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import.R
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# This script imports the data of the CovILD study
# toolbox ----
library(readxl)
library(foreign)
library(soucer)
library(stringi)
library(plyr)
library(tidyverse)
library(rlang)
library(trafo)
library(caret)
library(rstatix)
library(furrr)
library(clustTools)
source_all('./tools/tools.R',
message = TRUE, crash = TRUE)
insert_head()
# data containers ----
ptsd <- list()
globals <- list()
# globals ----
## graphics
globals$corr_colors <- c('negative' = 'steelblue4',
'positive' = 'firebrick4',
'ns' = 'gray60')
globals$common_text <- element_text(size = 8,
face = 'plain',
color = 'black')
globals$common_margin <- ggplot2::margin(t = 4, l = 3, r = 2, unit = 'mm')
globals$common_theme <- theme_classic() +
theme(axis.text = globals$common_text,
axis.title = globals$common_text,
plot.title = element_text(size = 8,
face = 'bold',
color = 'black',
hjust = 0),
plot.subtitle = globals$common_text,
plot.tag = element_text(size = 8,
face = 'plain',
color = 'black',
hjust = 0),
plot.tag.position = 'bottom',
legend.text = globals$common_text,
legend.title = globals$common_text,
strip.text = globals$common_text,
strip.background = element_rect(fill = 'gray95', color = 'gray80'),
plot.margin = globals$common_margin,
panel.grid.major = element_line(color = 'gray90'))
## cluster colors
globals$clust_colors <- c('neutral' = 'cadetblue3',
'PTG' = 'darkolivegreen3',
'PTS' = 'coral4')
## injury regions
globals$injury_vars <- c('injury_head',
'injury_face',
'injury_neck',
'injury_upper_limbs',
'injury_chest',
'injury_spine',
'injury_abdomen',
'injury_lower_limbs',
'injury_external_other')
globals$injury_labs <- c('Head',
'Face',
'Neck',
'Upper limbs',
'Chest',
'Spine region',
'Abdomen',
'Lower limbs',
'Other external') %>%
set_names(globals$injury_vars)
## data partition colors
globals$part_colors <- c(training = 'steelblue',
train = 'steelblue',
test = 'coral3',
cv = 'gray60')
globals$part_labels <- c(training = 'training',
train = 'training',
test = 'test',
cv = '10-fold CV')
# reading the recoding scheme -----
insert_msg('Reading the recoding scheme')
ptsd$var_coding <- read_excel('./data/var_recoding.xlsx') %>%
mutate(args1 = stri_replace_all(args1, regex = '“|”', replacement = ''),
args1 = stri_replace_all(args1, regex = '‘|’', replacement = "'"),
args1 = map(args1, function(x) if(any(is.na(x))) NULL else x)) %>%
mutate(args2 = stri_replace_all(args2, regex = '“|”', replacement = ''),
args2 = stri_replace_all(args2, regex = '‘|’', replacement = "'"),
args2 = map(args2, function(x) if(any(is.na(x))) NULL else x)) %>%
mutate(args3 = stri_replace_all(args3, regex = '“|”', replacement = ''),
args3 = stri_replace_all(args3, regex = '‘|’', replacement = "'"),
args3 = map(args3, function(x) if(any(is.na(x))) NULL else x)) %>%
mutate(args4 = stri_replace_all(args4, regex = '“|”', replacement = ''),
args4 = stri_replace_all(args4, regex = '‘|’', replacement = "'"),
args4 = map(args4, function(x) if(any(is.na(x))) NULL else x)) %>%
mutate(args = pmap(list(x = args1,
y = args2,
v = args3,
z = args4),
function(x, y, v, z) list(x, y, v, z))) %>%
select(old_var, new_var, trans_fun, args)
# Reading the SPSS files ------
insert_msg('Reading the SPSS files')
ptsd$raw_data <-
read.spss('./data/PTSD Datei mit Unfallcodes NUR Eingeladene23.08.sav',
to.data.frame = TRUE) %>%
as_tibble
# Generating a variable label table -----
insert_msg('Variable label table')
ptsd$var_labels <- tibble(old_var = names(attr(ptsd$raw_data,
'variable.labels')) %>%
make.names,
question_text = unname(attr(ptsd$raw_data,
'variable.labels')))
# serial clearing according to the variable recoding scheme ------
insert_msg('Serial recoding')
ptsd$cleared <- ptsd$var_coding %>%
pmap(recode_var,
data = ptsd$raw_data) %>%
reduce(left_join,
by = 'Passwort')
# removal of the nonsense age -----
insert_msg('Removal of the zero-age and child age records')
ptsd$cleared <- ptsd$cleared %>%
filter(is.na(age) | age >= 18)
# manual polishing -----
insert_msg('Manual polishing')
ptsd$cleared <- ptsd$cleared %>%
mutate(age_class = cut(age,
c(-Inf, 30, 65, Inf),
c('18-30', '31-65', '>65')),
ward_date = pss2date(ward_date),
hospitalization = ifelse(hospitalization == 'X', 'yes', 'no'),
hospitalization = factor(hospitalization, c('no', 'yes')),
survey_date = pss2date(survey_date),
obs_time = floor(as.numeric(survey_date - ward_date)),
accident_month = stri_split_fixed(accident_date,
pattern = '-',
simplify = TRUE)[, 2],
accident_month = as.numeric(accident_month),
accident_season = ifelse(accident_month %in% c(12, 1, 2),
'winter',
ifelse(accident_month %in% c(3, 4, 5),
'spring',
ifelse(accident_month %in% c(6, 7, 8),
'summer',
'fall'))),
accident_season = ifelse(is.na(accident_season),
'no information', accident_season),
accident_season = factor(accident_season,
c('spring', 'summer', 'fall',
'winter', 'no information')),
accident_hour = stri_extract(accident_time,
regex = '^\\d{2}'),
accident_hour = as.numeric(accident_hour),
accident_daytime = ifelse(accident_hour %in% 6:18,
'day', 'night'),
accident_daytime = ifelse(is.na(accident_daytime),
'no information', accident_daytime),
accident_daytime = factor(accident_daytime,
c('day', 'night', 'no information')),
weight_class = stri_replace(weight_class, regex = '\\s{1}$', replacement = ''),
weight_class = ifelse(weight_class == '', NA, weight_class),
weight_class = factor(weight_class,
c('30-40', '40-50', '50-60',
'60-70', '70-80', '80-90',
'90-100', '100-110', '110-120')),
height_class = stri_replace(height_class, regex = '\\s{1}$', replacement = ''),
height_class = ifelse(height_class == '', NA, height_class),
height_class = factor(height_class),
mother_tongue = factor(mother_tongue, c('German', 'English', 'Other')),
education = car::recode(as.character(education),
"'primary' = 'primary/apprenticeship';
'apprenticeship' = 'primary/apprenticeship'"),
education = factor(education,
c('primary/apprenticeship',
'secondary', 'tertiary')),
employment_status = car::recode(as.character(employment_status),
"'household' = 'unemployed';
'unemployed' = 'unemployed'"),
employment_status = factor(employment_status,
c('employed',
'unemployed',
'student',
'retired')),
household_income_class = car::recode(as.character(household_income_class),
"'no income' = 'no income';
'< 15000 Euro' = '< 30000 EUR';
'15000 - 30000 Euro' = '< 30000 EUR';
'30000 - 45000 Euro' = '30000 - 45000 EUR';
'> 45000 Euro' = '\u2265 45000 EUR'"),
household_income_class = factor(household_income_class,
c('no income',
'< 30000 EUR',
'30000 - 45000 EUR',
'\u2265 45000 EUR')),
high_income = ifelse(as.character(household_income_class) == '\u2265 45000 EUR',
'yes', 'no'),
high_income = factor(high_income, c('no', 'yes')),
injury_severity_ais = as.numeric(injury_severity_ais),
injury_severity_ais = ifelse(injury_severity_ais > 10,
NA, injury_severity_ais),
injury_sev_strata = cut(injury_severity_ais,
c(0, 1, 2, 3, 10),
c('0', '1', '2', '3+'),
right = FALSE),
injury_sev_strata = droplevels(injury_sev_strata),
surgery_diagnosis = ifelse(surgery_diagnosis %in% c('0', ''),
NA, surgery_diagnosis),
accident_culprit = ifelse(accident_culprit == 'Anderes: !!TEXT',
'other reason',
accident_culprit),
accident_culprit = ifelse(accident_culprit == 'Durch einen von einem/einer Tour Partner/in verursachten Fehler',
'tour partner',
accident_culprit),
accident_culprit = car::recode(accident_culprit,
"'Durch eine Naturkatastrophe' = 'natural diseaster';
'Durch einen durch Dritte verursachten Fehler' = 'third party';
'Durch einen Schicksalsschlag' = 'blow of fate';
'Durch einen von Ihnen verursachten Fehler' = 'self';
'' = NA; '0' = NA"),
accident_culprit = ifelse(as.character(accident_culprit) != 'self',
'non-self', 'self'),
accident_culprit = factor(accident_culprit, c('self', 'non-self')),
accident_injured_persons = ifelse(is.na(accident_injured_persons),
'no information', accident_injured_persons),
accident_injured_persons = factor(accident_injured_persons,
c('only self',
'self and partner',
'3+ persons',
'no information')),
accident_rescue = car::recode(accident_rescue,
"'tour partner' = 'companion';
'third party' = 'companion'"),
accident_rescue = factor(accident_rescue, c('self',
'companion',
'rescue team')),
accident_rescue_mode = ifelse(accident_rescue != 'rescue team',
'no rescue team involved',
accident_rescue_mode),
accident_rescue_mode = ifelse(accident_rescue_mode == 'Anderes: !!TEXT',
'other', accident_rescue_mode),
accident_rescue_mode = car::recode(accident_rescue_mode,
"'no rescue team involved' = 'no professional rescue';
'on ground with stretcher' = 'on ground';
'on ground by foot' = 'on ground'"),
accident_rescue_mode = factor(accident_rescue_mode, c('no professional rescue',
'airborne',
'on ground',
'other')),
accident_rescue_waiting_time = stri_replace(accident_rescue_waiting_time,
regex = '\\s{1}$',
replacement = ''),
accident_rescue_waiting_time = ifelse(accident_rescue != 'rescue team',
'no rescue team involved',
accident_rescue_waiting_time),
accident_rescue_waiting_time = factor(accident_rescue_waiting_time,
c('no rescue team involved',
'0-30', '30-60', '60-120')),
caution_post_accident = factor(caution_post_accident,
c('no change', 'more cautious', 'less cautious')),
flashback_frequency = ifelse(unwilling_flashback == 'no',
'no flashbacks', flashback_frequency),
flashback_frequency = car::recode(as.character(flashback_frequency),
"'no flashbacks' = 'none';
'> 1 – 2 per week' = '> 1/month';
'several per month' = '> 1/month';
'1 – 2 per month' = '> 1/month';
'1 – 2 per year' = '> 1/year'"),
flashback_frequency = factor(flashback_frequency,
c('none', '> 1/year', '> 1/month')),
psych_support_post_accident = ifelse(psych_therapy_post_accident == 'yes',
'yes',
as.character(psych_support_post_accident)),
psych_support_post_accident = factor(psych_support_post_accident,
c('no', 'yes')),
psych_support_need = ifelse(psych_support_post_accident == 'yes',
'no', as.character(psych_support_need)),
psych_support_need = factor(psych_support_need, c('no', 'yes')),
somatic_comorbidity_type = ifelse(somatic_comorbidity_type == 'Anderes: !!TEXT',
'other',
somatic_comorbidity_type),
somatic_comorbidity_type = ifelse(somatic_comorbidity == 'no',
'none',
somatic_comorbidity_type),
somatic_comorbidity_type = factor(somatic_comorbidity_type,
c('none', 'CVD', 'metabolic',
'pulmonary', 'neurological',
'rheumatoid', 'skin',
'cancer', 'other')),
prime_trauma_event = factor(prime_trauma_event,
c('no information',
'severe accident',
'physical assult',
'sexual molestation',
'rape',
'severe disease',
'natural diseaster',
'war')),
prime_trauma_event_past = ifelse(prime_trauma_event == 'no information',
'no information',
as.character(prime_trauma_event_past)),
prime_trauma_event_past = factor(prime_trauma_event_past,
c('no information',
'0 – 1 years ago',
'1 – 5 years ago',
'5 – 10 years ago',
'10+ years ago')),
smoking_length_years = ifelse(smoking_status == 'no',
0, smoking_length_years))
# types of somatic comorbidities ------
insert_msg('Types of somatic comorbidities')
ptsd$cleared <- ptsd$cleared %>%
mutate(somatic_comorbidity_type = ifelse(somatic_comorbidity == 'no',
'none',
ifelse(stri_detect(somatic_comorbidity_type,
fixed = 'Anderes'),
as.character(somatic_comorbidity_type_text),
as.character(somatic_comorbidity_type))),
somatic_comorbidity_type = car::recode(somatic_comorbidity_type,
"'0' = 'other';
'tinitus' = 'neurological';
'Rückenschmerzen' = 'musculoskeletal';
'Arthrose' = 'musculoskeletal';
'Morbus Whipple' = 'gastrointestinal';
'Probleme mit den Halswirbeln und Schulter' = 'musculoskeletal';
'-verletzung des meniskus' = 'musculoskeletal';
'Erkrankung des Bewegungsapparates' = 'musculoskeletal';
'Darmerkrankung' = 'gastrointestinal';
'Hüftverletzung mit Hüftkopfnekrose' = 'musculoskeletal';
'Colitis ulcerosa' = 'gastrointestinal';
'Kreuzschmerzen' = 'musculoskeletal';
'Morbus Chron' = 'gastrointestinal';
'Bewegungsapparat' = 'musculoskeletal';
'HWS, BWS' = 'musculoskeletal';
'ich leide immer noch an den folgen des Unfalles von 20.09.2020 (krankenstand)' = 'other';
'Band schiben vorfal. Not related to the ski accident, ut happened two years after.' = 'musculoskeletal';
'Niere Dialyse' = 'metabolic';
'regelmäßige Schmerzen an der Wirbelsäule' = 'musculoskeletal';
'Diverse Schleimbeutelentzündungen' = 'musculoskeletal';
'Endometriose' = 'other';
'Allergie / Heuschnupfen' = 'other';
'angeborener Herzfehler (DILV)' = 'CVD';
'Magen Darm Beschwerden, eventuell Reizdarm' = 'gastrointestinal';
'immer noch wegen dem Sturz beim Eislaufen (Instabilität Ulnakopf, viele Revisionsoperationen, Chronische Schmerzen.)' = 'musculoskeletal';
'Skoliose + Versteifung der Wirbelsäule' = 'musculoskeletal';
'Konzentrations Störung' = 'other';
'' = 'other'"),
somatic_comorbidity_type = factor(somatic_comorbidity_type,
c('none',
'musculoskeletal',
'CVD',
'gastrointestinal',
'neurological',
'metabolic',
'pulmonary',
'cancer',
'rheumatoid',
'skin',
'other')))
# profession text extraction -------
insert_msg('Profession text extraction')
## medical vs non-medical profession
ptsd$cleared <- ptsd$cleared %>%
mutate(medical_profession = stri_detect(tolower(profession_text),
regex = '(arzt)|(schwester)|(therapeut)|(ärzt)|(pfleg)'),
medical_profession_A = stri_detect(tolower(profession_text_A),
regex = '(arzt)|(schwester)|(therapeut)|(ärzt)|(pfleg)'),
medical_profession = ifelse(medical_profession | medical_profession_A,
'yes', 'no'),
medical_profession = factor(medical_profession, c('no', 'yes')))
# major categories of sport types ------
insert_msg('Recoding of the sport type')
## classification according to KURASI
ptsd$cleared <- ptsd$cleared %>%
mutate(sport_detail = sport_type,
sport_type = car::recode(as.character(sport_type),
"'alpine skiing' = 'alpine skiing/snowboarding';
'biking' = 'biking';
'sledding' = 'sledding';
'rock climbing' = 'climbing';
'hiking' = 'hiking';
'MTB' = 'biking';
'sailing' = 'water sport';
'crosssountry skiing' = 'cross-country skiing';
'surfing' = 'water sport';
'skating' = 'other';
'mountaineering' = 'mountaineering';
'skitouring' = 'ski touring/freeride';
'paragliding' = 'air sports';
'sport climbing/bouldering' = 'climbing';
'snowboarding' = 'alpine skiing/snowboarding';
'ice climbing' = 'ice climbing';
'other mountain' = 'other';
'swimming' = 'water sport';
'figeln' = 'ski touring/freeride';
'ski jumping' = 'other';
'other winter' = 'other';
'other water' = 'water sports'"),
sport_type = factor(sport_type,
c('alpine skiing/snowboarding',
'ski touring/freeride',
'cross-country skiing',
'sledding',
'ice climbing',
'hiking',
'climbing',
'mountaineering',
'biking',
'air sports',
'water sports',
'other')))
# Counts of injured body regions ------
insert_msg('Counts of injured body regions')
ptsd$cleared$injured_count <- ptsd$cleared[globals$injury_vars] %>%
map(as.numeric) %>%
map(~.x - 1) %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(injured_count = ifelse(!is.na(injured_count),
injured_count,
ifelse(!is.na(injury_severity_ais),
1, NA)))
# additional severity readouts ------
insert_msg('Additional severity readouts')
## they are: need for a surgery defined as a presence of surgery diagnosis
## number of surgical diagnoses
ptsd$cleared <- ptsd$cleared %>%
mutate(surgery_done = ifelse(is.na(surgery_diagnosis), 'no', 'yes'),
surgery_done = factor(surgery_done, c('no', 'yes')),
surgery_complexity = stri_replace(surgery_diagnosis,
regex = '/$',
replacement = ''),
surgery_complexity = stri_split(surgery_complexity, fixed = '/'),
surgery_complexity = map_dbl(surgery_complexity, length),
surgery_complexity = ifelse(surgery_done == 'no',
0, surgery_complexity),
surgery_complexity = cut(surgery_complexity,
c(-Inf, 0, 1, Inf),
c('none', '1', '2+')))
# PCL5-DSM5 item factor level setup -------
insert_msg('DSM5 levels')
for(i in paste0('dsm5_q', 1:20)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := factor(.data[[i]],
c('not at all',
'a little bit',
'moderately',
'quite a bit',
'extremely')))
}
## total score and cluster sub-score calculation
ptsd$dsm5_numeric <- ptsd$cleared %>%
select(starts_with('dsm')) %>%
map_dfc(~as.numeric(.x) - 1)
ptsd$dsm5_numeric$dsm5_total <- ptsd$dsm5_numeric[, 1:20] %>%
reduce(`+`)
ptsd$dsm5_numeric$dsm5_B <- ptsd$dsm5_numeric[, 1:5] %>%
reduce(`+`)
ptsd$dsm5_numeric$dsm5_C <- ptsd$dsm5_numeric[, 6:7] %>%
reduce(`+`)
ptsd$dsm5_numeric$dsm5_D <- ptsd$dsm5_numeric[, 8:14] %>%
reduce(`+`)
ptsd$dsm5_numeric$dsm5_E <- ptsd$dsm5_numeric[, 15:20] %>%
reduce(`+`)
ptsd$cleared <- cbind(ptsd$cleared,
ptsd$dsm5_numeric[c('dsm5_total',
'dsm5_B',
'dsm5_C',
'dsm5_D',
'dsm5_E')])
## class definition
ptsd$dsm5_bi <- ptsd$dsm5_numeric %>%
map_dfc(~as.numeric(.x >= 2))
ptsd$dsm5_bi$dsm5_B_class <- ptsd$dsm5_bi[, 1:5] %>%
reduce(`+`)
ptsd$dsm5_bi$dsm5_B_class <-
ifelse(ptsd$dsm5_bi$dsm5_B_class > 0, 'positive', 'negative') %>%
factor
ptsd$dsm5_bi$dsm5_C_class <- ptsd$dsm5_bi[, 6:7] %>%
reduce(`+`)
ptsd$dsm5_bi$dsm5_C_class <-
ifelse(ptsd$dsm5_bi$dsm5_C_class > 0, 'positive', 'negative') %>%
factor
ptsd$dsm5_bi$dsm5_D_class <- ptsd$dsm5_bi[, 8:14] %>%
reduce(`+`)
ptsd$dsm5_bi$dsm5_D_class <-
ifelse(ptsd$dsm5_bi$dsm5_D_class > 1, 'positive', 'negative') %>%
factor
ptsd$dsm5_bi$dsm5_E_class <- ptsd$dsm5_bi[, 15:20] %>%
reduce(`+`)
ptsd$dsm5_bi$dsm5_E_class <-
ifelse(ptsd$dsm5_bi$dsm5_E_class > 1, 'positive', 'negative') %>%
factor
ptsd$cleared <- cbind(ptsd$cleared,
ptsd$dsm5_bi[c('dsm5_B_class',
'dsm5_C_class',
'dsm5_D_class',
'dsm5_E_class')]) %>%
mutate(dsm5_total_class = binarize(dsm5_total, 31, 'negative;positive'),
dsm5_total_class = factor(dsm5_total_class),
dsm5_cluster_class = ptsd$dsm5_bi[c('dsm5_B_class',
'dsm5_C_class',
'dsm5_D_class',
'dsm5_E_class')] %>%
map(~as.numeric(.x) - 1) %>%
reduce(`+`),
dsm5_cluster_class = ifelse(dsm5_cluster_class > 0,
'positive', 'negative'),
dsm5_cluster_class = factor(dsm5_cluster_class))
ptsd$dsm5_numeric <- NULL
ptsd$dsm5_bi <- NULL
# PSS4 item factor level setup -----
insert_msg('PSS4 levels')
for(i in paste0('pss4_q', 1:4)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := factor(.data[[i]],
c('never',
'almost never',
'sometimes',
'fairly often',
'very often')))
}
## calculating the score,
## stratification be the median split
ptsd$pss4_numeric <- ptsd$cleared %>%
select(starts_with('pss4')) %>%
mutate(pss4_q1 = as.numeric(pss4_q1) - 1,
pss4_q2 = as.numeric(fct_rev(pss4_q2)) - 1,
pss4_q3 = as.numeric(fct_rev(pss4_q3)) - 1,
pss4_q4 = as.numeric(pss4_q4) - 1) %>%
as_tibble
ptsd$cleared$pss4_total <- ptsd$pss4_numeric %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(pss4_total_class = cut(pss4_total,
c(-Inf, median(ptsd$cleared$pss4_total, na.rm = TRUE), Inf),
c('low stress', 'high stress')))
ptsd$pss4_numeric <- NULL
# PTGI item factor level setup ------
insert_msg('PTGI factor level setup')
for(i in paste0('ptgi_q', 1:21)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := factor(.data[[i]],
c('none',
'very small',
'small',
'moderate',
'great',
'very great')))
}
## calculating the total score
## and the factor sub-scores
ptsd$ptgi_numeric <- ptsd$cleared %>%
select(starts_with('ptgi')) %>%
map_dfc(~as.numeric(.x) - 1)
ptsd$ptgi_numeric$ptgi_total <- ptsd$ptgi_numeric[, 1:21] %>%
reduce(`+`)
ptsd$ptgi_numeric$ptgi_fctI <- ptsd$ptgi_numeric[, c(6, 8, 9, 15, 16, 20, 21)] %>%
reduce(`+`)
ptsd$ptgi_numeric$ptgi_fctII <- ptsd$ptgi_numeric[, c(3, 7, 11, 14, 17)] %>%
reduce(`+`)
ptsd$ptgi_numeric$ptgi_fctIII <- ptsd$ptgi_numeric[, c(4, 10, 12, 19)] %>%
reduce(`+`)
ptsd$ptgi_numeric$ptgi_fctIV <- ptsd$ptgi_numeric[, c(5, 18)] %>%
reduce(`+`)
ptsd$ptgi_numeric$ptgi_fctV <- ptsd$ptgi_numeric[, c(1, 2, 13)] %>%
reduce(`+`)
ptsd$cleared <- cbind(ptsd$cleared,
ptsd$ptgi_numeric[c('ptgi_total',
'ptgi_fctI',
'ptgi_fctII',
'ptgi_fctIII',
'ptgi_fctIV',
'ptgi_fctV')])
ptsd$ptgi_numeric <- NULL
# DIAX/CIDI item factor setup -----
insert_msg('DIAX/CIDI items')
for(i in paste0('cidi_q', 1:8)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := factor(.data[[i]],
c('no',
'whitnessed',
'yes')))
}
ptsd$cleared <- ptsd$cleared %>%
mutate(cidi_extra = factor(cidi_extra, c('no', 'yes')))
ptsd$cidi_numeric <- ptsd$cleared %>%
select(starts_with('cidi_q'), cidi_extra) %>%
map_dfc(car::recode,
"'no' = 0; 'yes' = 1; 'whitnessed' = 1",
as.factor = FALSE)
ptsd$cleared$cidi_total <- ptsd$cidi_numeric %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(cidi_class = ifelse(cidi_total > 0, 'positive', 'negative'),
cidi_class = factor(cidi_class),
traumatic_event = car::recode(cidi_class,
"'negative' = 'no';
'positive' = 'yes'"),
traumatic_number = cut(cidi_total,
c(-Inf, 0, 1, 2, Inf),
c('none', '1', '2', '3+')))
ptsd$cidi_numeric <- NULL
## update of information on the prime traumatic event:
## DIAX-negative observations are set to 'none'
ptsd$cleared <- ptsd$cleared %>%
mutate(prime_trauma_event = ifelse(traumatic_event == 'no',
'none',
as.character(prime_trauma_event)),
prime_trauma_event = factor(prime_trauma_event,
c('none', levels(ptsd$cleared$prime_trauma_event))),
prime_trauma_event_past = ifelse(traumatic_event == 'no',
'none',
as.character(prime_trauma_event_past)),
prime_trauma_event_past = factor(prime_trauma_event_past,
c('none', levels(ptsd$cleared$prime_trauma_event_past))))
# SOC9l score calculation ----
insert_msg('SOC-9L score calculation')
ptsd$cleared$soc9l_total <- ptsd$cleared %>%
select(starts_with('soc9l')) %>%
reduce(`+`)
# BRCS level setting, score and classes -----
insert_msg('BRCS level setup and score')
for(i in paste0('brcs_q', 1:4)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := factor(.data[[i]],
c('not at all',
'a little bit',
'a bit',
'quite a bit',
'very well')))
}
## score calculation, class definition
ptsd$cleared$brcs_total <- ptsd$cleared %>%
select(starts_with('brcs')) %>%
map_dfc(as.numeric) %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(brcs_total_class = cut(brcs_total,
c(-Inf, 13, 16, Inf),
c('low', 'medium', 'high')))
# PHQD events/somatization module, levels and score ------
insert_msg('PDQD events, levels and score')
for(i in paste0('phqd_events_q', 1:13)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := factor(.data[[i]],
c('not affected',
'a bit',
'a lot')))
}
## score calculation, classification with the cutoff >10, Kroenke et al
ptsd$cleared$phq_events_total <- ptsd$cleared %>%
select(starts_with('phqd_events')) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(phq_events_total_class = cut(phq_events_total,
c(-Inf, 9, Inf),
c('negative', 'positive')))
# PHQD psychical satisfaction, depression and anxiety ------
insert_msg('PDQD psychical satisfaction, levels and score')
for(i in paste0('phqd_psych_satisf_q', 1:16)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := factor(.data[[i]],
c('not at all',
'some days',
'more than half of days',
'almost every day')))
}
## score calculation: total
ptsd$cleared$phqd_psych_satisf_total <- ptsd$cleared %>%
select(starts_with('phqd_psych_satisf')) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
## score calculation: PHQ9 and stratification with the >= 10 cutoff (Manea et al.)
## PHQ-2 and GAD-2 scores for comparison with the German mental health
## monitoring. Stratification of the PHQ-2 score with the cutoff >= 3.
## PHQ-8 for comparison with the Austrian microcensus data:
## cutoffs from Kroenke K, Spitzer RL, Psychiatric Annals 2002;32:509-521
ptsd$cleared$phq9_total <- ptsd$cleared %>%
select(all_of(paste0('phqd_psych_satisf_q', 1:9))) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
ptsd$cleared$phq2_total <- ptsd$cleared %>%
select(all_of(paste0('phqd_psych_satisf_q', c(1, 2)))) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
ptsd$cleared$phq8_total <- ptsd$cleared %>%
select(all_of(paste0('phqd_psych_satisf_q', c(1:8)))) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(phq9_total_class = cut(phq9_total,
c(-Inf, 9, Inf),
c('negative', 'positive')),
phq2_total_class = ifelse(phq2_total >= 3,
'positive', 'negative'),
phq2_total_class = factor(phq2_total_class,
c('negative', 'positive')),
phq8_total_class = cut(phq8_total,
c(-Inf, 4, 9, 14, 20, Inf),
c('none',
'mild',
'moderate',
'moderately severe',
'severe')))
## score calculation, GAD-7 and stratification with the >= 10 cutoff (Spitzer et al.)
ptsd$cleared$gad7_total <- ptsd$cleared %>%
select(all_of(paste0('phqd_psych_satisf_q', 10:16))) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
ptsd$cleared$gad2_total <- ptsd$cleared %>%
select(all_of(paste0('phqd_psych_satisf_q', c(1, 3)))) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(gad7_total_class = cut(gad7_total,
c(-Inf, 9, Inf),
c('negative', 'positive')),
gad2_total_class = ifelse(gad2_total >= 3,
'positive', 'negative'),
gad2_total_class = factor(gad2_total_class,
c('negative', 'positive')))
# PHQD panic and anxiety attack scores ------
insert_msg('PHQD anxiety and anxiety attack scores')
ptsd$cleared <- ptsd$cleared %>%
mutate(phqd_panic_q1 = factor(phqd_panic_q1, c('no', 'yes')))
for(i in paste0('phqd_panic_q', 2:4)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := as.character(.data[[i]]),
!!i := ifelse(as.character(phqd_panic_q1) == 'no',
'no', .data[[i]]),
!!i := factor(.data[[i]], c('no', 'yes')))
}
for(i in paste0('phqd_anx_attack_q', 1:11)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := as.character(.data[[i]]),
!!i := ifelse(as.character(phqd_panic_q1) == 'yes',
.data[[i]], 'no'),
!!i := factor(.data[[i]],
c('no', 'yes')))
}
## score calculation
ptsd$cleared$phqd_panic_total <- ptsd$cleared %>%
select(starts_with('phqd_panic')) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
ptsd$cleared$phqd_anx_total <- ptsd$cleared %>%
select(starts_with('phqd_anx')) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
## defining the panic
## as proposed in https://de.wikipedia.org/wiki/PHQ-Panikmodul
ptsd$cleared <- ptsd$cleared %>%
mutate(phqd_panic_total_class = ifelse(phqd_panic_total == 4 & phqd_anx_total > 3,
'yes', 'no'),
phqd_panic_total_class = factor(phqd_panic_total_class,
c('no', 'yes')))
# CAGE score calculation ------
insert_msg('Cage score calculation')
for(i in paste0('cage_q', 1:4)) {
ptsd$cleared <- ptsd$cleared %>%
mutate(!!i := factor(.data[[i]],
c('no', 'yes')))
}
## score calculation
ptsd$cleared$cage_total <- ptsd$cleared %>%
select(starts_with('cage')) %>%
map_dfc(~as.numeric(.x) - 1) %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(cage_total_class = binarize(cage_total, 1, 'negative;positive'))
# RS13 score calculation -----
insert_msg('RS13 score calculation')
ptsd$cleared$rs13_total <- ptsd$cleared %>%
select(starts_with('rs13')) %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(rs13_total_class = cut(rs13_total,
c(-Inf, 66, 72, Inf),
c('low', 'moderate', 'high')))
# EUROHIS-QOL-8 score calculation -----
insert_msg('EUROHIS-QOL8 score calculation')
## total score calculation (mean of the domains)
## subscores: each question separately
##
## the item score are inverted, i.e. very poor: 1 and excellent: 5
ptsd$cleared$eurohis_total <- ptsd$cleared %>%
select(starts_with('eurohis')) %>%
map(function(x) 6 - x) %>%
reduce(`+`)
ptsd$cleared <- ptsd$cleared %>%
mutate(eurohis_total = eurohis_total/8,
eurohis_qol = 6 - eurohis_q1,
eurohis_health = 6 - eurohis_q2,
eurohis_energy = 6 - eurohis_q3,
eurohis_finances = 6 - eurohis_q4,
eurohis_activity = 6 - eurohis_q5,
eurohis_selfesteem = 6 - eurohis_q6,
eurohis_relationship = 6 - eurohis_q7,
eurohis_housing = 6 - eurohis_q8)
# Coding interactions for modeling -------
insert_msg('Coding for interactions')
## appending the table with interactions (expert opinion)
## may be optionally investigated by linear modeling
ptsd$cleared <- ptsd$cleared %>%
mutate(support_injury = car::recode(as.character(injury_sev_strata),
"'1' = '1';
'2' = '2+';