The final sample consisted of 28 patients with OCD and 28 healthy control (HC) participants.
Patients with OCD and HC participants were individually matched for gender, age, and level of education.
Patients were recruited from the specialized OCD outpatient clinic at Humboldt-Universität zu Berlin. All patients were diagnosed with OCD as the primary diagnosis, as assessed with the Structured Clinical Interview for DSM-IV (SCID-I; Wittchen et al., 1997).
General inclusion criteria were age between 18 and 65 years and normal or corrected-to-normal vision.
Exclusion criteria for all participants were: lifetime diagnosis of any psychotic, bipolar, or substance-related disorder; use of benzodiazepines in the last week or of neuroleptic medication in the last three months; history of head trauma or neurological disease; any general exclusion criteria for tDCS.
Further exclusion criteria for healthy control participants were any current or past psychiatric disorder or current or past psychotherapeutic treatment.
This table corresponds to Table 1 in the manuscript.
# Load data
participant_data <- read.spss("./data/Probandendaten.sav", to.data.frame = TRUE)
questionnaires <- read.spss("./data/psychoEQExport.sav", to.data.frame = TRUE, add.undeclared.levels = "no")
# Select relevant variables
participant_data <- participant_data[, c("Code", "Alter", "Geschlecht",
"Schuljahre", "YBOCS_gesamt", "YBOCS_Gedanken", "YBOCS_Handlungen")]
questionnaires <- questionnaires[, c("CODE", "BD2SUMT0", "OCISUMT0")]
# Merge dataframes and rename columns
participant_data$Code <- gsub(" ", "", participant_data$Code, fixed = TRUE) # delete spaces for merging
demographics <- left_join(participant_data, questionnaires, by = c("Code" = "CODE")) %>%
# Rename columns
dplyr::rename("participant_id" = Code,
"Age (years)" = Alter,
"Gender" = Geschlecht,
"Years of education" = Schuljahre,
"Y-BOCS total score" = YBOCS_gesamt,
"Y-BOCS obsessions" = YBOCS_Gedanken,
"Y-BOCS compulsions" = YBOCS_Handlungen,
"BDI-II" = BD2SUMT0,
"OCI-R" = OCISUMT0) %>%
# Add group variable
dplyr::mutate(group = as.factor(ifelse(substr(participant_id, 13, 13) == "C", "HC", "OCD"))) %>%
# Exclude P_02 and C_02 and row with NA
dplyr::filter(participant_id != "ModERN_tDCS_P02" & participant_id != "ModERN_tDCS_C02" & !is.na(Gender)) %>%
# Select and reorder columns
dplyr::select(c("group", "Age (years)", "Gender", "Years of education", "BDI-II", "OCI-R",
"Y-BOCS total score", "Y-BOCS obsessions", "Y-BOCS compulsions"))
### Numeric variables
# Extract demographics per group
demographics_groups <- list()
demographics_groups <- psych::describeBy(demographics %>% dplyr::select(-c("Gender", "group")),
demographics$group)
OCD <- as.data.frame(demographics_groups$`OCD`)
HC <- as.data.frame(demographics_groups$`HC`)
# Create table with demographics formatted as "M (SD)"
demographics_groups <- as.data.frame(cbind(
Characteristic = rownames(OCD),
OCD = paste0(format(round(OCD$mean, 2), nsmall = 2), " (", format(round(OCD$sd, 2), nsmall = 2), ")"),
HC = paste0(format(round(HC$mean, 2), nsmall = 2), " (", format(round(HC$sd, 2), nsmall = 2), ")")
))
# Calculate t tests
t_tests <- demographics %>%
dplyr::select("Age (years)", "Years of education", "BDI-II", "OCI-R", "group") %>%
tidyr::gather(key = Characteristic, value = value, -group) %>%
dplyr::group_by(group, Characteristic) %>%
dplyr::summarise(value = list(value)) %>%
tidyr::spread(group, value) %>%
dplyr::ungroup() %>%
dplyr::group_by(Characteristic) %>%
dplyr::mutate("Test statistic" = t.test(unlist(HC), unlist(OCD))$statistic,
df = t.test(unlist(HC), unlist(OCD))$parameter,
p = t.test(unlist(HC), unlist(OCD))$p.value) %>%
dplyr::select(-c(HC, OCD)) %>%
dplyr::ungroup()
# Combine demographics and t test results
demographics_numeric <- left_join(demographics_groups, t_tests, by = "Characteristic")
### Count variables (gender)
# Calculate chi-squared test and extract counts formatted as "female:male"
gender_test <- chisq.test(demographics$Gender, demographics$group)
gender <- demographics %>% dplyr::group_by(group) %>% dplyr::count(Gender)
gender_OCD <- paste0(gender[gender$group == "OCD" & gender$Gender == "weiblich", ]$n, ":",
gender[gender$group == "OCD" & gender$Gender == "männlich", ]$n)
gender_HC <- paste0(gender[gender$group == "HC" & gender$Gender == "weiblich", ]$n, ":",
gender[gender$group == "HC" & gender$Gender == "männlich", ]$n)
# Combine demographics and chi-squared test
demographics_factorial <- as.data.frame(cbind(
Characteristic = "Gender (female:male)",
OCD = gender_OCD,
HC = gender_HC,
"Test statistic" = gender_test$statistic,
df = gender_test$parameter,
p = gender_test$p.value
))
### Create and display table
# Re-order rows and format p values
table <- rbind(demographics_numeric[c(1), ],
demographics_factorial,
demographics_numeric[c(2:nrow(demographics_numeric)), ])
table[, c("df", "Test statistic", "p")] <- as.numeric(unlist(table[, c("df", "Test statistic", "p")]))
table[, c("p")] <- format.pval(table[, c("p")], eps = 0.001, digits = 3)
# Display table
my_table_template(table, caption = "Demographic and Clinical Characteristics of Patients With OCD and
HC Participants ", digits = 2) %>%
add_footnote("Note. Values are means with standard deviations in parentheses except for gender.
Years of education refer to primary and secondary education, not to higher education.
Test statistic refers to Welch’s t test except for gender, which was analyzed using
a chi-squared test. BDI-II = Beck Depression Inventory-II; OCI-R = Obsessive-Compulsive
Inventory-Revised; Y-BOCS = Yale-Brown Obsessive Compulsive Scale.", notation = "none")| Characteristic | OCD | HC | Test statistic | df | p |
|---|---|---|---|---|---|
| Age (years) | 33.29 ( 8.57) | 33.07 (8.20) | -0.10 | 53.90 | 0.924 |
| Gender (female:male) | 17:11 | 17:11 | 0.00 | 1.00 | 1.000 |
| Years of education | 12.14 ( 1.46) | 12.14 (1.08) | 0.00 | 49.74 | 1.000 |
| BDI-II | 14.14 (11.34) | 1.86 (2.69) | -5.58 | 30.03 | <0.001 |
| OCI-R | 25.75 ( 9.95) | 6.25 (5.65) | -9.02 | 42.75 | <0.001 |
| Y-BOCS total score | 23.36 ( 3.84) | NaN ( NA) | NA | NA | NA |
| Y-BOCS obsessions | 11.43 ( 1.81) | NaN ( NA) | NA | NA | NA |
| Y-BOCS compulsions | 11.86 ( 2.53) | NaN ( NA) | NA | NA | NA |
|
Note. Values are means with standard deviations in parentheses except for gender. Years of education refer to primary and secondary education, not to higher education. Test statistic refers to Welch’s t test except for gender, which was analyzed using a chi-squared test. BDI-II = Beck Depression Inventory-II; OCI-R = Obsessive-Compulsive Inventory-Revised; Y-BOCS = Yale-Brown Obsessive Compulsive Scale. |
Wittchen, H., Zaudig, M., & Fydrich, T. (1997). Strukturiertes Klinisches Interview für DSM–IV. [User’s guide for the Structured Clinical Interview for DSM-IV–German modified version]. Hogrefe.
sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
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## Matrix products: default
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## locale:
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## [3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
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## other attached packages:
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## [5] knitr_1.30 dplyr_1.0.6
##
## loaded via a namespace (and not attached):
## [1] highr_0.8 pillar_1.6.0 compiler_3.6.1 tools_3.6.1
## [5] digest_0.6.27 nlme_3.1-140 lattice_0.20-38 evaluate_0.14
## [9] lifecycle_1.0.0 tibble_3.0.4 viridisLite_0.3.0 pkgconfig_2.0.3
## [13] rlang_0.4.11 rstudioapi_0.11 parallel_3.6.1 yaml_2.2.1
## [17] xfun_0.19 stringr_1.4.0 httr_1.4.2 xml2_1.3.2
## [21] generics_0.1.0 vctrs_0.3.8 grid_3.6.1 webshot_0.5.2
## [25] tidyselect_1.1.0 glue_1.4.2 R6_2.5.0 fansi_0.4.1
## [29] rmarkdown_2.5 purrr_0.3.4 magrittr_2.0.1 scales_1.1.1
## [33] ellipsis_0.3.2 htmltools_0.5.0 mnormt_2.0.2 rvest_1.0.2
## [37] colorspace_1.4-1 renv_0.12.0 utf8_1.1.4 stringi_1.5.3
## [41] munsell_0.5.0 tmvnsim_1.0-2 crayon_1.3.4