3_whoqol_itm16",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 29 126 152 411 178 890 1786
## [2,] Percent 1.6 7.1 8.5 23 10 49.8 100
17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v3_whoqol_itm17)
v3_quol_recode(v3_clin$v3_whoqol_bref_who17_alltag,v3_con$v3_whoqol_bref_who17,"v3_whoqol_itm17",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 18 93 145 414 227 889 1786
## [2,] Percent 1 5.2 8.1 23.2 12.7 49.8 100
18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v3_whoqol_itm18)
v3_quol_recode(v3_clin$v3_whoqol_bref_who18_arbeitsfhgk,v3_con$v3_whoqol_bref_who18,"v3_whoqol_itm18",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 51 146 173 305 208 903 1786
## [2,] Percent 2.9 8.2 9.7 17.1 11.6 50.6 100
19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v3_whoqol_itm19)
v3_quol_recode(v3_clin$v3_whoqol_bref_who19_selbstzufried,v3_con$v3_whoqol_bref_who19,"v3_whoqol_itm19",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 27 98 202 418 147 894 1786
## [2,] Percent 1.5 5.5 11.3 23.4 8.2 50.1 100
20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v3_whoqol_itm20)
v3_quol_recode(v3_clin$v3_whoqol_bref_who20_pers_bezieh,v3_con$v3_whoqol_bref_who20,"v3_whoqol_itm20",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 20 80 181 405 202 898 1786
## [2,] Percent 1.1 4.5 10.1 22.7 11.3 50.3 100
21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v3_whoqol_itm21)
v3_quol_recode(v3_clin$v3_whoqol_bref_who21_sexualleben,v3_con$v3_whoqol_bref_who21,"v3_whoqol_itm21",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 86 135 252 252 148 913 1786
## [2,] Percent 4.8 7.6 14.1 14.1 8.3 51.1 100
22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v3_whoqol_itm22)
v3_quol_recode(v3_clin$v3_whoqol_bref_who22_freunde,v3_con$v3_whoqol_bref_who22,"v3_whoqol_itm22",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 20 53 175 420 225 893 1786
## [2,] Percent 1.1 3 9.8 23.5 12.6 50 100
23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v3_whoqol_itm23)
v3_quol_recode(v3_clin$v3_whoqol_bref_who23_wohnbeding,v3_con$v3_whoqol_bref_who23,"v3_whoqol_itm23",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 24 68 126 387 290 891 1786
## [2,] Percent 1.3 3.8 7.1 21.7 16.2 49.9 100
24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v3_whoqol_itm24)
v3_quol_recode(v3_clin$v3_whoqol_bref_who24_gesundhdiens,v3_con$v3_whoqol_bref_who24,"v3_whoqol_itm24",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 6 15 91 405 377 892 1786
## [2,] Percent 0.3 0.8 5.1 22.7 21.1 49.9 100
25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v3_whoqol_itm25)
v3_quol_recode(v3_clin$v3_whoqol_bref_who25_transport,v3_con$v3_whoqol_bref_who25,"v3_whoqol_itm25",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 9 30 104 355 396 892 1786
## [2,] Percent 0.5 1.7 5.8 19.9 22.2 49.9 100
26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v3_whoqol_itm26)
Coding reversed so that higher scores mean symptoms less often.
v3_quol_recode(v3_clin$v3_whoqol_bref_who26_neg_gefuehle,v3_con$v3_whoqol_bref_who26,"v3_whoqol_itm26",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 21 103 217 341 211 893 1786
## [2,] Percent 1.2 5.8 12.2 19.1 11.8 50 100
Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).
Global (continuous [4-20],v3_whoqol_dom_glob)
v3_whoqol_dom_glob_df<-data.frame(as.numeric(v3_whoqol_itm1),as.numeric(v3_whoqol_itm2))
v3_who_glob_no_nas<-rowSums(is.na(v3_whoqol_dom_glob_df))
v3_whoqol_dom_glob<-ifelse((v3_who_glob_no_nas==0) | (v3_who_glob_no_nas==1),
rowMeans(v3_whoqol_dom_glob_df,na.rm=T)*4,NA)
v3_whoqol_dom_glob<-round(v3_whoqol_dom_glob,2)
summary(v3_whoqol_dom_glob)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.00 16.00 14.65 18.00 20.00 892
Physical Health (continuous [4-20],v3_whoqol_dom_phys)
v3_whoqol_dom_phys_df<-data.frame(as.numeric(v3_whoqol_itm3),as.numeric(v3_whoqol_itm10),as.numeric(v3_whoqol_itm16),as.numeric(v3_whoqol_itm15),as.numeric(v3_whoqol_itm17),as.numeric(v3_whoqol_itm4),as.numeric(v3_whoqol_itm18))
v3_who_phys_no_nas<-rowSums(is.na(v3_whoqol_dom_phys_df))
v3_whoqol_dom_phys<-ifelse((v3_who_phys_no_nas==0) | (v3_who_phys_no_nas==1),
rowMeans(v3_whoqol_dom_phys_df,na.rm=T)*4,NA)
v3_whoqol_dom_phys<-round(v3_whoqol_dom_phys,2)
summary(v3_whoqol_dom_phys)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 13.71 16.00 15.52 17.71 20.00 900
Psychological (continuous [4-20],v3_whoqol_dom_psy)
v3_whoqol_dom_psy_df<-data.frame(as.numeric(v3_whoqol_itm5),as.numeric(v3_whoqol_itm7),as.numeric(v3_whoqol_itm19),as.numeric(v3_whoqol_itm11),as.numeric(v3_whoqol_itm26),as.numeric(v3_whoqol_itm6))
v3_who_psy_no_nas<-rowSums(is.na(v3_whoqol_dom_psy_df))
v3_whoqol_dom_psy<-ifelse((v3_who_psy_no_nas==0) | (v3_who_psy_no_nas==1),
rowMeans(v3_whoqol_dom_psy_df,na.rm=T)*4,NA)
v3_whoqol_dom_psy<-round(v3_whoqol_dom_psy,2)
summary(v3_whoqol_dom_psy)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.67 12.67 15.33 14.67 16.67 20.00 895
Social relationships (continuous [4-20],v3_whoqol_dom_soc)
v3_whoqol_dom_soc_df<-data.frame(as.numeric(v3_whoqol_itm20),as.numeric(v3_whoqol_itm22),as.numeric(v3_whoqol_itm21))
v3_who_soc_no_nas<-rowSums(is.na(v3_whoqol_dom_soc_df))
v3_whoqol_dom_soc<-ifelse((v3_who_soc_no_nas==0) | (v3_who_soc_no_nas==1),
rowMeans(v3_whoqol_dom_soc_df,na.rm=T)*4,NA)
v3_whoqol_dom_soc<-round(v3_whoqol_dom_soc,2)
summary(v3_whoqol_dom_soc)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.00 14.67 14.57 17.33 20.00 894
Environment (continuous [4-20],v3_whoqol_dom_env)
v3_whoqol_dom_env_df<-data.frame(as.numeric(v3_whoqol_itm8),as.numeric(v3_whoqol_itm23),as.numeric(v3_whoqol_itm12),as.numeric(v3_whoqol_itm24),as.numeric(v3_whoqol_itm13),as.numeric(v3_whoqol_itm14),as.numeric(v3_whoqol_itm9),as.numeric(v3_whoqol_itm25))
v3_who_env_no_nas<-rowSums(is.na(v3_whoqol_dom_env_df))
v3_whoqol_dom_env<-ifelse((v3_who_env_no_nas==0) | (v3_who_env_no_nas==1),
rowMeans(v3_whoqol_dom_env_df,na.rm=T)*4,NA)
v3_whoqol_dom_env<-round(v3_whoqol_dom_env,2)
summary(v3_whoqol_dom_env)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 6.00 14.50 16.50 16.19 18.00 20.00 896
Create dataset
v3_whoqol<-data.frame(v3_whoqol_itm1,v3_whoqol_itm2,v3_whoqol_itm3,v3_whoqol_itm4,
v3_whoqol_itm5,v3_whoqol_itm6,v3_whoqol_itm7,v3_whoqol_itm8,
v3_whoqol_itm9,v3_whoqol_itm10,v3_whoqol_itm11,v3_whoqol_itm12,
v3_whoqol_itm13,v3_whoqol_itm14,v3_whoqol_itm15,v3_whoqol_itm16,
v3_whoqol_itm17,v3_whoqol_itm18,v3_whoqol_itm19,v3_whoqol_itm20,
v3_whoqol_itm21,v3_whoqol_itm22,v3_whoqol_itm23,v3_whoqol_itm24,
v3_whoqol_itm25,v3_whoqol_itm26,v3_whoqol_dom_glob,
v3_whoqol_dom_phys,v3_whoqol_dom_psy,v3_whoqol_dom_soc,
v3_whoqol_dom_env)
v3_df<-data.frame(v3_id,
v3_rec,
v3_clin_ill_ep,
v3_con_problems,
v3_dem,
v3_leprcp,
v3_suic,
v3_med,
v3_subst,
v3_symp_panss,
v3_symp_ids_c,
v3_symp_ymrs,
v3_ill_sev,
v3_nrpsy,
v3_sf12,
v3_cts,
v3_med_adh,
v3_bdi2,
v3_asrm,
v3_mss,
v3_leq,
v3_whoqol)
## [1] 1323
## [1] 466
v4_clin<-subset(v4_clin, as.character(v4_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v4_clin)[1]
## [1] 1320
v4_con<-subset(v4_con, as.character(v4_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v4_con)[1]
## [1] 466
v4_id<-as.factor(c(as.character(v4_clin$mnppsd),as.character(v4_con$mnppsd)))
v4_interv_date<-c(as.Date(as.character(v4_clin$v4_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v4_con$v4_rekru_visit_rekr_datum), "%Y%m%d"))
v4_age_years_clin<-as.numeric(substr(v4_clin$v4_ausschluss1_rekr_datum,1,4))-
as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))
v4_age_years_con<-as.numeric(substr(v4_con$v4_rekru_visit_rekr_datu,1,4))-
as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))
v4_age_years<-c(v4_age_years_clin,v4_age_years_con)
v4_age<-ifelse(c(as.numeric(substr(v4_clin$v4_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v4_con$v4_rekru_visit_rekr_datu,5,6)))<
c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
v4_age_years-1,v4_age_years)
summary(v4_age)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 19.00 31.00 45.00 43.85 54.00 79.00 946
Create dataset
v4_rec<-data.frame(v4_age,v4_interv_date)
Study participant are asked whether an acute illness episode occurred since the last study visit. Possible answers are “Y”-yes, “N”-no and “C”-chronic symptomatology. The latter category is for people which continually experience symptoms. If the answer was yes, additional questions were asked about the episodes, if not these are omitted. For participants with chronic symptomatology, the participant is asked about the nature of the chronic symptomatology (manic/depressive/mixed/psychotic) and answers are coded in the questions “Did you experience … symptoms during this illness episode?” (first illness episode).
Importantly, if the participant experienced multiple illness episodes since the last study visit, a set of questions (see below) is supposed to be answered for each illness episode. As most interviewers answered these questions only for a maximum of two illness episodes and few participants experienced more than two illness episodes, data are included only for the first two illness episodes.
“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v4_clin_ill_ep_snc_lst)
v4_clin_ill_ep_snc_lst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_ill_ep_snc_lst<-ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==1,"Y",
ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==2,"N",
ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==3,"C",v4_clin_ill_ep_snc_lst)))
v4_clin_ill_ep_snc_lst<-factor(v4_clin_ill_ep_snc_lst)
descT(v4_clin_ill_ep_snc_lst)
## -999 C N Y <NA>
## [1,] No. cases 466 77 342 164 737 1786
## [2,] Percent 26.1 4.3 19.1 9.2 41.3 100
“If yes, how many illness episodes? (continuous [no. illness episodes], v4_clin_no_ep)”
v4_clin_no_ep<-ifelse(v4_clin_ill_ep_snc_lst=="Y",c(v4_clin$v4_aktu_situat_anzahl_episoden,rep(-999,dim(v4_con)[1])),-999)
descT(v4_clin_no_ep)
## -999 1 2 3 5 <NA>
## [1,] No. cases 885 122 27 9 2 741 1786
## [2,] Percent 49.6 6.8 1.5 0.5 0.1 41.5 100
In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).
“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_man)
v4_clin_fst_ill_ep_man<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_man<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y",
-999)
descT(v4_clin_fst_ill_ep_man)
## -999 Y <NA>
## [1,] No. cases 1032 26 728 1786
## [2,] Percent 57.8 1.5 40.8 100
“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_dep)
v4_clin_fst_ill_ep_dep<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_dep<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y",
-999)
descT(v4_clin_fst_ill_ep_dep)
## -999 Y <NA>
## [1,] No. cases 972 86 728 1786
## [2,] Percent 54.4 4.8 40.8 100
“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).
v4_clin_fst_ill_ep_mx<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_mx<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y",
-999)
descT(v4_clin_fst_ill_ep_mx)
## -999 Y <NA>
## [1,] No. cases 1048 10 728 1786
## [2,] Percent 58.7 0.6 40.8 100
“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_psy)
v4_clin_fst_ill_ep_psy<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_psy<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y",
-999)
descT(v4_clin_fst_ill_ep_psy)
## -999 Y <NA>
## [1,] No. cases 1004 54 728 1786
## [2,] Percent 56.2 3 40.8 100
“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v4_clin_fst_ill_ep_dur)
v4_clin_fst_ill_ep_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_dur<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",
ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
ifelse(v4_clin_ill_ep_snc_lst=="N",-999,v4_clin_fst_ill_ep_dur))))
v4_clin_fst_ill_ep_dur<-ordered(v4_clin_fst_ill_ep_dur,
levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_fst_ill_ep_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 808 34 35 90
## [2,] Percent 45.2 1.9 2 5
## <NA>
## [1,] 819 1786
## [2,] 45.9 100
“During this episode, were you hospitalized?” (dichotomous, v4_clin_fst_ill_ep_hsp)
v4_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="N",-999,
ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v4_con)[1]))==2,"N",
ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", v4_clin_fst_ill_ep_hsp)))
v4_clin_fst_ill_ep_hsp<-factor(v4_clin_fst_ill_ep_hsp)
descT(v4_clin_fst_ill_ep_hsp)
## -999 N Y <NA>
## [1,] No. cases 808 96 64 818 1786
## [2,] Percent 45.2 5.4 3.6 45.8 100
“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v4_clin_fst_ill_ep_hsp_dur)
v4_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_hsp_dur<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",
ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
-999)))
v4_clin_fst_ill_ep_hsp_dur<-ordered(v4_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_fst_ill_ep_hsp_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 981 6 20 34
## [2,] Percent 54.9 0.3 1.1 1.9
## <NA>
## [1,] 745 1786
## [2,] 41.7 100
The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):
Reason for hospitalization: symptom worsensing (checkbox [Y], v4_clin_fst_ill_ep_symp_wrs)
v4_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_symp_wrs<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y",-999)
descT(v4_clin_fst_ill_ep_symp_wrs)
## -999 Y <NA>
## [1,] No. cases 1001 56 729 1786
## [2,] Percent 56 3.1 40.8 100
Reason for hospitalization: self-endangerment (checkbox [Y], v4_clin_fst_ill_ep_slf_end)
v4_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_slf_end<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",
-999)
descT(v4_clin_fst_ill_ep_slf_end)
## -999 Y <NA>
## [1,] No. cases 1046 12 728 1786
## [2,] Percent 58.6 0.7 40.8 100
Reason for hospitalization: suicidality (checkbox [Y], v4_clin_fst_ill_ep_suic)
v4_clin_fst_ill_ep_suic<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_suic<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",
-999)
descT(v4_clin_fst_ill_ep_suic)
## -999 Y <NA>
## [1,] No. cases 1052 6 728 1786
## [2,] Percent 58.9 0.3 40.8 100
Reason for hospitalization: endangerment of others (checkbox [Y], v4_clin_fst_ill_ep_oth_end)
v4_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_oth_end<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
descT(v4_clin_fst_ill_ep_oth_end)
## -999 Y <NA>
## [1,] No. cases 1057 1 728 1786
## [2,] Percent 59.2 0.1 40.8 100
Reason for hospitalization: medication change (checkbox [Y], v4_clin_fst_ill_ep_med_chg)
v4_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_med_chg<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
descT(v4_clin_fst_ill_ep_med_chg)
## -999 Y <NA>
## [1,] No. cases 1051 7 728 1786
## [2,] Percent 58.8 0.4 40.8 100
Reason for hospitalization: other (checkbox [Y], v4_clin_fst_ill_ep_othr)
v4_clin_fst_ill_ep_othr<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_othr<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
descT(v4_clin_fst_ill_ep_othr)
## -999 Y <NA>
## [1,] No. cases 1045 13 728 1786
## [2,] Percent 58.5 0.7 40.8 100
“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_man)
v4_clin_sec_ill_ep_man<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_man<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",-999)
descT(v4_clin_sec_ill_ep_man)
## -999 Y <NA>
## [1,] No. cases 917 3 866 1786
## [2,] Percent 51.3 0.2 48.5 100
“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_dep) #frstill
v4_clin_sec_ill_ep_dep<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_dep<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",
-999)
descT(v4_clin_sec_ill_ep_dep)
## -999 Y <NA>
## [1,] No. cases 896 24 866 1786
## [2,] Percent 50.2 1.3 48.5 100
“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).
v4_clin_sec_ill_ep_mx<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_mx<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",
-999)
descT(v4_clin_sec_ill_ep_mx)
## -999 Y <NA>
## [1,] No. cases 917 3 866 1786
## [2,] Percent 51.3 0.2 48.5 100
“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_psy)
v4_clin_sec_ill_ep_psy<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_psy<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",
-999)
descT(v4_clin_sec_ill_ep_psy)
## -999 Y <NA>
## [1,] No. cases 914 6 866 1786
## [2,] Percent 51.2 0.3 48.5 100
“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v4_clin_sec_ill_ep_dur)
v4_clin_sec_ill_ep_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_dur<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",
ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
ifelse(v4_clin_ill_ep_snc_lst=="N",-999,v4_clin_sec_ill_ep_dur))))
v4_clin_sec_ill_ep_dur<-ordered(v4_clin_sec_ill_ep_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_sec_ill_ep_dur)
## less than two weeks two to four weeks more than four weeks <NA>
## [1,] No. cases 9 7 18 1752
## [2,] Percent 0.5 0.4 1 98.1
##
## [1,] 1786
## [2,] 100
“During this episode, were you hospitalized?” (dichotomous, v4_clin_sec_ill_ep_hsp)
v4_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="N",-999,
ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v4_con)[1]))==2,"N",
ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y", v4_clin_sec_ill_ep_hsp)))
v4_clin_sec_ill_ep_hsp<-factor(v4_clin_sec_ill_ep_hsp)
descT(v4_clin_sec_ill_ep_hsp)
## -999 N Y <NA>
## [1,] No. cases 808 24 11 943 1786
## [2,] Percent 45.2 1.3 0.6 52.8 100
“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v4_clin_sec_ill_ep_hsp_dur)
v4_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_hsp_dur<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",
ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
-999)))
v4_clin_sec_ill_ep_hsp_dur<-ordered(v4_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_sec_ill_ep_hsp_dur)
## -999 less than two weeks two to four weeks more than four weeks
## [1,] No. cases 909 1 6 4
## [2,] Percent 50.9 0.1 0.3 0.2
## <NA>
## [1,] 866 1786
## [2,] 48.5 100
The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes): Reason for hospitalization: symptom worsensing (checkbox [Y], v4_clin_sec_ill_ep_symp_wrs)
v4_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_symp_wrs<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",-999)
descT(v4_clin_sec_ill_ep_symp_wrs)
## -999 Y <NA>
## [1,] No. cases 912 8 866 1786
## [2,] Percent 51.1 0.4 48.5 100
Reason for hospitalization: self-endangerment (checkbox [Y], v4_clin_sec_ill_ep_slf_end)
v4_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_slf_end<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",
-999)
descT(v4_clin_sec_ill_ep_slf_end)
## -999 Y <NA>
## [1,] No. cases 919 1 866 1786
## [2,] Percent 51.5 0.1 48.5 100
Reason for hospitalization: suicidality (checkbox [Y], v4_clin_sec_ill_ep_suic)
v4_clin_sec_ill_ep_suic<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_suic<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",
-999)
descT(v4_clin_sec_ill_ep_suic)
## -999 Y <NA>
## [1,] No. cases 919 1 866 1786
## [2,] Percent 51.5 0.1 48.5 100
Reason for hospitalization: endangerment of others (checkbox [Y], v4_clin_sec_ill_ep_oth_end)
v4_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_oth_end<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
descT(v4_clin_sec_ill_ep_oth_end)
## -999 <NA>
## [1,] No. cases 920 866 1786
## [2,] Percent 51.5 48.5 100
Reason for hospitalization: medication change (checkbox [Y], v4_clin_sec_ill_ep_med_chg)
v4_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_med_chg<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
descT(v4_clin_sec_ill_ep_med_chg)
## -999 <NA>
## [1,] No. cases 920 866 1786
## [2,] Percent 51.5 48.5 100
Reason for hospitalization: other (checkbox [Y], v4_clin_sec_ill_ep_othr)
v4_clin_sec_ill_ep_othr<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_othr<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
descT(v4_clin_sec_ill_ep_othr)
## -999 Y <NA>
## [1,] No. cases 916 4 866 1786
## [2,] Percent 51.3 0.2 48.5 100
v4_clin_add_oth_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_add_oth_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_aufent,rep(-999,dim(v4_con)[1]))==1,"Y","N")
descT(v4_clin_add_oth_hsp)
## N Y <NA>
## [1,] No. cases 1029 15 742 1786
## [2,] Percent 57.6 0.8 41.5 100
v4_clin_oth_hsp_nmb<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_oth_hsp_nmb<-ifelse(v4_clin_add_oth_hsp=="Y",
c(v4_clin$v4_aktu_situat_aenderung_anzahl,rep(-999,dim(v4_con)[1])),-999)
descT(v4_clin_oth_hsp_nmb)
## -999 1 2 3 <NA>
## [1,] No. cases 1029 8 1 1 747 1786
## [2,] Percent 57.6 0.4 0.1 0.1 41.8 100
v4_clin_oth_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_oth_hsp_dur<-
ifelse(v4_clin_add_oth_hsp=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",
ifelse(v4_clin_add_oth_hsp=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
ifelse(v4_clin_add_oth_hsp=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
ifelse(v4_clin_add_oth_hsp=="N",-999,v4_clin_add_oth_hsp))))
v4_clin_oth_hsp_dur<-ordered(v4_clin_oth_hsp_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_oth_hsp_dur)
## less than two weeks two to four weeks more than four weeks <NA>
## [1,] No. cases 2 3 8 1773
## [2,] Percent 0.1 0.2 0.4 99.3
##
## [1,] 1786
## [2,] 100
v4_clin_othr_psy_med<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_othr_psy_med<-ifelse(v4_clin_add_oth_hsp=="Y" & v4_clin_add_oth_hsp=="Y" &
c(v4_clin$v4_aktu_situat_aenderung_medikament,rep(-999,dim(v4_con)[1]))==1,"Y",
ifelse(v4_clin_add_oth_hsp=="N",-999,v4_clin_othr_psy_med))
descT(v4_clin_othr_psy_med)
## -999 Y <NA>
## [1,] No. cases 1029 2 755 1786
## [2,] Percent 57.6 0.1 42.3 100
This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.
v4_clin_cur_psy_trm<-rep(NA,dim(v4_clin)[1])
v4_con_cur_psy_trm<-rep(NA,dim(v4_con)[1])
v4_clin_cur_psy_trm<-ifelse(v4_clin$v4_aktu_situat_psybehandlung==0,"1",
ifelse(v4_clin$v4_aktu_situat_psybehandlung==3,"2",
ifelse(v4_clin$v4_aktu_situat_psybehandlung==2,"3",
ifelse(v4_clin$v4_aktu_situat_psybehandlung==1,"4",v4_clin_cur_psy_trm))))
v4_con_cur_psy_trm<-ifelse(v4_con$v4_bildung_beruf_psybehandlung==0,"1",
ifelse(v4_con$v4_bildung_beruf_psybehandlung==3,"2",
ifelse(v4_con$v4_bildung_beruf_psybehandlung==2,"3",
ifelse(v4_con$v4_bildung_beruf_psybehandlung==1,"4",v4_con_cur_psy_trm))))
v4_cur_psy_trm<-factor(c(v4_clin_cur_psy_trm,v4_con_cur_psy_trm),ordered=T)
descT(v4_cur_psy_trm)
## 1 2 3 4 <NA>
## [1,] No. cases 283 517 6 23 957 1786
## [2,] Percent 15.8 28.9 0.3 1.3 53.6 100
Create dataset
v4_clin_ill_ep<-data.frame(v4_clin_ill_ep_snc_lst,
v4_clin_no_ep,
v4_clin_fst_ill_ep_man,
v4_clin_fst_ill_ep_dep,
v4_clin_fst_ill_ep_mx,
v4_clin_fst_ill_ep_psy,
v4_clin_fst_ill_ep_dur,
v4_clin_fst_ill_ep_hsp,
v4_clin_fst_ill_ep_hsp_dur,
v4_clin_fst_ill_ep_symp_wrs,
v4_clin_fst_ill_ep_slf_end,
v4_clin_fst_ill_ep_suic,
v4_clin_fst_ill_ep_oth_end,
v4_clin_fst_ill_ep_med_chg,
v4_clin_fst_ill_ep_othr,
v4_clin_sec_ill_ep_man,
v4_clin_sec_ill_ep_dep,
v4_clin_sec_ill_ep_mx,
v4_clin_sec_ill_ep_psy,
v4_clin_sec_ill_ep_dur,
v4_clin_sec_ill_ep_hsp,
v4_clin_sec_ill_ep_hsp_dur,
v4_clin_sec_ill_ep_symp_wrs,
v4_clin_sec_ill_ep_slf_end,
v4_clin_sec_ill_ep_suic,
v4_clin_sec_ill_ep_oth_end,
v4_clin_sec_ill_ep_med_chg,
v4_clin_sec_ill_ep_othr,
v4_clin_add_oth_hsp,
v4_clin_oth_hsp_nmb,
v4_clin_oth_hsp_dur,
v4_clin_othr_psy_med,
v4_cur_psy_trm)
See Visit 1 marital status item for general explanation of the next two items.
Did your marital status change since the last study visit? (dichotomous, v4_cng_mar_stat)
v4_clin_cng_mar_stat<-rep(NA,dim(v4_clin)[1])
v4_clin_cng_mar_stat<-ifelse(v4_clin$v4_aktu_situat_fam_stand==1, "Y",
ifelse(v4_clin$v4_aktu_situat_fam_stand==2, "N", v4_clin_cng_mar_stat))
v4_con_cng_mar_stat<-rep(NA,dim(v4_con)[1])
v4_con_cng_mar_stat<-ifelse(v4_con$v4_famil_wohn_fam_stand==1, "Y",
ifelse(v4_con$v4_famil_wohn_fam_stand==2, "N", v4_con_cng_mar_stat))
v4_cng_mar_stat<-factor(c(v4_clin_cng_mar_stat,v4_con_cng_mar_stat))
v4_clin_marital_stat<-rep(NA,dim(v4_clin)[1])
v4_clin_marital_stat<-ifelse(v4_clin$v4_aktu_situat_fam_familienstand==1,"Married",
ifelse(v4_clin$v4_aktu_situat_fam_familienstand==2,"Married_living_sep",
ifelse(v4_clin$v4_aktu_situat_fam_familienstand==3,"Single",
ifelse(v4_clin$v4_aktu_situat_fam_familienstand==4,"Divorced",
ifelse(v4_clin$v4_aktu_situat_fam_familienstand==5,"Widowed",v4_clin_marital_stat)))))
v4_con_marital_stat<-rep(NA,dim(v4_con)[1])
v4_con_marital_stat<-ifelse(v4_con$v4_famil_wohn_fam_famstand==1,"Married",
ifelse(v4_con$v4_famil_wohn_fam_famstand==2,"Married_living_sep",
ifelse(v4_con$v4_famil_wohn_fam_famstand==3,"Single",
ifelse(v4_con$v4_famil_wohn_fam_famstand==4,"Divorced",
ifelse(v4_con$v4_famil_wohn_fam_famstand==5,"Widowed",v4_con_marital_stat)))))
v4_marital_stat<-factor(c(v4_clin_marital_stat,v4_con_marital_stat))
desc(v4_marital_stat)
## Divorced Married Married_living_sep Single Widowed NA's
## [1,] No. cases 110 207 21 480 13 955 1786
## [2,] Percent 6.2 11.6 1.2 26.9 0.7 53.5 100
v4_clin_partner<-rep(NA,dim(v4_clin)[1])
v4_clin_partner<-ifelse(v4_clin$v4_aktu_situat_fam_fest_partner==1,"Y",
ifelse(v4_clin$v4_aktu_situat_fam_fest_partner==2,"N",v4_clin_partner))
v4_con_partner<-rep(NA,dim(v4_con)[1])
v4_con_partner<-ifelse(v4_con$v4_famil_wohn_fam_partner==1,"Y",
ifelse(v4_con$v4_famil_wohn_fam_partner==2,"N",v4_con_partner))
v4_partner<-factor(c(v4_clin_partner,v4_con_partner))
descT(v4_partner)
## N Y <NA>
## [1,] No. cases 388 429 969 1786
## [2,] Percent 21.7 24 54.3 100
v4_no_bio_chld<-c(v4_clin$v4_aktu_situat_fam_kind_gesamt,v4_con$v4_famil_wohn_fam_lkind)
descT(v4_no_bio_chld)
## 0 1 2 3 4 5 <NA>
## [1,] No. cases 521 146 100 56 6 4 953 1786
## [2,] Percent 29.2 8.2 5.6 3.1 0.3 0.2 53.4 100
v4_no_adpt_chld<-c(v4_clin$v4_aktu_situat_fam_adopt_gesamt,v4_con$v4_famil_wohn_fam_adkind)
descT(v4_no_adpt_chld)
## 0 1 2 <NA>
## [1,] No. cases 822 2 2 960 1786
## [2,] Percent 46 0.1 0.1 53.8 100
v4_stp_chld<-c(v4_clin$v4_aktu_situat_fam_stift_gesamt,v4_con$v4_famil_wohn_fam_skind)
descT(v4_stp_chld)
## 0 1 2 3 4 5 <NA>
## [1,] No. cases 764 40 19 4 1 1 957 1786
## [2,] Percent 42.8 2.2 1.1 0.2 0.1 0.1 53.6 100
v4_clin_chg_hsng<-rep(NA,dim(v4_clin)[1])
v4_clin_chg_hsng<-ifelse(v4_clin$v4_wohnsituation_wohn_aenderung==1,"Y",
ifelse(v4_clin$v4_wohnsituation_wohn_aenderung==2,"N",v4_clin_chg_hsng))
v4_con_chg_hsng<-rep(NA,dim(v4_con)[1])
v4_con_chg_hsng<-ifelse(v4_con$v4_famil_wohn_wohn_stand==1,"Y",
ifelse(v4_con$v4_famil_wohn_wohn_stand==2,"N",v4_con_chg_hsng))
v4_chg_hsng<-factor(c(v4_clin_chg_hsng,v4_con_chg_hsng))
descT(v4_chg_hsng)
## N Y <NA>
## [1,] No. cases 731 104 951 1786
## [2,] Percent 40.9 5.8 53.2 100
v4_clin_liv_aln<-rep(NA,dim(v4_clin)[1])
v4_clin_liv_aln<-ifelse(v4_clin$v4_wohnsituation_wohn_allein==1,"Y",
ifelse(v4_clin$v4_wohnsituation_wohn_allein==0,"N",v4_clin_liv_aln))
v4_con_liv_aln<-rep(NA,dim(v4_con)[1])
v4_con_liv_aln<-ifelse(v4_con$v4_famil_wohn_wohn_allein==1,"Y",
ifelse(v4_con$v4_famil_wohn_wohn_allein==0,"N",v4_con_liv_aln))
v4_liv_aln<-factor(c(v4_clin_liv_aln,v4_con_liv_aln))
descT(v4_liv_aln)
## N Y <NA>
## [1,] No. cases 510 333 943 1786
## [2,] Percent 28.6 18.6 52.8 100
Did your employment situation change since the last study visit?
v4_clin_chg_empl_stat<-rep(NA,dim(v4_clin)[1])
v4_clin_chg_empl_stat<-ifelse(v4_clin$v4_wohnsituation_erwerb_aenderung==1, "Y",
ifelse(v4_clin$v4_wohnsituation_erwerb_aenderung==2, "N",v4_clin_chg_empl_stat))
v4_con_chg_empl_stat<-rep(NA,dim(v4_con)[1])
v4_con_chg_empl_stat<-ifelse(v4_con$v4_bildung_beruf_bild_stand==1, "Y",
ifelse(v4_con$v4_bildung_beruf_bild_stand==2, "N",v4_con_chg_empl_stat))
v4_chg_empl_stat<-factor(c(v4_clin_chg_empl_stat,v4_con_chg_empl_stat))
descT(v4_chg_empl_stat)
## N Y <NA>
## [1,] No. cases 727 100 959 1786
## [2,] Percent 40.7 5.6 53.7 100
Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.
v4_clin_curr_paid_empl<-rep(NA,dim(v4_clin)[1])
v4_clin_curr_paid_empl<-ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==1,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==2,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==3,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==4,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==5,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==6,-999,
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==7,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==8,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==9,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==10,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==11,"N",v4_clin_curr_paid_empl)))))))))))
v4_con_curr_paid_empl<-rep(NA,dim(v4_con)[1])
v4_con_curr_paid_empl<-ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==1,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==2,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==3,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==4,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==5,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==6,-999,
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==7,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==8,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==9,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==10,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==11,"N",v4_con_curr_paid_empl)))))))))))
v4_curr_paid_empl<-factor(c(v4_clin_curr_paid_empl,v4_con_curr_paid_empl))
descT(v4_curr_paid_empl)
## -999 N Y <NA>
## [1,] No. cases 18 383 427 958 1786
## [2,] Percent 1 21.4 23.9 53.6 100
NB: Not available (-999) in control participants
v4_clin_disabl_pens<-rep(NA,dim(v4_clin)[1])
v4_clin_disabl_pens<-ifelse(v4_clin$v4_wohnsituation_rente_psych==1,"Y",
ifelse(v4_clin$v4_wohnsituation_rente_psych==2,"N",v4_clin_disabl_pens))
v4_con_disabl_pens<-rep(-999,dim(v4_con)[1])
v4_disabl_pens<-factor(c(v4_clin_disabl_pens,v4_con_disabl_pens))
descT(v4_disabl_pens)
## -999 N Y <NA>
## [1,] No. cases 466 225 250 845 1786
## [2,] Percent 26.1 12.6 14 47.3 100
v4_clin_spec_emp<-rep(NA,dim(v4_clin)[1])
v4_clin_spec_emp<-ifelse(v4_clin$v4_wohnsituation_erwerb_werk==1,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerb_werk==2,"N",v4_clin_spec_emp))
v4_con_spec_emp<-rep(NA,dim(v4_con)[1])
v4_con_spec_emp<-ifelse(v4_con$v4_bildung_beruf_erwerb_wfbm==1,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_wfbm==2,"N",v4_con_spec_emp))
v4_spec_emp<-factor(c(v4_clin_spec_emp,v4_con_spec_emp))
descT(v4_spec_emp)
## N Y <NA>
## [1,] No. cases 372 65 1349 1786
## [2,] Percent 20.8 3.6 75.5 100
Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.
v4_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v4_clin)[1])
v4_clin_wrk_abs_pst_6_mths<-ifelse((v4_clin$v4_wohnsituation_erwerb_unbekannt==1 | v4_clin$v4_wohnsituation_erwerb_rente==1 |
v4_clin$v4_wohnsituation_erwerb_fehlen>26),-999, v4_clin$v4_wohnsituation_erwerb_fehlen)
v4_con_wrk_abs_pst_6_mths<-rep(NA,dim(v4_con)[1])
v4_con_wrk_abs_pst_6_mths<-ifelse((v4_con$v4_bildung_beruf_erwerb_ausfallu==1 | v4_con$v4_bildung_beruf_erwerb_rente==1 |
v4_con$v4_bildung_beruf_erwerb_ausfallm>26),-999, v4_con$v4_bildung_beruf_erwerb_ausfallm)
v4_wrk_abs_pst_6_mths<-c(v4_clin_wrk_abs_pst_6_mths,v4_con_wrk_abs_pst_6_mths)
descT(v4_wrk_abs_pst_6_mths)
## -999 0 1 2 3 4 5 6 8 10 12 13 15 16 20 22
## [1,] No. cases 309 259 17 9 6 7 2 6 3 3 6 1 1 1 2 1
## [2,] Percent 17.3 14.5 1 0.5 0.3 0.4 0.1 0.3 0.2 0.2 0.3 0.1 0.1 0.1 0.1 0.1
## 24 26 <NA>
## [1,] 11 7 1135 1786
## [2,] 0.6 0.4 63.5 100
Important: if receiving pension, this question refers to impairments in the household
v4_clin_cur_work_restr<-rep(NA,dim(v4_clin)[1])
v4_clin_cur_work_restr<-ifelse(v4_clin$v4_wohnsituation_erwerb_psysymptom==1,"Y",
ifelse(v4_clin$v4_wohnsituation_erwerb_psysymptom==2,"N",v4_clin_cur_work_restr))
v4_con_cur_work_restr<-rep(NA,dim(v4_con)[1])
v4_con_cur_work_restr<-ifelse(v4_con$v4_bildung_beruf_erwerb_psyeinsch==1,"Y",
ifelse(v4_con$v4_bildung_beruf_erwerb_psyeinsch==2,"N",v4_con_cur_work_restr))
v4_cur_work_restr<-factor(c(v4_clin_cur_work_restr,v4_con_cur_work_restr))
descT(v4_cur_work_restr)
## N Y <NA>
## [1,] No. cases 512 242 1032 1786
## [2,] Percent 28.7 13.5 57.8 100
v4_weight<-c(v4_clin$v4_wohnsituation_erwerb_gewicht,v4_con$v4_bildung_beruf_erwerb_gewicht)
summary(v4_weight)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 25.00 69.00 81.00 83.87 96.00 193.00 971
v4_clin_waist<-v4_clin$v4_wohnsituation_erwerb_tailumf
v4_con_waist<-v4_con$v4_bildung_beruf_erwerb_taille
v4_waist<-c(v4_clin_waist,v4_con_waist)
summary(v4_waist)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 62.00 77.00 87.00 90.29 101.50 150.00 1435
We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.
v4_bmi<-v4_weight/(v1_height/100)^2
summary(v4_bmi)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 8.65 23.04 26.58 27.79 31.25 70.02 974
Create dataset
v4_dem<-data.frame(v4_cng_mar_stat,v4_marital_stat,v4_partner,v4_no_bio_chld,v4_no_adpt_chld,v4_stp_chld,v4_chg_hsng,v4_liv_aln,
v4_chg_empl_stat,v4_curr_paid_empl,v4_disabl_pens,v4_spec_emp,v4_wrk_abs_pst_6_mths,v4_cur_work_restr,
v4_weight,v4_bmi,v4_waist)
The OPCRIT is an operational criteria checklist (and computer program) for psychotic illness (McGuffin, Farmer, & Harvey, 1991). We use item 90 of the OPCRIT to broadly assess the course of disorder from onset to the current state. All available information is to be used to answer the item (interview, medical records etc.).
IMPORTANT: this item was assessed in CLINICAL participants only, all CONTROL participants are assigned -999.
In clinical participants, this item has the following gradation: “single episode wirh good remission”-1, “multiple episodes with good remission between episodes”-2,“multiple episodes with partial remission between episodes”-3, “ongoing chronic disease”-4, “ongoing chronic disease with deterioration”-5 and “not estimable”-99. In the current dataset, 99 is replaced with -999. Note: this item is to be rated hierarchically, meaning if the past course of disease is to be rated with 2 but the present course of disease would require a 4, 4 is the right assessment.
v4_opcrit<-c(v4_clin$v4_opcrit_opcrit_verlauf,rep(-999,dim(v4_con)[1]))
v4_opcrit[v4_opcrit==99]<--999
descT(v4_opcrit)
## -999 1 2 3 4 5 <NA>
## [1,] No. cases 471 21 201 199 126 10 758 1786
## [2,] Percent 26.4 1.2 11.3 11.1 7.1 0.6 42.4 100
Please see Visit 2 for explanation.
**Life events: Occurred before illness episode? (dichotomous, v4_evnt_prcp_b4_*)**
for(i in 1:length(grep("v4_ergaenz_leq_leq_zeit_31055_",names(v4_clin)))){
b4_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_zeit_31055_",names(v4_clin))[i]],
paste("v4_evnt_prcp_b4_",i,sep=""))
}
**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v4_evnt_prcp_f_*)**
for(i in 1:length(grep("v4_ergaenz_leq_leq_ausloeser_31055_",names(v4_clin)))){
prcp_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_ausloeser_31055_",names(v4_clin))[i]],
paste("v4_evnt_prcp_f_",i,sep=""))
}
**Life events: LEQ item number (categorical [LEQ item number], v4_evnt_prcp_it_*)**
for(i in 1:length(grep("v4_ergaenz_leq_leq_item_31055_",names(v4_clin)))){
leq_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_item_31055_",names(v4_clin))[i]],
paste("v4_evnt_prcp_it_",i,sep=""))
}
Create dataset
v4_leprcp<-data.frame(v4_evnt_prcp_it_1,v4_evnt_prcp_b4_1,v4_evnt_prcp_f_1,
v4_evnt_prcp_it_2,v4_evnt_prcp_b4_2,v4_evnt_prcp_f_2,
v4_evnt_prcp_it_3,v4_evnt_prcp_b4_3,v4_evnt_prcp_f_3,
v4_evnt_prcp_it_4,v4_evnt_prcp_b4_4,v4_evnt_prcp_f_4,
v4_evnt_prcp_it_5,v4_evnt_prcp_b4_5,v4_evnt_prcp_f_5,
v4_evnt_prcp_it_6,v4_evnt_prcp_b4_6,v4_evnt_prcp_f_6,
v4_evnt_prcp_it_7,v4_evnt_prcp_b4_7,v4_evnt_prcp_f_7,
v4_evnt_prcp_it_8,v4_evnt_prcp_b4_8,v4_evnt_prcp_f_8,
v4_evnt_prcp_it_9,v4_evnt_prcp_b4_9,v4_evnt_prcp_f_9,
v4_evnt_prcp_it_10,v4_evnt_prcp_b4_10,v4_evnt_prcp_f_10,
v4_evnt_prcp_it_11,v4_evnt_prcp_b4_11,v4_evnt_prcp_f_11,
v4_evnt_prcp_it_12,v4_evnt_prcp_b4_12,v4_evnt_prcp_f_12,
v4_evnt_prcp_it_13,v4_evnt_prcp_b4_13,v4_evnt_prcp_f_13,
v4_evnt_prcp_it_14,v4_evnt_prcp_b4_14,v4_evnt_prcp_f_14,
v4_evnt_prcp_it_15,v4_evnt_prcp_b4_15,v4_evnt_prcp_f_15,
v4_evnt_prcp_it_16,v4_evnt_prcp_b4_16,v4_evnt_prcp_f_16,
v4_evnt_prcp_it_17,v4_evnt_prcp_b4_17,v4_evnt_prcp_f_17,
v4_evnt_prcp_it_18,v4_evnt_prcp_b4_18,v4_evnt_prcp_f_18,
v4_evnt_prcp_it_19,v4_evnt_prcp_b4_19,v4_evnt_prcp_f_19,
v4_evnt_prcp_it_20,v4_evnt_prcp_b4_20,v4_evnt_prcp_f_20,
v4_evnt_prcp_it_21,v4_evnt_prcp_b4_21,v4_evnt_prcp_f_21,
v4_evnt_prcp_it_22,v4_evnt_prcp_b4_22,v4_evnt_prcp_f_22,
v4_evnt_prcp_it_23,v4_evnt_prcp_b4_23,v4_evnt_prcp_f_23,
v4_evnt_prcp_it_24,v4_evnt_prcp_b4_24,v4_evnt_prcp_f_24,
v4_evnt_prcp_it_25,v4_evnt_prcp_b4_25,v4_evnt_prcp_f_25,
v4_evnt_prcp_it_26,v4_evnt_prcp_b4_26,v4_evnt_prcp_f_26,
v4_evnt_prcp_it_27,v4_evnt_prcp_b4_27,v4_evnt_prcp_f_27,
v4_evnt_prcp_it_28,v4_evnt_prcp_b4_28,v4_evnt_prcp_f_28,
v4_evnt_prcp_it_29,v4_evnt_prcp_b4_29,v4_evnt_prcp_f_29,
v4_evnt_prcp_it_30,v4_evnt_prcp_b4_30,v4_evnt_prcp_f_30,
v4_evnt_prcp_it_31,v4_evnt_prcp_b4_31,v4_evnt_prcp_f_31)
Please note that all of the following questions refer to suicide attempts and suicidal ideation occurring since the last study visit.
Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.
v4_suic_ide_snc_lst_vst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_suic_ide_snc_lst_vst<-ifelse(c(v4_clin$v4_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v4_con)[1]))==1, "N",
ifelse(c(v4_clin$v4_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v4_con)[1]))==3, "Y", v4_suic_ide_snc_lst_vst))
v4_suic_ide_snc_lst_vst<-factor(v4_suic_ide_snc_lst_vst)
descT(v4_suic_ide_snc_lst_vst)
## -999 N Y <NA>
## [1,] No. cases 466 434 144 742 1786
## [2,] Percent 26.1 24.3 8.1 41.5 100
This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v4_scid_suic_ide<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_scid_suic_ide<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==1, "1",
ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==2, "2",
ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==3, "3",
ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==4, "4",-999))))
v4_scid_suic_ide<-factor(v4_scid_suic_ide,ordered=T)
descT(v4_scid_suic_ide)
## -999 1 2 3 4 <NA>
## [1,] No. cases 900 89 23 13 19 742 1786
## [2,] Percent 50.4 5 1.3 0.7 1.1 41.5 100
This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.
v4_scid_suic_thght_mth<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_scid_suic_thght_mth<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==1, "1",
ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==2, "2",
ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==3, "3",-999)))
v4_scid_suic_thght_mth<-factor(v4_scid_suic_thght_mth,ordered=T)
descT(v4_scid_suic_thght_mth)
## -999 1 2 3 <NA>
## [1,] No. cases 900 83 45 15 743 1786
## [2,] Percent 50.4 4.6 2.5 0.8 41.6 100
This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v4_scid_suic_note_thgts<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_scid_suic_note_thgts<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==1, "1",
ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==2, "2",
ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==3, "3",
ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==4, "4",-999))))
v4_scid_suic_note_thgts<-factor(v4_scid_suic_note_thgts,ordered=T)
descT(v4_scid_suic_note_thgts)
## -999 1 2 4 <NA>
## [1,] No. cases 900 132 5 3 746 1786
## [2,] Percent 50.4 7.4 0.3 0.2 41.8 100
This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.
v4_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_suic_attmpt_snc_lst_vst<-ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==1, "1",
ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==2, "2",
ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==3, "3",-999)))
v4_suic_attmpt_snc_lst_vst<-factor(v4_suic_attmpt_snc_lst_vst,ordered=T)
descT(v4_suic_attmpt_snc_lst_vst)
## -999 1 2 3 <NA>
## [1,] No. cases 466 564 4 4 748 1786
## [2,] Percent 26.1 31.6 0.2 0.2 41.9 100
This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.
v4_no_suic_attmpt<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_no_suic_attmpt<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999, ifelse(v4_suic_attmpt_snc_lst_vst>1, c(v4_clin$v4_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v4_con)[1])),v4_no_suic_attmpt))
v4_no_suic_attmpt<-factor(v4_no_suic_attmpt,ordered=T)
descT(v4_no_suic_attmpt)
## -999 1 3 <NA>
## [1,] No. cases 1030 7 1 748 1786
## [2,] Percent 57.7 0.4 0.1 41.9 100
This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.
v4_prep_suic_attp_ord<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_prep_suic_attp_ord<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999,
ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==1, "1",
ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==2, "2",
ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==3, "3",
ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==4, "4",
v4_prep_suic_attp_ord)))))
v4_prep_suic_attp_ord<-factor(v4_prep_suic_attp_ord,ordered=T)
descT(v4_prep_suic_attp_ord)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1030 3 1 1 2 749 1786
## [2,] Percent 57.7 0.2 0.1 0.1 0.1 41.9 100
This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.
v4_suic_note_attmpt<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_suic_note_attmpt<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999,
ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==1, "1",
ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==2, "2",
ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==3, "3",
ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==4, "4",
v4_suic_note_attmpt)))))
v4_suic_note_attmpt<-factor(v4_suic_note_attmpt,ordered=T)
descT(v4_suic_note_attmpt)
## -999 1 2 4 <NA>
## [1,] No. cases 1030 5 1 1 749 1786
## [2,] Percent 57.7 0.3 0.1 0.1 41.9 100
Create dataset
v4_suic<-data.frame(v4_suic_ide_snc_lst_vst,v4_scid_suic_ide,v4_scid_suic_thght_mth,v4_scid_suic_note_thgts,
v4_suic_attmpt_snc_lst_vst,v4_no_suic_attmpt,v4_prep_suic_attp_ord,
v4_suic_note_attmpt)
The code below creates the following variables that summarize the medication of each individual:
Number of antidepressants prescribed (continuous [number], v4_Antidepressants) Number of antipsychotics prescribed (continuous [number], v4_Antipsychotics) Number of mood stabilizers prescribed (continuous [number], v4_Mood_stabilizers) Number of tranquilizers prescribed (continuous [number], v4_Tranquilizers) Number of other psychiatric medications (continuous [number], v4_Other_psychiatric)
#get the following variables from v4_clin
#1. Medication name ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name ["_depot_medi_2"]
#4. Depot category ["_depot_kategorie_2"]
#5. Bedarf name ["_bedarf_medi_1"]
#6. Bedarf category ["_bedarf_kategorie_1"]
v4_clin_medication_variables_1<-as.data.frame(v4_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v4_clin))])
dim(v4_clin_medication_variables_1)
## [1] 1320 61
#recode the variables that are coded as characters/logicals in the "v4_clin_medication_variables_1" as factors
v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_15<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_15)
v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_15<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_15)
v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_16<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_16)
v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_16<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_16)
v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_17<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_17)
v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_17<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_17)
v4_clin_medication_variables_1$v4_medikabehand3_depot_medi_200170_3<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_depot_medi_200170_3)
v4_clin_medication_variables_1$v4_medikabehand3_depot_kategorie_200170_3<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_depot_kategorie_200170_3)
v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_8<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_8)
v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_8<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_8)
v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_9<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_9)
v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_9)
v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_10<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_10)
v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_10)
#make the duplicated data frame
v4_clin_medications_duplicated_1<-as.data.frame(t(apply(v4_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v4_clin_medications_duplicated_1)
## [1] 1320 30
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_".
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_clin, not as character
v4_clin_medication_variables_1[,!c(TRUE, FALSE)][v4_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v4_clin_medication_variables_1)
## [1] 1320 61
#bind columns id and medication names, but not categories together
v4_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v4_clin_medication_variables_1[,1], v4_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v4_clin_medication_name_1)
## [1] 1320 31
#get the medication categories from the "_medication_variables_1" dataframe
v4_clin_medication_categories_1<-as.data.frame(v4_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v4_clin_medication_categories_1)
## [1] 1320 31
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_clin, not as character
#Important: v4_clin_medication_name_1=="NA" replaced with is.na(v4_clin_medication_name_1)
v4_clin_medication_categories_1[is.na(v4_clin_medication_name_1)] <- NA
#write.csv(v4_clin_medication_categories_1, file="v4_clin_medication_group_1.csv")
#Make a count table of medications
v4_clin_med_table<-data.frame("mnppsd"=v4_clin$mnppsd)
v4_clin_med_table$v4_Antidepressants<-rowSums(v4_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v4_clin_med_table$v4_Antipsychotics<-rowSums(v4_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v4_clin_med_table$v4_Mood_stabilizers<-rowSums(v4_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v4_clin_med_table$v4_Tranquilizers<-rowSums(v4_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v4_clin_med_table$v4_Other_psychiatric<-rowSums(v4_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
#get the following variables from v4_con
#1. Medication name ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name ["_depot_medi_2"]
#4. Depot category ["_depot_kategorie_2"]
#5. Bedarf name ["_bedarf_medi_2"]
#6. Bedarf category ["_bedarf_kategorie_2"]
v4_con_medication_variables_1<-as.data.frame(v4_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v4_con))])
dim(v4_con_medication_variables_1) #[1] 320 29
## [1] 466 29
#recode the variables that are coded as characters/logicals in the "v4_con_medication_variables_1" as factors
v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_7<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_7)
v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_7<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_7)
v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_8<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_8)
v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_8<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_8)
v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_2<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_2)
v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_2<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_2)
v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_4<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_4)
v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_4)
#make the duplicated data frame
v4_con_medications_duplicated_1<-as.data.frame(t(apply(v4_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v4_con_medications_duplicated_1)
## [1] 466 14
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_".
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_con, not as character
v4_con_medication_variables_1[,!c(TRUE, FALSE)][v4_con_medications_duplicated_1=="TRUE"] <- NA
dim(v4_con_medication_variables_1)
## [1] 466 29
#bind columns id and medication names, but not categories together
v4_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v4_con_medication_variables_1[,1], v4_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v4_con_medication_name_1)
## [1] 466 15
#get the medication categories from the "_medication_variables_1" dataframe
v4_con_medication_categories_1<-as.data.frame(v4_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v4_con_medication_categories_1)
## [1] 466 15
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_con, not as character
#Important: v4_con_medication_name_1=="NA" replaced with is.na(v4_con_medication_name_1)
v4_con_medication_categories_1[is.na(v4_con_medication_name_1)] <- NA
#write.csv(v4_con_medication_categories_1, file="v4_con_medication_group_1.csv")
#Make a count table of medications
v4_con_med_table<-data.frame("mnppsd"=v4_con$mnppsd)
v4_con_med_table$v4_Antidepressants<-rowSums(v4_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v4_con_med_table$v4_Antipsychotics<-rowSums(v4_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v4_con_med_table$v4_Mood_stabilizers<-rowSums(v4_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v4_con_med_table$v4_Tranquilizers<-rowSums(v4_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v4_con_med_table$v4_Other_psychiatric<-rowSums(v4_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
Bind v4_clin and v4_con together by rows
v4_drugs<-rbind(v4_clin_med_table,v4_con_med_table)
dim(v4_drugs)
## [1] 1786 6
#check if the id column of v4_drugs and v1_id match
table(droplevels(v4_drugs[,1])==v1_id)
##
## TRUE
## 1786
v4_clin_adv<-ifelse(v4_clin$v4_medikabehand_medi2_nebenwirk==1,"Y","N")
v4_con_adv<-rep("-999",dim(v4_con)[1])
v4_adv<-factor(c(v4_clin_adv,v4_con_adv))
descT(v4_adv)
## -999 N Y <NA>
## [1,] No. cases 466 159 236 925 1786
## [2,] Percent 26.1 8.9 13.2 51.8 100
v4_clin_medchange<-rep(NA,dim(v4_clin)[1])
v4_clin_medchange<-ifelse(v4_clin$v4_medikabehand_medi3_mediaenderung==1,"Y","N")
v4_con_medchange<-rep("-999",dim(v4_con)[1])
v4_medchange<-as.factor(c(v4_clin_medchange,v4_con_medchange))
descT(v4_medchange)
## -999 N Y <NA>
## [1,] No. cases 466 177 217 926 1786
## [2,] Percent 26.1 9.9 12.2 51.8 100
Please see the section in Visit 1 for explanation.
v4_clin_lith<-rep(NA,dim(v4_clin)[1])
v4_clin_lith<-ifelse(v4_clin$v4_medikabehand_med_zusatz_lithium==1,"Y","N")
v4_con_lith<-rep("-999",dim(v4_con)[1])
v4_lith<-as.factor(c(v4_clin_lith,v4_con_lith))
v4_lith<-as.factor(v4_lith)
descT(v4_lith)
## -999 N Y <NA>
## [1,] No. cases 466 256 146 918 1786
## [2,] Percent 26.1 14.3 8.2 51.4 100
Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.
v4_clin_lith_prd<-rep(NA,dim(v4_clin)[1])
v4_con_lith_prd<-rep(-999,dim(v4_con)[1])
v4_clin_lith_prd<-ifelse(v4_clin_lith=="N", -999, ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==2,1,
ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==1,2,
ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==0,3,NA))))
v4_lith_prd<-factor(c(v4_clin_lith_prd,v4_con_lith_prd))
descT(v4_lith_prd)
## -999 1 2 3 <NA>
## [1,] No. cases 722 42 26 78 918 1786
## [2,] Percent 40.4 2.4 1.5 4.4 51.4 100
The ALDA scale (Grof et al., 2002) measures reponse to lithium and was thus only used in clinical participants (see below). Control subjects, and clinical participants without a bipolar disorder (see below) have a -999 in this item. The scale is formally called “Retrospective criteria of long-term treatment response in research subjects with bipolar disorder”. The ALDA scale quantifies symptom improvement in the course of treatment (A score, range 0–10), which is then weighted against five criteria (B score) that assess confounding factors, each scored 0,1, or 2. The total score is then derived by subtracting the total B score from the A score. Negative scores are set to 0 by default so that the total score ranges from 0 to 10 (Hou et al., 2016).
This questionnaire was only assessed if
The scale was also assessed in some clinical participants with other diagnoses, because the bipolar diagnosis criterion had not been formalized at the start of the study.
Now, the ALDA items are coded so that all individuals with values on these item are included in the dataset. If no value is given (NA), and the fourth visit took place, all diagnoses other that BP-I and BP-II (this includes controls), including BP1 and BP2 individuals that never (or for too little time) received lithium, are coded -999.
All scale levels are described as being continuous, but the true level of the scales are probably ordinal.
The ALDA Total Score is given as in the paper CRF, please check yourself if it was correctly calculated
A score (continuous [0,1,2,3,4,5,6,7,8,9,10], v4_alda_A)
v4_clin_alda_A<-rep(NA,dim(v4_clin)[1])
v4_con_alda_A<-rep(-999,dim(v4_con)[1])
v4_clin_alda_A<-ifelse(is.na(v4_clin$v4_lithium_lithium_crit_a_score)==F, v4_clin$v4_lithium_lithium_crit_a_score,
ifelse(is.na(v4_interv_date),NA,
ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
| (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |
v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
v4_alda_A<-c(v4_clin_alda_A,v4_con_alda_A)
summary(v4_alda_A[v4_alda_A>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 5.000 7.000 6.285 8.000 10.000 753
B1 score (continuous [0,1,2], v4_alda_B1)
v4_clin_alda_B1<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B1<-rep(-999,dim(v4_con)[1])
v4_clin_alda_B1<-ifelse(is.na(v4_clin$v4_lithium_b1_score)==F, v4_clin$v4_lithium_b1_score,
ifelse(is.na(v4_interv_date),NA,
ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
| (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |
v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
v4_alda_B1<-c(v4_clin_alda_B1,v4_con_alda_B1)
summary(v4_alda_B1[v4_alda_B1>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.3197 1.0000 2.0000 754
B2 score (continuous [0,1,2], v4_alda_B2)
v4_clin_alda_B2<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B2<-rep(-999,dim(v4_con)[1])
v4_clin_alda_B2<-ifelse(is.na(v4_clin$v4_lithium_b2_score)==F, v4_clin$v4_lithium_b2_score,
ifelse(is.na(v4_interv_date),NA,
ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
| (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |
v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
v4_alda_B2<-c(v4_clin_alda_B2,v4_con_alda_B2)
summary(v4_alda_B2[v4_alda_B2>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.00 0.00 0.45 1.00 2.00 756
B3 score (continuous [0,1,2], v4_alda_B3)
v4_clin_alda_B3<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B3<-rep(-999,dim(v4_con)[1])
v4_clin_alda_B3<-ifelse(is.na(v4_clin$v4_lithium_b3_score)==F, v4_clin$v4_lithium_b3_score,
ifelse(is.na(v4_interv_date),NA,
ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
| (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |
v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
v4_alda_B3<-c(v4_clin_alda_B3,v4_con_alda_B3)
summary(v4_alda_B3[v4_alda_B3>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.2459 0.0000 1.0000 754
B4 score (continuous [0,1,2], v4_alda_B4)
v4_clin_alda_B4<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B4<-rep(-999,dim(v4_con)[1])
v4_clin_alda_B4<-ifelse(is.na(v4_clin$v4_lithium_b4_score)==F, v4_clin$v4_lithium_b4_score,
ifelse(is.na(v4_interv_date),NA,
ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
| (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |
v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
v4_alda_B4<-c(v4_clin_alda_B4,v4_con_alda_B4)
summary(v4_alda_B4[v4_alda_B4>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.2846 0.0000 2.0000 753
B5 score (continuous [0,1,2], v4_alda_B5)
v4_clin_alda_B5<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B5<-rep(-999,dim(v4_con)[1])
v4_clin_alda_B5<-ifelse(is.na(v4_clin$v4_lithium_b5_score)==F, v4_clin$v4_lithium_b5_score,
ifelse(is.na(v4_interv_date),NA,
ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
| (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |
v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
v4_alda_B5<-c(v4_clin_alda_B5,v4_con_alda_B5)
summary(v4_alda_B5[v4_alda_B5>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.000 1.336 2.000 2.000 754
Create dataset
v4_med<-data.frame(v4_drugs[,2:6],v4_adv,v4_medchange,v4_lith,v4_lith_prd,v4_alda_A,v4_alda_B1,v4_alda_B2,v4_alda_B3,v4_alda_B4,v4_alda_B5)
Here, separate datasets for clinical and control participants are created that contain the raw medication information at visit 4, as specified in the phenotype database.
Please note: The ALDA scale is not contained in the dataset of clinical participants.
For each medication that the individual took at visit 4 (including non-psychiatric drugs), the information given below is assessed.
The last character of each variable name always refers to the medication in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.
The medications were not assessed in any specific order, i.e. the order was determined by the individual participant (whatever she or he mentioned first). To classify medications, we used a catalogue, from which the categories and subcategories a medication belongs to were selected (see below).
Below, the variable names of clinical/control participants, respectively, are given in quotes, and the coding is explained in the parentheses.
1.Was the individual treated with any medication? (-1-not assessed, 1-yes, 2-no)
“v4_medikabehand3_keine_med”/“v4_medikabehand3_keine_med”
Regular medication: Name of the medication (character)
“v4_medikabehand3_med_medi_199998”/“v4_medikabehand3_med_medi_200705”
Regular medication: Category to which the medication belongs (character)
“v4_medikabehand3_med_kategorie_199998”/“v4_medikabehand3_med_kategorie_200705”
Regular medication: Subcategory to which the medication belongs (character)
“v4_medikabehand3_med_kategorie_sub_199998”/“v4_medikabehand3_med_kategorie_sub_200705”
Regular medication: Psychiatric medication? (0-no, 1-yes) “v4_medikabehand3_med_zusatz_199998”/“v4_medikabehand3_med_zusatz_200705”
Regular medication: Dose in the morning (unitless)
“v4_medikabehand3_s_medi1_morgens_199998”/“v4_medikabehand3_s_medi1_morgens_200705”
Regular medication: Dose at midday (unitless)
“v4_medikabehand3_smedi1_mittags_199998”/“v4_medikabehand3_smedi1_mittags_200705”
Regular medication: Dose in the evening (unitless)
“v4_medikabehand3_smedi1_abends_199998”/“v4_medikabehand3_smedi1_abends_200705”
Regular medication: Dose at night (unitless)
“v4_medikabehand3_smedi1_nachts_199998”/“v4_medikabehand3_smedi1_nachts_200705”
Regular medication: Unit of the medication asked in the last four questions (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
“v4_medikabehand3_smedi1_einheit_199998”/“v4_medikabehand3_smedi1_einheit_200705”
Regular medication: Total dose of the medication per day (unitless)
“v4_medikabehand3_smedi1_gesamtdosis_199998”/“v4_medikabehand3_smedi1_gesamtdosis_200705”
Regular medication: Unit of the medication asked in the last question (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE)
“v4_medikabehand3_smedi1_einheit1_199998”/“v4_medikabehand3_smedi1_einheit1_200705”
Regular medication: Medication name, if not contained in our catalog (character)
“v4_medikabehand3_medikament_text_199998”/“v4_medikabehand3_medikament_text_200705”
Depot medication: Name of the medication (character) “v4_medikabehand3_depot_medi_200170”/"v4_medikabehand3_depot_medi_201224
Depot medication: Category to which the medication belongs (character) “v4_medikabehand3_depot_kategorie_200170”/"v4_medikabehand3_depot_kategorie_201224
Depot medication: Subcategory to which the medication belongs (character)
“v4_medikabehand3_depot_kategorie_sub_200170”/"v4_medikabehand3_depot_kategorie_sub_201224
Depot medication: Psychiatric medication? (0-no, 1-yes) “v4_medikabehand3_depot_zusatz_200170”/“v4_medikabehand3_depot_zusatz_201224”
Depot medication: Total Dose (unitless) “v4_medikabehand3_s_depot_gesamtdosis_200170”/“v4_medikabehand3_s_depot_gesamtdosis_201224”
Depot medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v4_medikabehand3_s_depot_einheit_200170”/ “v4_medikabehand3_s_depot_einheit_201224”
Interval, at which the depot medication is given (days) “v4_medikabehand3_s_depot_tage_200170”/“v4_medikabehand3_s_depot_tage_201224”
Medication name, if not contained in our catalog (character) “v4_medikabehand3_medikament_text_200170”/“v4_medikabehand3_medikament_text_201224”
Pro re nata (PRN) medication: Name of the medication (character) “v4_medikabehand3_bedarf_medi_199584”/“v4_medikabehand3_bedarf_medi_201187”
Pro re nata (PRN) medication: Category to which the medication belongs (character)
“v4_medikabehand3_bedarf_kategorie_199584”/“v4_medikabehand3_bedarf_kategorie_201187”
Pro re nata (PRN) medication: Subcategory to which the medication belongs (character) “v4_medikabehand3_bedarf_kategorie_sub_199584”/“v4_medikabehand3_bedarf_kategorie_sub_201187”
Pro re nata (PRN) medication: Psychiatric medication? (0-no, 1-yes) “v4_medikabehand3_bedarf_zusatz_199584”/“v4_medikabehand3_bedarf_zusatz_201187”
Pro re nata (PRN) medication: Total dose up to (unitless) “v4_medikabehand3_s_bedarf_gesamtdosis_199584”/"v4_medikabehand3_s_bedarf_kommentar_201187
Pro re nata (PRN) medication: Unit (1-milligram, 2-microgram, 3-millimol, 4-gram, 5-IE) “v4_medikabehand3_s_bedarf_einheit1_199584”/“v4_medikabehand3_s_bedarf_einheit1_201187”
Pro re nata (PRN) medication: Comment (character) “v4_medikabehand3_s_bedarf_kommentar_199584”/“v4_medikabehand3_s_bedarf_kommentar_201187”
Pro re nata (PRN) medication: Medication name, if not contained in our catalog (character) “v4_medikabehand3_medikament_text_199584”/“v4_medikabehand3_medikament_text_201187”
Make datasets containing only information on medication
v4_med_clin_orig<-data.frame(v4_clin$mnppsd,v4_clin[,147:455])
names(v4_med_clin_orig)[1]<-"v1_id"
v4_med_con_orig<-data.frame(v4_con$mnppsd,v4_con[,75:219])
names(v4_med_con_orig)[1]<-"v1_id"
Save raw medication datasets of visit 4
save(v4_med_clin_orig, file="200715_v4.1_psycourse_clin_raw_med_visit4.RData")
save(v4_med_con_orig, file="200715_v4.1_psycourse_con_raw_med_visit4.RData")
Write .csv file
write.table(v4_med_clin_orig,file="200715_v4.1_psycourse_clin_raw_med_visit4.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v4_med_con_orig,file="200715_v4.1_psycourse_con_raw_med_visit4.csv", quote=F, row.names=F, col.names=T, sep="\t")
For more explanation, see Visit 1
This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.
v4_clin_smk_strt_stp<-rep(NA,dim(v4_clin)[1])
v4_clin_smk_strt_stp<-ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==1,"NS",
ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==2,"NN",
ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==3,"YSP",
ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==4,"YST",v4_clin_smk_strt_stp))))
#ATTENTION: answering alternative: e-cigarette only in controls
v4_con_smk_strt_stp<-rep(NA,dim(v4_con)[1])
v4_con_smk_strt_stp<-ifelse(v4_con$v4_tabalk_folge_tabak1==1 | v4_con$v4_tabalk_folge_tabak1==2,"NS",
ifelse(v4_con$v4_tabalk_folge_tabak1==3,"NN",
ifelse(v4_con$v4_tabalk_folge_tabak1==4,"YSP",
ifelse(v4_con$v4_tabalk_folge_tabak1==5,"YST",v4_con_smk_strt_stp))))
v4_smk_strt_stp<-c(v4_clin_smk_strt_stp,v4_con_smk_strt_stp)
descT(v4_smk_strt_stp)
## NN NS YSP YST <NA>
## [1,] No. cases 277 525 20 8 956 1786
## [2,] Percent 15.5 29.4 1.1 0.4 53.5 100
In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.
Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.
v4_no_cig<-c(rep(NA,dim(v4_clin)[1]),rep(NA,dim(v4_con)[1]))
v4_no_cig<-ifelse((v4_smk_strt_stp=="NN" | v4_smk_strt_stp=="YSP"), -999,
ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") &
c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==1,
c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*365,
ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") &
c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==2,
c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*52,
ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") &
c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==3,
c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*12,
v4_no_cig))))
summary(v4_no_cig[v4_no_cig>=0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 3650 5475 6320 9125 21900 1181
This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.
v4_alc_pst6_mths<-c(v4_clin$v4_tabalk1_ta9_alkkonsum,v4_con$v4_tabalk_folge_alkohol4)
v4_alc_pst6_mths<-factor(v4_alc_pst6_mths, ordered=T)
descT(v4_alc_pst6_mths)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 214 160 90 154 130 49 34 955 1786
## [2,] Percent 12 9 5 8.6 7.3 2.7 1.9 53.5 100
This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.
v4_alc_5orm<-ifelse(v4_alc_pst6_mths<4,-999,
ifelse(is.na(c(v4_clin$v4_tabalk1_ta10_alk_haeufigk_m1,v4_con$v4_tabalk_folge_alkohol5))==T,
c(v4_clin$v4_tabalk1_ta11_alk_haeufigk_f1,v4_con$v4_tabalk_folge_alkohol6),
c(v4_clin$v4_tabalk1_ta10_alk_haeufigk_m1,v4_con$v4_tabalk_folge_alkohol5)))
v4_alc_5orm<-factor(v4_alc_5orm, ordered=T)
descT(v4_alc_5orm)
## -999 1 2 3 4 5 6 7 8 9 <NA>
## [1,] No. cases 464 182 56 40 13 24 23 18 4 7 955 1786
## [2,] Percent 26 10.2 3.1 2.2 0.7 1.3 1.3 1 0.2 0.4 53.5 100
For more information see visit 2.
“During the past six months, did you take ANY illicit drugs?” (dichotomous, v4_pst6_ill_drg)
v4_pst6_ill_drg<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_pst6_ill_drg<-ifelse(c(v4_clin$v4_drogen1_dg1_konsum,v4_con$v4_drogen_folge_drogenkonsum)==2, "Y", "N")
descT(v4_pst6_ill_drg)
## N Y <NA>
## [1,] No. cases 768 63 955 1786
## [2,] Percent 43 3.5 53.5 100
Create dataset
v4_subst<-data.frame(v4_smk_strt_stp,
v4_no_cig,
v4_alc_pst6_mths,
v4_alc_5orm,
v4_pst6_ill_drg)
Here, separate datasets for clinical and control participants are created that contain the raw information on illicit drugs at visit 4, exactly as specified in the phenotype database.
For each illicit drug ever taken, the information given below is assessed.
The last character of each variable name always refers to the drug in question, so items ending with _1 refer to the first drug, _2 to the second drug and so on. In the variable descriptions below, these endings are omitted.
The drugs are not assessed in any specific order, i.e. the order is determined by the individual participant (whatever she or he mentions first).
Below, the variable names of clinical/control participants are given in quotes, and the coding is explained in the parentheses.
1. Whether the individual consumed illicit drugs since the last visit. (Coding: 1-no, 2-yes) “v4_drogen1_dg1_konsum”/“v4_drogen_folge_drogenkonsum”
2. The name of the drug: (character)
“v4_drogen1_s_dg_droge_28483”/“v4_drogen_folge_droge_117794”
The category to which the drug belongs (each item below is a checkbox: 0-not checked, 1-checked):
3. Stimulants: “v4_drogen1_s_dg_drogekt1_28483”/“v4_drogen_folge_droge1_117794”
4. Cannabis: “v4_drogen2_s_dg_drogekt1_28483”/“v4_drogen_folge_droge2_117794”
5. Opiates and pain reliefers: “v4_drogen3_s_dg_drogekt1_28483”/“v4_drogen_folge_droge3_117794”
6. Cocaine: “v4_drogen1_s_dg_drogekt1_28483”/“v4_drogen_folge_droge4_117794”
7. Hallucinogens: “v4_drogen1_s_dg_drogekt5_28483”/“v4_drogen_folge_droge5_117794”
8. Inhalants: “v4_drogen6_s_dg_drogekt1_28483”/“v4_drogen_folge_droge6_117794”
9. Tranquilizers: “v4_drogen7_s_dg_drogekt1_28483”/“v4_drogen_folge_droge7_117794”
10. Other: “v4_drogen8_s_dg_drogekt1_28483”/“v4_drogen_folge_droge8_117794”
11. “Referring to the time since the last study visit, how often did you consume it?”
“v4_drogen1_s_dga_haeufigk_28483”/“v4_drogen_folge_droge_haeufig_117794”
The coding is given below:
1 - tried 1 time
2 - less than once a month
3 - about once a month
4 - at least 2 times but less than 10 times a month
5 - at least 10 times a month
12. “Referring to the period of time since the last study visit, did you have to take more of the drug to achieve the same effect?” (Coding: 1-no, 2-yes). “v4_drogen1_s_dgf_l6m_dosis_28483”/“v4_drogen_folge_droge_dosis_117794”
Important: There is an error in the original phenotype database, that affects the coding of item 10 (above). In all drugs the exports of the phenotype database do not reflect the input into the graphical user interface. Below, the incorrect variable is replaced with the corrected one
Make datasets containing only information on illicit drugs
v4_drg_clin<-v4_clin[,733:788]
v4_drg_con<-v4_con[,315:392]
Clinical participants
v4_clin_ill_drugs_orig<-data.frame(v4_clin$mnppsd,v4_drg_clin)
names(v4_clin_ill_drugs_orig)[1]<-"v1_id"
#recode wrongly coded item 10
for(i in c(0:4)){
v4_clin_ill_drugs_orig[,12+i*11]<-ifelse(v4_clin_ill_drugs_orig[,12+i*11]==5,1,
ifelse(v4_clin_ill_drugs_orig[,12+i*11]==4,5,
ifelse(v4_clin_ill_drugs_orig[,12+i*11]==3,4,
ifelse(v4_clin_ill_drugs_orig[,12+i*11]==2,3,
ifelse(v4_clin_ill_drugs_orig[,12+i*11]==1,2,NA)))))}
Control participants
v4_con_ill_drugs_orig<-data.frame(v4_con$mnppsd,v4_drg_con)
names(v4_con_ill_drugs_orig)[1]<-"v1_id"
#recode wrongly coded item 10
for(i in c(0:6)){
v4_con_ill_drugs_orig[,12+i*11]<-ifelse(v4_con_ill_drugs_orig[,12+i*11]==5,1,
ifelse(v4_con_ill_drugs_orig[,12+i*11]==4,5,
ifelse(v4_con_ill_drugs_orig[,12+i*11]==3,4,
ifelse(v4_con_ill_drugs_orig[,12+i*11]==2,3,
ifelse(v4_con_ill_drugs_orig[,12+i*11]==1,2,NA)))))}
Save raw illicit drug dataset from visit 4
save(v4_clin_ill_drugs_orig, file="200715_v4.1_psycourse_clin_raw_ill_drg_visit4.RData")
save(v4_con_ill_drugs_orig, file="200715_v4.1_psycourse_con_raw_ill_drg_visit4.RData")
Write .csv file
write.table(v4_clin_ill_drugs_orig,file="200715_v4.1_psycourse_clin_raw_ill_drg_visit4.csv", quote=F, row.names=F, col.names=T, sep="\t")
write.table(v4_con_ill_drugs_orig,file="200715_v4.1_psycourse_con_raw_ill_drg_visit4.csv", quote=F, row.names=F, col.names=T, sep="\t")
For more information on the scale, please see Visit 1
P1 Delusions (ordinal [1,2,3,4,5,6,7], v4_panss_p1)
v4_panss_p1<-c(v4_clin$v4_panss_p_p1_wahnideen,v4_con$v4_panss_p_p1_wahnideen)
v4_panss_p1<-factor(v4_panss_p1, ordered=T)
descT(v4_panss_p1)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 652 31 50 28 7 9 2 1007 1786
## [2,] Percent 36.5 1.7 2.8 1.6 0.4 0.5 0.1 56.4 100
P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v4_panss_p2)
v4_panss_p2<-c(v4_clin$v4_panss_p_p2_form_denkst,v4_con$v4_panss_p_p2_form_denkst)
v4_panss_p2<-factor(v4_panss_p2, ordered=T)
descT(v4_panss_p2)
## 1 2 3 4 5 <NA>
## [1,] No. cases 615 47 73 33 11 1007 1786
## [2,] Percent 34.4 2.6 4.1 1.8 0.6 56.4 100
P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v4_panss_p3)
v4_panss_p3<-c(v4_clin$v4_panss_p_p3_halluz,v4_con$v4_panss_p_p3_halluz)
v4_panss_p3<-factor(v4_panss_p3, ordered=T)
descT(v4_panss_p3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 690 20 32 19 15 3 1007 1786
## [2,] Percent 38.6 1.1 1.8 1.1 0.8 0.2 56.4 100
P4 Excitement (ordinal [1,2,3,4,5,6,7], v4_panss_p4)
v4_panss_p4<-c(v4_clin$v4_panss_p_p4_erregung,v4_con$v4_panss_p_p4_erregung)
v4_panss_p4<-factor(v4_panss_p4, ordered=T)
descT(v4_panss_p4)
## 1 2 3 4 5 <NA>
## [1,] No. cases 643 43 72 19 2 1007 1786
## [2,] Percent 36 2.4 4 1.1 0.1 56.4 100
P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v4_panss_p5)
v4_panss_p5<-c(v4_clin$v4_panss_p_p5_groessenideen,v4_con$v4_panss_p_p5_groessenideen)
v4_panss_p5<-factor(v4_panss_p5, ordered=T)
descT(v4_panss_p5)
## 1 2 3 4 5 <NA>
## [1,] No. cases 730 22 16 6 5 1007 1786
## [2,] Percent 40.9 1.2 0.9 0.3 0.3 56.4 100
P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v4_panss_p6)
v4_panss_p6<-c(v4_clin$v4_panss_p_p6_misstr_verfolg,v4_con$v4_panss_p_p6_misstr_verfolg)
v4_panss_p6<-factor(v4_panss_p6, ordered=T)
descT(v4_panss_p6)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 655 30 64 18 9 3 1007 1786
## [2,] Percent 36.7 1.7 3.6 1 0.5 0.2 56.4 100
P7 Hostility (ordinal [1,2,3,4,5,6,7], v4_panss_p7)
v4_panss_p7<-c(v4_clin$v4_panss_p_p7_feindseligkeit,v4_con$v4_panss_p_p7_feindseligkeit)
v4_panss_p7<-factor(v4_panss_p7, ordered=T)
descT(v4_panss_p7)
## 1 2 3 4 5 <NA>
## [1,] No. cases 720 23 31 4 1 1007 1786
## [2,] Percent 40.3 1.3 1.7 0.2 0.1 56.4 100
PANSS Positive sum score (continuous [7-49], v4_panss_sum_pos)
v4_panss_sum_pos<-as.numeric.factor(v4_panss_p1)+
as.numeric.factor(v4_panss_p2)+
as.numeric.factor(v4_panss_p3)+
as.numeric.factor(v4_panss_p4)+
as.numeric.factor(v4_panss_p5)+
as.numeric.factor(v4_panss_p6)+
as.numeric.factor(v4_panss_p7)
summary(v4_panss_sum_pos)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.000 7.000 7.000 9.003 10.000 27.000 1007
N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v4_panss_n1)
v4_panss_n1<-c(v4_clin$v4_panss_n_n1_affektverflachung,v4_con$v4_panss_n_n1_affektverflachung)
v4_panss_n1<-factor(v4_panss_n1, ordered=T)
descT(v4_panss_n1)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 493 67 98 51 66 4 1007 1786
## [2,] Percent 27.6 3.8 5.5 2.9 3.7 0.2 56.4 100
N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v4_panss_n2)
v4_panss_n2<-c(v4_clin$v4_panss_n_n2_emot_rueckzug,v4_con$v4_panss_n_n2_emot_rueckzug)
v4_panss_n2<-factor(v4_panss_n2, ordered=T)
descT(v4_panss_n2)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 567 55 75 57 21 4 1007 1786
## [2,] Percent 31.7 3.1 4.2 3.2 1.2 0.2 56.4 100
N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v4_panss_n3)
v4_panss_n3<-c(v4_clin$v4_panss_n_n3_mang_aff_rapp,v4_con$v4_panss_n_n3_mang_aff_rapp)
v4_panss_n3<-factor(v4_panss_n3, ordered=T)
descT(v4_panss_n3)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 592 49 92 24 15 3 1011 1786
## [2,] Percent 33.1 2.7 5.2 1.3 0.8 0.2 56.6 100
N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v4_panss_n4)
v4_panss_n4<-c(v4_clin$v4_panss_n_n4_soz_pass_apath,v4_con$v4_panss_n_n4_soz_pass_apath)
v4_panss_n4<-factor(v4_panss_n4, ordered=T)
descT(v4_panss_n4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 549 62 95 37 28 6 1009 1786
## [2,] Percent 30.7 3.5 5.3 2.1 1.6 0.3 56.5 100
N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v4_panss_n5)
v4_panss_n5<-c(v4_clin$v4_panss_n_n5_abstr_denken,v4_con$v4_panss_n_n5_abstr_denken)
v4_panss_n5<-factor(v4_panss_n5, ordered=T)
descT(v4_panss_n5)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 523 81 110 43 15 2 1012 1786
## [2,] Percent 29.3 4.5 6.2 2.4 0.8 0.1 56.7 100
N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v4_panss_n6)
v4_panss_n6<-c(v4_clin$v4_panss_n_n6_spon_fl_sprache,v4_con$v4_panss_n_n6_spon_fl_sprache)
v4_panss_n6<-factor(v4_panss_n6, ordered=T)
descT(v4_panss_n6)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 640 30 61 30 16 3 1006 1786
## [2,] Percent 35.8 1.7 3.4 1.7 0.9 0.2 56.3 100
N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v4_panss_n7)
v4_panss_n7<-c(v4_clin$v4_panss_n_n7_stereotyp_ged,v4_con$v4_panss_n_n7_stereotyp_ged)
v4_panss_n7<-factor(v4_panss_n7, ordered=T)
descT(v4_panss_n7)
## 1 2 3 4 5 <NA>
## [1,] No. cases 653 33 71 20 1 1008 1786
## [2,] Percent 36.6 1.8 4 1.1 0.1 56.4 100
PANSS Negative sum score (continuous [7-49], v4_panss_sum_neg)
v4_panss_sum_neg<-as.numeric.factor(v4_panss_n1)+
as.numeric.factor(v4_panss_n2)+
as.numeric.factor(v4_panss_n3)+
as.numeric.factor(v4_panss_n4)+
as.numeric.factor(v4_panss_n5)+
as.numeric.factor(v4_panss_n6)+
as.numeric.factor(v4_panss_n7)
summary(v4_panss_sum_neg)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.00 7.00 9.00 10.99 13.00 34.00 1015
G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v4_panss_g1)
v4_panss_g1<-c(v4_clin$v4_panss_g_g1_sorge_gesundh,v4_con$v4_panss_g_g1_sorge_gesundh)
v4_panss_g1<-factor(v4_panss_g1, ordered=T)
descT(v4_panss_g1)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 589 75 76 27 6 1 1012 1786
## [2,] Percent 33 4.2 4.3 1.5 0.3 0.1 56.7 100
G2 Anxiety (ordinal [1,2,3,4,5,6,7], v4_panss_g2)
v4_panss_g2<-c(v4_clin$v4_panss_g_g2_angst,v4_con$v4_panss_g_g2_angst)
v4_panss_g2<-factor(v4_panss_g2, ordered=T)
descT(v4_panss_g2)
## 1 2 3 4 5 <NA>
## [1,] No. cases 511 65 137 40 24 1009 1786
## [2,] Percent 28.6 3.6 7.7 2.2 1.3 56.5 100
G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v4_panss_g3)
v4_panss_g3<-c(v4_clin$v4_panss_g_g3_schuldgefuehle,v4_con$v4_panss_g_g3_schuldgefuehle)
v4_panss_g3<-factor(v4_panss_g3, ordered=T)
descT(v4_panss_g3)
## 1 2 3 4 5 <NA>
## [1,] No. cases 602 39 77 42 15 1011 1786
## [2,] Percent 33.7 2.2 4.3 2.4 0.8 56.6 100
G4 Tension (ordinal [1,2,3,4,5,6,7], v4_panss_g4)
v4_panss_g4<-c(v4_clin$v4_panss_g_g4_anspannung,v4_con$v4_panss_g_g4_anspannung)
v4_panss_g4<-factor(v4_panss_g4, ordered=T)
descT(v4_panss_g4)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 556 63 115 33 8 3 1008 1786
## [2,] Percent 31.1 3.5 6.4 1.8 0.4 0.2 56.4 100
G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v4_panss_g5)
v4_panss_g5<-c(v4_clin$v4_panss_g_g5_manier_koerperh,v4_con$v4_panss_g_g5_manier_koerperh)
v4_panss_g5<-factor(v4_panss_g5, ordered=T)
descT(v4_panss_g5)
## 1 2 3 4 6 <NA>
## [1,] No. cases 723 19 27 5 2 1010 1786
## [2,] Percent 40.5 1.1 1.5 0.3 0.1 56.6 100
G6 Depression (ordinal [1,2,3,4,5,6,7], v4_panss_g6)
v4_panss_g6<-c(v4_clin$v4_panss_g_g6_depression,v4_con$v4_panss_g_g6_depression)
v4_panss_g6<-factor(v4_panss_g6, ordered=T)
descT(v4_panss_g6)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 502 48 108 66 46 5 2 1009 1786
## [2,] Percent 28.1 2.7 6 3.7 2.6 0.3 0.1 56.5 100
G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v4_panss_g7)
v4_panss_g7<-c(v4_clin$v4_panss_g_g7_mot_verlangs,v4_con$v4_panss_g_g7_mot_verlangs)
v4_panss_g7<-factor(v4_panss_g7, ordered=T)
descT(v4_panss_g7)
## 1 2 3 4 5 <NA>
## [1,] No. cases 584 47 96 46 5 1008 1786
## [2,] Percent 32.7 2.6 5.4 2.6 0.3 56.4 100
G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v4_panss_g8)
v4_panss_g8<-c(v4_clin$v4_panss_g_g8_unkoop_verh,v4_con$v4_panss_g_g8_unkoop_verh)
v4_panss_g8<-factor(v4_panss_g8, ordered=T)
descT(v4_panss_g8)
## 1 2 3 4 5 <NA>
## [1,] No. cases 743 17 15 1 2 1008 1786
## [2,] Percent 41.6 1 0.8 0.1 0.1 56.4 100
G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v4_panss_g9)
v4_panss_g9<-c(v4_clin$v4_panss_g_g9_ungew_denkinh,v4_con$v4_panss_g_g9_ungew_denkinh)
v4_panss_g9<-factor(v4_panss_g9, ordered=T)
descT(v4_panss_g9)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 656 31 64 18 7 2 1008 1786
## [2,] Percent 36.7 1.7 3.6 1 0.4 0.1 56.4 100
G10 Disorientation (ordinal [1,2,3,4,5,6,7], v4_panss_g10)
v4_panss_g10<-c(v4_clin$v4_panss_g_g10_desorient,v4_con$v4_panss_g_g10_desorient)
v4_panss_g10<-factor(v4_panss_g10, ordered=T)
descT(v4_panss_g10)
## 1 2 3 4 <NA>
## [1,] No. cases 745 23 9 1 1008 1786
## [2,] Percent 41.7 1.3 0.5 0.1 56.4 100
G11 Poor attention (ordinal [1,2,3,4,5,6,7], v4_panss_g11)
v4_panss_g11<-c(v4_clin$v4_panss_g_g11_mang_aufmerks,v4_con$v4_panss_g_g11_mang_aufmerks)
v4_panss_g11<-factor(v4_panss_g11, ordered=T)
descT(v4_panss_g11)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 533 55 132 49 1 1 1015 1786
## [2,] Percent 29.8 3.1 7.4 2.7 0.1 0.1 56.8 100
G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v4_panss_g12)
v4_panss_g12<-c(v4_clin$v4_panss_g_g12_mang_urt_einsi,v4_con$v4_panss_g_g12_mang_urt_einsi)
v4_panss_g12<-factor(v4_panss_g12, ordered=T)
descT(v4_panss_g12)
## 1 2 3 4 5 7 <NA>
## [1,] No. cases 679 37 35 20 5 1 1009 1786
## [2,] Percent 38 2.1 2 1.1 0.3 0.1 56.5 100
G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v4_panss_g13)
v4_panss_g13<-c(v4_clin$v4_panss_g_g13_willensschwae,v4_con$v4_panss_g_g13_willensschwae)
v4_panss_g13<-factor(v4_panss_g13, ordered=T)
descT(v4_panss_g13)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 676 24 64 10 2 1 1009 1786
## [2,] Percent 37.8 1.3 3.6 0.6 0.1 0.1 56.5 100
G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v4_panss_g14)
v4_panss_g14<-c(v4_clin$v4_panss_g_g14_mang_impulsk,v4_con$v4_panss_g_g14_mang_impulsk)
v4_panss_g14<-factor(v4_panss_g14, ordered=T)
descT(v4_panss_g14)
## 1 2 3 4 6 <NA>
## [1,] No. cases 683 29 56 5 2 1011 1786
## [2,] Percent 38.2 1.6 3.1 0.3 0.1 56.6 100
G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v4_panss_g15)
v4_panss_g15<-c(v4_clin$v4_panss_g_g15_selbstbezog,v4_con$v4_panss_g_g15_selbstbezog)
v4_panss_g15<-factor(v4_panss_g15, ordered=T)
descT(v4_panss_g15)
## 1 2 3 4 5 <NA>
## [1,] No. cases 704 31 33 4 3 1011 1786
## [2,] Percent 39.4 1.7 1.8 0.2 0.2 56.6 100
G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v4_panss_g16)
v4_panss_g16<-c(v4_clin$v4_panss_g_g16_aktsoz_vermeid,v4_con$v4_panss_g_g16_aktsoz_vermeid)
v4_panss_g16<-factor(v4_panss_g16, ordered=T)
descT(v4_panss_g16)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 619 39 73 26 15 3 1011 1786
## [2,] Percent 34.7 2.2 4.1 1.5 0.8 0.2 56.6 100
PANSS General Psychopathology sum score (continuous [16-112], v4_panss_sum_gen)
v4_panss_sum_gen<-as.numeric.factor(v4_panss_g1)+
as.numeric.factor(v4_panss_g2)+
as.numeric.factor(v4_panss_g3)+
as.numeric.factor(v4_panss_g4)+
as.numeric.factor(v4_panss_g5)+
as.numeric.factor(v4_panss_g6)+
as.numeric.factor(v4_panss_g7)+
as.numeric.factor(v4_panss_g8)+
as.numeric.factor(v4_panss_g9)+
as.numeric.factor(v4_panss_g10)+
as.numeric.factor(v4_panss_g11)+
as.numeric.factor(v4_panss_g12)+
as.numeric.factor(v4_panss_g13)+
as.numeric.factor(v4_panss_g14)+
as.numeric.factor(v4_panss_g15)+
as.numeric.factor(v4_panss_g16)
summary(v4_panss_sum_gen)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 16 16 19 22 25 50 1031
Create PANSS Total score (continuous [30-210], v4_panss_sum_tot)
v4_panss_sum_tot<-v4_panss_sum_pos+v4_panss_sum_neg+v4_panss_sum_gen
summary(v4_panss_sum_tot)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 30.00 30.50 36.00 41.83 48.00 100.00 1035
Create dataset
v4_symp_panss<-data.frame(v4_panss_p1,v4_panss_p2,v4_panss_p3,v4_panss_p4,v4_panss_p5,v4_panss_p6,v4_panss_p7,
v4_panss_n1,v4_panss_n2,v4_panss_n3,v4_panss_n4,v4_panss_n5,v4_panss_n6,v4_panss_n7,
v4_panss_g1,v4_panss_g2,v4_panss_g3,v4_panss_g4,v4_panss_g5,v4_panss_g6,v4_panss_g7,
v4_panss_g8,v4_panss_g9,v4_panss_g10,v4_panss_g11,v4_panss_g12,v4_panss_g13,v4_panss_g14,
v4_panss_g15,v4_panss_g16,v4_panss_sum_pos,v4_panss_sum_neg,v4_panss_sum_gen,
v4_panss_sum_tot)
For more information on the scale, please see Visit 1
Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v4_idsc_itm1)
v4_idsc_itm1<-c(v4_clin$v4_ids_c_s1_ids1_einschlafschw,v4_con$v4_ids_c_s1_ids1_einschlafschw)
v4_idsc_itm1<-factor(v4_idsc_itm1, ordered=T)
descT(v4_idsc_itm1)
## 0 1 2 3 <NA>
## [1,] No. cases 567 92 63 54 1010 1786
## [2,] Percent 31.7 5.2 3.5 3 56.6 100
Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v4_idsc_itm2)
v4_idsc_itm2<-c(v4_clin$v4_ids_c_s1_ids2_naechtl_aufw,v4_con$v4_ids_c_s1_ids2_naechtl_aufw)
v4_idsc_itm2<-factor(v4_idsc_itm2, ordered=T)
descT(v4_idsc_itm2)
## 0 1 2 3 <NA>
## [1,] No. cases 497 114 108 57 1010 1786
## [2,] Percent 27.8 6.4 6 3.2 56.6 100
Item 3 Early morning insomnia (ordinal [0,1,2,3], v4_idsc_itm3)
v4_idsc_itm3<-c(v4_clin$v4_ids_c_s1_ids3_frueh_aufw,v4_con$v4_ids_c_s1_ids3_frueh_aufw)
v4_idsc_itm3<-factor(v4_idsc_itm3, ordered=T)
descT(v4_idsc_itm3)
## 0 1 2 3 <NA>
## [1,] No. cases 640 60 46 29 1011 1786
## [2,] Percent 35.8 3.4 2.6 1.6 56.6 100
Item 4 Hypersomnia (ordinal [0,1,2,3], v4_idsc_itm4)
v4_idsc_itm4<-c(v4_clin$v4_ids_c_s1_ids4_hypersomnie,v4_con$v4_ids_c_s1_ids4_hypersomnie)
v4_idsc_itm4<-factor(v4_idsc_itm4, ordered=T)
descT(v4_idsc_itm4)
## 0 1 2 3 <NA>
## [1,] No. cases 521 175 65 16 1009 1786
## [2,] Percent 29.2 9.8 3.6 0.9 56.5 100
Item 5 Mood (sad) (ordinal [0,1,2,3], v4_idsc_itm5)
v4_idsc_itm5<-c(v4_clin$v4_ids_c_s1_ids5_stimmung_trgk,v4_con$v4_ids_c_s1_ids5_stimmung_trgk)
v4_idsc_itm5<-factor(v4_idsc_itm5, ordered=T)
descT(v4_idsc_itm5)
## 0 1 2 3 <NA>
## [1,] No. cases 496 172 83 26 1009 1786
## [2,] Percent 27.8 9.6 4.6 1.5 56.5 100
Item 6 Mood (irritable) (ordinal [0,1,2,3], v4_idsc_itm6)
v4_idsc_itm6<-c(v4_clin$v4_ids_c_s1_ids6_stimmung_grzt,v4_con$v4_ids_c_s1_ids6_stimmung_grzt)
v4_idsc_itm6<-factor(v4_idsc_itm6, ordered=T)
descT(v4_idsc_itm6)
## 0 1 2 3 <NA>
## [1,] No. cases 552 161 47 15 1011 1786
## [2,] Percent 30.9 9 2.6 0.8 56.6 100
Item 7 Mood (anxious) (ordinal [0,1,2,3], v4_idsc_itm7)
v4_idsc_itm7<-c(v4_clin$v4_ids_c_s1_ids7_stimmung_agst,v4_con$v4_ids_c_s1_ids7_stimmung_agst)
v4_idsc_itm7<-factor(v4_idsc_itm7, ordered=T)
descT(v4_idsc_itm7)
## 0 1 2 3 <NA>
## [1,] No. cases 506 179 66 24 1011 1786
## [2,] Percent 28.3 10 3.7 1.3 56.6 100
Item 8 Reactivity of mood (ordinal [0,1,2,3], v4_idsc_itm8)
v4_idsc_itm8<-c(v4_clin$v4_ids_c_s1_ids8_reakt_stimmung,v4_con$v4_ids_c_s1_ids8_reakt_stimmung)
v4_idsc_itm8<-factor(v4_idsc_itm8, ordered=T)
descT(v4_idsc_itm8)
## 0 1 2 3 <NA>
## [1,] No. cases 635 90 41 10 1010 1786
## [2,] Percent 35.6 5 2.3 0.6 56.6 100
Item 9 Mood Variation (ordinal [0,1,2,3], v4_idsc_itm9)
v4_idsc_itm9<-c(v4_clin$v4_ids_c_s1_ids9_stimmungsschw,v4_con$v4_ids_c_s1_ids9_stimmungsschw)
v4_idsc_itm9<-factor(v4_idsc_itm9, ordered=T)
descT(v4_idsc_itm9)
## 0 1 2 3 <NA>
## [1,] No. cases 635 56 24 60 1011 1786
## [2,] Percent 35.6 3.1 1.3 3.4 56.6 100
Item 9A (categorical [M, A, N], v4_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).
v4_idsc_itm9a_pre<-c(v4_clin$v4_ids_c_s1_ids9a_stimmungsschw,v4_con$v4_ids_c_s1_ids9a_stimmungsschw)
v4_idsc_itm9a<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==1, "M", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==2, "A", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==3, "N", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-factor(v4_idsc_itm9a, ordered=F)
descT(v4_idsc_itm9a)
## -999 A M N <NA>
## [1,] No. cases 635 12 86 17 1036 1786
## [2,] Percent 35.6 0.7 4.8 1 58 100
Item 9B (dichotomous, v4_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.
v4_idsc_itm9b_pre<-c(v4_clin$v4_ids_c_s1_ids9b_stimmungsschw,v4_con$v4_ids_c_s1_ids9b_stimmungsschw)
v4_idsc_itm9b<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm9b<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9b_pre==0, "N", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9b))
v4_idsc_itm9b<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9b_pre==1, "Y", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9b))
v4_idsc_itm9b<-factor(v4_idsc_itm9b, ordered=F)
descT(v4_idsc_itm9b)
## -999 N Y <NA>
## [1,] No. cases 635 43 52 1056 1786
## [2,] Percent 35.6 2.4 2.9 59.1 100
Item 10 Quality of mood (ordinal [0,1,2,3], v4_idsc_itm10)
v4_idsc_itm10<-c(v4_clin$v4_ids_c_s1_ids10_quali_stimmung,v4_con$v4_ids_c_s1_ids10_quali_stimmung)
v4_idsc_itm10<-factor(v4_idsc_itm10, ordered=T)
descT(v4_idsc_itm10)
## 0 1 2 3 <NA>
## [1,] No. cases 673 42 25 25 1021 1786
## [2,] Percent 37.7 2.4 1.4 1.4 57.2 100
Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.
The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.
Item 11 (ordinal [0,1,2,3], v4_idsc_itm11)
v4_idsc_app_verm<-c(v4_clin$v4_ids_c_s2_ids11_appetit_verm,v4_con$v4_ids_c_s2_ids11_appetit_verm)
v4_idsc_app_gest<-c(v4_clin$v4_ids_c_s2_ids12_appetit_steig,v4_con$v4_ids_c_s2_ids12_appetit_steig)
v4_idsc_itm11<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm11<-ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==T, NA,
ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==F, -999,
ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==T,
v4_idsc_app_verm,
ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &
(v4_idsc_app_verm>v4_idsc_app_gest), v4_idsc_app_verm, ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F & (v4_idsc_app_gest>=v4_idsc_app_verm),-999,v4_idsc_itm11)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v4_idsc_itm11)
## -999 0 1 2 3 <NA>
## [1,] No. cases 227 464 60 19 5 1011 1786
## [2,] Percent 12.7 26 3.4 1.1 0.3 56.6 100
Item 12 (ordinal [0,1,2,3], v4_idsc_itm12)
v4_idsc_itm12<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm12<-ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==T, NA,
ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==F,
v4_idsc_app_gest,
ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==T,
-999,
ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &
(v4_idsc_app_verm>v4_idsc_app_gest), -999,
ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F & (v4_idsc_app_gest>=v4_idsc_app_verm),
v4_idsc_app_gest,v4_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v4_idsc_itm12)
## -999 0 1 2 3 <NA>
## [1,] No. cases 548 110 72 26 19 1011 1786
## [2,] Percent 30.7 6.2 4 1.5 1.1 56.6 100
Item 13 (ordinal [0,1,2,3], v4_idsc_itm13)
v4_idsc_gew_abn<-c(v4_clin$v4_ids_c_s2_ids13_gewichtsabn,v4_con$v4_ids_c_s2_ids13_gewichtsabn)
v4_idsc_gew_zun<-c(v4_clin$v4_ids_c_s2_ids14_gewichtszun,v4_con$v4_ids_c_s2_ids14_gewichtszun)
v4_idsc_itm13<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm13<-ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==T, NA,
ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==F, -999,
ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==T,
v4_idsc_gew_abn,
ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F &
(v4_idsc_gew_abn>v4_idsc_gew_zun), v4_idsc_gew_abn, ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F & (v4_idsc_gew_zun >= v4_idsc_gew_abn),-999,v4_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v4_idsc_itm13)
## -999 0 1 2 3 <NA>
## [1,] No. cases 249 437 40 34 16 1010 1786
## [2,] Percent 13.9 24.5 2.2 1.9 0.9 56.6 100
Item 14 (ordinal [0,1,2,3], v4_idsc_itm14)
v4_idsc_itm14<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm14<-ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==T, NA,
ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==F,
v4_idsc_gew_zun,
ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==T,
-999,
ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F &
(v4_idsc_gew_abn>v4_idsc_gew_zun), -999,
ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F & (v4_idsc_gew_zun>=v4_idsc_gew_abn),
v4_idsc_gew_zun,v4_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!
descT(v4_idsc_itm14)
## -999 0 1 2 3 <NA>
## [1,] No. cases 527 137 63 28 21 1010 1786
## [2,] Percent 29.5 7.7 3.5 1.6 1.2 56.6 100
Item 15 Concentration/decision making (ordinal [0,1,2,3], v4_idsc_itm15)
v4_idsc_itm15<-c(v4_clin$v4_ids_c_s2_ids15_konz_entscheid,v4_con$v4_ids_c_s2_ids15_konz_entscheid)
v4_idsc_itm15<-factor(v4_idsc_itm15, ordered=T)
descT(v4_idsc_itm15)
## 0 1 2 3 <NA>
## [1,] No. cases 441 203 110 20 1012 1786
## [2,] Percent 24.7 11.4 6.2 1.1 56.7 100
Item 16 Outlook (self) (ordinal [0,1,2,3], v4_idsc_itm16)
v4_idsc_itm16<-c(v4_clin$v4_ids_c_s2_ids16_selbstbild,v4_con$v4_ids_c_s2_ids16_selbstbild)
v4_idsc_itm16<-factor(v4_idsc_itm16, ordered=T)
descT(v4_idsc_itm16)
## 0 1 2 3 <NA>
## [1,] No. cases 577 118 33 46 1012 1786
## [2,] Percent 32.3 6.6 1.8 2.6 56.7 100
Item 17 Outlook (future) (ordinal [0,1,2,3], v4_idsc_itm17)
v4_idsc_itm17<-c(v4_clin$v4_ids_c_s2_ids17_zukunftssicht,v4_con$v4_ids_c_s2_ids17_zukunftssicht)
v4_idsc_itm17<-factor(v4_idsc_itm17, ordered=T)
descT(v4_idsc_itm17)
## 0 1 2 3 <NA>
## [1,] No. cases 526 185 55 8 1012 1786
## [2,] Percent 29.5 10.4 3.1 0.4 56.7 100
Item 18 Suicidal ideation (ordinal [0,1,2,3], v4_idsc_itm18)
v4_idsc_itm18<-c(v4_clin$v4_ids_c_s2_ids18_selbstmordged,v4_con$v4_ids_c_s2_ids18_selbstmordged)
v4_idsc_itm18<-factor(v4_idsc_itm18, ordered=T)
descT(v4_idsc_itm18)
## 0 1 2 3 <NA>
## [1,] No. cases 702 40 32 2 1010 1786
## [2,] Percent 39.3 2.2 1.8 0.1 56.6 100
Item 19 Involvement (ordinal [0,1,2,3], v4_idsc_itm19)
v4_idsc_itm19<-c(v4_clin$v4_ids_c_s2_ids19_interess_aktiv,v4_con$v4_ids_c_s2_ids19_interess_aktiv)
v4_idsc_itm19<-factor(v4_idsc_itm19, ordered=T)
descT(v4_idsc_itm19)
## 0 1 2 3 <NA>
## [1,] No. cases 626 100 29 20 1011 1786
## [2,] Percent 35.1 5.6 1.6 1.1 56.6 100
Item 20 Energy/fatigability (ordinal [0,1,2,3], v4_idsc_itm20)
v4_idsc_itm20<-c(v4_clin$v4_ids_c_s2_ids20_energ_ermued,v4_con$v4_ids_c_s2_ids20_energ_ermued)
v4_idsc_itm20<-factor(v4_idsc_itm20, ordered=T)
descT(v4_idsc_itm20)
## 0 1 2 3 <NA>
## [1,] No. cases 514 154 97 11 1010 1786
## [2,] Percent 28.8 8.6 5.4 0.6 56.6 100
Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v4_idsc_itm21)
v4_idsc_itm21<-c(v4_clin$v4_ids_c_s3_ids21_vergn_genuss,v4_con$v4_ids_c_s3_ids21_vergn_genuss)
v4_idsc_itm21<-factor(v4_idsc_itm21, ordered=T)
descT(v4_idsc_itm21)
## 0 1 2 3 <NA>
## [1,] No. cases 642 83 38 12 1011 1786
## [2,] Percent 35.9 4.6 2.1 0.7 56.6 100
Item 22 Sexual interest (ordinal [0,1,2,3], v4_idsc_itm22)
v4_idsc_itm22<-c(v4_clin$v4_ids_c_s3_ids22_sex_interesse,v4_con$v4_ids_c_s3_ids22_sex_interesse)
v4_idsc_itm22<-factor(v4_idsc_itm22, ordered=T)
descT(v4_idsc_itm22)
## 0 1 2 3 <NA>
## [1,] No. cases 581 56 73 60 1016 1786
## [2,] Percent 32.5 3.1 4.1 3.4 56.9 100
Item 23 Psychomotor slowing (ordinal [0,1,2,3], v4_idsc_itm23)
v4_idsc_itm23<-c(v4_clin$v4_ids_c_s3_ids23_psymo_hemm,v4_con$v4_ids_c_s3_ids23_psymo_hemm)
v4_idsc_itm23<-factor(v4_idsc_itm23, ordered=T)
descT(v4_idsc_itm23)
## 0 1 2 3 <NA>
## [1,] No. cases 603 134 34 4 1011 1786
## [2,] Percent 33.8 7.5 1.9 0.2 56.6 100
Item 24 Psychomotor agitation (ordinal [0,1,2,3], v4_idsc_itm24)
v4_idsc_itm24<-c(v4_clin$v4_ids_c_s3_ids24_psymo_agitht,v4_con$v4_ids_c_s3_ids24_psymo_agitht)
v4_idsc_itm24<-factor(v4_idsc_itm24, ordered=T)
descT(v4_idsc_itm24)
## 0 1 2 3 <NA>
## [1,] No. cases 640 100 27 2 1017 1786
## [2,] Percent 35.8 5.6 1.5 0.1 56.9 100
Item 25 Somatic complaints (ordinal [0,1,2,3], v4_idsc_itm25)
v4_idsc_itm25<-c(v4_clin$v4_ids_c_s3_ids25_som_beschw,v4_con$v4_ids_c_s3_ids25_som_beschw)
v4_idsc_itm25<-factor(v4_idsc_itm25, ordered=T)
descT(v4_idsc_itm25)
## 0 1 2 3 <NA>
## [1,] No. cases 501 207 49 16 1013 1786
## [2,] Percent 28.1 11.6 2.7 0.9 56.7 100
Item 26 Sympathetic arousal (ordinal [0,1,2,3], v4_idsc_itm26)
v4_idsc_itm26<-c(v4_clin$v4_ids_c_s3_ids26_veg_erreg,v4_con$v4_ids_c_s3_ids26_veg_erreg)
v4_idsc_itm26<-factor(v4_idsc_itm26, ordered=T)
descT(v4_idsc_itm26)
## 0 1 2 3 <NA>
## [1,] No. cases 568 164 31 11 1012 1786
## [2,] Percent 31.8 9.2 1.7 0.6 56.7 100
Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v4_idsc_itm27)
v4_idsc_itm27<-c(v4_clin$v4_ids_c_s3_ids27_panik_phob,v4_con$v4_ids_c_s3_ids27_panik_phob)
v4_idsc_itm27<-factor(v4_idsc_itm27, ordered=T)
descT(v4_idsc_itm27)
## 0 1 2 3 <NA>
## [1,] No. cases 687 55 25 9 1010 1786
## [2,] Percent 38.5 3.1 1.4 0.5 56.6 100
Item 28 Gastrointestinal (ordinal [0,1,2,3], v4_idsc_itm28)
v4_idsc_itm28<-c(v4_clin$v4_ids_c_s3_ids28_verdauung,v4_con$v4_ids_c_s3_ids28_verdauung)
v4_idsc_itm28<-factor(v4_idsc_itm28, ordered=T)
descT(v4_idsc_itm28)
## 0 1 2 3 <NA>
## [1,] No. cases 664 73 31 8 1010 1786
## [2,] Percent 37.2 4.1 1.7 0.4 56.6 100
Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v4_idsc_itm29)
v4_idsc_itm29<-c(v4_clin$v4_ids_c_s3_ids29_pers_bezieh,v4_con$v4_ids_c_s3_ids29_pers_bezieh)
v4_idsc_itm29<-factor(v4_idsc_itm29, ordered=T)
descT(v4_idsc_itm29)
## 0 1 2 3 <NA>
## [1,] No. cases 617 89 46 19 1015 1786
## [2,] Percent 34.5 5 2.6 1.1 56.8 100
Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v4_idsc_itm30)
v4_idsc_itm30<-c(v4_clin$v4_ids_c_s3_ids30_schwgf_k_energ,v4_con$v4_ids_c_s3_ids30_schwgf_k_energ)
v4_idsc_itm30<-factor(v4_idsc_itm30, ordered=T)
descT(v4_idsc_itm30)
## 0 1 2 3 <NA>
## [1,] No. cases 649 77 36 13 1011 1786
## [2,] Percent 36.3 4.3 2 0.7 56.6 100
Create IDS-C30 total score (continuous [0-84], v4_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.
v4_idsc_sum<-as.numeric.factor(v4_idsc_itm1)+
as.numeric.factor(v4_idsc_itm2)+
as.numeric.factor(v4_idsc_itm3)+
as.numeric.factor(v4_idsc_itm4)+
as.numeric.factor(v4_idsc_itm5)+
as.numeric.factor(v4_idsc_itm6)+
as.numeric.factor(v4_idsc_itm7)+
as.numeric.factor(v4_idsc_itm8)+
as.numeric.factor(v4_idsc_itm9)+
as.numeric.factor(v4_idsc_itm10)+
ifelse(is.na(v4_idsc_itm11)==T & is.na(v4_idsc_itm12)==T, NA,
ifelse((v4_idsc_itm11==-999 & v4_idsc_itm12!=-999), v4_idsc_itm12,
ifelse((v4_idsc_itm11!=-999 & v4_idsc_itm12==-999),v4_idsc_itm11, NA)))+
ifelse(is.na(v4_idsc_itm13)==T & is.na(v4_idsc_itm14)==T, NA,
ifelse((v4_idsc_itm13==-999 & v4_idsc_itm14!=-999), v4_idsc_itm14,
ifelse((v4_idsc_itm13!=-999 & v4_idsc_itm14==-999),v4_idsc_itm13, NA)))+
as.numeric.factor(v4_idsc_itm15)+
as.numeric.factor(v4_idsc_itm16)+
as.numeric.factor(v4_idsc_itm17)+
as.numeric.factor(v4_idsc_itm18)+
as.numeric.factor(v4_idsc_itm19)+
as.numeric.factor(v4_idsc_itm20)+
as.numeric.factor(v4_idsc_itm21)+
as.numeric.factor(v4_idsc_itm22)+
as.numeric.factor(v4_idsc_itm23)+
as.numeric.factor(v4_idsc_itm24)+
as.numeric.factor(v4_idsc_itm25)+
as.numeric.factor(v4_idsc_itm26)+
as.numeric.factor(v4_idsc_itm27)+
as.numeric.factor(v4_idsc_itm28)+
as.numeric.factor(v4_idsc_itm29)+
as.numeric.factor(v4_idsc_itm30)
summary(v4_idsc_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 3.00 7.00 10.19 14.00 55.00 1065
Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)
v4_idsc_itm11<-factor(v4_idsc_itm11,ordered=T)
v4_idsc_itm12<-factor(v4_idsc_itm12,ordered=T)
v4_idsc_itm13<-factor(v4_idsc_itm13,ordered=T)
v4_idsc_itm14<-factor(v4_idsc_itm14,ordered=T)
Create dataset
v4_symp_ids_c<-data.frame(v4_idsc_itm1,v4_idsc_itm2,v4_idsc_itm3,v4_idsc_itm4,v4_idsc_itm5,v4_idsc_itm6,v4_idsc_itm7,
v4_idsc_itm8,v4_idsc_itm9,v4_idsc_itm9a,v4_idsc_itm9b,v4_idsc_itm10,v4_idsc_itm11,v4_idsc_itm12,
v4_idsc_itm13,v4_idsc_itm14,v4_idsc_itm15,v4_idsc_itm16,v4_idsc_itm17,v4_idsc_itm18,v4_idsc_itm19,
v4_idsc_itm20,v4_idsc_itm21,v4_idsc_itm22,v4_idsc_itm23,v4_idsc_itm24,v4_idsc_itm25,v4_idsc_itm26,
v4_idsc_itm27,v4_idsc_itm28,v4_idsc_itm29,v4_idsc_itm30,v4_idsc_sum)
For more information on the scale, please see Visit 1
Item 1 Elevated mood (ordinal [0,1,2,3,4], v4_ymrs_itm1)
v4_ymrs_itm1<-c(v4_clin$v4_ymrs_ymrs1_gehob_stimm,v4_con$v4_ymrs_ymrs1_gehob_stimm)
v4_ymrs_itm1<-factor(v4_ymrs_itm1, ordered=T)
descT(v4_ymrs_itm1)
## 0 1 2 <NA>
## [1,] No. cases 655 85 34 1012 1786
## [2,] Percent 36.7 4.8 1.9 56.7 100
Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v4_ymrs_itm2)
v4_ymrs_itm2<-c(v4_clin$v4_ymrs_ymrs2_gest_aktiv,v4_con$v4_ymrs_ymrs2_gest_aktiv)
v4_ymrs_itm2<-factor(v4_ymrs_itm2, ordered=T)
descT(v4_ymrs_itm2)
## 0 1 2 3 4 <NA>
## [1,] No. cases 695 55 16 5 1 1014 1786
## [2,] Percent 38.9 3.1 0.9 0.3 0.1 56.8 100
Item 3 Sexual interest (ordinal [0,1,2,3,4], v4_ymrs_itm3)
v4_ymrs_itm3<-c(v4_clin$v4_ymrs_ymrs3_sex_interesse,v4_con$v4_ymrs_ymrs3_sex_interesse)
v4_ymrs_itm3<-factor(v4_ymrs_itm3, ordered=T)
descT(v4_ymrs_itm3)
## 0 1 2 3 <NA>
## [1,] No. cases 736 16 16 2 1016 1786
## [2,] Percent 41.2 0.9 0.9 0.1 56.9 100
Item 4 Sleep (ordinal [0,1,2,3,4], v4_ymrs_itm4)
v4_ymrs_itm4<-c(v4_clin$v4_ymrs_ymrs4_schlaf,v4_con$v4_ymrs_ymrs4_schlaf)
v4_ymrs_itm4<-factor(v4_ymrs_itm4, ordered=T)
descT(v4_ymrs_itm4)
## 0 1 2 3 <NA>
## [1,] No. cases 719 22 22 11 1012 1786
## [2,] Percent 40.3 1.2 1.2 0.6 56.7 100
Item 5 Irritability (ordinal [0,2,4,6,8], v4_ymrs_itm5)
v4_ymrs_itm5<-c(v4_clin$v4_ymrs_ymrs5_reizbarkeit,v4_con$v4_ymrs_ymrs5_reizbarkeit)
v4_ymrs_itm5<-factor(v4_ymrs_itm5, ordered=T)
descT(v4_ymrs_itm5)
## 0 2 4 6 <NA>
## [1,] No. cases 648 116 9 1 1012 1786
## [2,] Percent 36.3 6.5 0.5 0.1 56.7 100
Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v4_ymrs_itm6)
v4_ymrs_itm6<-c(v4_clin$v4_ymrs_ymrs6_sprechweise,v4_con$v4_ymrs_ymrs6_sprechweise)
v4_ymrs_itm6<-factor(v4_ymrs_itm6, ordered=T)
descT(v4_ymrs_itm6)
## 0 2 4 6 8 <NA>
## [1,] No. cases 671 56 40 6 1 1012 1786
## [2,] Percent 37.6 3.1 2.2 0.3 0.1 56.7 100
Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v4_ymrs_itm7)
v4_ymrs_itm7<-c(v4_clin$v4_ymrs_ymrs7_sprachstoer,v4_con$v4_ymrs_ymrs7_sprachstoer)
v4_ymrs_itm7<-factor(v4_ymrs_itm7, ordered=T)
descT(v4_ymrs_itm7)
## 0 1 2 <NA>
## [1,] No. cases 714 48 12 1012 1786
## [2,] Percent 40 2.7 0.7 56.7 100
Item 8 Content (ordinal [0,2,4,6,8], v4_ymrs_itm8)
v4_ymrs_itm8<-c(v4_clin$v4_ymrs_ymrs8_inhalte,v4_con$v4_ymrs_ymrs8_inhalte)
v4_ymrs_itm8<-factor(v4_ymrs_itm8, ordered=T)
descT(v4_ymrs_itm8)
## 0 2 4 6 8 <NA>
## [1,] No. cases 743 12 1 8 10 1012 1786
## [2,] Percent 41.6 0.7 0.1 0.4 0.6 56.7 100
Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v4_ymrs_itm9)
v4_ymrs_itm9<-c(v4_clin$v4_ymrs_ymrs9_exp_aggr_verh,v4_con$v4_ymrs_ymrs9_exp_aggr_verh)
v4_ymrs_itm9<-factor(v4_ymrs_itm9, ordered=T)
descT(v4_ymrs_itm9)
## 0 2 4 6 <NA>
## [1,] No. cases 751 20 1 1 1013 1786
## [2,] Percent 42 1.1 0.1 0.1 56.7 100
Item 10 Appearance (ordinal [0,1,2,3,4], v4_ymrs_itm10)
v4_ymrs_itm10<-c(v4_clin$v4_ymrs_ymrs10_erscheinung,v4_con$v4_ymrs_ymrs10_erscheinung)
v4_ymrs_itm10<-factor(v4_ymrs_itm10, ordered=T)
descT(v4_ymrs_itm10)
## 0 1 2 3 <NA>
## [1,] No. cases 700 51 17 4 1014 1786
## [2,] Percent 39.2 2.9 1 0.2 56.8 100
Item 11 Insight (ordinal [0,1,2,3,4], v4_ymrs_itm11)
v4_ymrs_itm11<-c(v4_clin$v4_ymrs_ymrs11_krkh_einsicht,v4_con$v4_ymrs_ymrs11_krkh_einsicht)
v4_ymrs_itm11<-factor(v4_ymrs_itm11, ordered=T)
descT(v4_ymrs_itm11)
## 0 1 2 3 4 <NA>
## [1,] No. cases 743 16 8 2 4 1013 1786
## [2,] Percent 41.6 0.9 0.4 0.1 0.2 56.7 100
Create YMRS total score (continuous [0-60], v4_ymrs_sum)
v4_ymrs_sum<-(as.numeric.factor(v4_ymrs_itm1)+
as.numeric.factor(v4_ymrs_itm2)+
as.numeric.factor(v4_ymrs_itm3)+
as.numeric.factor(v4_ymrs_itm4)+
as.numeric.factor(v4_ymrs_itm5)+
as.numeric.factor(v4_ymrs_itm6)+
as.numeric.factor(v4_ymrs_itm7)+
as.numeric.factor(v4_ymrs_itm8)+
as.numeric.factor(v4_ymrs_itm9)+
as.numeric.factor(v4_ymrs_itm10)+
as.numeric.factor(v4_ymrs_itm11))
summary(v4_ymrs_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 0.000 1.859 2.000 24.000 1020
Create dataset
v4_symp_ymrs<-data.frame(v4_ymrs_itm1,
v4_ymrs_itm2,
v4_ymrs_itm3,
v4_ymrs_itm4,
v4_ymrs_itm5,
v4_ymrs_itm6,
v4_ymrs_itm7,
v4_ymrs_itm8,
v4_ymrs_itm9,
v4_ymrs_itm10,
v4_ymrs_itm11,
v4_ymrs_sum)
Please see Visit 1 for more details and explicit rating instructions.
v4_cgi_s<-c(v4_clin$v4_cgi1_cgi1_schweregrad,rep(-999,dim(v4_con)[1]))
v4_cgi_s[v4_cgi_s==0]<- -999
v4_cgi_s<-factor(v4_cgi_s, ordered=T)
descT(v4_cgi_s)
## -999 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 467 16 59 187 152 134 35 1 735 1786
## [2,] Percent 26.1 0.9 3.3 10.5 8.5 7.5 2 0.1 41.2 100
Here, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “very much improved”-1 to “very much worse”-7.
v4_cgi_c<-c(v4_clin$v4_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v4_con)[1]))
v4_cgi_c[v4_cgi_c==0]<- -999
v4_cgi_c<-factor(v4_cgi_c, ordered=T)
descT(v4_cgi_c)
## -999 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 493 9 68 108 242 96 16 2 752 1786
## [2,] Percent 27.6 0.5 3.8 6 13.5 5.4 0.9 0.1 42.1 100
Please see Visit 1 for more details and explicit rating instructions.
v4_gaf<-c(v4_clin$v4_gaf_gaf_code,v4_con$v4_gaf_gaf_code)
v4_gaf[v4_gaf==0]<- -999
summary(v4_gaf[v4_gaf>0])
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 14.00 55.00 68.00 67.14 81.00 99.00 1006
boxplot(v4_gaf[v4_gaf>0 & v1_stat=="CLINICAL"], v4_gaf[v4_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))
v4_ill_sev<-data.frame(v4_cgi_s,v4_cgi_c,v4_gaf)
There are no differences compared to the test battery assessed in Visit 2 or Visit 3.
General comments on the testing (character, v4_nrpsy_com) If there were no comments, this item was coded -999.
Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v4_nrpsy_lng)
v4_nrpsy_lng<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_nrpsy_lng<-ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==0, "mother tongue",
ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==1, "good",
ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==2, "sufficient",
ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==3, "not sufficient",v4_nrpsy_lng))))
v4_nrpsy_lng<-factor(v4_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))
descT(v4_nrpsy_lng)
## mother tongue good sufficient not sufficient <NA>
## [1,] No. cases 762 43 4 1 976 1786
## [2,] Percent 42.7 2.4 0.2 0.1 54.6 100
Motivation of the participant (ordinal [“poor”,“average”,“good”], v4_nrpsy_mtv)
v4_nrpsy_mtv_pre<-c(v4_clin$v4_npu1_np_mot,v4_con$v4_npu_folge_np_mot)
v4_nrpsy_mtv<-ifelse(v4_nrpsy_mtv_pre==0, "poor",
ifelse(v4_nrpsy_mtv_pre==1, "average",
ifelse(v4_nrpsy_mtv_pre==2, "good", NA)))
v4_nrpsy_mtv<-factor(v4_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))
descT(v4_nrpsy_mtv)
## poor average good <NA>
## [1,] No. cases 13 65 726 982 1786
## [2,] Percent 0.7 3.6 40.6 55 100
For a description of the test and the variables, see Visit 2.
Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.
VLMT_introcheck (categorical [0, 1, 9], v4_nrpsy_vlmt_check)
v4_nrpsy_vlmt_check<-c(v4_clin$v4_vlmt_vlmt_introcheck1,v4_con$v4_npu_folge_np_vlmt)
descT(v4_nrpsy_vlmt_check)
## 0 1 9 <NA>
## [1,] No. cases 49 769 18 950 1786
## [2,] Percent 2.7 43.1 1 53.2 100
Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v4_nrpsy_vlmt_corr)
v4_nrpsy_vlmt_corr<-c(v4_clin$v4_vlmt_vlmt3_sw_a5d,v4_con$v4_npu_folge_np_vlmt_gl)
summary(v4_nrpsy_vlmt_corr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 43.00 53.00 52.16 63.00 76.00 1004
Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v4_nrpsy_vlmt_lss_d)
v4_nrpsy_vlmt_lss_d<-c(v4_clin$v4_vlmt_vlmt5_aw_ilsd6,v4_con$v4_npu_folge_np_vlmt_vni)
summary(v4_nrpsy_vlmt_lss_d)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -4.000 0.000 1.000 1.588 3.000 9.000 1010
Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v4_nrpsy_vlmt_lss_t)
v4_nrpsy_vlmt_lss_t<-c(v4_clin$v4_vlmt_vlmt6_aw_vwd7,v4_con$v4_npu_folge_np_vlmt_vnzv)
summary(v4_nrpsy_vlmt_lss_t)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -4.000 0.000 1.000 1.786 3.000 14.000 1014
Recognition performance (corrected for falsely recognized words) (continuous [number of words], v4_nrpsy_vlmt_rec)
v4_nrpsy_vlmt_rec<-c(v4_clin$v4_vlmt_vlmt8_kwl,v4_con$v4_npu_folge_np_vlmt_kw)
summary(v4_nrpsy_vlmt_rec)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -13.00 10.00 13.00 11.74 15.00 15.00 1016
For a description of the test, see Visit 1.
TMT Part A, time (continuous [seconds], v4_nrpsy_tmt_A_rt)
v4_nrpsy_tmt_A_rt<-c(v4_clin$v4_npu1_tmt_001,v4_con$v4_npu_folge_np_tmt_001)
summary(v4_nrpsy_tmt_A_rt)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 20.00 26.00 29.59 36.00 151.00 977
TMT Part A, errors (continuous [number of errors], v4_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).
v4_nrpsy_tmt_A_err<-c(v4_clin$v4_npu1_tmt_af_001,v4_con$v4_npu_folge_np_tmtfehler_001)
summary(v4_nrpsy_tmt_A_err)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.0914 0.0000 3.0000 976
TMT Part B, time (continuous [seconds], v4_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.
v4_nrpsy_tmt_B_rt<-c(v4_clin$v4_npu1_tmt_002,v4_con$v4_npu_folge_tmt_002)
v4_nrpsy_tmt_B_rt[v4_nrpsy_tmt_B_rt>300]<-300
summary(v4_nrpsy_tmt_B_rt)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2.00 46.00 61.00 71.55 83.00 300.00 1009
TMT Part B, errors (continuous [number of errors], v4_nrpsy_tmt_B_err)
v4_nrpsy_tmt_B_err<-c(v4_clin$v4_npu1_tmt_af_002,v4_con$v4_npu_folge_tmt_af_002)
summary(v4_nrpsy_tmt_B_err)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.5013 1.0000 15.0000 1010
For a description of the test, see Visit 1.
Forward (continuous [number of items], v4_nrpsy_dgt_sp_frw)
v4_nrpsy_dgt_sp_frw<-c(v4_clin$v4_npu1_zns_001,v4_con$v4_npu_folge_np_wie_001)
summary(v4_nrpsy_dgt_sp_frw)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.000 8.000 10.000 9.675 11.000 15.000 989
Backward (continuous [number of items], v4_nrpsy_dgt_sp_bck)
v4_nrpsy_dgt_sp_bck<-c(v4_clin$v4_npu1_zns_002,v4_con$v4_npu_folge_np_wie_002)
summary(v4_nrpsy_dgt_sp_bck)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 5.000 6.000 6.853 8.000 14.000 990
For a description of the test, see Visit 1.
v4_introcheck3<-c(v4_clin$v4_npu1_np_introcheck3,v4_con$v4_npu_folge_np_hawier)
v4_nrpsy_dg_sym_pre<-c(v4_clin$v4_npu1_zst_001,v4_con$v4_npu_folge_np_hawier_001)
v4_nrpsy_dg_sym<-ifelse(v4_introcheck3==1, v4_nrpsy_dg_sym_pre,
ifelse(v4_introcheck3==9,-999,
ifelse(v4_introcheck3==0,NA,NA)))
summary(subset(v4_nrpsy_dg_sym,v4_nrpsy_dg_sym>=0))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.00 53.25 70.00 70.55 88.00 133.00
Create dataset
v4_nrpsy<-data.frame(v4_nrpsy_com,
v4_nrpsy_lng,
v4_nrpsy_mtv,
v4_nrpsy_vlmt_check,
v4_nrpsy_vlmt_corr,
v4_nrpsy_vlmt_lss_d,
v4_nrpsy_vlmt_lss_t,
v4_nrpsy_vlmt_rec,
v4_nrpsy_tmt_A_rt,
v4_nrpsy_tmt_A_err,
v4_nrpsy_tmt_B_rt,
v4_nrpsy_tmt_B_err,
v4_nrpsy_dgt_sp_frw,
v4_nrpsy_dgt_sp_bck,
v4_nrpsy_dg_sym)
Participants were asked to fill out questionnaires on the following topics: current medication adherence (compliance), current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 3 and 4) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1, 2 and 3, all questionnaires are checked on whether they were filled out correctly or not and only those correctly filled-out are included in this dataset.
For explanation, please refer to the section in Visit 1
“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v4_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.
v4_sf12_recode(v4_con$v4_sf12_sf_allgemein,"v4_sf12_itm0")
## -999 1 2 3 4 5 6 7 8 9 10 <NA>
## [1,] No. cases 1320 1 2 5 5 6 9 36 85 65 33 219 1786
## [2,] Percent 73.9 0.1 0.1 0.3 0.3 0.3 0.5 2 4.8 3.6 1.8 12.3 100
“In general, would you say your health is…” (ordinal [1,2,3,4,5], v4_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.
v4_sf12_recode(v4_con$v4_sf12_sf1,"v4_sf12_itm1")
## -999 1 2 3 4 5 <NA>
## [1,] No. cases 1320 58 106 75 11 1 215 1786
## [2,] Percent 73.9 3.2 5.9 4.2 0.6 0.1 12 100
“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”
“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v4_sf12_itm2)
Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.
v4_sf12_recode(v4_con$v4_sf12_sf2,"v4_sf12_itm2")
## -999 2 3 <NA>
## [1,] No. cases 1320 20 231 215 1786
## [2,] Percent 73.9 1.1 12.9 12 100
“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v4_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.
v4_sf12_recode(v4_con$v4_sf12_sf3,"v4_sf12_itm3")
## -999 1 2 3 <NA>
## [1,] No. cases 1320 1 23 227 215 1786
## [2,] Percent 73.9 0.1 1.3 12.7 12 100
During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?
“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v4_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.
v4_sf12_recode(v4_con$v4_sf12_sf4,"v4_sf12_itm4")
## -999 1 2 <NA>
## [1,] No. cases 1320 37 212 217 1786
## [2,] Percent 73.9 2.1 11.9 12.2 100
“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v4_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.
v4_sf12_recode(v4_con$v4_sf12_sf5,"v4_sf12_itm5")
## -999 1 2 <NA>
## [1,] No. cases 1320 18 229 219 1786
## [2,] Percent 73.9 1 12.8 12.3 100
During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?
“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v4_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.
v4_sf12_recode(v4_con$v4_sf12_sf6,"v4_sf12_itm6")
## -999 1 2 <NA>
## [1,] No. cases 1320 24 225 217 1786
## [2,] Percent 73.9 1.3 12.6 12.2 100
“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v4_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.
v4_sf12_recode(v4_con$v4_sf12_sf7,"v4_sf12_itm7")
## -999 1 2 <NA>
## [1,] No. cases 1320 16 232 218 1786
## [2,] Percent 73.9 0.9 13 12.2 100
“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v4_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.
v4_sf12_recode(v4_con$v4_sf12_st8,"v4_sf12_itm8")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 134 54 33 23 4 1 217 1786
## [2,] Percent 73.9 7.5 3 1.8 1.3 0.2 0.1 12.2 100
The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS
Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.
“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v4_sf12_itm9)
v4_sf12_recode(v4_con$v4_sf12_st9,"v4_sf12_itm9")
## -999 1 2 3 4 5 <NA>
## [1,] No. cases 1320 27 152 43 22 5 217 1786
## [2,] Percent 73.9 1.5 8.5 2.4 1.2 0.3 12.2 100
“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v4_sf12_itm10)
v4_sf12_recode(v4_con$v4_sf12_st10,"v4_sf12_itm10")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 15 100 69 50 13 2 217 1786
## [2,] Percent 73.9 0.8 5.6 3.9 2.8 0.7 0.1 12.2 100
“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v4_sf12_itm11)
v4_sf12_recode(v4_con$v4_sf12_st11,"v4_sf12_itm11")
## -999 1 2 3 4 5 6 <NA>
## [1,] No. cases 1320 1 5 8 26 114 95 217 1786
## [2,] Percent 73.9 0.1 0.3 0.4 1.5 6.4 5.3 12.2 100
“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v4_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.
There is an error in the phenotype database regarding this item. The answering alternatives 3, 4, and 5 appear as 4, 5, and 6 in the database exports. These errors are corrected below.
v4_sf12_recode(v4_con$v4_sf12_st12,"v4_sf12_itm12")
## -999 2 4 5 6 <NA>
## [1,] No. cases 1320 6 11 44 182 223 1786
## [2,] Percent 73.9 0.3 0.6 2.5 10.2 12.5 100
#recode error in phenotype database
v4_sf12_itm12[v4_sf12_itm12==4]<-3
v4_sf12_itm12[v4_sf12_itm12==5]<-4
v4_sf12_itm12[v4_sf12_itm12==6]<-5
descT(v4_sf12_itm12)
## -999 2 3 4 5 <NA>
## [1,] No. cases 1320 6 11 44 182 223 1786
## [2,] Percent 73.9 0.3 0.6 2.5 10.2 12.5 100
Create dataset
v4_sf12<-data.frame(v4_sf12_itm0,
v4_sf12_itm1,
v4_sf12_itm2,
v4_sf12_itm3,
v4_sf12_itm4,
v4_sf12_itm5,
v4_sf12_itm6,
v4_sf12_itm7,
v4_sf12_itm8,
v4_sf12_itm9,
v4_sf12_itm10,
v4_sf12_itm11,
v4_sf12_itm12)
For a description of the questionnaire, see Visit 1. Controls all have “-999”, as here the questionaire was introduced from the start of data collection.
Religion Christianity (dichotomous, v4_rel_chr)
v4_rel_chris<-c(v4_clin$v4_religion_christ,rep(-999,dim(v4_con)[1]))
v4_rel_chr<-ifelse(v4_rel_chris==1, "Y",ifelse(v4_rel_chris==0,"N",ifelse(v4_rel_chris==-999,"-999",NA)))
descT(v4_rel_chr)
## -999 N Y <NA>
## [1,] No. cases 466 41 346 933 1786
## [2,] Percent 26.1 2.3 19.4 52.2 100
Religion Islam (dichotomous, v4_rel_isl)
v4_rel_islam<-c(v4_clin$v4_religion_islam_jn,rep(-999,dim(v4_con)[1]))
v4_rel_isl<-ifelse(v4_rel_islam==1, "Y",ifelse(v4_rel_islam==0,"N",ifelse(v4_rel_islam==-999,"-999",NA)))
descT(v4_rel_isl)
## -999 N Y <NA>
## [1,] No. cases 466 138 9 1173 1786
## [2,] Percent 26.1 7.7 0.5 65.7 100
Other religion (categorical,[v4_rel_oth])
v4_rel_var<-c(v4_clin$v4_religion_religion,rep(-999,dim(v4_con)[1]))
v4_rel_oth<-ifelse(v4_rel_var==1, "Judaism",
ifelse(v4_rel_var==2, "Hinduism",
ifelse(v4_rel_var==3, "Buddhism",
ifelse(v4_rel_var==4, "Other",
ifelse(v4_rel_var==5, "No denomination",
ifelse(v4_rel_var==-999, "-999", NA))))))
descT(v4_rel_oth)
## -999 Buddhism Hinduism Judaism No denomination Other <NA>
## [1,] No. cases 466 8 2 1 110 13 1186 1786
## [2,] Percent 26.1 0.4 0.1 0.1 6.2 0.7 66.4 100
“How actively do you practice your belief?” (ordinal [1,2,3,4,5], v4_rel_act) This is an ordinal item with the following answer possibilities and the assigned gadation: “not at all”-1,“little active”-2,“moderately active”-3,“rather active”-4,“very actively”-5.
v4_rel_act<-c(v4_clin$v4_religion_religion_aktiv,rep(-999,dim(v4_con)[1]))
descT(v4_rel_act)
## -999 1 2 3 4 5 <NA>
## [1,] No. cases 466 125 155 127 69 25 819 1786
## [2,] Percent 26.1 7 8.7 7.1 3.9 1.4 45.9 100
Create dataset
v4_rlgn<-data.frame(v4_rel_chr,v4_rel_isl,v4_rel_oth,v4_rel_act)
For a description of the questionnaire, see Visit 1.
Past seven days (ordinal [1,2,3,4,5,6], v4_med_pst_wk)
v4_med_chk<-c(v4_clin$v4_compl_verwer_fragebogen,rep(1,dim(v4_con)[1]))
v4_med_pst_wk_pre<-c(v4_clin$v4_compl_psychopharm_7_tag,rep(-999,dim(v4_con)[1]))
v4_med_pst_wk<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_med_pst_wk<-ifelse((is.na(v4_med_chk) | v4_med_chk!=2),
v4_med_pst_wk_pre, v4_med_pst_wk)
descT(v4_med_pst_wk)
## -999 1 2 3 4 6 <NA>
## [1,] No. cases 466 484 52 19 1 5 759 1786
## [2,] Percent 26.1 27.1 2.9 1.1 0.1 0.3 42.5 100
Past six months (ordinal [1,2,3,4,5,6], v4_med_pst_sx_mths)
v4_med_pre<-c(v4_clin$v4_compl_psychopharm_6_mon,rep(-999,dim(v4_con)[1]))
v4_med_pst_sx_mths<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_med_pst_sx_mths<-ifelse((is.na(v4_med_chk) | v4_med_chk!=2),
v4_med_pre, v4_med_pst_sx_mths)
descT(v4_med_pst_sx_mths)
## -999 1 2 3 4 6 <NA>
## [1,] No. cases 466 432 71 45 10 3 759 1786
## [2,] Percent 26.1 24.2 4 2.5 0.6 0.2 42.5 100
Create dataset
v4_med_adh<-data.frame(v4_med_pst_wk,v4_med_pst_sx_mths)
For explanation, please refer to the section in Visit 1
1. Sadness (ordinal [0,1,2,3], v4_bdi2_itm1)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi1_traurigkeit,v4_con$v4_bdi2_s1_bdi1,"v4_bdi2_itm1")
## 0 1 2 3 <NA>
## [1,] No. cases 609 178 18 12 969 1786
## [2,] Percent 34.1 10 1 0.7 54.3 100
2. Pessimism (ordinal [0,1,2,3], v4_bdi2_itm2)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi2_pessimismus,v4_con$v4_bdi2_s1_bdi2,"v4_bdi2_itm2")
## 0 1 2 3 <NA>
## [1,] No. cases 627 124 53 12 970 1786
## [2,] Percent 35.1 6.9 3 0.7 54.3 100
3. Past failure (ordinal [0,1,2,3], v4_bdi2_itm3)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi3_versagensgef,v4_con$v4_bdi2_s1_bdi3,"v4_bdi2_itm3")
## 0 1 2 3 <NA>
## [1,] No. cases 571 152 80 13 970 1786
## [2,] Percent 32 8.5 4.5 0.7 54.3 100
4. Loss of pleasure (ordinal [0,1,2,3], v4_bdi2_itm4)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi4_verlust_freude,v4_con$v4_bdi2_s1_bdi4,"v4_bdi2_itm4")
## 0 1 2 3 <NA>
## [1,] No. cases 546 203 41 23 973 1786
## [2,] Percent 30.6 11.4 2.3 1.3 54.5 100
5. Guilty feelings (ordinal [0,1,2,3], v4_bdi2_itm5)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi5_schuldgef,v4_con$v4_bdi2_s1_bdi5,"v4_bdi2_itm5")
## 0 1 2 3 <NA>
## [1,] No. cases 624 157 21 12 972 1786
## [2,] Percent 34.9 8.8 1.2 0.7 54.4 100
6. Punishment feelings (ordinal [0,1,2,3], v4_bdi2_itm6)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi6_bestrafungsgef,v4_con$v4_bdi2_s1_bdi6,"v4_bdi2_itm6")
## 0 1 2 3 <NA>
## [1,] No. cases 688 91 6 31 970 1786
## [2,] Percent 38.5 5.1 0.3 1.7 54.3 100
7. Self-dislike (ordinal [0,1,2,3], v4_bdi2_itm7)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi7_selbstablehnung,v4_con$v4_bdi2_s1_bdi7,"v4_bdi2_itm7")
## 0 1 2 3 <NA>
## [1,] No. cases 655 102 48 12 969 1786
## [2,] Percent 36.7 5.7 2.7 0.7 54.3 100
8. Self-criticalness (ordinal [0,1,2,3], v4_bdi2_itm8)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi8_selbstvorwuerfe,v4_con$v4_bdi2_s1_bdi8,"v4_bdi2_itm8")
## 0 1 2 3 <NA>
## [1,] No. cases 581 174 49 14 968 1786
## [2,] Percent 32.5 9.7 2.7 0.8 54.2 100
9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v4_bdi2_itm9)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi9_selbstmordged,v4_con$v4_bdi2_s1_bdi9,"v4_bdi2_itm9")
## 0 1 2 3 <NA>
## [1,] No. cases 695 110 9 3 969 1786
## [2,] Percent 38.9 6.2 0.5 0.2 54.3 100
10. Crying (ordinal [0,1,2,3], v4_bdi2_itm10)
v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi10_weinen,v4_con$v4_bdi2_s1_bdi10,"v4_bdi2_itm10")
## 0 1 2 3 <NA>
## [1,] No. cases 666 80 15 51 974 1786
## [2,] Percent 37.3 4.5 0.8 2.9 54.5 100
11. Agitation (ordinal [0,1,2,3], v4_bdi2_itm11)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi11_unruhe,v4_con$v4_bdi2_s2_bdi11,"v4_bdi2_itm11")
## 0 1 2 3 <NA>
## [1,] No. cases 628 155 17 12 974 1786
## [2,] Percent 35.2 8.7 1 0.7 54.5 100
12. Loss of interest (ordinal [0,1,2,3], v4_bdi2_itm12)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi12_interessverl,v4_con$v4_bdi2_s2_bdi12,"v4_bdi2_itm12")
## 0 1 2 3 <NA>
## [1,] No. cases 595 153 36 29 973 1786
## [2,] Percent 33.3 8.6 2 1.6 54.5 100
13. Indecisiveness (ordinal [0,1,2,3], v4_bdi2_itm13)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi13_entschlussunf,v4_con$v4_bdi2_s2_bdi13,"v4_bdi2_itm13")
## 0 1 2 3 <NA>
## [1,] No. cases 572 169 44 26 975 1786
## [2,] Percent 32 9.5 2.5 1.5 54.6 100
14. Worthlessness (ordinal [0,1,2,3], v4_bdi2_itm14)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi14_wertlosigkeit,v4_con$v4_bdi2_s2_bdi14,"v4_bdi2_itm14")
## 0 1 2 3 <NA>
## [1,] No. cases 650 101 51 9 975 1786
## [2,] Percent 36.4 5.7 2.9 0.5 54.6 100
15. Loss of energy (ordinal [0,1,2,3], v4_bdi2_itm15)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi15_energieverlust,v4_con$v4_bdi2_s2_bdi15,"v4_bdi2_itm15")
## 0 1 2 3 <NA>
## [1,] No. cases 498 235 68 9 976 1786
## [2,] Percent 27.9 13.2 3.8 0.5 54.6 100
16. Changes in sleeping pattern (ordinal [0,1,2,3], v4_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep". There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.
v4_itm_bdi2_chk<-c(v4_clin$v4_bdi2_s1_verwer_fragebogen,v4_con$v4_bdi2_s1_bdi_korrekt)
v4_itm_bdi2_itm16_clin_con<-c(v4_clin$v4_bdi2_s2_bdi16_schlafgewohn,v4_con$v4_bdi2_s2_bdi16)
v4_bdi2_itm16<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_bdi2_itm16<-ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & v4_itm_bdi2_itm16_clin_con==0, 0,
ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) &
(v4_itm_bdi2_itm16_clin_con==1 | v4_itm_bdi2_itm16_clin_con==100), 1,
ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) &
(v4_itm_bdi2_itm16_clin_con==2 | v4_itm_bdi2_itm16_clin_con==200), 2,
ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) &
(v4_itm_bdi2_itm16_clin_con==3 | v4_itm_bdi2_itm16_clin_con==300), 3, v4_bdi2_itm16))))
v4_bdi2_itm16<-factor(v4_bdi2_itm16,ordered=T)
descT(v4_bdi2_itm16)
## 0 1 2 3 <NA>
## [1,] No. cases 474 247 57 33 975 1786
## [2,] Percent 26.5 13.8 3.2 1.8 54.6 100
17. Irritability (ordinal [0,1,2,3], v4_bdi2_itm17)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi17_reizbarkeit,v4_con$v4_bdi2_s2_bdi17,"v4_bdi2_itm17")
## 0 1 2 3 <NA>
## [1,] No. cases 656 129 19 8 974 1786
## [2,] Percent 36.7 7.2 1.1 0.4 54.5 100
18. Change in appetite (ordinal [0,1,2,3], v4_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.
v4_itm_bdi2_itm18_clin_con<-c(v4_clin$v4_bdi2_s2_bdi18_appetit,v4_con$v4_bdi2_s2_bdi18)
v4_bdi2_itm18<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_bdi2_itm18<-ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & v4_itm_bdi2_itm18_clin_con==0, 0,
ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) &
(v4_itm_bdi2_itm18_clin_con==1 | v4_itm_bdi2_itm18_clin_con==100), 1,
ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) &
(v4_itm_bdi2_itm18_clin_con==2 | v4_itm_bdi2_itm18_clin_con==200), 2,
ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) &
(v4_itm_bdi2_itm18_clin_con==3 | v4_itm_bdi2_itm18_clin_con==300), 3, v4_bdi2_itm18))))
v4_bdi2_itm18<-factor(v4_bdi2_itm18,ordered=T)
descT(v4_bdi2_itm18)
## 0 1 2 3 <NA>
## [1,] No. cases 558 200 34 19 975 1786
## [2,] Percent 31.2 11.2 1.9 1.1 54.6 100
19. Concentration difficulty (ordinal [0,1,2,3], v4_bdi2_itm19)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi19_konzschw,v4_con$v4_bdi2_s2_bdi19,"v4_bdi2_itm19")
## 0 1 2 3 <NA>
## [1,] No. cases 506 217 83 6 974 1786
## [2,] Percent 28.3 12.2 4.6 0.3 54.5 100
20. Tiredness or fatigue (ordinal [0,1,2,3], v4_bdi2_itm20)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi20_ermued_ersch,v4_con$v4_bdi2_s2_bdi20,"v4_bdi2_itm20")
## 0 1 2 3 <NA>
## [1,] No. cases 504 246 47 14 975 1786
## [2,] Percent 28.2 13.8 2.6 0.8 54.6 100
21. Loss of interest in sex (ordinal [0,1,2,3], v4_bdi2_itm21)
v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi21_sex_interess,v4_con$v4_bdi2_s2_bdi21,"v4_bdi2_itm21")
## 0 1 2 3 <NA>
## [1,] No. cases 585 112 46 62 981 1786
## [2,] Percent 32.8 6.3 2.6 3.5 54.9 100
BDI-II sum score calculation (continuous [0-63], v4_bdi2_sum)
v4_bdi2_sum<-as.numeric.factor(v4_bdi2_itm1)+
as.numeric.factor(v4_bdi2_itm2)+
as.numeric.factor(v4_bdi2_itm3)+
as.numeric.factor(v4_bdi2_itm4)+
as.numeric.factor(v4_bdi2_itm5)+
as.numeric.factor(v4_bdi2_itm6)+
as.numeric.factor(v4_bdi2_itm7)+
as.numeric.factor(v4_bdi2_itm8)+
as.numeric.factor(v4_bdi2_itm9)+
as.numeric.factor(v4_bdi2_itm10)+
as.numeric.factor(v4_bdi2_itm11)+
as.numeric.factor(v4_bdi2_itm12)+
as.numeric.factor(v4_bdi2_itm13)+
as.numeric.factor(v4_bdi2_itm14)+
as.numeric.factor(v4_bdi2_itm15)+
as.numeric.factor(v4_bdi2_itm16)+
as.numeric.factor(v4_bdi2_itm17)+
as.numeric.factor(v4_bdi2_itm18)+
as.numeric.factor(v4_bdi2_itm19)+
as.numeric.factor(v4_bdi2_itm20)+
as.numeric.factor(v4_bdi2_itm21)
summary(v4_bdi2_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 4.000 7.701 11.000 54.000 1007
Create dataset
v4_bdi2<-data.frame(v4_bdi2_itm1,v4_bdi2_itm2,v4_bdi2_itm3,v4_bdi2_itm4,v4_bdi2_itm5,
v4_bdi2_itm6,v4_bdi2_itm7,v4_bdi2_itm8,v4_bdi2_itm9,v4_bdi2_itm10,
v4_bdi2_itm11,v4_bdi2_itm12,v4_bdi2_itm13,v4_bdi2_itm14,
v4_bdi2_itm15,v4_bdi2_itm16,v4_bdi2_itm17,v4_bdi2_itm18,
v4_bdi2_itm19,v4_bdi2_itm20,v4_bdi2_itm21, v4_bdi2_sum)
For explanation, please refer to the section in Visit 1
1. Positive Mood (ordinal [0,1,2,3,4], v4_asrm_itm1)
v4_asrm_recode(v4_clin$v4_asrm_asrm1_gluecklich,v4_con$v4_asrm_asrm1,"v4_asrm_itm1")
## 0 1 2 3 4 <NA>
## [1,] No. cases 617 142 33 11 5 978 1786
## [2,] Percent 34.5 8 1.8 0.6 0.3 54.8 100
2 Self-Confidence (ordinal [0,1,2,3,4], v4_asrm_itm2)
v4_asrm_recode(v4_clin$v4_asrm_asrm2_selbstbewusst,v4_con$v4_asrm_asrm2,"v4_asrm_itm2")
## 0 1 2 3 4 <NA>
## [1,] No. cases 636 140 25 4 2 979 1786
## [2,] Percent 35.6 7.8 1.4 0.2 0.1 54.8 100
3. Sleep (ordinal [0,1,2,3,4], v4_asrm_itm3)
v4_asrm_recode(v4_clin$v4_asrm_asrm3_schlaf,v4_con$v4_asrm_asrm3,"v4_asrm_itm3")
## 0 1 2 3 4 <NA>
## [1,] No. cases 691 93 15 4 4 979 1786
## [2,] Percent 38.7 5.2 0.8 0.2 0.2 54.8 100
4. Speech (ordinal [0,1,2,3,4], v4_asrm_itm4)
v4_asrm_recode(v4_clin$v4_asrm_asrm4_reden,v4_con$v4_asrm_asrm4,"v4_asrm_itm4")
## 0 1 2 3 4 <NA>
## [1,] No. cases 667 122 14 3 1 979 1786
## [2,] Percent 37.3 6.8 0.8 0.2 0.1 54.8 100
5. Activity Level (ordinal [0,1,2,3,4], v4_asrm_itm5)
v4_asrm_recode(v4_clin$v4_asrm_asrm5_aktiv,v4_con$v4_asrm_asrm5,"v4_asrm_itm5")
## 0 1 2 3 4 <NA>
## [1,] No. cases 629 145 24 6 4 978 1786
## [2,] Percent 35.2 8.1 1.3 0.3 0.2 54.8 100
Create ASRM sum score (continuous [0-20],v4_asrm_sum)
v4_asrm_sum<-as.numeric.factor(v4_asrm_itm1)+
as.numeric.factor(v4_asrm_itm2)+
as.numeric.factor(v4_asrm_itm3)+
as.numeric.factor(v4_asrm_itm4)+
as.numeric.factor(v4_asrm_itm5)
summary(v4_asrm_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.00 0.00 1.25 2.00 13.00 983
Create dataset
v4_asrm<-data.frame(v4_asrm_itm1,v4_asrm_itm2,v4_asrm_itm3,v4_asrm_itm4,v4_asrm_itm5,v4_asrm_sum)
For explanation, please refer to the section in Visit 1
1. “I had more energy” (dichotomous, v4_mss_itm1)
v4_mss_recode(v4_clin$v4_mss_s1_mss1_energie,v4_con$v4_mss_s1_mss1,"v4_mss_itm1")
## N Y <NA>
## [1,] No. cases 663 138 985 1786
## [2,] Percent 37.1 7.7 55.2 100
2. “I had trouble sitting still” (dichotomous, v4_mss_itm2)
v4_mss_recode(v4_clin$v4_mss_s1_mss2_ruhig_sitzen,v4_con$v4_mss_s1_mss2,"v4_mss_itm2")
## N Y <NA>
## [1,] No. cases 705 95 986 1786
## [2,] Percent 39.5 5.3 55.2 100
3. “I drove faster” (dichotomous, v4_mss_itm3)
v4_mss_recode(v4_clin$v4_mss_s1_mss3_auto_fahren,v4_con$v4_mss_s1_mss3,"v4_mss_itm3")
## N Y <NA>
## [1,] No. cases 752 20 1014 1786
## [2,] Percent 42.1 1.1 56.8 100
4. “I drank more alcoholic beverages” (dichotomous, v4_mss_itm4)
v4_mss_recode(v4_clin$v4_mss_s1_mss4_alkohol,v4_con$v4_mss_s1_mss4,"v4_mss_itm4")
## N Y <NA>
## [1,] No. cases 736 59 991 1786
## [2,] Percent 41.2 3.3 55.5 100
5. “I changed clothes several times a day” (dichotomous, v4_mss_itm5)
v4_mss_recode(v4_clin$v4_mss_s1_mss5_umziehen, v4_con$v4_mss_s1_mss5,"v4_mss_itm5")
## N Y <NA>
## [1,] No. cases 749 49 988 1786
## [2,] Percent 41.9 2.7 55.3 100
6. “I wore brighter clothes/make-up” (dichotomous, v4_mss_itm6)
v4_mss_recode(v4_clin$v4_mss_s1_mss6_bunter,v4_con$v4_mss_s1_mss6,"v4_mss_itm6")
## N Y <NA>
## [1,] No. cases 768 31 987 1786
## [2,] Percent 43 1.7 55.3 100
7. “I played music louder” (dichotomous, v4_mss_itm7)
v4_mss_recode(v4_clin$v4_mss_s1_mss7_musik_lauter,v4_con$v4_mss_s1_mss7,"v4_mss_itm7")
## N Y <NA>
## [1,] No. cases 706 94 986 1786
## [2,] Percent 39.5 5.3 55.2 100
8. “I ate faster than usual” (dichotomous, v4_mss_itm8)
v4_mss_recode(v4_clin$v4_mss_s1_mss8_hastiger_essen,v4_con$v4_mss_s1_mss8,"v4_mss_itm8")
## N Y <NA>
## [1,] No. cases 724 76 986 1786
## [2,] Percent 40.5 4.3 55.2 100
9. “I ate more than usual” (dichotomous, v4_mss_itm9)
v4_mss_recode(v4_clin$v4_mss_s1_mss9_mehr_essen,v4_con$v4_mss_s1_mss9,"v4_mss_itm9")
## N Y <NA>
## [1,] No. cases 656 142 988 1786
## [2,] Percent 36.7 8 55.3 100
10. “I slept fewer hours than usual” (dichotomous, v4_mss_itm10)
v4_mss_recode(v4_clin$v4_mss_s1_mss10_weniger_schlaf,v4_con$v4_mss_s1_mss10,"v4_mss_itm10")
## N Y <NA>
## [1,] No. cases 717 83 986 1786
## [2,] Percent 40.1 4.6 55.2 100
11. “I started things that I didn’t finish” (dichotomous, v4_mss_itm11)
v4_mss_recode(v4_clin$v4_mss_s1_mss11_unbeendet,v4_con$v4_mss_s1_mss11,"v4_mss_itm11")
## N Y <NA>
## [1,] No. cases 657 143 986 1786
## [2,] Percent 36.8 8 55.2 100
12. “I gave away my own possessions” (dichotomous, v4_mss_itm12)
v4_mss_recode(v4_clin$v4_mss_s1_mss12_weggeben,v4_con$v4_mss_s1_mss12,"v4_mss_itm12")
## N Y <NA>
## [1,] No. cases 743 57 986 1786
## [2,] Percent 41.6 3.2 55.2 100
13. “I bought gifts for people” (dichotomous, v4_mss_itm13)
v4_mss_recode(v4_clin$v4_mss_s1_mss13_geschenke,v4_con$v4_mss_s1_mss13,"v4_mss_itm13")
## N Y <NA>
## [1,] No. cases 739 60 987 1786
## [2,] Percent 41.4 3.4 55.3 100
14. “I spent money more freely” (dichotomous, v4_mss_itm14)
v4_mss_recode(v4_clin$v4_mss_s1_mss14_mehr_geld,v4_con$v4_mss_s1_mss14,"v4_mss_itm14")
## N Y <NA>
## [1,] No. cases 643 157 986 1786
## [2,] Percent 36 8.8 55.2 100
15. “I accumulated debts” (dichotomous, v4_mss_itm15)
v4_mss_recode(v4_clin$v4_mss_s1_mss15_schulden,v4_con$v4_mss_s1_mss15,"v4_mss_itm15")
## N Y <NA>
## [1,] No. cases 757 43 986 1786
## [2,] Percent 42.4 2.4 55.2 100
16. “I made unwise business decisions” (dichotomous, v4_mss_itm16)
v4_mss_recode(v4_clin$v4_mss_s1_mss16_unkluge_entsch,v4_con$v4_mss_s1_mss16,"v4_mss_itm16")
## N Y <NA>
## [1,] No. cases 767 32 987 1786
## [2,] Percent 42.9 1.8 55.3 100
17. “I partied more” (dichotomous, v4_mss_itm17)
v4_mss_recode(v4_clin$v4_mss_s1_mss17_parties,v4_con$v4_mss_s1_mss17,"v4_mss_itm17")
## N Y <NA>
## [1,] No. cases 769 32 985 1786
## [2,] Percent 43.1 1.8 55.2 100
18. “I enjoyed flirting” (dichotomous, v4_mss_itm18)
v4_mss_recode(v4_clin$v4_mss_s1_mss18_flirten,v4_con$v4_mss_s1_mss18,"v4_mss_itm18")
## N Y <NA>
## [1,] No. cases 760 38 988 1786
## [2,] Percent 42.6 2.1 55.3 100
19. “I masturbated more often” (dichotomous, v4_mss_itm19)
v4_mss_recode(v4_clin$v4_mss_s2_mss19_selbstbefried,v4_con$v4_mss_s2_mss19,"v4_mss_itm19")
## N Y <NA>
## [1,] No. cases 755 32 999 1786
## [2,] Percent 42.3 1.8 55.9 100
20. “I was more interested in sex than usual” (dichotomous, v4_mss_itm20)
v4_mss_recode(v4_clin$v4_mss_s2_mss20_sex_interess,v4_con$v4_mss_s2_mss20,"v4_mss_itm20")
## N Y <NA>
## [1,] No. cases 726 60 1000 1786
## [2,] Percent 40.6 3.4 56 100
21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v4_mss_itm21)
v4_mss_recode(v4_clin$v4_mss_s2_mss21_sexpartner,v4_con$v4_mss_s2_mss21,"v4_mss_itm21")
## N Y <NA>
## [1,] No. cases 781 7 998 1786
## [2,] Percent 43.7 0.4 55.9 100
22. “I spent more time on the phone” (dichotomous, v4_mss_itm22)
v4_mss_recode(v4_clin$v4_mss_s2_mss22_mehr_telefon,v4_con$v4_mss_s2_mss22,"v4_mss_itm22")
## N Y <NA>
## [1,] No. cases 694 94 998 1786
## [2,] Percent 38.9 5.3 55.9 100
23. “I spoke louder than usual” (dichotomous, v4_mss_itm23)
v4_mss_recode(v4_clin$v4_mss_s2_mss23_sprache_lauter,v4_con$v4_mss_s2_mss23,"v4_mss_itm23")
## N Y <NA>
## [1,] No. cases 732 53 1001 1786
## [2,] Percent 41 3 56 100
24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v4_mss_itm24)
v4_mss_recode(v4_clin$v4_mss_s2_mss24_spr_schneller,v4_con$v4_mss_s2_mss24,"v4_mss_itm24")
## N Y <NA>
## [1,] No. cases 743 48 995 1786
## [2,] Percent 41.6 2.7 55.7 100
25. “1 enjoyed punning or rhyming” (dichotomous, v4_mss_itm25)
v4_mss_recode(v4_clin$v4_mss_s2_mss25_witze,v4_con$v4_mss_s2_mss25,"v4_mss_itm25")
## N Y <NA>
## [1,] No. cases 729 61 996 1786
## [2,] Percent 40.8 3.4 55.8 100
26. “I butted into conversations” (dichotomous, v4_mss_itm26)
v4_mss_recode(v4_clin$v4_mss_s2_mss26_einmischen,v4_con$v4_mss_s2_mss26,"v4_mss_itm26")
## N Y <NA>
## [1,] No. cases 756 35 995 1786
## [2,] Percent 42.3 2 55.7 100
27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v4_mss_itm27)
v4_mss_recode(v4_clin$v4_mss_s2_mss27_red_pausenlos,v4_con$v4_mss_s2_mss27,"v4_mss_itm27")
## N Y <NA>
## [1,] No. cases 769 21 996 1786
## [2,] Percent 43.1 1.2 55.8 100
28. “I enjoyed being the centre of attention” (dichotomous, v4_mss_itm28)
v4_mss_recode(v4_clin$v4_mss_s2_mss28_mittelpunkt,v4_con$v4_mss_s2_mss28,"v4_mss_itm28")
## N Y <NA>
## [1,] No. cases 748 42 996 1786
## [2,] Percent 41.9 2.4 55.8 100
29. “I liked to joke and laugh” (dichotomous, v4_mss_itm29)
v4_mss_recode(v4_clin$v4_mss_s2_mss29_herumalbern,v4_con$v4_mss_s2_mss29,"v4_mss_itm29")
## N Y <NA>
## [1,] No. cases 708 83 995 1786
## [2,] Percent 39.6 4.6 55.7 100
30. “People found me entertaining” (dichotomous, v4_mss_itm30)
v4_mss_recode(v4_clin$v4_mss_s2_mss30_unterhaltsamer,v4_con$v4_mss_s2_mss30,"v4_mss_itm30")
## N Y <NA>
## [1,] No. cases 739 50 997 1786
## [2,] Percent 41.4 2.8 55.8 100
31. “I felt as if I was on top of the world” (dichotomous, v4_mss_itm31)
v4_mss_recode(v4_clin$v4_mss_s2_mss31_obenauf,v4_con$v4_mss_s2_mss31,"v4_mss_itm31")
## N Y <NA>
## [1,] No. cases 739 49 998 1786
## [2,] Percent 41.4 2.7 55.9 100
32. “I was more cheerful than my usual self” (dichotomous, v4_mss_itm32)
v4_mss_recode(v4_clin$v4_mss_s2_mss32_froehlicher,v4_con$v4_mss_s2_mss32,"v4_mss_itm32")
## N Y <NA>
## [1,] No. cases 686 102 998 1786
## [2,] Percent 38.4 5.7 55.9 100
33. “Other people got on my nerves” (dichotomous, v4_mss_itm33)
v4_mss_recode(v4_clin$v4_mss_s2_mss33_ungeduldiger,v4_con$v4_mss_s2_mss33,"v4_mss_itm33")
## N Y <NA>
## [1,] No. cases 640 150 996 1786
## [2,] Percent 35.8 8.4 55.8 100
34. “I was getting into arguments” (dichotomous, v4_mss_itm34)
v4_mss_recode(v4_clin$v4_mss_s2_mss34_streiten,v4_con$v4_mss_s2_mss34,"v4_mss_itm34")
## N Y <NA>
## [1,] No. cases 735 56 995 1786
## [2,] Percent 41.2 3.1 55.7 100
35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v4_mss_itm35)
v4_mss_recode(v4_clin$v4_mss_s2_mss35_ideen,v4_con$v4_mss_s2_mss35,"v4_mss_itm35")
## N Y <NA>
## [1,] No. cases 687 103 996 1786
## [2,] Percent 38.5 5.8 55.8 100
36. “My thoughts raced through my mind” (dichotomous, v4_mss_itm36)
v4_mss_recode(v4_clin$v4_mss_s2_mss36_gedanken,v4_con$v4_mss_s2_mss36,"v4_mss_itm36")
## N Y <NA>
## [1,] No. cases 626 161 999 1786
## [2,] Percent 35.1 9 55.9 100
37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v4_mss_itm37)
v4_mss_recode(v4_clin$v4_mss_s2_mss37_konzentration,v4_con$v4_mss_s2_mss37,"v4_mss_itm37")
## N Y <NA>
## [1,] No. cases 689 101 996 1786
## [2,] Percent 38.6 5.7 55.8 100
38. “I thought I was an especially important person” (dichotomous, v4_mss_itm38)
v4_mss_recode(v4_clin$v4_mss_s2_mss38_etw_besonderes,v4_con$v4_mss_s2_mss38,"v4_mss_itm38")
## N Y <NA>
## [1,] No. cases 744 45 997 1786
## [2,] Percent 41.7 2.5 55.8 100
39. “I thought I could change the world” (dichotomous, v4_mss_itm39)
v4_mss_recode(v4_clin$v4_mss_s2_mss39_welt_veraender,v4_con$v4_mss_s2_mss39,"v4_mss_itm39")
## N Y <NA>
## [1,] No. cases 763 28 995 1786
## [2,] Percent 42.7 1.6 55.7 100
40. “I thought I was right most of the time” (dichotomous, v4_mss_itm40)
v4_mss_recode(v4_clin$v4_mss_s2_mss40_recht_haben,v4_con$v4_mss_s2_mss40,"v4_mss_itm40")
## N Y <NA>
## [1,] No. cases 759 29 998 1786
## [2,] Percent 42.5 1.6 55.9 100
41. “I thought I was superior to others” (dichotomous, v4_mss_itm41)
v4_mss_recode(v4_clin$v4_mss_s3_mss41_ueberlegen,v4_con$v4_mss_s3_mss41,"v4_mss_itm41")
## N Y <NA>
## [1,] No. cases 773 23 990 1786
## [2,] Percent 43.3 1.3 55.4 100
42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v4_mss_itm42)
v4_mss_recode(v4_clin$v4_mss_s3_mss42_uebermut,v4_con$v4_mss_s3_mss42,"v4_mss_itm42")
## N Y <NA>
## [1,] No. cases 757 39 990 1786
## [2,] Percent 42.4 2.2 55.4 100
43. “I thought I knew what other people were thinking” (dichotomous, v4_mss_itm43)
v4_mss_recode(v4_clin$v4_mss_s3_mss43_ged_lesen_akt,v4_con$v4_mss_s3_mss43,"v4_mss_itm43")
## N Y <NA>
## [1,] No. cases 752 44 990 1786
## [2,] Percent 42.1 2.5 55.4 100
44. “I thought other people knew what I was thinking” (dichotomous, v4_mss_itm44)
v4_mss_recode(v4_clin$v4_mss_s3_mss44_ged_lesen_pas,v4_con$v4_mss_s3_mss44,"v4_mss_itm44")
## N Y <NA>
## [1,] No. cases 756 40 990 1786
## [2,] Percent 42.3 2.2 55.4 100
45. “I thought someone wanted to harm me” (dichotomous, v4_mss_itm45)
v4_mss_recode(v4_clin$v4_mss_s3_mss45_etw_antun,v4_con$v4_mss_s3_mss45,"v4_mss_itm45")
## N Y <NA>
## [1,] No. cases 762 34 990 1786
## [2,] Percent 42.7 1.9 55.4 100
46. “I heard voices when people weren’t there” (dichotomous, v4_mss_itm46)
v4_mss_recode(v4_clin$v4_mss_s3_mss46_stimmen,v4_con$v4_mss_s3_mss46,"v4_mss_itm46")
## N Y <NA>
## [1,] No. cases 742 53 991 1786
## [2,] Percent 41.5 3 55.5 100
47. “I had false beliefs concerning who I was” (dichotomous, v4_mss_itm47)
v4_mss_recode(v4_clin$v4_mss_s3_mss47_jmd_anders,v4_con$v4_mss_s3_mss47,"v4_mss_itm47")
## N Y <NA>
## [1,] No. cases 778 17 991 1786
## [2,] Percent 43.6 1 55.5 100
48. “I knew I was getting ill” (dichotomous, v4_mss_itm48)
v4_mss_recode(v4_clin$v4_mss_s3_mss48_krank_einsicht,v4_con$v4_mss_s3_mss48,"v4_mss_itm48")
## N Y <NA>
## [1,] No. cases 713 75 998 1786
## [2,] Percent 39.9 4.2 55.9 100
Create MSS sum score (continuous [0-48],v4_mss_sum)
v4_mss_sum<-ifelse(v4_mss_itm1=="Y",1,0)+
ifelse(v4_mss_itm2=="Y",1,0)+
ifelse(v4_mss_itm3=="Y",1,0)+
ifelse(v4_mss_itm4=="Y",1,0)+
ifelse(v4_mss_itm5=="Y",1,0)+
ifelse(v4_mss_itm6=="Y",1,0)+
ifelse(v4_mss_itm7=="Y",1,0)+
ifelse(v4_mss_itm8=="Y",1,0)+
ifelse(v4_mss_itm9=="Y",1,0)+
ifelse(v4_mss_itm10=="Y",1,0)+
ifelse(v4_mss_itm11=="Y",1,0)+
ifelse(v4_mss_itm12=="Y",1,0)+
ifelse(v4_mss_itm13=="Y",1,0)+
ifelse(v4_mss_itm14=="Y",1,0)+
ifelse(v4_mss_itm15=="Y",1,0)+
ifelse(v4_mss_itm16=="Y",1,0)+
ifelse(v4_mss_itm17=="Y",1,0)+
ifelse(v4_mss_itm18=="Y",1,0)+
ifelse(v4_mss_itm19=="Y",1,0)+
ifelse(v4_mss_itm20=="Y",1,0)+
ifelse(v4_mss_itm21=="Y",1,0)+
ifelse(v4_mss_itm22=="Y",1,0)+
ifelse(v4_mss_itm23=="Y",1,0)+
ifelse(v4_mss_itm24=="Y",1,0)+
ifelse(v4_mss_itm25=="Y",1,0)+
ifelse(v4_mss_itm26=="Y",1,0)+
ifelse(v4_mss_itm27=="Y",1,0)+
ifelse(v4_mss_itm28=="Y",1,0)+
ifelse(v4_mss_itm29=="Y",1,0)+
ifelse(v4_mss_itm30=="Y",1,0)+
ifelse(v4_mss_itm31=="Y",1,0)+
ifelse(v4_mss_itm32=="Y",1,0)+
ifelse(v4_mss_itm33=="Y",1,0)+
ifelse(v4_mss_itm34=="Y",1,0)+
ifelse(v4_mss_itm35=="Y",1,0)+
ifelse(v4_mss_itm36=="Y",1,0)+
ifelse(v4_mss_itm37=="Y",1,0)+
ifelse(v4_mss_itm38=="Y",1,0)+
ifelse(v4_mss_itm39=="Y",1,0)+
ifelse(v4_mss_itm40=="Y",1,0)+
ifelse(v4_mss_itm41=="Y",1,0)+
ifelse(v4_mss_itm42=="Y",1,0)+
ifelse(v4_mss_itm43=="Y",1,0)+
ifelse(v4_mss_itm44=="Y",1,0)+
ifelse(v4_mss_itm45=="Y",1,0)+
ifelse(v4_mss_itm46=="Y",1,0)+
ifelse(v4_mss_itm47=="Y",1,0)+
ifelse(v4_mss_itm48=="Y",1,0)
summary(v4_mss_sum)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 2.000 3.538 5.000 36.000 1065
Create dataset
v4_mss<-data.frame(v4_mss_itm1,v4_mss_itm2,v4_mss_itm3,v4_mss_itm4,v4_mss_itm5,v4_mss_itm6,
v4_mss_itm7,v4_mss_itm8,v4_mss_itm9,v4_mss_itm10,v4_mss_itm11,
v4_mss_itm12,v4_mss_itm13,v4_mss_itm14,v4_mss_itm15,v4_mss_itm16,
v4_mss_itm17,v4_mss_itm18,v4_mss_itm19,v4_mss_itm20,v4_mss_itm21,
v4_mss_itm22,v4_mss_itm23,v4_mss_itm24,v4_mss_itm25,v4_mss_itm26,
v4_mss_itm27,v4_mss_itm28,v4_mss_itm29,v4_mss_itm30,v4_mss_itm31,
v4_mss_itm32,v4_mss_itm33,v4_mss_itm34,v4_mss_itm35,v4_mss_itm36,
v4_mss_itm37,v4_mss_itm38,v4_mss_itm39,v4_mss_itm40,v4_mss_itm41,
v4_mss_itm42,v4_mss_itm43,v4_mss_itm44,v4_mss_itm45,v4_mss_itm46,
v4_mss_itm47,v4_mss_itm48, v4_mss_sum)
For explanation, please refer to the section in Visit 1
1. “Major personal illness or injury”
1A Nature (dichotomous [“good”,“bad”], v4_leq_A_1A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq1a_schw_krankh,v4_con$v4_leq_a_leq1a,"v4_leq_A_1A")
## -999 bad good <NA>
## [1,] No. cases 569 166 26 1025 1786
## [2,] Percent 31.9 9.3 1.5 57.4 100
1B Impact (ordinal [0,1,2,3], v4_leq_A_1B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq1e_schw_krankh,v4_con$v4_leq_a_leq1e,"v4_leq_A_1B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 567 12 38 67 77 1025 1786
## [2,] Percent 31.7 0.7 2.1 3.8 4.3 57.4 100
2. “Major change in eating habits”
2A Nature (dichotomous [“good”,“bad”], v4_leq_A_2A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq2a_ernaehrung,v4_con$v4_leq_a_leq2a,"v4_leq_A_2A")
## -999 bad good <NA>
## [1,] No. cases 583 75 103 1025 1786
## [2,] Percent 32.6 4.2 5.8 57.4 100
2B Impact (ordinal [0,1,2,3], v4_leq_A_2B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq2e_ernaehrung,v4_con$v4_leq_a_leq2e,"v4_leq_A_2B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 583 13 49 71 45 1025 1786
## [2,] Percent 32.6 0.7 2.7 4 2.5 57.4 100
3. “Major change in sleeping habits”
3A Nature (dichotomous [“good”,“bad”], v4_leq_A_3A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq3a_schlaf,v4_con$v4_leq_a_leq3a,"v4_leq_A_3A")
## -999 bad good <NA>
## [1,] No. cases 574 112 75 1025 1786
## [2,] Percent 32.1 6.3 4.2 57.4 100
3B Impact (ordinal [0,1,2,3], v4_leq_A_3B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq3e_schlaf,v4_con$v4_leq_a_leq3e,"v4_leq_A_3B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 573 20 52 57 59 1025 1786
## [2,] Percent 32.1 1.1 2.9 3.2 3.3 57.4 100
4. “Major change in usual type and/or amount of recreation”
4A Nature (dichotomous [“good”,“bad”], v4_leq_A_4A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq4a_freizeit,v4_con$v4_leq_a_leq4a,"v4_leq_A_4A")
## -999 bad good <NA>
## [1,] No. cases 560 73 128 1025 1786
## [2,] Percent 31.4 4.1 7.2 57.4 100
4B Impact (ordinal [0,1,2,3], v4_leq_A_4B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq4e_freizeit,v4_con$v4_leq_a_leq4e,"v4_leq_A_4B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 559 14 48 77 63 1025 1786
## [2,] Percent 31.3 0.8 2.7 4.3 3.5 57.4 100
5. “Major dental work”
5A Nature (dichotomous [“good”,“bad”], v4_leq_A_5A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq5a_zahnarzt,v4_con$v4_leq_a_leq5a,"v4_leq_A_5A")
## -999 bad good <NA>
## [1,] No. cases 642 55 64 1025 1786
## [2,] Percent 35.9 3.1 3.6 57.4 100
5B Impact (ordinal [0,1,2,3], v4_leq_A_5B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq5e_zahnarzt,v4_con$v4_leq_a_leq5e,"v4_leq_A_5B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 637 31 42 30 21 1025 1786
## [2,] Percent 35.7 1.7 2.4 1.7 1.2 57.4 100
6. “(Female) Pregnancy”
6A Nature (dichotomous [“good”,“bad”], v4_leq_A_6A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq6a_schwanger,v4_con$v4_leq_a_leq6a,"v4_leq_A_6A")
## -999 good <NA>
## [1,] No. cases 755 6 1025 1786
## [2,] Percent 42.3 0.3 57.4 100
6B Impact (ordinal [0,1,2,3], v4_leq_A_6B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq6e_schwanger,v4_con$v4_leq_a_leq6e,"v4_leq_A_6B")
## -999 0 2 3 <NA>
## [1,] No. cases 754 1 1 5 1025 1786
## [2,] Percent 42.2 0.1 0.1 0.3 57.4 100
7. “(Female) Miscarriage or abortion”
7A Nature (dichotomous [“good”,“bad”], v4_leq_A_7A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq7a_fehlg_abtr,v4_con$v4_leq_a_leq7a,"v4_leq_A_7A")
## -999 <NA>
## [1,] No. cases 761 1025 1786
## [2,] Percent 42.6 57.4 100
7B Impact (ordinal [0,1,2,3], v4_leq_A_7B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq7e_fehlg_abtr,v4_con$v4_leq_a_leq7e,"v4_leq_A_7B")
## -999 0 <NA>
## [1,] No. cases 760 1 1025 1786
## [2,] Percent 42.6 0.1 57.4 100
8. “(Female) Started menopause”
8A Nature (dichotomous [“good”,“bad”], v4_leq_A_8A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq8a_wechseljahre,v4_con$v4_leq_a_leq8a,"v4_leq_A_8A")
## -999 bad good <NA>
## [1,] No. cases 735 19 7 1025 1786
## [2,] Percent 41.2 1.1 0.4 57.4 100
8B Impact (ordinal [0,1,2,3], v4_leq_A_8B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq8e_wechseljahre,v4_con$v4_leq_a_leq8e,"v4_leq_A_8B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 733 5 8 8 7 1025 1786
## [2,] Percent 41 0.3 0.4 0.4 0.4 57.4 100
9. “Major difficulties with birth control pills or devices”
9A Nature (dichotomous [“good”,“bad”], v4_leq_A_9A)
v4_leq_a_recode(v4_clin$v4_leq_a_leq9a_verhuetung,v4_con$v4_leq_a_leq9a,"v4_leq_A_9A")
## -999 bad good <NA>
## [1,] No. cases 752 3 6 1025 1786
## [2,] Percent 42.1 0.2 0.3 57.4 100
9B Impact (ordinal [0,1,2,3], v4_leq_A_9B)
v4_leq_b_recode(v4_clin$v4_leq_a_leq9e_verhuetung,v4_con$v4_leq_a_leq9e,"v4_leq_A_9B")
## -999 0 1 3 <NA>
## [1,] No. cases 750 5 5 1 1025 1786
## [2,] Percent 42 0.3 0.3 0.1 57.4 100
Create dataset
v4_leq_A<-data.frame(v4_leq_A_1A,v4_leq_A_1B,v4_leq_A_2A,v4_leq_A_2B,v4_leq_A_3A,
v4_leq_A_3B,v4_leq_A_4A,v4_leq_A_4B,v4_leq_A_5A,v4_leq_A_5B,
v4_leq_A_6A,v4_leq_A_6B,v4_leq_A_7A,v4_leq_A_7B,v4_leq_A_8A,
v4_leq_A_8B,v4_leq_A_9A,v4_leq_A_9B)
10. “Difficulty finding a job”
10A Nature (dichotomous [“good”,“bad”], v4_leq_B_10A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq10a_arbeitssuche,v4_con$v4_leq_b_leq10a,"v4_leq_B_10A")
## -999 bad good <NA>
## [1,] No. cases 676 71 14 1025 1786
## [2,] Percent 37.8 4 0.8 57.4 100
10B Impact (ordinal [0,1,2,3], v4_leq_B_10B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq10e_arbeitssuche,v4_con$v4_leq_b_leq10e,"v4_leq_B_10B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 674 10 22 28 27 1025 1786
## [2,] Percent 37.7 0.6 1.2 1.6 1.5 57.4 100
11. “Beginning work outside the home”
11A Nature (dichotomous [“good”,“bad”], v4_leq_B_11A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq11a_arbeit_aussen,v4_con$v4_leq_b_leq11a,"v4_leq_B_11A")
## -999 bad good <NA>
## [1,] No. cases 680 13 68 1025 1786
## [2,] Percent 38.1 0.7 3.8 57.4 100
11B Impact (ordinal [0,1,2,3], v4_leq_B_11B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq11e_arbeit_aussen,v4_con$v4_leq_b_leq11e,"v4_leq_B_11B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 678 10 19 23 31 1025 1786
## [2,] Percent 38 0.6 1.1 1.3 1.7 57.4 100
12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v4_leq_B_12A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq12a_arbeitswechs,v4_con$v4_leq_b_leq12a,"v4_leq_B_12A")
## -999 bad good <NA>
## [1,] No. cases 659 17 85 1025 1786
## [2,] Percent 36.9 1 4.8 57.4 100
12B Impact (ordinal [0,1,2,3], v4_leq_B_12B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq12e_arbeitswechs,v4_con$v4_leq_b_leq12e,"v4_leq_B_12B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 657 7 19 35 43 1025 1786
## [2,] Percent 36.8 0.4 1.1 2 2.4 57.4 100
13. “Changing your work hours or conditions”
13A Nature (dichotomous [“good”,“bad”], v4_leq_B_13A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq13a_veraend_arb,v4_con$v4_leq_b_leq13a,"v4_leq_B_13A")
## -999 bad good <NA>
## [1,] No. cases 626 40 95 1025 1786
## [2,] Percent 35.1 2.2 5.3 57.4 100
13B Impact (ordinal [0,1,2,3], v4_leq_B_13B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq13e_veraend_arb,v4_con$v4_leq_b_leq13e,"v4_leq_B_13B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 625 6 41 43 46 1025 1786
## [2,] Percent 35 0.3 2.3 2.4 2.6 57.4 100
14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v4_leq_B_14A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq14a_veraend_ba,v4_con$v4_leq_b_leq14a,"v4_leq_B_14A")
## -999 bad good <NA>
## [1,] No. cases 619 27 115 1025 1786
## [2,] Percent 34.7 1.5 6.4 57.4 100
14B Impact (ordinal [0,1,2,3], v4_leq_B_14B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq14e_veraend_ba,v4_con$v4_leq_b_leq14e,"v4_leq_B_14B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 617 9 37 57 41 1025 1786
## [2,] Percent 34.5 0.5 2.1 3.2 2.3 57.4 100
15. “Troubles at work with your employer or co-worker”
15A Nature (dichotomous [“good”,“bad”], v4_leq_B_15A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq15a_schw_arbeit,v4_con$v4_leq_b_leq15a,"v4_leq_B_15A")
## -999 bad good <NA>
## [1,] No. cases 682 66 13 1025 1786
## [2,] Percent 38.2 3.7 0.7 57.4 100
15B Impact (ordinal [0,1,2,3], v4_leq_B_15B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq15e_schw_arbeit,v4_con$v4_leq_b_leq15e,"v4_leq_B_15B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 681 8 29 28 15 1025 1786
## [2,] Percent 38.1 0.4 1.6 1.6 0.8 57.4 100
16. “Major business readjustment”
16A Nature (dichotomous [“good”,“bad”], v4_leq_B_16A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq16a_betr_reorg,v4_con$v4_leq_b_leq16a,"v4_leq_B_16A")
## -999 bad good <NA>
## [1,] No. cases 731 19 11 1025 1786
## [2,] Percent 40.9 1.1 0.6 57.4 100
16B Impact (ordinal [0,1,2,3], v4_leq_B_16B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq16e_betr_reorg,v4_con$v4_leq_b_leq16e,"v4_leq_B_16B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 729 5 10 7 10 1025 1786
## [2,] Percent 40.8 0.3 0.6 0.4 0.6 57.4 100
17. “Being fired or laid off from work”
17A Nature (dichotomous [“good”,“bad”], v4_leq_B_17A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq17a_kuendigung,v4_con$v4_leq_b_leq17a,"v4_leq_B_17A")
## -999 bad good <NA>
## [1,] No. cases 731 16 14 1025 1786
## [2,] Percent 40.9 0.9 0.8 57.4 100
17B Impact (ordinal [0,1,2,3], v4_leq_B_17B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq17e_kuendigung,v4_con$v4_leq_b_leq17e,"v4_leq_B_17B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 730 8 4 6 13 1025 1786
## [2,] Percent 40.9 0.4 0.2 0.3 0.7 57.4 100
18. “Retirement from work”
18A Nature (dichotomous [“good”,“bad”], v4_leq_B_18A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq18a_ende_beruf,v4_con$v4_leq_b_leq18a,"v4_leq_B_18A")
## -999 bad good <NA>
## [1,] No. cases 744 4 13 1025 1786
## [2,] Percent 41.7 0.2 0.7 57.4 100
18B Impact (ordinal [0,1,2,3], v4_leq_B_18B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq18e_ende_beruf,v4_con$v4_leq_b_leq18e,"v4_leq_B_18B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 743 1 5 2 10 1025 1786
## [2,] Percent 41.6 0.1 0.3 0.1 0.6 57.4 100
19. “Taking courses by mail or studying at home to help you in your work”
19A Nature (dichotomous [“good”,“bad”], v4_leq_B_19A)
v4_leq_a_recode(v4_clin$v4_leq_b_leq19a_fortbildung,v4_con$v4_leq_b_leq19a,"v4_leq_B_19A")
## -999 bad good <NA>
## [1,] No. cases 716 7 38 1025 1786
## [2,] Percent 40.1 0.4 2.1 57.4 100
19B Impact (ordinal [0,1,2,3], v4_leq_B_19B)
v4_leq_b_recode(v4_clin$v4_leq_b_leq19e_fortbildung,v4_con$v4_leq_b_leq19e,"v4_leq_B_19B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 714 6 15 14 12 1025 1786
## [2,] Percent 40 0.3 0.8 0.8 0.7 57.4 100
v4_leq_B<-data.frame(v4_leq_B_10A,v4_leq_B_10B,v4_leq_B_11A,v4_leq_B_11B,v4_leq_B_12A,
v4_leq_B_12B,v4_leq_B_13A,v4_leq_B_13B,v4_leq_B_14A,v4_leq_B_14B,
v4_leq_B_15A,v4_leq_B_15B,v4_leq_B_16A,v4_leq_B_16B,v4_leq_B_17A,
v4_leq_B_17B,v4_leq_B_18A,v4_leq_B_18B,v4_leq_B_19A,v4_leq_B_19B)
20. “Beginning or ceasing school, college, or training program”
20A Nature (dichotomous [“good”,“bad”], v4_leq_C_20A)
v4_leq_a_recode(v4_clin$v4_leq_c_d_leq20a_beginn_ende,v4_con$v4_leq_c_d_leq20a,"v4_leq_C_20A")
## -999 bad good <NA>
## [1,] No. cases 713 2 46 1025 1786
## [2,] Percent 39.9 0.1 2.6 57.4 100
20B Impact (ordinal [0,1,2,3], v4_leq_C_20B)
v4_leq_b_recode(v4_clin$v4_leq_c_d_leq20e_beginn_ende,v4_con$v4_leq_c_d_leq20e,"v4_leq_C_20B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 712 1 9 15 24 1025 1786
## [2,] Percent 39.9 0.1 0.5 0.8 1.3 57.4 100
21. “Change of school, college, or training program”
21A Nature (dichotomous [“good”,“bad”], v4_leq_C_21A)
v4_leq_a_recode(v4_clin$v4_leq_c_d_leq21a_schulwechsel,v4_con$v4_leq_c_d_leq21a,"v4_leq_C_21A")
## -999 bad good <NA>
## [1,] No. cases 749 2 10 1025 1786
## [2,] Percent 41.9 0.1 0.6 57.4 100
21B Impact (ordinal [0,1,2,3], v4_leq_C_21B)
v4_leq_b_recode(v4_clin$v4_leq_c_d_leq21e_schulwechsel,v4_con$v4_leq_c_d_leq21e,"v4_leq_C_21B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 748 1 2 2 8 1025 1786
## [2,] Percent 41.9 0.1 0.1 0.1 0.4 57.4 100
22. “Change in career goal or academic major”
A Nature (dichotomous [“good”,“bad”], v4_leq_C_22A)
v4_leq_a_recode(v4_clin$v4_leq_c_d_leq22a_aend_karriere,v4_con$v4_leq_c_d_leq22a,"v4_leq_C_22A")
## -999 bad good <NA>
## [1,] No. cases 739 2 20 1025 1786
## [2,] Percent 41.4 0.1 1.1 57.4 100
B Impact (ordinal [0,1,2,3], v4_leq_C_22B)
v4_leq_b_recode(v4_clin$v4_leq_c_d_leq22e_aend_karriere,v4_con$v4_leq_c_d_leq22e,"v4_leq_C_22B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 738 1 5 7 10 1025 1786
## [2,] Percent 41.3 0.1 0.3 0.4 0.6 57.4 100
23. “Problem in school, college, or training program”
23A Nature (dichotomous [“good”,“bad”], v4_leq_C_23A)
v4_leq_a_recode(v4_clin$v4_leq_c_d_leq23a_schulprob,v4_con$v4_leq_c_d_leq23a,"v4_leq_C_23A")
## -999 bad good <NA>
## [1,] No. cases 747 12 2 1025 1786
## [2,] Percent 41.8 0.7 0.1 57.4 100
23B Impact (ordinal [0,1,2,3], v4_leq_C_23B)
v4_leq_b_recode(v4_clin$v4_leq_c_d_leq23e_schulprob,v4_con$v4_leq_c_d_leq23e,"v4_leq_C_23B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 746 1 4 3 7 1025 1786
## [2,] Percent 41.8 0.1 0.2 0.2 0.4 57.4 100
Create dataset
v4_leq_C<-data.frame(v4_leq_C_20A,v4_leq_C_20B,v4_leq_C_21A,v4_leq_C_21B,v4_leq_C_22A,v4_leq_C_22B,v4_leq_C_23A,v4_leq_C_23B)
24. “Difficulty finding housing”
24A Nature (dichotomous [“good”,“bad”], v4_leq_D_24A)
v4_leq_a_recode(v4_clin$v4_leq_c_d_leq24a_schw_wsuche,v4_con$v4_leq_c_d_leq24a,"v4_leq_D_24A")
## -999 bad good <NA>
## [1,] No. cases 714 39 8 1025 1786
## [2,] Percent 40 2.2 0.4 57.4 100
24B Impact (ordinal [0,1,2,3], v4_leq_D_24B)
v4_leq_b_recode(v4_clin$v4_leq_c_d_leq24e_schw_wsuche,v4_con$v4_leq_c_d_leq24e,"v4_leq_D_24B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 712 4 9 15 21 1025 1786
## [2,] Percent 39.9 0.2 0.5 0.8 1.2 57.4 100
25. “Changing residence within the same town or city”
A Nature (dichotomous [“good”,“bad”], v4_leq_D_25A)
v4_leq_a_recode(v4_clin$v4_leq_c_d_leq25a_umzug_nah,v4_con$v4_leq_c_d_leq25a,"v4_leq_D_25A")
## -999 bad good <NA>
## [1,] No. cases 709 5 47 1025 1786
## [2,] Percent 39.7 0.3 2.6 57.4 100
B Impact (ordinal [0,1,2,3], v4_leq_D_25B)
v4_leq_b_recode(v4_clin$v4_leq_c_d_leq25e_umzug_nah,v4_con$v4_leq_c_d_leq25e,"v4_leq_D_25B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 707 3 6 18 27 1025 1786
## [2,] Percent 39.6 0.2 0.3 1 1.5 57.4 100
26. “Moving to a different town, city, state, or country”
26A Nature (dichotomous [“good”,“bad”], v4_leq_D_26A)
v4_leq_a_recode(v4_clin$v4_leq_c_d_leq26a_umzug_fern,v4_con$v4_leq_c_d_leq26a,"v4_leq_D_26A")
## -999 bad good <NA>
## [1,] No. cases 740 4 17 1025 1786
## [2,] Percent 41.4 0.2 1 57.4 100
26B Impact (ordinal [0,1,2,3], v4_leq_D_26B)
v4_leq_b_recode(v4_clin$v4_leq_c_d_leq26e_umzug_fern,v4_con$v4_leq_c_d_leq26e,"v4_leq_D_26B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 739 2 2 5 13 1025 1786
## [2,] Percent 41.4 0.1 0.1 0.3 0.7 57.4 100
27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”
27A Nature (dichotomous [“good”,“bad”], v4_leq_D_27A)
v4_leq_a_recode(v4_clin$v4_leq_c_d_leq27a_veraend_lu,v4_con$v4_leq_c_d_leq27a,"v4_leq_D_27A")
## -999 bad good <NA>
## [1,] No. cases 666 34 61 1025 1786
## [2,] Percent 37.3 1.9 3.4 57.4 100
27B Impact (ordinal [0,1,2,3], v4_leq_D_27B)
v4_leq_b_recode(v4_clin$v4_leq_c_d_leq27e_veraend_lu,v4_con$v4_leq_c_d_leq27e,"v4_leq_D_27B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 663 1 19 32 46 1025 1786
## [2,] Percent 37.1 0.1 1.1 1.8 2.6 57.4 100
Create dataset
v4_leq_D<-data.frame(v4_leq_D_24A,v4_leq_D_24B,v4_leq_D_25A,v4_leq_D_25B,v4_leq_D_26A,
v4_leq_D_26B,v4_leq_D_27A,v4_leq_D_27B)
28. “Began a new, close, personal relationship”
28A Nature (dichotomous [“good”,“bad”], v4_leq_E_28A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq28a_neue_bez,v4_con$v4_leq_e_leq28a,"v4_leq_E_28A")
## -999 bad good <NA>
## [1,] No. cases 708 4 49 1025 1786
## [2,] Percent 39.6 0.2 2.7 57.4 100
28B Impact (ordinal [0,1,2,3], v4_leq_E_28B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq28e_neue_bez,v4_con$v4_leq_e_leq28e,"v4_leq_E_28B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 707 3 9 14 28 1025 1786
## [2,] Percent 39.6 0.2 0.5 0.8 1.6 57.4 100
29. “Became engaged”
29A Nature (dichotomous [“good”,“bad”], v4_leq_E_29A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq29a_verlobung,v4_con$v4_leq_e_leq29a,"v4_leq_E_29A")
## -999 bad good <NA>
## [1,] No. cases 753 1 7 1025 1786
## [2,] Percent 42.2 0.1 0.4 57.4 100
29B Impact (ordinal [0,1,2,3], v4_leq_E_29B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq29e_verlobung,v4_con$v4_leq_e_leq29e,"v4_leq_E_29B")
## -999 0 2 3 <NA>
## [1,] No. cases 752 2 3 4 1025 1786
## [2,] Percent 42.1 0.1 0.2 0.2 57.4 100
30. “Girlfriend or boyfriend problems”
30A Nature (dichotomous [“good”,“bad”], v4_leq_E_30A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq30a_prob_partner,v4_con$v4_leq_e_leq30a,"v4_leq_E_30A")
## -999 bad good <NA>
## [1,] No. cases 702 54 5 1025 1786
## [2,] Percent 39.3 3 0.3 57.4 100
30B Impact (ordinal [0,1,2,3], v4_leq_E_30B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq30e_prob_partner,v4_con$v4_leq_e_leq30e,"v4_leq_E_30B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 701 7 15 16 22 1025 1786
## [2,] Percent 39.2 0.4 0.8 0.9 1.2 57.4 100
31. “Breaking up with a girlfriend or breaking an engagement”
31A Nature (dichotomous [“good”,“bad”], v4_leq_E_31A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq31a_trennung,v4_con$v4_leq_e_leq31a,"v4_leq_E_31A")
## -999 bad good <NA>
## [1,] No. cases 730 22 9 1025 1786
## [2,] Percent 40.9 1.2 0.5 57.4 100
31B Impact (ordinal [0,1,2,3], v4_leq_E_31B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq31e_trennung,v4_con$v4_leq_e_leq31e,"v4_leq_E_31B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 729 3 6 7 16 1025 1786
## [2,] Percent 40.8 0.2 0.3 0.4 0.9 57.4 100
32. “(Male) Wife or girlfriend’s pregnancy”
32A Nature (dichotomous [“good”,“bad”], v4_leq_E_32A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq32a_schwanger_p,v4_con$v4_leq_e_leq32a,"v4_leq_E_32A")
## -999 good <NA>
## [1,] No. cases 758 3 1025 1786
## [2,] Percent 42.4 0.2 57.4 100
32B Impact (ordinal [0,1,2,3], v4_leq_E_32B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq32e_schwanger_p,v4_con$v4_leq_e_leq32e,"v4_leq_E_32B")
## -999 2 3 <NA>
## [1,] No. cases 758 2 1 1025 1786
## [2,] Percent 42.4 0.1 0.1 57.4 100
33. “(Male) Wife or girlfriend having a miscarriage or abortion”
33A Nature (dichotomous [“good”,“bad”], v4_leq_E_33A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq33a_fehlg_abtr_p,v4_con$v4_leq_e_leq33a,"v4_leq_E_33A")
## -999 <NA>
## [1,] No. cases 761 1025 1786
## [2,] Percent 42.6 57.4 100
33B Impact (ordinal [0,1,2,3], v4_leq_E_33B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq33e_fehlg_abtr_p,v4_con$v4_leq_e_leq33e,"v4_leq_E_33B")
## -999 <NA>
## [1,] No. cases 761 1025 1786
## [2,] Percent 42.6 57.4 100
34. “Getting married (or beginning to live with someone)”
34A Nature (dichotomous [“good”,“bad”], v4_leq_E_34A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq34a_heirat,v4_con$v4_leq_e_leq34a,"v4_leq_E_34A")
## -999 good <NA>
## [1,] No. cases 750 11 1025 1786
## [2,] Percent 42 0.6 57.4 100
34B Impact (ordinal [0,1,2,3], v4_leq_E_34B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq34e_heirat,v4_con$v4_leq_e_leq34e,"v4_leq_E_34B")
## -999 0 2 3 <NA>
## [1,] No. cases 749 1 6 5 1025 1786
## [2,] Percent 41.9 0.1 0.3 0.3 57.4 100
35. “A change in closeness with your partner”
35A Nature (dichotomous [“good”,“bad”], v4_leq_E_35A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq35a_veraend_naehe,v4_con$v4_leq_e_leq35a,"v4_leq_E_35A")
## -999 bad good <NA>
## [1,] No. cases 694 23 44 1025 1786
## [2,] Percent 38.9 1.3 2.5 57.4 100
35B Impact (ordinal [0,1,2,3], v4_leq_E_35B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq35e_veraend_naehe,v4_con$v4_leq_e_leq35e,"v4_leq_E_35B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 693 3 12 20 33 1025 1786
## [2,] Percent 38.8 0.2 0.7 1.1 1.8 57.4 100
36. “Infidelity”
36A Nature (dichotomous [“good”,“bad”], v4_leq_E_36A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq36a_untreue,v4_con$v4_leq_e_leq36a,"v4_leq_E_36A")
## -999 bad good <NA>
## [1,] No. cases 752 6 3 1025 1786
## [2,] Percent 42.1 0.3 0.2 57.4 100
36B Impact (ordinal [0,1,2,3], v4_leq_E_36B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq36e_untreue,v4_con$v4_leq_e_leq36e,"v4_leq_E_36B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 751 2 2 3 3 1025 1786
## [2,] Percent 42 0.1 0.1 0.2 0.2 57.4 100
37. “Trouble with in-laws”
37A Nature (dichotomous [“good”,“bad”], v4_leq_E_37A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq37a_konf_schwiege,v4_con$v4_leq_e_leq37a,"v4_leq_E_37A")
## -999 bad good <NA>
## [1,] No. cases 748 12 1 1025 1786
## [2,] Percent 41.9 0.7 0.1 57.4 100
37B Impact (ordinal [0,1,2,3], v4_leq_E_37B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq37e_konf_schwiege,v4_con$v4_leq_e_leq37e,"v4_leq_E_37B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 747 1 5 7 1 1025 1786
## [2,] Percent 41.8 0.1 0.3 0.4 0.1 57.4 100
38. “Separation from spouse or partner due to conflict”
38A Nature (dichotomous [“good”,“bad”], v4_leq_E_38A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq38a_trennung_str,v4_con$v4_leq_e_leq38a,"v4_leq_E_38A")
## -999 bad good <NA>
## [1,] No. cases 751 6 4 1025 1786
## [2,] Percent 42 0.3 0.2 57.4 100
38B Impact (ordinal [0,1,2,3], v4_leq_E_38B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq38e_trennung_str,v4_con$v4_leq_e_leq38e,"v4_leq_E_38B")
## -999 0 1 3 <NA>
## [1,] No. cases 750 2 3 6 1025 1786
## [2,] Percent 42 0.1 0.2 0.3 57.4 100
39. “Separation from spouse or partner due to work, travel, etc.”
39A Nature (dichotomous [“good”,“bad”], v4_leq_E_39A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq39a_trennung_ber,v4_con$v4_leq_e_leq39a,"v4_leq_E_39A")
## -999 bad good <NA>
## [1,] No. cases 755 3 3 1025 1786
## [2,] Percent 42.3 0.2 0.2 57.4 100
39B Impact (ordinal [0,1,2,3], v4_leq_E_39B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq39e_trennung_ber,v4_con$v4_leq_e_leq39e,"v4_leq_E_39B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 754 1 1 1 4 1025 1786
## [2,] Percent 42.2 0.1 0.1 0.1 0.2 57.4 100
40. “Reconciliation with spouse or partner”
40A Nature (dichotomous [“good”,“bad”], v4_leq_E_40A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq40a_versoehnung,v4_con$v4_leq_e_leq40a,"v4_leq_E_40A")
## -999 bad good <NA>
## [1,] No. cases 745 1 15 1025 1786
## [2,] Percent 41.7 0.1 0.8 57.4 100
40B Impact (ordinal [0,1,2,3], v4_leq_E_40B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq40a_versoehnung,v4_con$v4_leq_e_leq40e,"v4_leq_E_40B")
## -999 0 1 3 <NA>
## [1,] No. cases 745 1 11 4 1025 1786
## [2,] Percent 41.7 0.1 0.6 0.2 57.4 100
41. “Divorce”
41A Nature (dichotomous [“good”,“bad”], v4_leq_E_41A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq41a_scheidung,v4_con$v4_leq_e_leq41a,"v4_leq_E_41A")
## -999 bad <NA>
## [1,] No. cases 759 2 1025 1786
## [2,] Percent 42.5 0.1 57.4 100
41B Impact (ordinal [0,1,2,3], v4_leq_E_41B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq41e_scheidung,v4_con$v4_leq_e_leq41e,"v4_leq_E_41B")
## -999 0 3 <NA>
## [1,] No. cases 758 1 2 1025 1786
## [2,] Percent 42.4 0.1 0.1 57.4 100
42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”
42A Nature (dichotomous [“good”,“bad”], v4_leq_E_42A)
v4_leq_a_recode(v4_clin$v4_leq_e_leq42a_veraend_taet,v4_con$v4_leq_e_leq42a,"v4_leq_E_42A")
## -999 bad good <NA>
## [1,] No. cases 729 10 22 1025 1786
## [2,] Percent 40.8 0.6 1.2 57.4 100
42B Impact (ordinal [0,1,2,3], v4_leq_E_42B)
v4_leq_b_recode(v4_clin$v4_leq_e_leq42e_veraend_taet,v4_con$v4_leq_e_leq42e,"v4_leq_E_42B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 728 3 7 13 10 1025 1786
## [2,] Percent 40.8 0.2 0.4 0.7 0.6 57.4 100
Create dataset
v4_leq_E<-data.frame(v4_leq_E_28A,v4_leq_E_28B,v4_leq_E_29A,v4_leq_E_29B,v4_leq_E_30A,
v4_leq_E_30B,v4_leq_E_31A,v4_leq_E_31B,v4_leq_E_32A,v4_leq_E_32B,
v4_leq_E_33A,v4_leq_E_33B,v4_leq_E_34A,v4_leq_E_34B,v4_leq_E_35A,
v4_leq_E_35B,v4_leq_E_36A,v4_leq_E_36B,v4_leq_E_37A,v4_leq_E_37B,
v4_leq_E_38A,v4_leq_E_38B,v4_leq_E_39A,v4_leq_E_39B,v4_leq_E_40A,
v4_leq_E_40B,v4_leq_E_41A,v4_leq_E_41B,v4_leq_E_42A,v4_leq_E_42B)
43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”
43A Nature (dichotomous [“good”,“bad”], v4_leq_F_43A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq43a_neu_fmitglied,v4_con$v4_leq_f_g_leq43a,"v4_leq_F_43A")
## -999 bad good <NA>
## [1,] No. cases 723 2 36 1025 1786
## [2,] Percent 40.5 0.1 2 57.4 100
43B Impact (ordinal [0,1,2,3], v4_leq_F_43B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq43e_neu_fmitglied,v4_con$v4_leq_f_g_leq43e,"v4_leq_F_43B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 722 3 11 2 23 1025 1786
## [2,] Percent 40.4 0.2 0.6 0.1 1.3 57.4 100
44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”
44A Nature (dichotomous [“good”,“bad”], v4_leq_F_44A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq44a_auszug_fm,v4_con$v4_leq_f_g_leq44a,"v4_leq_F_44A")
## -999 bad good <NA>
## [1,] No. cases 750 5 6 1025 1786
## [2,] Percent 42 0.3 0.3 57.4 100
44B Impact (ordinal [0,1,2,3], v4_leq_F_44B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq44e_auszug_fm,v4_con$v4_leq_f_g_leq44e,"v4_leq_F_44B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 748 2 3 4 4 1025 1786
## [2,] Percent 41.9 0.1 0.2 0.2 0.2 57.4 100
45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”
45A Nature (dichotomous [“good”,“bad”], v4_leq_F_45A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq45a_gz_verh_fm,v4_con$v4_leq_f_g_leq45a,"v4_leq_F_45A")
## -999 bad good <NA>
## [1,] No. cases 652 98 11 1025 1786
## [2,] Percent 36.5 5.5 0.6 57.4 100
45B Impact (ordinal [0,1,2,3], v4_leq_F_45B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq45e_gz_verh_fm,v4_con$v4_leq_f_g_leq45e,"v4_leq_F_45B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 649 7 26 34 45 1025 1786
## [2,] Percent 36.3 0.4 1.5 1.9 2.5 57.4 100
46. “Death of spouse or partner”
46A Nature (dichotomous [“good”,“bad”], v4_leq_F_46A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq46a_tod_partner,v4_con$v4_leq_f_g_leq46a,"v4_leq_F_46A")
## -999 bad good <NA>
## [1,] No. cases 759 1 1 1025 1786
## [2,] Percent 42.5 0.1 0.1 57.4 100
46B Impact (ordinal [0,1,2,3], v4_leq_F_46B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq46e_tod_partner,v4_con$v4_leq_f_g_leq46e,"v4_leq_F_46B")
## -999 0 1 2 <NA>
## [1,] No. cases 758 1 1 1 1025 1786
## [2,] Percent 42.4 0.1 0.1 0.1 57.4 100
47. “Death of a child”
47A Nature (dichotomous [“good”,“bad”], v4_leq_F_47A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq47a_tod_kind,v4_con$v4_leq_f_g_leq47a,"v4_leq_F_47A")
## -999 <NA>
## [1,] No. cases 761 1025 1786
## [2,] Percent 42.6 57.4 100
47B Impact (ordinal [0,1,2,3], v4_leq_F_47B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq47e_tod_kind,v4_con$v4_leq_f_g_leq47e,"v4_leq_F_47B")
## -999 0 <NA>
## [1,] No. cases 760 1 1025 1786
## [2,] Percent 42.6 0.1 57.4 100
48. “Death of family member or close friend”
48A Nature (dichotomous [“good”,“bad”], v4_leq_F_48A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq48a_tod_fm_ef,v4_con$v4_leq_f_g_leq48a,"v4_leq_F_48A")
## -999 bad good <NA>
## [1,] No. cases 696 61 4 1025 1786
## [2,] Percent 39 3.4 0.2 57.4 100
48B Impact (ordinal [0,1,2,3], v4_leq_F_48B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq48e_tod_fm_ef,v4_con$v4_leq_f_g_leq48e,"v4_leq_F_48B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 695 6 18 17 25 1025 1786
## [2,] Percent 38.9 0.3 1 1 1.4 57.4 100
49. “Birth of a grandchild”
49A Nature (dichotomous [“good”,“bad”], v4_leq_F_49A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq49a_geb_enkel,v4_con$v4_leq_f_g_leq49a,"v4_leq_F_49A")
## -999 good <NA>
## [1,] No. cases 745 16 1025 1786
## [2,] Percent 41.7 0.9 57.4 100
49B Impact (ordinal [0,1,2,3], v4_leq_F_49B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq49e_geb_enkel,v4_con$v4_leq_f_g_leq49e,"v4_leq_F_49B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 744 4 2 4 7 1025 1786
## [2,] Percent 41.7 0.2 0.1 0.2 0.4 57.4 100
50. “Change in marital status of your parents”
50A Nature (dichotomous [“good”,“bad”], v4_leq_F_50A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq50a_fstand_eltern,v4_con$v4_leq_f_g_leq50a,"v4_leq_F_50A")
## -999 bad good <NA>
## [1,] No. cases 749 7 5 1025 1786
## [2,] Percent 41.9 0.4 0.3 57.4 100
50B Impact (ordinal [0,1,2,3], v4_leq_F_50B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq50e_fstand_eltern,v4_con$v4_leq_f_g_leq50e,"v4_leq_F_50B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 748 3 6 3 1 1025 1786
## [2,] Percent 41.9 0.2 0.3 0.2 0.1 57.4 100
Create dataset
v4_leq_F<-data.frame(v4_leq_F_43A,v4_leq_F_43B,v4_leq_F_44A,v4_leq_F_44B,v4_leq_F_45A,
v4_leq_F_45B,v4_leq_F_46A,v4_leq_F_46B,v4_leq_F_47A,v4_leq_F_47B,
v4_leq_F_48A,v4_leq_F_48B,v4_leq_F_49A,v4_leq_F_49B,v4_leq_F_50A,
v4_leq_F_50B)
51. “Change in child care arrangements”
51A Nature (dichotomous [“good”,“bad”], v4_leq_G_51A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq51a_kindbetr,v4_con$v4_leq_f_g_leq51a,"v4_leq_G_51A")
## -999 bad good <NA>
## [1,] No. cases 749 4 8 1025 1786
## [2,] Percent 41.9 0.2 0.4 57.4 100
51B Impact (ordinal [0,1,2,3], v4_leq_G_51B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq51e_kindbetr,v4_con$v4_leq_f_g_leq51e,"v4_leq_G_51B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 748 3 4 4 2 1025 1786
## [2,] Percent 41.9 0.2 0.2 0.2 0.1 57.4 100
52. “Conflicts with spouse or partner about parenting”
52A Nature (dichotomous [“good”,“bad”], v4_leq_G_52A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq52a_konf_eschaft,v4_con$v4_leq_f_g_leq52a,"v4_leq_G_52A")
## -999 bad good <NA>
## [1,] No. cases 742 16 3 1025 1786
## [2,] Percent 41.5 0.9 0.2 57.4 100
52B Impact (ordinal [0,1,2,3], v4_leq_G_52B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq52e_konf_eschaft,v4_con$v4_leq_f_g_leq52e,"v4_leq_G_52B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 741 4 6 6 4 1025 1786
## [2,] Percent 41.5 0.2 0.3 0.3 0.2 57.4 100
53. “Conflicts with child’s grandparents (or other important person) about parenting”
53A Nature (dichotomous [“good”,“bad”], v4_leq_G_53A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq53a_konf_geltern,v4_con$v4_leq_f_g_leq53a,"v4_leq_G_53A")
## -999 bad <NA>
## [1,] No. cases 756 5 1025 1786
## [2,] Percent 42.3 0.3 57.4 100
53B Impact (ordinal [0,1,2,3], v4_leq_G_53B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq53e_konf_geltern,v4_con$v4_leq_f_g_leq53e,"v4_leq_G_53B")
## -999 0 1 3 <NA>
## [1,] No. cases 755 1 3 2 1025 1786
## [2,] Percent 42.3 0.1 0.2 0.1 57.4 100
54. “Taking on full responsibility for parenting as a single parent”
54A Nature (dichotomous [“good”,“bad”], v4_leq_G_54A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq54a_alleinerz,v4_con$v4_leq_f_g_leq54a,"v4_leq_G_54A")
## -999 bad good <NA>
## [1,] No. cases 758 1 2 1025 1786
## [2,] Percent 42.4 0.1 0.1 57.4 100
54B Impact (ordinal [0,1,2,3], v4_leq_G_54B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq54e_alleinerz,v4_con$v4_leq_f_g_leq54e,"v4_leq_G_54B")
## -999 0 1 3 <NA>
## [1,] No. cases 757 1 2 1 1025 1786
## [2,] Percent 42.4 0.1 0.1 0.1 57.4 100
55. “Custody battles with former spouse or partner”
55A Nature (dichotomous [“good”,“bad”], v4_leq_G_55A)
v4_leq_a_recode(v4_clin$v4_leq_f_g_leq55a_sorgerecht,v4_con$v4_leq_f_g_leq55a,"v4_leq_G_55A")
## -999 bad good <NA>
## [1,] No. cases 754 6 1 1025 1786
## [2,] Percent 42.2 0.3 0.1 57.4 100
55B Impact (ordinal [0,1,2,3], v4_leq_G_55B)
v4_leq_b_recode(v4_clin$v4_leq_f_g_leq55e_sorgerecht,v4_con$v4_leq_f_g_leq55e,"v4_leq_G_55B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 753 2 1 4 1 1025 1786
## [2,] Percent 42.2 0.1 0.1 0.2 0.1 57.4 100
Create dataset
v4_leq_G<-data.frame(v4_leq_G_51A,v4_leq_G_51B,v4_leq_G_52A,v4_leq_G_52B,v4_leq_G_53A,
v4_leq_G_53B,v4_leq_G_54A,v4_leq_G_54B,v4_leq_G_55A,v4_leq_G_55B)
69. “Major change in finances (increased or decreased income)”
69A Nature (dichotomous [“good”,“bad”], v4_leq_I_69A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq69a_finanz_sit,v4_con$v4_leq_i_j_k_leq69a,"v4_leq_I_69A")
## -999 bad good <NA>
## [1,] No. cases 576 90 95 1025 1786
## [2,] Percent 32.3 5 5.3 57.4 100
69B Impact (ordinal [0,1,2,3], v4_leq_I_69B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq69e_finanz_sit,v4_con$v4_leq_i_j_k_leq69e,"v4_leq_I_69B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 574 6 41 68 72 1025 1786
## [2,] Percent 32.1 0.3 2.3 3.8 4 57.4 100
70. “Took on a moderate purchase, such as TV, car, freezer, etc.”
70A Nature (dichotomous [“good”,“bad”], v4_leq_I_70A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq70a_finanz_verpfl,v4_con$v4_leq_i_j_k_leq70a,"v4_leq_I_70A")
## -999 bad good <NA>
## [1,] No. cases 712 20 29 1025 1786
## [2,] Percent 39.9 1.1 1.6 57.4 100
70B Impact (ordinal [0,1,2,3], v4_leq_I_70B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq70e_finanz_verpfl,v4_con$v4_leq_i_j_k_leq70e,"v4_leq_I_70B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 710 9 21 15 6 1025 1786
## [2,] Percent 39.8 0.5 1.2 0.8 0.3 57.4 100
71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”
71A Nature (dichotomous [“good”,“bad”], v4_leq_I_71A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq71a_hypothek,v4_con$v4_leq_i_j_k_leq71a,"v4_leq_I_71A")
## -999 bad good <NA>
## [1,] No. cases 747 6 8 1025 1786
## [2,] Percent 41.8 0.3 0.4 57.4 100
71B Impact (ordinal [0,1,2,3], v4_leq_I_71B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq71e_hypothek,v4_con$v4_leq_i_j_k_leq71e,"v4_leq_I_71B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 745 2 4 4 6 1025 1786
## [2,] Percent 41.7 0.1 0.2 0.2 0.3 57.4 100
72. “Experienced a foreclosure on a mortgage or loan”
72A Nature (dichotomous [“good”,“bad”], v4_leq_I_72A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq72a_hypoth_kuend,v4_con$v4_leq_i_j_k_leq72a,"v4_leq_I_72A")
## -999 bad good <NA>
## [1,] No. cases 752 4 5 1025 1786
## [2,] Percent 42.1 0.2 0.3 57.4 100
72B Impact (ordinal [0,1,2,3], v4_leq_I_72B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq72e_hypoth_kuend,v4_con$v4_leq_i_j_k_leq72e,"v4_leq_I_72B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 751 2 1 4 3 1025 1786
## [2,] Percent 42 0.1 0.1 0.2 0.2 57.4 100
73. “Credit rating difficulties”
73A Nature (dichotomous [“good”,“bad”], v4_leq_I_73A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq73a_kreditwuerdk,v4_con$v4_leq_i_j_k_leq73a,"v4_leq_I_73A")
## -999 bad good <NA>
## [1,] No. cases 739 20 2 1025 1786
## [2,] Percent 41.4 1.1 0.1 57.4 100
73B Impact (ordinal [0,1,2,3], v4_leq_I_73B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq73e_kreditwuerdk,v4_con$v4_leq_i_j_k_leq73e,"v4_leq_I_73B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 738 2 10 5 6 1025 1786
## [2,] Percent 41.3 0.1 0.6 0.3 0.3 57.4 100
Create dataset
v4_leq_I<-data.frame(v4_leq_I_69A,v4_leq_I_69B,v4_leq_I_70A,v4_leq_I_70B,v4_leq_I_71A,
v4_leq_I_71B,v4_leq_I_72A,v4_leq_I_72B,v4_leq_I_73A,v4_leq_I_73B)
74. “Being robbed or victim of identity theft”
74A Nature (dichotomous [“good”,“bad”], v4_leq_J_74A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq74a_opf_diebstahl,v4_con$v4_leq_i_j_k_leq74a,"v4_leq_J_74A")
## -999 bad <NA>
## [1,] No. cases 736 25 1025 1786
## [2,] Percent 41.2 1.4 57.4 100
74B Impact (ordinal [0,1,2,3], v4_leq_J_74B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq74e_opf_diebstahl,v4_con$v4_leq_i_j_k_leq74e,"v4_leq_J_74B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 735 5 8 8 5 1025 1786
## [2,] Percent 41.2 0.3 0.4 0.4 0.3 57.4 100
75. “Being a victim of a violent act (rape, assault, etc.)”
75A Nature (dichotomous [“good”,“bad”], v4_leq_J_75A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq75a_opf_gewalttat,v4_con$v4_leq_i_j_k_leq75a,"v4_leq_J_75A")
## -999 bad <NA>
## [1,] No. cases 753 8 1025 1786
## [2,] Percent 42.2 0.4 57.4 100
75B Impact (ordinal [0,1,2,3], v4_leq_J_75B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq75e_opf_gewalttat,v4_con$v4_leq_i_j_k_leq75e,"v4_leq_J_75B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 752 4 1 2 2 1025 1786
## [2,] Percent 42.1 0.2 0.1 0.1 0.1 57.4 100
76. “Involved in an accident”
76A Nature (dichotomous [“good”,“bad”], v4_leq_J_76A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq76a_unfall,v4_con$v4_leq_i_j_k_leq76a,"v4_leq_J_76A")
## -999 bad good <NA>
## [1,] No. cases 740 19 2 1025 1786
## [2,] Percent 41.4 1.1 0.1 57.4 100
76B Impact (ordinal [0,1,2,3], v4_leq_J_76B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq76e_unfall,v4_con$v4_leq_i_j_k_leq76e,"v4_leq_J_76B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 738 13 4 4 2 1025 1786
## [2,] Percent 41.3 0.7 0.2 0.2 0.1 57.4 100
77. “Involved in a law suit”
77A Nature (dichotomous [“good”,“bad”], v4_leq_J_77A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq77a_rechtsstreit,v4_con$v4_leq_i_j_k_leq77a,"v4_leq_J_77A")
## -999 bad good <NA>
## [1,] No. cases 725 27 9 1025 1786
## [2,] Percent 40.6 1.5 0.5 57.4 100
77B Impact (ordinal [0,1,2,3], v4_leq_J_77B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq77e_rechtsstreit,v4_con$v4_leq_i_j_k_leq77e,"v4_leq_J_77B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 723 6 14 9 9 1025 1786
## [2,] Percent 40.5 0.3 0.8 0.5 0.5 57.4 100
78. “Involved in a minor violation of the law (traffic tickets, disturbing the peace, etc)”
78A Nature (dichotomous [“good”,“bad”], v4_leq_J_78A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq78a_owi,v4_con$v4_leq_i_j_k_leq78a,"v4_leq_J_78A")
## -999 bad good <NA>
## [1,] No. cases 732 27 2 1025 1786
## [2,] Percent 41 1.5 0.1 57.4 100
78B Impact (ordinal [0,1,2,3], v4_leq_J_78B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq78e_owi,v4_con$v4_leq_i_j_k_leq78e,"v4_leq_J_78B")
## -999 0 1 2 3 <NA>
## [1,] No. cases 731 7 15 7 1 1025 1786
## [2,] Percent 40.9 0.4 0.8 0.4 0.1 57.4 100
79. “Legal troubles resulting in your being arrested or held in jail”
79A Nature (dichotomous [“good”,“bad”], v4_leq_J_79A)
v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq79a_konf_gesetz,v4_con$v4_leq_i_j_k_leq79a,"v4_leq_J_79A")
## -999 bad <NA>
## [1,] No. cases 758 3 1025 1786
## [2,] Percent 42.4 0.2 57.4 100
79B Impact (ordinal [0,1,2,3], v4_leq_J_79B)
v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq79e_konf_gesetz,v4_con$v4_leq_i_j_k_leq79e,"v4_leq_J_79B")
## -999 0 1 <NA>
## [1,] No. cases 757 3 1 1025 1786
## [2,] Percent 42.4 0.2 0.1 57.4 100
Create dataset
v4_leq_J<-data.frame(v4_leq_J_74A,v4_leq_J_74B,v4_leq_J_75A,v4_leq_J_75B,v4_leq_J_76A,
v4_leq_J_76B,v4_leq_J_77A,v4_leq_J_77B,v4_leq_J_78A,v4_leq_J_78B,
v4_leq_J_79A,v4_leq_J_79B)
Create LEQ dataset
v4_leq<-data.frame(v4_leq_A,v4_leq_B,v4_leq_C,v4_leq_D,v4_leq_E,v4_leq_F,v4_leq_G,
v4_leq_H,v4_leq_I,v4_leq_J)
For explanation, please refer to the section in Visit 1
1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v4_whoqol_itm1)
v4_quol_recode(v4_clin$v4_whoqol_bref_who1_lebensqualitaet,v4_con$v4_whoqol_bref_who1,"v4_whoqol_itm1",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 11 52 202 363 174 984 1786
## [2,] Percent 0.6 2.9 11.3 20.3 9.7 55.1 100
2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v4_whoqol_itm2)”
v4_quol_recode(v4_clin$v4_whoqol_bref_who2_gesundheit,v4_con$v4_whoqol_bref_who2,"v4_whoqol_itm2",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 30 145 168 329 130 984 1786
## [2,] Percent 1.7 8.1 9.4 18.4 7.3 55.1 100
3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v4_whoqol_itm3)
Coding reversed so that higher scores mean less impairment by pain.
v4_quol_recode(v4_clin$v4_whoqol_bref_who3_schmerzen,v4_con$v4_whoqol_bref_who3,"v4_whoqol_itm3",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 9 44 76 157 511 989 1786
## [2,] Percent 0.5 2.5 4.3 8.8 28.6 55.4 100
4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v4_whoqol_itm4)”
Coding reversed so that higher scores mean less dependence on medical treatment.
v4_quol_recode(v4_clin$v4_whoqol_bref_who4_med_behand,v4_con$v4_whoqol_bref_who4,"v4_whoqol_itm4",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 73 143 109 166 302 993 1786
## [2,] Percent 4.1 8 6.1 9.3 16.9 55.6 100
5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v4_whoqol_itm5)
v4_quol_recode(v4_clin$v4_whoqol_bref_who5_lebensgenuss,v4_con$v4_whoqol_bref_who5,"v4_whoqol_itm5",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 21 84 206 345 136 994 1786
## [2,] Percent 1.2 4.7 11.5 19.3 7.6 55.7 100
6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v4_whoqol_itm6)
v4_quol_recode(v4_clin$v4_whoqol_bref_who6_lebenssinn,v4_con$v4_whoqol_bref_who6,"v4_whoqol_itm6",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 31 72 167 303 212 1001 1786
## [2,] Percent 1.7 4 9.4 17 11.9 56 100
7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v4_whoqol_itm7)
v4_quol_recode(v4_clin$v4_whoqol_bref_who7_konzentration,v4_con$v4_whoqol_bref_who7,"v4_whoqol_itm7",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 13 103 273 330 78 989 1786
## [2,] Percent 0.7 5.8 15.3 18.5 4.4 55.4 100
8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v4_whoqol_itm8)
v4_quol_recode(v4_clin$v4_whoqol_bref_who8_sicherheit,v4_con$v4_whoqol_bref_who8,"v4_whoqol_itm8",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 13 47 173 378 185 990 1786
## [2,] Percent 0.7 2.6 9.7 21.2 10.4 55.4 100
9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v4_whoqol_itm9)
v4_quol_recode(v4_clin$v4_whoqol_bref_who9_umweltbed,v4_con$v4_whoqol_bref_who9,"v4_whoqol_itm9",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 11 24 153 402 206 990 1786
## [2,] Percent 0.6 1.3 8.6 22.5 11.5 55.4 100
10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v4_whoqol_itm10)
v4_quol_recode(v4_clin$v4_whoqol_bref_who10_energie,v4_con$v4_whoqol_bref_who10,"v4_whoqol_itm10",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 19 55 188 313 224 987 1786
## [2,] Percent 1.1 3.1 10.5 17.5 12.5 55.3 100
11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v4_whoqol_itm11)
v4_quol_recode(v4_clin$v4_whoqol_bref_who11_aussehen,v4_con$v4_whoqol_bref_who11,"v4_whoqol_itm11",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 19 54 176 333 214 990 1786
## [2,] Percent 1.1 3 9.9 18.6 12 55.4 100
12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v4_whoqol_itm12)
v4_quol_recode(v4_clin$v4_whoqol_bref_who12_genug_geld,v4_con$v4_whoqol_bref_who12,"v4_whoqol_itm12",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 21 102 180 288 206 989 1786
## [2,] Percent 1.2 5.7 10.1 16.1 11.5 55.4 100
13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v4_whoqol_itm13)
v4_quol_recode(v4_clin$v4_whoqol_bref_who13_infozugang,v4_con$v4_whoqol_bref_who13,"v4_whoqol_itm13",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 4 16 78 281 418 989 1786
## [2,] Percent 0.2 0.9 4.4 15.7 23.4 55.4 100
14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v4_whoqol_itm14)
v4_quol_recode(v4_clin$v4_whoqol_bref_who14_freizeitaktiv,v4_con$v4_whoqol_bref_who14,"v4_whoqol_itm14",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 7 46 175 281 289 988 1786
## [2,] Percent 0.4 2.6 9.8 15.7 16.2 55.3 100
15. “How well are you able to get around? (ordinal [1,2,3,4,5], v4_whoqol_itm15)”
v4_quol_recode(v4_clin$v4_whoqol_bref_who15_fortbewegung,v4_con$v4_whoqol_bref_who15,"v4_whoqol_itm15",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 3 36 119 275 365 988 1786
## [2,] Percent 0.2 2 6.7 15.4 20.4 55.3 100
16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v4_whoqol_itm16)
v4_quol_recode(v4_clin$v4_whoqol_bref_who16_schlaf,v4_con$v4_whoqol_bref_who16,"v4_whoqol_itm16",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 32 120 121 383 148 982 1786
## [2,] Percent 1.8 6.7 6.8 21.4 8.3 55 100
17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v4_whoqol_itm17)
v4_quol_recode(v4_clin$v4_whoqol_bref_who17_alltag,v4_con$v4_whoqol_bref_who17,"v4_whoqol_itm17",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 20 99 135 338 210 984 1786
## [2,] Percent 1.1 5.5 7.6 18.9 11.8 55.1 100
18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v4_whoqol_itm18)
v4_quol_recode(v4_clin$v4_whoqol_bref_who18_arbeitsfhgk,v4_con$v4_whoqol_bref_who18,"v4_whoqol_itm18",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 54 128 152 302 165 985 1786
## [2,] Percent 3 7.2 8.5 16.9 9.2 55.2 100
19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v4_whoqol_itm19)
v4_quol_recode(v4_clin$v4_whoqol_bref_who19_selbstzufried,v4_con$v4_whoqol_bref_who19,"v4_whoqol_itm19",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 28 95 169 382 129 983 1786
## [2,] Percent 1.6 5.3 9.5 21.4 7.2 55 100
20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v4_whoqol_itm20)
v4_quol_recode(v4_clin$v4_whoqol_bref_who20_pers_bezieh,v4_con$v4_whoqol_bref_who20,"v4_whoqol_itm20",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 17 75 153 367 184 990 1786
## [2,] Percent 1 4.2 8.6 20.5 10.3 55.4 100
21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v4_whoqol_itm21)
v4_quol_recode(v4_clin$v4_whoqol_bref_who21_sexualleben,v4_con$v4_whoqol_bref_who21,"v4_whoqol_itm21",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 78 110 229 243 130 996 1786
## [2,] Percent 4.4 6.2 12.8 13.6 7.3 55.8 100
22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v4_whoqol_itm22)
v4_quol_recode(v4_clin$v4_whoqol_bref_who22_freunde,v4_con$v4_whoqol_bref_who22,"v4_whoqol_itm22",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 17 49 170 346 220 984 1786
## [2,] Percent 1 2.7 9.5 19.4 12.3 55.1 100
23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v4_whoqol_itm23)
v4_quol_recode(v4_clin$v4_whoqol_bref_who23_wohnbeding,v4_con$v4_whoqol_bref_who23,"v4_whoqol_itm23",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 18 59 125 340 261 983 1786
## [2,] Percent 1 3.3 7 19 14.6 55 100
24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v4_whoqol_itm24)
v4_quol_recode(v4_clin$v4_whoqol_bref_who24_gesundhdiens,v4_con$v4_whoqol_bref_who24,"v4_whoqol_itm24",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 13 22 78 366 326 981 1786
## [2,] Percent 0.7 1.2 4.4 20.5 18.3 54.9 100
25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v4_whoqol_itm25)
v4_quol_recode(v4_clin$v4_whoqol_bref_who25_transport,v4_con$v4_whoqol_bref_who25,"v4_whoqol_itm25",0)
## 1 2 3 4 5 NA's
## [1,] No. cases 13 36 73 359 321 984 1786
## [2,] Percent 0.7 2 4.1 20.1 18 55.1 100
26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v4_whoqol_itm26)
Coding reversed so that higher scores mean symptoms less often.
v4_quol_recode(v4_clin$v4_whoqol_bref_who26_neg_gefuehle,v4_con$v4_whoqol_bref_who26,"v4_whoqol_itm26",1)
## 1 2 3 4 5 NA's
## [1,] No. cases 21 105 165 326 185 984 1786
## [2,] Percent 1.2 5.9 9.2 18.3 10.4 55.1 100
Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).
Global (continuous [4-20],v4_whoqol_dom_glob)
v4_whoqol_dom_glob_df<-data.frame(as.numeric(v4_whoqol_itm1),as.numeric(v4_whoqol_itm2))
v4_who_glob_no_nas<-rowSums(is.na(v4_whoqol_dom_glob_df))
v4_whoqol_dom_glob<-ifelse((v4_who_glob_no_nas==0) | (v4_who_glob_no_nas==1),
rowMeans(v4_whoqol_dom_glob_df,na.rm=T)*4,NA)
v4_whoqol_dom_glob<-round(v4_whoqol_dom_glob,2)
summary(v4_whoqol_dom_glob)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.00 16.00 14.54 16.00 20.00 981
Physical Health (continuous [4-20],v4_whoqol_dom_phys)
v4_whoqol_dom_phys_df<-data.frame(as.numeric(v4_whoqol_itm3),as.numeric(v4_whoqol_itm10),as.numeric(v4_whoqol_itm16),as.numeric(v4_whoqol_itm15),as.numeric(v4_whoqol_itm17),as.numeric(v4_whoqol_itm4),as.numeric(v4_whoqol_itm18))
v4_who_phys_no_nas<-rowSums(is.na(v4_whoqol_dom_phys_df))
v4_whoqol_dom_phys<-ifelse((v4_who_phys_no_nas==0) | (v4_who_phys_no_nas==1),
rowMeans(v4_whoqol_dom_phys_df,na.rm=T)*4,NA)
v4_whoqol_dom_phys<-round(v4_whoqol_dom_phys,2)
summary(v4_whoqol_dom_phys)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.14 13.14 16.00 15.39 17.71 20.00 987
Psychological (continuous [4-20],v4_whoqol_dom_psy)
v4_whoqol_dom_psy_df<-data.frame(as.numeric(v4_whoqol_itm5),as.numeric(v4_whoqol_itm7),as.numeric(v4_whoqol_itm19),as.numeric(v4_whoqol_itm11),as.numeric(v4_whoqol_itm26),as.numeric(v4_whoqol_itm6))
v4_who_psy_no_nas<-rowSums(is.na(v4_whoqol_dom_psy_df))
v4_whoqol_dom_psy<-ifelse((v4_who_psy_no_nas==0) | (v4_who_psy_no_nas==1),
rowMeans(v4_whoqol_dom_psy_df,na.rm=T)*4,NA)
v4_whoqol_dom_psy<-round(v4_whoqol_dom_psy,2)
summary(v4_whoqol_dom_psy)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.67 15.33 14.63 16.67 20.00 991
Social relationships (continuous [4-20],v4_whoqol_dom_soc)
v4_whoqol_dom_soc_df<-data.frame(as.numeric(v4_whoqol_itm20),as.numeric(v4_whoqol_itm22),as.numeric(v4_whoqol_itm21))
v4_who_soc_no_nas<-rowSums(is.na(v4_whoqol_dom_soc_df))
v4_whoqol_dom_soc<-ifelse((v4_who_soc_no_nas==0) | (v4_who_soc_no_nas==1),
rowMeans(v4_whoqol_dom_soc_df,na.rm=T)*4,NA)
v4_whoqol_dom_soc<-round(v4_whoqol_dom_soc,2)
summary(v4_whoqol_dom_soc)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.00 12.00 14.67 14.62 17.33 20.00 985
Environment (continuous [4-20],v4_whoqol_dom_env)
v4_whoqol_dom_env_df<-data.frame(as.numeric(v4_whoqol_itm8),as.numeric(v4_whoqol_itm23),as.numeric(v4_whoqol_itm12),as.numeric(v4_whoqol_itm24),as.numeric(v4_whoqol_itm13),as.numeric(v4_whoqol_itm14),as.numeric(v4_whoqol_itm9),as.numeric(v4_whoqol_itm25))
v4_who_env_no_nas<-rowSums(is.na(v4_whoqol_dom_env_df))
v4_whoqol_dom_env<-ifelse((v4_who_env_no_nas==0) | (v4_who_env_no_nas==1),
rowMeans(v4_whoqol_dom_env_df,na.rm=T)*4,NA)
v4_whoqol_dom_env<-round(v4_whoqol_dom_env,2)
summary(v4_whoqol_dom_env)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.00 14.50 16.50 16.11 18.00 20.00 989
Create dataset
v4_whoqol<-data.frame(v4_whoqol_itm1,v4_whoqol_itm2,v4_whoqol_itm3,v4_whoqol_itm4,
v4_whoqol_itm5,v4_whoqol_itm6,v4_whoqol_itm7,v4_whoqol_itm8,
v4_whoqol_itm9,v4_whoqol_itm10,v4_whoqol_itm11,v4_whoqol_itm12,
v4_whoqol_itm13,v4_whoqol_itm14,v4_whoqol_itm15,v4_whoqol_itm16,
v4_whoqol_itm17,v4_whoqol_itm18,v4_whoqol_itm19,v4_whoqol_itm20,
v4_whoqol_itm21,v4_whoqol_itm22,v4_whoqol_itm23,v4_whoqol_itm24,
v4_whoqol_itm25,v4_whoqol_itm26,v4_whoqol_dom_glob,
v4_whoqol_dom_phys,v4_whoqol_dom_psy,v4_whoqol_dom_soc,
v4_whoqol_dom_env)
v4_df<-data.frame(v4_id,
v4_rec,
v4_clin_ill_ep,
v4_con_problems,
v4_dem,
v4_opcrit,
v4_leprcp,
v4_suic,
v4_med,
v4_subst,
v4_symp_panss,
v4_symp_ids_c,
v4_symp_ymrs,
v4_ill_sev,
v4_nrpsy,
v4_sf12,
v4_rlgn,
v4_med_adh,
v4_bdi2,
v4_asrm,
v4_mss,
v4_leq,
v4_whoqol)
ctmp1<-merge(x=v1_df, y=v2_df, by.x="v1_id", by.y="v2_id", all.x=T)
ctmp2<-merge(x=ctmp1, y=v3_df, by.x="v1_id", by.y="v3_id", all.x=T)
phen<-merge(x=ctmp2, y=v4_df, by.x="v1_id", by.y="v4_id",all.x=T)
To simplify the process of data analysis and subject selection, we here provide the IDs of individuals that have been included in various biological analyses (e.g. all sample that were whole-genome genotyped have an ID in the column “gwas_id”).
1446 individuals contained in this dataset have been genotyped on the Illumina PsychChip (https://www.illumina.com/products/by-type/microarray-kits/infinium-psycharray.html).
IMPORTANT:
1. Some individuals will be removed during QC of genotype data. Therefore, discrepancies with the number of individuals in the genotype dataset may exist. 2. Related individuals remain in the latest genotype dataset. Exclude by yourself if neccessary.
#make a dataframe for all analysis ids
ids<-data.frame(v1_id)
ids<-merge(x=ids,y=gwas_id,all.x=T, by.x="v1_id",by.y="id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$gwas_id)==F))
## [1] 1446
The exomes of 104 bipolar PsyCourse individuals were sequenced in the context of a larger study.
#merge to dataframe ids
ids<-merge(x=ids,y=ex_id,all.x=T, by="v1_id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$exome_id)==F))
## [1] 104
The smallRNAomes of a total of 1322 individuals contained in this dataset were sequenced from biomaterial collected AT THE FIRST VISIT. The variable gives the names of the corresponding .fastq files. The dummy variables “v2_smRNAome_id”, v3_smRNAome_id" and “v4_epic_id” are also created below to enable to include data properly in the long format dataset.
## [1] 1361 2
#merge to dataframe ids
ids<-merge(x=ids,y=v1_smRNAome_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_smRNAome_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_smRNAome_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_smRNAome_id,all.x=T, by="v1_id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_smRNAome_id)==F))
## [1] 1322
In this analysis, 96 biploar individuals were analyzed at wo measurement points (visit 1 and visit 3) using the Illumina EPIC array. The dummy variables “v2_epic_id” and “v4_epic_id” are also created below to enable to include data properly in the long dataset.
## [1] 96 2
#merge to dataframe ids
ids<-merge(x=ids,y=v1_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_epic_id,all.x=T, by="v1_id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_epic_id)==F))
## [1] 96
length(subset(ids$v1_id,is.na(ids$v3_epic_id)==F))
## [1] 96
In this analysis, the RB1CC1 gene was sequenced in 63 clinical participants.
#merge to dataframe ids
ids<-merge(x=ids,y=rb1cc1_id,all.x=T, by="v1_id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$rb1cc1_id)==F))
## [1] 63
In this analysis, the mRNA transcriptomes of 542 individuals contained in this dataset (538 from visit 1, 4 from visit 3) were sequenced. The dummy variables “v2_lexo_id” and “v4_lexo_id” are also created below to enable to include data properly in the long dataset.
#merge to dataframe ids
ids<-merge(x=ids,y=v1_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_lexo_seq_id,all.x=T, by="v1_id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_lexo_id)==F))
## [1] 538
length(subset(ids$v1_id,is.na(ids$v3_lexo_id)==F))
## [1] 4
In a pilot study, a plasma proteome profiling pipeline was applied to 220 PsyCourse participants. Of these, 74 were from visit 1, 37 from visit 2, 72 from visit 3, 36 from visit 4, and one from an extra study visit between regular visits. This last individual was excluded. We do not have analysis IDs from these individuals, if they are contained in the analysis, the respective field contains a “Y”.
##
## 1 2 3 4 ZV
## 74 37 72 36 1
#merge to dataframe ids
ids<-merge(x=ids,y=v1_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_prot_id,all.x=T, by="v1_id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_prot_id)==F))
## [1] 74
length(subset(ids$v1_id,is.na(ids$v2_prot_id)==F))
## [1] 37
length(subset(ids$v1_id,is.na(ids$v3_prot_id)==F))
## [1] 72
length(subset(ids$v1_id,is.na(ids$v4_prot_id)==F))
## [1] 36
In a total of 224 PsyCourse individuals (214 from visit 1, 9 from visit 2, and 1 from visit 3), a selected panel of ~100 serum proteins was determined using a set of 155 antibodies in a high-throughput antibody-based assay. This suspension bead array technology enabled a multiplexed protein profiling of these proteins.
#merge to dataframe ids
ids<-merge(x=ids,y=v1_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_ab_prof_id,all.x=T, by="v1_id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_ab_prof_id)==F))
## [1] 214
length(subset(ids$v1_id,is.na(ids$v2_ab_prof_id)==F))
## [1] 9
length(subset(ids$v1_id,is.na(ids$v3_ab_prof_id)==F))
## [1] 1
length(subset(ids$v1_id,is.na(ids$v4_ab_prof_id)==F))
## [1] 0
Plasma lipid profiles were analyzed for a total of 620 PsyCourse individuals, 410 from visit 1, 270 from visit 2, 49 from visit 3, 26 from visit 4. Diagnoses include schizophrenia and bipolar disorder, as well as controls. We do not have ID codes from these individuals, if they are contained in the analysis, the respective field contains a “Y”. )
## [1] 271 2
## [1] 270 2
#merge to dataframe ids
ids<-merge(x=ids,y=v1_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_lip_id,all.x=T, by="v1_id")
#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_lip_id)==F))
## [1] 410
length(subset(ids$v1_id,is.na(ids$v2_lip_id)==F))
## [1] 270
length(subset(ids$v1_id,is.na(ids$v3_lip_id)==F))
## [1] 49
length(subset(ids$v1_id,is.na(ids$v4_lip_id)==F))
## [1] 26
save(psycrs4.1_wd, file="200715_v4.1_psycourse_wd.RData")
Write wide format .csv file
write.table(psycrs4.1_wd,file="200715_v4.1_psycourse_wd.csv", quote=F, row.names=F, col.names=T, sep="\t")
To create a long dataset, it has to be determimed which variables are assessd at one, two, three and four visits in the PsyCourse 4.1 dataset. Subsequently, one has to integrate variables that were measured at two or three measurement points with those assessed at four time points.*
For variables that were repeatedy measured at three points in time, dummy first measurement point variables were created, all coded -999, so that these can be treated as repeated measures.
Only four variables were measured at two times, and these are items on religion. These items do not assess change, but were only added at a later measurement point so that people who did not have the chance to complete it at the first measurement point could also be assessed (the questionnaire was introduced some time after the study had started). Below, these variables are collapsed into cross-sectional variables.
Get a list of variables measured one, two, three, or four times. These are identified by counting the variables that are similarly named after the "_" character.
#get variables names measured one time
crs<-names(subset(table(substring(names(psycrs4.1_wd),4)),table(substring(names(psycrs4.1_wd),4))==1))
length(crs)
## [1] 216
#get variables names measured two times
lng2<-names(subset(table(substring(names(psycrs4.1_wd),4)),table(substring(names(psycrs4.1_wd),4))==2))
length(lng2)
## [1] 4
#get variables names measured three times
lng3<-names(subset(table(substring(names(psycrs4.1_wd),4)),table(substring(names(psycrs4.1_wd),4))==3))
length(lng3)
## [1] 147
#get variables names measured four times
lng4<-names(subset(table(substring(names(psycrs4.1_wd),4)),table(substring(names(psycrs4.1_wd),4))==4))
length(lng4)
## [1] 411
#Check whether the sum of these variable is equal to the total number of variables in the wide dataset
length(c(crs,lng2,lng2,lng3,lng3,lng3,lng4,lng4,lng4,lng4))==dim(psycrs4.1_wd)[2]
## [1] TRUE
After inspectionThese variables and saw that variables that were asked on follow-up but not the first visit: For each variable name, create a “v1_” variable filled with -999.
#Modify items in lng3 so that each vector element has a "v1_" added in front of it
lng3_new_v1_varnames<-paste("v1",lng3, sep="_")
#Add new variables to psycrs4.1_wd and fill them with -999
psycrs4.1_wd[lng3_new_v1_varnames] <- -999
I had a look and these four variables are the religion variables, the questionnaire of which is asked at visit 1 but also at visit 4.
psycrs4.1_wd$v1_rel_act<-ifelse(is.na(psycrs4.1_wd$v1_rel_act) &
is.na(psycrs4.1_wd$v4_rel_act)==F &
psycrs4.1_wd$v4_rel_act!=-999,psycrs4.1_wd$v4_rel_act,psycrs4.1_wd$v1_rel_act)
psycrs4.1_wd$v1_rel_chr<-ifelse(is.na(psycrs4.1_wd$v1_rel_chr) &
is.na(psycrs4.1_wd$v4_rel_chr)==F &
psycrs4.1_wd$v4_rel_chr!=-999,psycrs4.1_wd$v4_rel_chr,psycrs4.1_wd$v1_rel_chr)
psycrs4.1_wd$v1_rel_isl<-as.factor(ifelse(is.na(psycrs4.1_wd$v1_rel_isl) &
is.na(psycrs4.1_wd$v4_rel_isl)==F &
psycrs4.1_wd$v4_rel_isl!=-999,as.character(psycrs4.1_wd$v4_rel_isl),as.character(psycrs4.1_wd$v1_rel_isl)))
psycrs4.1_wd$v1_rel_oth<-as.factor(ifelse(is.na(psycrs4.1_wd$v1_rel_oth) & is.na(psycrs4.1_wd$v4_rel_oth)==F &
psycrs4.1_wd$v4_rel_oth!=-999,as.character(psycrs4.1_wd$v4_rel_oth),as.character(psycrs4.1_wd$v1_rel_oth)))
psycrs4.1_wd$v4_rel_act<-NULL
psycrs4.1_wd$v4_rel_chr<-NULL
psycrs4.1_wd$v4_rel_isl<-NULL
psycrs4.1_wd$v4_rel_oth<-NULL
#get variables names measured four times
lng4_cor<-names(subset(table(substring(names(psycrs4.1_wd),4)),table(substring(names(psycrs4.1_wd),4))==4))
length(lng4_cor)
## [1] 558
#get variables names measured three times
lng3_cor<-names(subset(table(substring(names(psycrs4.1_wd),4)),table(substring(names(psycrs4.1_wd),4))==3))
length(lng3_cor)
## [1] 0
#get variables names measured two times
lng2_cor<-names(subset(table(substring(names(psycrs4.1_wd),4)),table(substring(names(psycrs4.1_wd),4))==2))
length(lng2_cor)
## [1] 0
#get variables names measured one time
crs_cor<-names(subset(table(substring(names(psycrs4.1_wd),4)),table(substring(names(psycrs4.1_wd),4))==1))
length(crs_cor)
## [1] 220
#Check whether the sum of these variable is equal to the total number of variables in the wide dataset
length(c(crs_cor,lng2_cor,lng2_cor,lng3_cor,lng3_cor,lng3_cor,lng4_cor,lng4_cor,lng4_cor,lng4_cor))==dim(psycrs4.1_wd)[2]
## [1] TRUE
lng4_cor_v1<-paste("v1",lng4_cor,sep="_")
lng4_cor_v2<-paste("v2",lng4_cor,sep="_")
lng4_cor_v3<-paste("v3",lng4_cor,sep="_")
lng4_cor_v4<-paste("v4",lng4_cor,sep="_")
names_lng<-c(lng4_cor_v1,lng4_cor_v2,lng4_cor_v3,lng4_cor_v4)
long<-subset(psycrs4.1_wd,select=names_lng)
#change names of longitudinally measured variables, so that visit info comes at the end
names(long)<-paste(substring(names(long),4),substr(names(long),2,2),sep=".")
#sort dataframe
long<-long[,sort(names(long))]
dim(long)
## [1] 1786 2232
#create a dataframe with cross-sectionally measured variables
cross<-subset(psycrs4.1_wd,select=!(names(psycrs4.1_wd)%in%names_lng))
dim(cross)
## [1] 1786 220
##Reunite long and cross
psycrs4.1_wd2<-cbind(cross,long)
dim(psycrs4.1_wd2)
## [1] 1786 2452
IMPORTANT: the column “visit” contains the time information
psycrs4.1_ln<-reshape(data=psycrs4.1_wd2,
direction="long",
varying=names(long),
timevar="visit",
sep=".")
dim(psycrs4.1_ln)
## [1] 7144 780
#Remove the last column that contains only consective numbers for each time point, and can safely be removed
psycrs4.1_ln<-psycrs4.1_ln[,-dim(psycrs4.1_ln)[2]]
#Is the number of rows four times that of the long dataframe?
dim(psycrs4.1_ln)[1]==dim(psycrs4.1_wd2)[1]*4
## [1] TRUE
save(psycrs4.1_ln, file="200715_v4.1_psycourse_ln.RData")
Write long format .csv file
write.table(psycrs4.1_ln,file="200715_v4.1_psycourse_ln.csv", quote=F, row.names=F, col.names=T, sep="\t")
Altman, E. G., Hedeker, D., Peterson, J. L., & Davis, J. M. (1997). The Altman Self-Rating Mania Scale. Biol Psychiatry, 42(10), 948–955. http://doi.org/10.1016/S0006-3223(96)00548-3
Angermeyer, M. C., Kilian, R., & Matschinger, H. (2000). WHOQOL-100 und WHOQUOL-BREF. Handbuch für die deutschsprachigen versionen der WHO Instrumente zur Erfassung von Lebensqualität. Göttingen: Hogrefe.
Bernstein, D. P., Fink, L., Handelsman, L., Foote, J., Lovejoy, M., Wenzel, K., … Ruggiero, J. (1994). Initial reliability and validity of a new retrospective measure of child abuse and neglect. Am J Psychiatry, 151(8), 1132–1136. http://doi.org/10.1176/ajp.151.8.1132
Bernstein, D. P., Stein, J. A., Newcomb, M. D., Walker, E., Pogge, D., Ahluvalia, T., … Zule, W. (2003). Development and validation of a brief screening version of the childhood trauma questionnaire. Child Abuse Negl, 27(2), 169–190.
Busner, J., & Targum, S. D. (2007). The clinical global impressions scale: Applying a research tool in clinical practice. Psychiatry (Edgmont), 4(7), 28–37.
Endicott, J., Spitzer, R. L., Fleiss, J. L., & Cohen, J. (1976). The global assessment scale. A procedure for measuring overall severity of psychiatric disturbance. Arch Gen Psychiatry, 33(6), 766–771.
Glaesmer, H., Schulz, A., Häuser, W., Freyberger, H. J., Brähler, E., & Grabe, H.-J. (2013). The childhood trauma screener (cts) - development and validation of cut-off-scores for classificatory diagnostics. Psychiatr Prax, 40(4), 220–226. http://doi.org/10.1055/s-0033-1343116
Grabe, H. J., Schulz, A., Schmidt, C. O., Appel, K., Driessen, M., Wingenfeld, K., … Freyberger, H. J. (2012). A brief instrument for the assessment of childhood abuse and neglect: The childhood trauma screener (cts). Psychiatr Prax, 39(3), 109–115. http://doi.org/10.1055/s-0031-1298984
Grof, P., Duffy, A., Cavazzoni, P., Grof, E., Garnham, J., MacDougall, M., … Alda, M. (2002). Is response to prophylactic lithium a familial trait? J Clin Psychiatry, 63(10), 942–947.
Hautzinger, M., Keller, F., & Kühner, C. (2006). Beck Depressions-Inventar (BDI-II). Harcourt Test Services Frankfurt.
Helmstaedter, C., Lendt, M., & Lux, S. (2001). Verbaler Lern- und Merkfähigkeitstest. Göttingen: Hogrefe.
Hou, L., Heilbronner, U., Degenhardt, F., Adli, M., Akiyama, K., Akula, N., … Schulze, T. G. (2016). Genetic variants associated with response to lithium treatment in bipolar disorder: A genome-wide association study. Lancet, 387(10023), 1085–1093. http://doi.org/10.1016/S0140-6736(16)00143-4
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The positive and negative syndrome scale (panss) for schizophrenia. Schizophr Bull, 13(2), 261–276.
Luborsky, L. (1962). Clinician’s judgments of mental health. Arch Gen Psychiatry, 7, 407–417.
McGuffin, P., Farmer, A., & Harvey, I. (1991). A polydiagnostic application of operational criteria in studies of psychotic illness. Development and reliability of the opcrit system. Arch Gen Psychiatry, 48(8), 764–770.
Norbeck, J. S. (1984). Modification of life event questionnaires for use with female respondents. Res Nurs Health, 7(1), 61–71.
Peters, E., Joseph, S., Day, S., & Garety, P. (2004). Measuring delusional ideation: The 21-item peters et al. Delusions inventory (pdi). Schizophrenia Bulletin, 30(4), 1005–1022. http://doi.org/10.1093/oxfordjournals.schbul.a007116
Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the big five inventory in english and german. J Res Pers, 41(1), 203–212.
Rammstedt, B., Kemper, C. J., Klein, M. C., Beierlein, C., & Kovaleva, A. (2012). Eine kurze Skala zur Messung der fünf Dimensionen der Persönlichkeit : Big-Five-Inventory-10 (BFI-10), 2012/23, 32. Retrieved from http://nbn-resolving.de/urn:nbn:de:0168-ssoar-312133
Sarason, I. G., Johnson, J. H., & Siegel, J. M. (1978). Assessing the impact of life changes: Development of the life experiences survey. J Consult Clin Psychol, 46(5), 932–946.
Shugar, G., Schertzer, S., Toner, B. B., & Di Gasbarro, I. (1992). Development, use, and factor analysis of a self-report inventory for mania. Compr Psychiatry, 33(5), 325–331.
Strauss, E., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary. Oxford University Press, USA.
Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978). A rating scale for mania: Reliability, validity and sensitivity. Br J Psychiatry, 133, 429–435.
The ALDA scala should only be assessed in clinical participants with a diagnosis of bipolar disorder↩
Self-reported weight is assessed at each study visit↩
Data not included in the present dataset, but were used to exclude control participants↩
Included during the course of the study, also included in Visit 4 to get information from people that did not fill out this cross-sectional questionnaire in Visit 1↩