final.Rmd

---
title: "Final Report CTN-0094"
author: "CTN-0094 Team"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
      toc: true
      toc_depth: 4
vignette: >
  %\VignetteIndexEntry{Final Report CTN-0094}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
params:
   randomizationOnly: TRUE
---

```{r setup, echo=FALSE}
knitr::opts_chunk$set(
	echo = FALSE,
	message = FALSE,
	warning = FALSE,
	collapse = TRUE,
  comment = "#>"
)
```

```{r tidyverse}
library(conflicted)
conflict_prefer("filter", "dplyr", quiet = TRUE)
conflict_prefer("lag", "dplyr", quiet = TRUE)
#suppressPackageStartupMessages(library(tidyverse))
library(dplyr)
library(ggplot2)
library(tibble)
library(forcats)
# suppress "`summarise()` has grouped output by " messages
options(dplyr.summarise.inform = FALSE)  
```

```{r other-packages}
library(ggthemes)  # for theme_few()
library(table1)  # for table1()
library(DiagrammeR)  # for grViz() 
library(broom)  # for tidy()
library(kableExtra)  # for add_header_above(), kable_styling()

conflict_prefer("chisq.test", "stats", quiet = TRUE)
conflict_prefer("fisher.test", "stats", quiet = TRUE)
library(janitor) # for tabyl

library(infer) # for chisq_test

library(psych) # for phi
```
```{r make-functions}

dropMissing <- function(data, thingy) {
  data %>%  
    filter({{thingy}} != "Missing/Not Answered") %>% 
    filter({{thingy}} != "Refused/Missing") %>% 
    filter({{thingy}} != "Missing/Not Answered/Not Administered") %>% 
    mutate({{thingy}} := fct_drop({{thingy}}))
}

# Anna This function can go away because the binary variables are make factors in preprocessing


# Convert 1/0 to Yes/No for Table 1
makeYesNo <- function(thingy) {
  factor(
    case_when(
      thingy == "Yes" ~ "Yes",
      thingy == "No" ~ "No", 
      TRUE ~ "Missing/Not Answered"
    ),
    levels = c("Yes", "No", "Missing/Not Answered")
  )
}

# Convert 1/0 to Yes/No for Table 1
makeYesNoASI <- function(thingy) {
  factor(
    case_when(
      thingy == "Yes" ~ "Yes",
      thingy == "No" ~ "No", 
      TRUE ~ "Missing/Not Answered/Not Administered"
    ),
    levels = c("Yes", "No", "Missing/Not Answered/Not Administered")
  )
}

makeRandomizedAnalysisSubset <- function(data) {
  inner_join(
    data, 
    whoRandomized_id, 
    by = "who"
  ) 
}

makeChiSq <- function(table, variable) {
  a_tibble <- dropMissing(
    {{table}},
    {{variable}}
    ) %>% 
    rename(variable := {{variable}})

  infer::chisq_test(a_tibble, variable ~ project)
}


makeMosaic <- function(variable, table, title_lgl = FALSE) {
  a_tibble <- dropMissing(
    {{table}},
    {{variable}}
  )

  a_table <- janitor::tabyl(a_tibble, {{variable}}, project) 
  
  a_mat <- a_table %>% 
    select(-{{variable}}) %>% 
    as.matrix(nrow = 2, ncol = 3)

  rownames(a_mat) <- 
    a_table %>% 
    pull({{variable}}) %>% 
    as.character()

  if(title_lgl) {
    plot_title <- colnames(a_table)[1]
  } else {
    plot_title <- NULL
  }
  
  mosaicplot(
    t(a_mat),
    shade = TRUE,
    main = plot_title
  )
  
}

# custom display for table1 function
my_render_cont <- function(x) {
  with(
    stats.apply.rounding(stats.default(x), digits = 2),
    c("", "Median (Q1 - Q3)" = sprintf("%s (%s - %s)", MEDIAN, Q1, Q3))
  )
}

```


```{r load-data}
library(public.ctn0094data)

# All data objects have the same name as their sources OTHER THAN "randomized"
all_drugs    <- public.ctn0094data::all_drugs
demograhics  <- public.ctn0094data::demographics
everybody    <- public.ctn0094data::everybody
first_survey <- public.ctn0094data::first_survey
pain         <- public.ctn0094data::pain
psychiatric  <- public.ctn0094data::psychiatric
qol          <- public.ctn0094data::qol
randomized   <- public.ctn0094data::randomization # different name
rbs          <- public.ctn0094data::rbs
rbs_iv       <- public.ctn0094data::rbs_iv
tlfb         <- public.ctn0094data::tlfb
uds_temp     <- public.ctn0094data::uds_temp
withdrawal   <- public.ctn0094data::withdrawal

visit <- 
  public.ctn0094data::visit %>% 
  select(who, when, visit, what)
```

```{r randomization-info}
screened27 <- filter(everybody, project == 27)
screened30 <- filter(everybody, project == 30)
screened51 <- filter(everybody, project == 51)


whoRandomized <- 
  inner_join(everybody, randomized, by = "who") %>% 
  filter(which == 1) 

whoRandomized_id <- 
  inner_join(everybody, randomized, by = "who") %>% 
  filter(which == 1) %>% 
  select(who)


in27 <- filter(whoRandomized, project == "27")
in30 <- filter(whoRandomized, project == "30")
in51 <- filter(whoRandomized, project == "51")


visits27 <- 
  in27 %>% 
  inner_join(visit, by = "who") %>% 
  select(who, project, treatment, visit, what) %>% 
  filter(visit != "BASELINE") %>% 
  filter(what == "visit") %>%  # people came to the appointment
  group_by(who, treatment) %>% 
  summarize(visits = n())

in30_2 <- 
  inner_join(everybody, randomized, by = "who") %>% 
  filter(which == 2) %>%  # second randomization
  inner_join(visit, by = "who") %>% 
  filter(project == "30") %>% 
  filter(visit == "P2Wk1A" & what == "visit")

hasDrugInfo <- all_drugs %>% 
  select(who) %>% 
  distinct()

hasRandomization <- randomization %>% 
  select(who) %>% 
  distinct()  

drugInfoWithoutRandomization <- anti_join(
  hasDrugInfo, 
  hasRandomization, 
  by = "who"
)

hasDemographis <- demographics %>% 
  select(who) %>% 
  distinct

demoInfoWithoutRandomization <- anti_join(
  hasDemographis, 
  hasRandomization, 
  by = "who"
)

extraUsefulPeople <- intersect(
  drugInfoWithoutRandomization, 
  demoInfoWithoutRandomization
)

```

```{r make-analysis}
# add trial -- project variable
analysis_demographics <- 
  inner_join(
    demographics, 
    everybody, 
    by = "who"
  ) %>% 
  mutate(
    Sex = factor(
      case_when(
        is_male == "No" ~ "Female", 
        is_male == "Yes" ~ "Male",
        TRUE ~ "Refused/Missing"
      ),
      levels = c("Female", "Male", "Refused/Missing")
    )
  ) %>% 
  mutate(
    Race = recode_factor(race, "Refused/missing" = "Refused/Missing")
  ) %>% 
  mutate(
    Ethnicity = factor(
      case_when(
        is_hispanic == "No" ~ "Not Hispanic",
        is_hispanic == "Yes" ~ "Hispanic",
        TRUE ~ "Refused/Missing"
      )
    )
  ) %>% 
  rename(Age = age) %>% 
  rename(Education = education) %>% 
  rename(Employment = job) %>% 
  mutate(
    `Usual Living Arragements` = factor(
      case_when(
        is_living_stable == "No" ~ "Not Stable", 
        is_living_stable == "Yes" ~ "Stable",
        TRUE ~ "Refused/Missing"
      ),
      levels = c("Stable", "Not Stable", "Refused/Missing")
    )
  ) %>% 
  rename(`Relationship Status` = marital) %>% 
  select(-c(is_male, race, is_living_stable))


analysis_tlfb <- inner_join(
  everybody, 
  tlfb, 
  by = "who"
) 

analysis_uds <- inner_join(
  everybody, 
  uds, 
  by = "who"
) 


analysis_fagerstrom <- 
  inner_join(
    everybody, 
    fagerstrom, 
    by = "who"
  )%>% 
  mutate(
    `Smoking Status` = factor(
      case_when(
        as.numeric(is_smoker) == 0 ~ "Nonsmoker", 
        as.numeric(is_smoker) == 1 ~ "Smoker",
        TRUE ~ "Refused/Missing"
      ),
      levels = c("Smoker", "Nonsmoker", "Refused/Missing")
    )
  ) %>%
  #mutate(
  #  `Cigarettes per Day` = 
  #    factor(
  #      case_when(
  #        as.character(`Smoking Status`) == "Nonsmoker" ~ "N/A",
  #        as.character(per_day) == "Missing" ~ "Refused/Missing",
  #        as.character(per_day) == "" ~ "Refused/Missing",
  #        TRUE ~ per_day
  #      ),
  #      levels = c("N/A", "10 OR LESS", "11-20", "21-30", "31 OR MORE")
  #    )
  #) %>%
  mutate(`Cigarettes per Day`  = 2) %>% 
  rename(`Fagerstrom Test for Nicotine Dependence` = ftnd)

analysis_withdrawal <- 
  inner_join(
    everybody, 
    withdrawal, 
    by = "who"
  ) %>% 
  mutate(
    Severity = factor(
      case_when(
        withdrawal == 0 ~ "None",
        withdrawal == 1 ~ "Mild",
        withdrawal == 2 ~ "Moderate",
        withdrawal == 3 ~ "Severe",
      ),
      levels = c("None", "Mild", "Moderate", "Severe")
    )
  ) 

analysis_withdrawal_prepost <- 
  inner_join(
    everybody, 
    withdrawal_pre_post, 
    by = "who"
  ) %>% 
  mutate(
    what = factor(
      case_when(
        what == "post" ~ "Post",
        what == "pre" ~ "Pre"
      ),
      levels = c("Pre", "Post")
    )
  ) %>% 
  mutate(
    Severity = factor(
      case_when(
        withdrawal == 0 ~ "None",
        withdrawal == 1 ~ "Mild",
        withdrawal == 2 ~ "Mod",
        withdrawal == 3 ~ "Sev",
      ),
      levels = c("None", "Mild", "Mod", "Sev")
    )
  )  
  
# psychiatric <- psychiatric %>% 
#   rename(has_schizophrenia = has_schizophrenia,
#          schizophrenia = schizophrenia)

analysis_psychiatric <-
  inner_join(
    everybody, 
    psychiatric, 
    by = "who"
  ) %>%   
  mutate(
    across(
      c(has_schizophrenia:has_epilepsy, has_opiates_dx:has_sedatives_dx),
      makeYesNo
    )
  ) %>%
  mutate(
    across(c(depression, anxiety, schizophrenia), makeYesNoASI)
  ) %>%
  rename(
    `History of Schiziphrenia` = has_schizophrenia,
    `History of Major Depression` = has_major_dep,
    `History of Bipolar Disorder` = has_bipolar,
    `History of Anxiety/Panic Disorder` = has_anx_pan,
    `History of Brain Damage` = has_brain_damage,
    `History of Epilepsy` = has_epilepsy,
    `ASI-Lite Depression` = depression,
    `ASI-Lite Anxiety` = anxiety,
    `ASI-Lite Schizophrenia` = schizophrenia, # fix spelling
    `DSM Opiate Diagnosis` = has_opiates_dx,
    `DSM Alcohol Diagnosis` = has_alcol_dx, # fix spelling
    `DSM Amphetamine Diagnosis` = has_amphetamines_dx,
    `DSM Cannabis Diagnosis` = has_cannabis_dx,
    `DSM Cocaine Diagnosis` = has_cocaine_dx,
    `DSM Sedatives Diagnosis` = has_sedatives_dx
  )


analysis_pain <-
  inner_join(
    everybody, 
    pain, 
    by = "who"
  ) %>% 
  mutate(
    Pain = factor(
      pain,
      levels = c("No Pain", "Very mild to Moderate Pain", "Severe Pain")
    )
  ) %>% 
  select(-pain)


analysis_rbs <- inner_join(
  everybody, 
  rbs, 
  by = "who"
) 

analysis_rbs_iv <- inner_join(
  everybody, 
  rbs_iv, 
  by = "who"
) 

analysis_qol <- inner_join(
  everybody, 
  qol, 
  by = "who"
) 

analysis_all_drugs <- inner_join(
  all_drugs, 
  everybody, 
  by = "who"
)

analysis_uds_temp <-
  inner_join(
    uds_temp, 
    everybody, 
    by = "who"
  ) %>% 
  mutate(
    `Temperature In Range`=
      factor(
        case_when(
          was_temp_ok == 0 ~ "No",
          was_temp_ok == 1 ~ "Yes"
        )
      )
    )


analysis_treatment <- inner_join(
  everybody, 
  treatment, 
  by = "who"
) 

```

```{r subset-to-randomized}
if (params$randomizationOnly == TRUE) {
  
  analysis_all_drugs    <- makeRandomizedAnalysisSubset(analysis_all_drugs)
  analysis_demographics <- makeRandomizedAnalysisSubset(analysis_demographics)
  analysis_fagerstrom   <- makeRandomizedAnalysisSubset(analysis_fagerstrom)
  analysis_pain         <- makeRandomizedAnalysisSubset(analysis_pain)
  analysis_psychiatric  <- makeRandomizedAnalysisSubset(analysis_psychiatric)
  analysis_qol          <- makeRandomizedAnalysisSubset(analysis_qol)
  analysis_rbs          <- makeRandomizedAnalysisSubset(analysis_rbs)
  analysis_rbs_iv       <- makeRandomizedAnalysisSubset(analysis_rbs_iv)
  analysis_tlfb         <- makeRandomizedAnalysisSubset(analysis_tlfb)
  analysis_treatment    <- makeRandomizedAnalysisSubset(analysis_treatment)  
  analysis_uds_temp     <- makeRandomizedAnalysisSubset(analysis_uds_temp)
  analysis_uds          <- makeRandomizedAnalysisSubset(analysis_uds)
  analysis_withdrawal   <- makeRandomizedAnalysisSubset(analysis_withdrawal)

  analysis_withdrawal_prepost <- makeRandomizedAnalysisSubset(
    analysis_withdrawal_prepost
  )
  
}
```



# Table 1 
Harmonized demographics data from the three National Drug Treatment Clinical Trials Network (CTN) opioid use disorder treatment trials (CTN-0027, CTN-0030, CTN-0051, , N= `r nrow(analysis_demographics)`)

```{r}
analysis_demographics %>% 
  select(-who) %>% 
  mutate(Sex = fct_drop(Sex)) %>% 
  mutate(Sex = fct_drop(Ethnicity)) %>% 
  table1(
    ~ Sex + Age + Ethnicity + Race + Education + `Relationship Status` + 
      Employment + `Usual Living Arragements` | project,
    data = .
  )
```

# Table 2 
Harmonized Urine Drug Screen (UDS) data from the three National Drug Treatment Clinical Trials Network (CTN) opioid use disorder treatment trials 

```{r}
analysis_all_drugs %>%
  filter(source %in% c("UDS", "UDSAB")) %>% 
  mutate(
    what = fct_drop(what),
    what = as.character(what)
  ) %>% 
  filter(! what %in% c("Alcohol", "Methadone")) %>% 
  mutate(what = if_else(what == "Thc", "THC", what)) %>% 
  mutate(what = if_else(what == "Mdma", "MDMA", what)) %>% 
  rename(Substance = what) %>% 
  table1(~  Substance | project, data = .)


table1(~  `Temperature In Range` | project, data = analysis_uds_temp)
```


# Table 3
Medical and Psychiatric Co-Morbidities in the three National Drug Treatment Clinical Trials Network (CTN) opioid use disorder treatment trials 

```{r}
analysis_psychiatric %>% 
  select(project, starts_with(c("History", "DSM"))) %>% 
  table1(~  . | project, data = .)
```


```{r}
sessionInfo()
```