Data and Function Loading

knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, out.width = "100%",               # Standard Markdown chunk behaviour        
                      warning = FALSE, message = FALSE)                         # Streamline R Markdown output with removed messages

options(width = 600)
library(haven)
library(gtsummary)
library(tidyverse)
library(rstatix)
library(coin)

data <- read.csv("MIVIDA Methods Paper Data.csv")

data <- data |> dplyr::filter(!is.na(pclscore))


Eff_size_r <- function(data, variable, by, ...) {
                     rstatix::wilcox_effsize(data, as.formula(glue::glue("{variable} ~ {by}", ci = TRUE, conf.level = 0.95)))$effsize
}

Eff_size_V <- function(data, variable, by, ...) {
                       table(data[[variable]], data[[by]]) |> 
                       rstatix::cramer_v()
}

## Internal Reliability for measures in analyses
#  PCL-5
data |> dplyr::select(pcl_1:pcl_20) |> 
ltm::cronbach.alpha(na.rm = TRUE)
## 
## Cronbach's alpha for the 'dplyr::select(data, pcl_1:pcl_20)' data-set
## 
## Items: 20
## Sample units: 562
## alpha: 0.949

#  PHQ-9
data |> dplyr::select(phq1:phq9) |> 
ltm::cronbach.alpha(na.rm = TRUE)
## 
## Cronbach's alpha for the 'dplyr::select(data, phq1:phq9)' data-set
## 
## Items: 9
## Sample units: 562
## alpha: 0.91

#  GAD-7
data |> dplyr::select(gad1:gad7) |> 
ltm::cronbach.alpha(na.rm = TRUE)
## 
## Cronbach's alpha for the 'dplyr::select(data, gad1:gad7)' data-set
## 
## Items: 7
## Sample units: 562
## alpha: 0.915

#  AUDIT
data |> dplyr::select(audit1:audit10) |> 
ltm::cronbach.alpha(na.rm = TRUE)
## 
## Cronbach's alpha for the 'dplyr::select(data, audit1:audit10)' data-set
## 
## Items: 10
## Sample units: 562
## alpha: 0.654

# Dissociative Experiences
data |> dplyr::select(des1:des8) |> 
ltm::cronbach.alpha(na.rm = TRUE)
## 
## Cronbach's alpha for the 'dplyr::select(data, des1:des8)' data-set
## 
## Items: 8
## Sample units: 562
## alpha: 0.898

Summary Statistics


table1 <-
data |> 
  mutate(Assignment      = factor(pclgroup, labels = c("Control", "Case")),
         Gender          = data$dem2.factor,
         Sexuality       = dem6.factor,
         Martial_Status  = dem4.factor,
         Age             = dem3,
         Ethnicity       = dem11.factor,
         Religion        = dem12.factor,
         Education       = dem14.factor,
         Economic_Stat   = dem17.factor,
         Living_Area     = dem8.factor,
         Victim_Registry = vic1.factor,
         FARC_Involved   = farc.factor) |> 
  # Factors with limited numbers collapsed to minimise participant identification
  mutate(Gender = recode_factor(Gender, 
                                "Man" = "Male",
                                "Female" = "Female",
                                "Transgender man" = "Other Gender Identity",
                                "Transgender woman" = "Other Gender Identity",
                                "Transgender/gender  fluid" = "Other Gender Identity",
                                "Other" = "Other Gender Identity"),
         Ethnicity = recode(Ethnicity, 
                            "Gitano" = "Other",
                            "Other - Please specify" = "Other"),
         Economic_Stat = recode_factor(Economic_Stat,
                                       "Stratum 1 (Very low)" = "Stratum 1-2 (Very Low - Low)",
                                       "Stratum 2 (Low)" = "Stratum 1-2 (Very Low - Low)",
                                       "Stratum 3 (Medium Low)" = "Stratum 3-4 (Medium Low - Medium)",
                                       "Stratum 4 (Medium)" = "Stratum 3-4 (Medium Low - Medium)"),
         Religion = recode_factor(Religion,
                                   "Catholic" = "Catholic",
                                   "Protestant" = "Other",
                                   "Jeovah Witness" = "Other",
                                   "Other  - Please Specify" = "Other"),
         Sexuality = recode_factor(Sexuality,
                                   "Heterosexual" = "Heterosexual")
         ) |> 
  select(Assignment, Gender, Sexuality, Martial_Status, Age, Ethnicity, Religion, 
         Education, Economic_Stat, Living_Area, Victim_Registry, FARC_Involved) |> 
  tbl_summary(by = Assignment,
    label = list(Economic_Stat ~ "Economic Status",
                 Living_Area   ~ "Area of Residence",
                 FARC_Involved ~ "Were you or a close family member (e.g. sibling or child) 
                                  forcibly recruited to be part of the revolutionary armed forces?"),
    type = c(Age) ~  "continuous" ,
    statistic = all_continuous() ~ "{mean} ({sd})",
    digits = list(all_continuous() ~ 2, all_categorical() ~ c(0, 2)),
    missing = "no") |> 
    add_overall()   |> 
    add_stat(fns = list(all_continuous()  ~ Eff_size_r,
                      all_categorical() ~ Eff_size_V)) |> 
  modify_header(add_stat_1 ~ "**Effect Size**"); table1

Characteristic	Overall N = 562¹	Control N = 366¹	Case N = 196¹	Effect Size
Gender				0.018
Male	164 (29.23%)	105 (28.77%)	59 (30.10%)
Female	392 (69.88%)	257 (70.41%)	135 (68.88%)
Other Gender Identity	5 (0.89%)	3 (0.82%)	2 (1.02%)
Sexuality				0.105
Heterosexual	447 (81.27%)	284 (80.00%)	163 (83.59%)
I prefer not to answer	75 (13.64%)	56 (15.77%)	19 (9.74%)
Other - Please specify	8 (1.45%)	4 (1.13%)	4 (2.05%)
Bisexual	11 (2.00%)	5 (1.41%)	6 (3.08%)
Homosexual (gay or lesbian)	9 (1.64%)	6 (1.69%)	3 (1.54%)
Martial_Status				0.080
I prefer not to say	5 (0.89%)	3 (0.82%)	2 (1.02%)
Married or union	252 (44.92%)	164 (44.93%)	88 (44.90%)
Other	4 (0.71%)	3 (0.82%)	1 (0.51%)
Separated or divorced	57 (10.16%)	37 (10.14%)	20 (10.20%)
Single	208 (37.08%)	140 (38.36%)	68 (34.69%)
Widowed/widowed	35 (6.24%)	18 (4.93%)	17 (8.67%)
Age	43.89 (14.03)	42.77 (14.10)	45.98 (13.70)	0.108
Ethnicity				0.084
Hispanico	143 (34.13%)	88 (32.96%)	55 (36.18%)
I prefer not to answer	51 (12.17%)	37 (13.86%)	14 (9.21%)
Indigena	86 (20.53%)	51 (19.10%)	35 (23.03%)
None - Does not identify with an ethnic group	133 (31.74%)	87 (32.58%)	46 (30.26%)
Other	6 (1.43%)	4 (1.50%)	2 (1.32%)
Religion				0.093
Catholic	279 (50.00%)	194 (53.30%)	85 (43.81%)
Other	22 (3.94%)	14 (3.85%)	8 (4.12%)
None	57 (10.22%)	36 (9.89%)	21 (10.82%)
Christian or Evangelical	200 (35.84%)	120 (32.97%)	80 (41.24%)
Education				0.098
Baccalaureate	223 (39.96%)	146 (40.22%)	77 (39.49%)
Higher Education	22 (3.94%)	15 (4.13%)	7 (3.59%)
None	14 (2.51%)	6 (1.65%)	8 (4.10%)
Other - Please specify	4 (0.72%)	2 (0.55%)	2 (1.03%)
Preschool	9 (1.61%)	6 (1.65%)	3 (1.54%)
Primary	150 (26.88%)	95 (26.17%)	55 (28.21%)
Professional Technical Level	30 (5.38%)	22 (6.06%)	8 (4.10%)
Technical	77 (13.80%)	50 (13.77%)	27 (13.85%)
University undergraduate	29 (5.20%)	21 (5.79%)	8 (4.10%)
Economic Status				0.085
Stratum 1-2 (Very Low - Low)	546 (98.20%)	358 (99.17%)	188 (96.41%)
Stratum 3-4 (Medium Low - Medium)	10 (1.80%)	3 (0.83%)	7 (3.59%)
Area of Residence				0.040
Rural	167 (29.93%)	103 (28.45%)	64 (32.65%)
Urban	391 (70.07%)	259 (71.55%)	132 (67.35%)
Victim_Registry				0.076
I am not registered because I am not a victim	23 (4.11%)	11 (3.02%)	12 (6.15%)
I am not registered but I am a victim	53 (9.48%)	34 (9.34%)	19 (9.74%)
Yes, I am registered	483 (86.40%)	319 (87.64%)	164 (84.10%)
Were you or a close family member (e.g. sibling or child) forcibly recruited to be part of the revolutionary armed forces?	220 (39.64%)	132 (36.67%)	88 (45.13%)	0.079
¹ n (%); Mean (SD)



#  gt::gtsave(as_gt(table1), filename = "Demographic_table.rtf")

data |> 
rstatix::wilcox_test(dem3 ~ pclgroup, alternative = "two.sided")
## # A tibble: 1 × 7
##   .y.   group1 group2    n1    n2 statistic      p
## * <chr> <chr>  <chr>  <int> <int>     <dbl>  <dbl>
## 1 dem3  0      1        365   196     31087 0.0105

options(scipen=10, digits = 4)

table2a <-
data |> 
  mutate(Assignment      = factor(pclgroup, labels = c("Control", "Case")),
         Gender          = dem2.factor,
         Sexuality       = dem6.factor,
         Age             = dem3,
         Ethnicity       = dem11.factor,
         Religion        = dem12.factor,
         Education       = dem14.factor,
         Economic_Stat   = dem17.factor,
         Living_Area     = dem8.factor,
         Victim_Registry = vic1.factor,
         FARC_Involved   = farc.factor)  |>  
  select(Assignment, ntrauma, lec1.factor, lec2a.factor, lec3a.factor, lec4a.factor,
         lec5a.factor, lec6a.factor, lec7a.factor, lec8a.factor,
         lec9a.factor, lec10a.factor, lec11a.factor, lec12a.factor,
         lec13a.factor, lec14a.factor, lec15a.factor, lec16a.factor,
         lec17a.factor, lec18a.factor, lec19a.factor, lec20a.factor,
         lec21a.factor, lec22a.factor) |> 
  tbl_summary(by = Assignment,
    label = list(lec1.factor = "Natural Disaster", lec2a.factor = "Fire or Explosion",
                 lec3a.factor = "Traffic Accident", lec4a.factor = "Serious Accident", 
                 lec5a.factor = "Exposure to Toxic Substances", lec6a.factor = "Physical Assault", 
                 lec7a.factor = "Armed Assault", lec8a.factor = "Sexual Assault",
                 lec9a.factor = "Unwanted Sexual Contact", lec10a.factor = "Combat", 
                 lec11a.factor = "Captivity", lec12a.factor = "Life Threatening Illness or Injury",
                 lec13a.factor = "Severe Human Suffering", lec14a.factor = "Sudden Violent Death", 
                 lec15a.factor = "Sudden Accidental Death", lec16a.factor = "Serious Injury, Harm, or Death Caused to Another",
                 lec17a.factor = "Parent or Partner Ridicule", lec18a.factor = "Physical Torture", 
                 lec19a.factor = "Psychological Torture", lec20a.factor = "House or Property Damaged",
                 lec21a.factor = "Forced Displacement", lec22a.factor = "Other Stressful Event",
                 worsttrauma.factor = "Index Trauma"),
    statistic = all_continuous() ~ "{mean} ({sd})",
    digits = list(all_continuous() ~ 2, all_categorical() ~ c(0, 2)),
    missing = "no") |> 
    add_overall() |> 
    add_p(list(all_categorical() ~ "chisq.test",
               all_continuous()  ~ "wilcox.test"),  include = everything()) |> 
    modify_header(statistic ~ "**Test Statistic**")   |> 
  add_stat(fns = list(all_continuous()  ~ Eff_size_r,
                      all_categorical() ~ Eff_size_V)) |> 
  modify_header(add_stat_1 ~ "**Effect Size**"); table2a

Characteristic	Overall N = 562¹	Control N = 366¹	Case N = 196¹	Test Statistic²	p-value²	Effect Size
ntrauma	8.23 (4.19)	7.22 (3.85)	10.10 (4.17)	21,853	<0.001	0.323
Natural Disaster	207 (36.83%)	128 (34.97%)	79 (40.31%)	1.34	0.2	0.049
Fire or Explosion	199 (35.41%)	117 (31.97%)	82 (41.84%)	5.01	0.025	0.094
Traffic Accident	196 (34.88%)	119 (32.51%)	77 (39.29%)	2.29	0.13	0.064
Serious Accident	221 (39.32%)	130 (35.52%)	91 (46.43%)	5.92	0.015	0.103
Exposure to Toxic Substances	96 (17.08%)	62 (16.94%)	34 (17.35%)	0.000	>0.9	0.000
Physical Assault	291 (51.78%)	174 (47.54%)	117 (59.69%)	7.07	0.008	0.112
Armed Assault	249 (44.31%)	142 (38.80%)	107 (54.59%)	12.3	<0.001	0.148
Sexual Assault	97 (17.26%)	53 (14.48%)	44 (22.45%)	5.13	0.024	0.096
Unwanted Sexual Contact	74 (13.17%)	39 (10.66%)	35 (17.86%)	5.18	0.023	0.096
Combat	298 (53.02%)	180 (49.18%)	118 (60.20%)	5.79	0.016	0.102
Captivity				12.6	0.002	0.150
No	323 (57.47%)	226 (61.75%)	97 (49.49%)
Yes, it happened to a family member	172 (30.60%)	108 (29.51%)	64 (32.65%)
Yes, it happened to me	67 (11.92%)	32 (8.74%)	35 (17.86%)
Life Threatening Illness or Injury	137 (24.38%)	62 (16.94%)	75 (38.27%)	30.3	<0.001	0.232
Severe Human Suffering	303 (53.91%)	167 (45.63%)	136 (69.39%)	28.1	<0.001	0.223
Sudden Violent Death	236 (41.99%)	119 (32.51%)	117 (59.69%)	37.6	<0.001	0.259
Sudden Accidental Death	143 (25.44%)	73 (19.95%)	70 (35.71%)	15.9	<0.001	0.168
Serious Injury, Harm, or Death Caused to Another	21 (3.74%)	6 (1.64%)	15 (7.65%)	11.2	<0.001	0.141
Parent or Partner Ridicule	202 (35.94%)	103 (28.14%)	99 (50.51%)	26.8	<0.001	0.218
Physical Torture	101 (17.97%)	43 (11.75%)	58 (29.59%)	26.4	<0.001	0.217
Psychological Torture	253 (45.02%)	137 (37.43%)	116 (59.18%)	23.5	<0.001	0.205
House or Property Damaged	188 (33.45%)	111 (30.33%)	77 (39.29%)	4.21	0.040	0.087
Forced Displacement	498 (88.61%)	328 (89.62%)	170 (86.73%)	0.785	0.4	0.037
Other Stressful Event	203 (36.12%)	103 (28.14%)	100 (51.02%)	28.0	<0.001	0.223
¹ Mean (SD); n (%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test



#  gt::gtsave(as_gt(table2a), filename = "TraumaEndorsement_table.rtf")


table2b <-
data |> 
  mutate(Assignment      = factor(pclgroup, labels = c("Control", "Case")),
         Gender          = dem2.factor,
         Sexuality       = dem6.factor,
         Age             = dem3,
         Ethnicity       = dem11.factor,
         Religion        = dem12.factor,
         Education       = dem14.factor,
         Economic_Stat   = dem17.factor,
         Living_Area     = dem8.factor,
         Victim_Registry = vic1.factor,
         FARC_Involved   = farc.factor,
         worsttrauma.factor = factor(worsttrauma.factor))  |>  
  select(Assignment, worsttrauma.factor) |> 
  tbl_summary(by = Assignment,
    label = list(worsttrauma.factor = "Index Trauma"),
    statistic = all_continuous() ~ "{mean} ({sd})",
    digits = list(all_continuous() ~ 2, all_categorical() ~ c(0, 2)),
    missing = "no") |> 
    add_overall(); table2b

Characteristic	Overall N = 562¹	Control N = 366¹	Case N = 196¹
Index Trauma
A life-threatening illness or injury	4 (0.74%)	3 (0.87%)	1 (0.52%)
A natural disaster (such as earthquakes, floods, volcanic eruptions, hurricanes, landslides)	18 (3.35%)	15 (4.35%)	3 (1.55%)
Accident at work, at home or during recreational activities	8 (1.49%)	2 (0.58%)	6 (3.11%)
Another stressful experience or event	6 (1.12%)	4 (1.16%)	2 (1.04%)
Any other type of unwanted or unpleasant sexual activity	5 (0.93%)	3 (0.87%)	2 (1.04%)
Armed assault (he has been shot, stabbed , threatened with a knife. Firearm or bomb)	17 (3.16%)	6 (1.74%)	11 (5.70%)
Captivity (kidnapping , hostage-taking, prisoner of war, being deprived of liberty)	12 (2.23%)	7 (2.03%)	5 (2.59%)
Combat or exposure to a war zone (such as military, combatant, or civilian)	29 (5.39%)	18 (5.22%)	11 (5.70%)
Exposure to toxic substances	2 (0.37%)	2 (0.58%)	0 (0.00%)
Fire, Explosion, or Bombs	58 (10.78%)	45 (13.04%)	13 (6.74%)
Forced displacement	233 (43.31%)	163 (47.25%)	70 (36.27%)
House or property damaged	1 (0.19%)	1 (0.29%)	0 (0.00%)
Physical assault (attack, robbery, beating, has been kicked or beaten)	13 (2.42%)	10 (2.90%)	3 (1.55%)
Physical torture	2 (0.37%)	1 (0.29%)	1 (0.52%)
Psychological torture	12 (2.23%)	6 (1.74%)	6 (3.11%)
Severe suffering	10 (1.86%)	4 (1.16%)	6 (3.11%)
Sexual assault (rape, attempted rape, forced to engage in any sexual act with use of force or threats )	20 (3.72%)	9 (2.61%)	11 (5.70%)
Traffic accident (includes accident of car, bus, boat or boat, train or plane )	14 (2.60%)	6 (1.74%)	8 (4.15%)
Unexpected accidental death	7 (1.30%)	5 (1.45%)	2 (1.04%)
Violent death and unexpected (murder, suicide)	57 (10.59%)	30 (8.70%)	27 (13.99%)
Your parents, partner or family member ridiculed repeatedly, verbally abused, or said you re not worth it	10 (1.86%)	5 (1.45%)	5 (2.59%)
¹ n (%)


#  gt::gtsave(as_gt(table2b), filename = "IndexTrauma_table.rtf")


table3 <-
data |> 
  mutate(Assignment  = factor(pclgroup, labels = c("Control", "Case")),
         Tot_ACES    = as.numeric(ace1+ace2+ace3+ace4+ace5+ace6+ace7+ace8+ace9+ace10, label = "ACE Score Total"),
         Tot_Dep     = as.numeric(phq1+phq2+phq3+phq4+phq5+phq6+phq7+phq8+phq9, label = "Depression Total"),
         Cut_Dep     = if_else(Tot_Dep >= 10, true = 1, false = 0, missing = NULL),
         Tot_Anx     = as.numeric(gad1+gad2+gad3+gad4+gad5+gad6+gad7, label = "Anxiety Total"),
         Cut_Anx     = if_else(Tot_Anx >= 10, true = 1, false = 0, missing = NULL),
         Tot_AUDIT   = as.numeric(audit1+audit2+audit3+audit4+audit5+audit6+audit7+audit8+audit9+audit10, label = "AUDIT Total"),
         Cut_AUDIT   = if_else(Tot_AUDIT >= 10, true = 1, false = 0, missing = NULL)) |> 
    rowwise() |> 
    mutate(N_disorder  = sum(pclgroup, Cut_Dep, Cut_Anx, Cut_AUDIT, na.rm = TRUE),
           Diss_Exp    = mean(c(des1,des2,des3,des4,des5,des6,des7,des8), na.rm = TRUE)) |> 
    ungroup() |>  
  select(Assignment, Tot_Dep, Cut_Dep, Tot_Anx, Cut_Anx, Cut_AUDIT, N_disorder, Diss_Exp) |> 
  tbl_summary(by = Assignment,
              label = list(Tot_Dep ~ "Total Depression Score",
                           Cut_Dep ~ "Probable Depression (>= 10)",
                           Tot_Anx ~ "Total Anxiety Score",
                           Cut_Anx ~ "Probable Anxiety (>= 10)",
                           Cut_AUDIT ~ "Probable Alcohol Use Disorder (>= 10)",
                           Diss_Exp ~ "Dissociative Experiences",
                           N_disorder ~ "Number of Probable Common Mental Health Diagnoses (0-4)"),
    type = list(Tot_Dep ~ "continuous", 
                Cut_Dep ~ "dichotomous",
                Tot_Anx ~ "continuous",
                Cut_Anx ~ "dichotomous",
                N_disorder ~ "continuous"),
    statistic = all_continuous() ~ "{mean} ({sd})",
    digits = list(all_continuous() ~ 2, all_categorical() ~ c(0, 2)),
    missing = "no") |> 
    add_overall() |> 
    add_p(list(all_categorical() ~ "chisq.test",
               all_continuous() ~ "wilcox.test"),  include = everything()) |> 
    modify_header(statistic ~ "**Test Statistic**") |> 
  add_stat(fns = list(all_continuous()  ~ Eff_size_r,
                      all_categorical() ~ Eff_size_V)) |> 
  modify_header(add_stat_1 ~ "**Effect Size**") |> 
    modify_caption("**Trauma and Diagnostic Characteristics**"); table3

**Trauma and Diagnostic Characteristics**
Characteristic	Overall N = 562¹	Control N = 366¹	Case N = 196¹	Test Statistic²	p-value²	Effect Size
Total Depression Score	6.96 (6.76)	4.12 (4.84)	12.01 (6.77)	10,233	<0.001	0.566
Probable Depression (>= 10)	157 (29.90%)	43 (12.80%)	114 (60.32%)	128	<0.001	0.494
Total Anxiety Score	6.60 (5.89)	4.13 (4.48)	11.15 (5.45)	10,079	<0.001	0.576
Probable Anxiety (>= 10)	148 (27.56%)	44 (12.64%)	104 (55.03%)	108	<0.001	0.449
Probable Alcohol Use Disorder (>= 10)	22 (13.66%)	11 (9.82%)	11 (22.45%)	3.60	0.058	0.150
Number of Probable Common Mental Health Diagnoses (0-4)	0.93 (1.16)	0.27 (0.59)	2.17 (0.92)	3,668	<0.001	0.806
Dissociative Experiences	1.07 (1.84)	0.88 (1.70)	1.43 (2.02)	26,943	<0.001	0.179
¹ Mean (SD); n (%)
² Wilcoxon rank sum test; Pearson’s Chi-squared test


#  gt::gtsave(as_gt(table3), filename = "Diagnoses_Table.rtf")


  data1 <- data |> 
  mutate(Assignment  = factor(pclgroup, labels = c("Control", "Case")),
         Tot_ACES    = as.numeric(ace1+ace2+ace3+ace4+ace5+ace6+ace7+ace8+ace9+ace10, label = "ACE Score Total"),
         Tot_Dep     = as.numeric(phq1+phq2+phq3+phq4+phq5+phq6+phq7+phq8+phq9, label = "Depression Total"),
         Cut_Dep     = if_else(Tot_Dep >= 10, true = 1, false = 0, missing = NULL),
         Tot_Anx     = as.numeric(gad1+gad2+gad3+gad4+gad5+gad6+gad7, label = "Anxiety Total"),
         Cut_Anx     = if_else(Tot_Anx >= 10, true = 1, false = 0, missing = NULL),
         Tot_AUDIT   = as.numeric(audit1+audit2+audit3+audit4+audit5+audit6+audit7+audit8+audit9+audit10, label = "AUDIT Total"),
         Cut_AUDIT   = if_else(Tot_AUDIT >= 10, true = 1, false = 0, missing = NULL)) |> 
    rowwise() |> 
    mutate(N_disorder  = sum(pclgroup, Cut_Dep, Cut_Anx, Cut_AUDIT, na.rm = TRUE)) |> 
    ungroup()

  
  
  data1 |> dplyr::select(N_disorder) |> 
    dplyr::mutate(Any_disorder = N_disorder >= 1) |> 
    tbl_summary(digits = list(all_continuous() ~ 2, all_categorical() ~ c(0, 2)),
                label = list(N_disorder ~ "Number of Probable Common Mental Health Diagnoses (0-4)",
                             Any_disorder ~ "Any Probable Diagnosis (Depression, Anxiety, PTSD, Alcohol Use)"),) |> 
    modify_caption("**Number of Participants With Any Probable Diagnosis, and Comorbidity Count**")

**Number of Participants With Any Probable Diagnosis, and Comorbidity Count**
Characteristic	N = 562¹
Number of Probable Common Mental Health Diagnoses (0-4)
0	294 (52.31%)
1	111 (19.75%)
2	64 (11.39%)
3	88 (15.66%)
4	5 (0.89%)
Any Probable Diagnosis (Depression, Anxiety, PTSD, Alcohol Use)	268 (47.69%)
¹ n (%)

Missing Data Analysis


data1 |> 
    select(pcl_1:pcl_20, audit1, phq1:phq9, gad1:gad7, des1:des8,
         dem2, dem6, dem4, dem3, dem11,
         dem12, dem14, dem17, dem8, vic1, farc,
         lec1, lec2a, lec3a, lec4a, lec5a, lec6a, lec7a,
         lec8a, lec9a, lec10a, lec11a, lec12a, lec13a, lec14a, lec15a, lec16a,
         lec17a, lec18a, lec19a, lec20a, lec21a, lec22a) |> 
  naniar::pct_miss()
## [1] 1.323

miss_dataplot <- data1 |> 
      select(pcl_1:pcl_20, N_disorder, phq1:phq9, gad1:gad7,
           dem2, dem6, dem4, dem3, dem11,
           dem12, dem14, dem17, dem8, vic1, farc,
           lec1, lec2a, lec3a, lec4a, lec5a, lec6a, lec7a,
           lec8a, lec9a, lec10a, lec11a, lec12a, lec13a, lec14a, lec15a, lec16a,
           lec17a, lec18a, lec19a, lec20a, lec21a, lec22a) |> 
  naniar::vis_miss(); miss_dataplot


MCAR_test <- data1 |> 
    select(pcl_1:pcl_20, audit1, phq1:phq9, gad1:gad7, des1:des8,
         dem2, dem6, dem4, dem3, dem11,
         dem12, dem14, dem17, dem8, vic1, farc,
         lec1, lec2a, lec3a, lec4a, lec5a, lec6a, lec7a,
         lec8a, lec9a, lec10a, lec11a, lec12a, lec13a, lec14a, lec15a, lec16a,
         lec17a, lec18a, lec19a, lec20a, lec21a, lec22a
         ) |> 
  naniar::mcar_test(); MCAR_test
## # A tibble: 1 × 4
##   statistic    df p.value missing.patterns
##       <dbl> <dbl>   <dbl>            <int>
## 1     6408.  7228    1.00               99

Figure 1

library(patchwork)
cbpal <- c("#0072b2", "#009e73")

fig1a <- data |> 
ggplot(aes(x = "", y = pclscore)) +
geom_violin(alpha = 0.3, fill = "#13315C") +  
geom_boxplot(alpha = 0.6, width = .4, outlier.shape = NA, color = "#13315C") +
  expand_limits(y = c(0, 80)) +
geom_hline(yintercept = 33, linetype="dashed", color = "#ef8a62", size = .9) + 
theme_minimal() + theme(legend.position="none", axis.line = element_line(colour = "grey50", size = 1)) +  
          labs(title=NULL, x = "Total Sample", y = NULL) + coord_flip()

fig1b <- data |> 
mutate(pclgroup = factor(pclgroup, labels = c("Control", "Case"))) |> 
ggplot(aes(x = pclgroup, y = pclscore, color = pclgroup, fill = pclgroup)) +
geom_violin(alpha = 0.3) +  
geom_boxplot(alpha = 0.6, width = .4, outlier.shape = NA) +  
    expand_limits(y = c(0, 80)) +
scale_color_manual(values = cbpal) + scale_fill_manual(values = cbpal) +
theme_minimal() + theme(legend.position="none", axis.line = element_line(colour = "grey50", size = 1)) + 
          labs(title=NULL, x = "Assignment", y = "PTSD Score") + coord_flip()

jpeg("Figure 1.jpeg", width=1200, height=480)
fig1 <- fig1a / fig1b
fig1 + plot_annotation(caption = "Note: Cut-off score of 33 used to indicated probable PTSD diagnosis (Murphy et al., 2017)")
dev.off()
## png 
##   2

Methodological Overview and Baseline Data Analysis of a Case-Control Investigation of Risk and Resiliency Factors for Traumatic Stress in Colombia

Supplemental File 1

2024-10-04

Data and Function Loading

Summary Statistics

Missing Data Analysis

Figure 1