inst/doc/generate_tmc_test_data.R

In teal.modules.clinical: 'teal' Modules for Standard Clinical Outputs

## ----include=FALSE------------------------------------------------------------
knitr::opts_template$set(
  remove_linter_comments = list(tidy = function(code, ...) gsub(pattern = "#\\s?nolint.*", replacement = "", code))
)

## ----setup, eval=FALSE, opts.label=c("remove_linter_comments")----------------
# library(dplyr)
# library(teal.data)
# 
# study_duration_secs <- lubridate::seconds(lubridate::years(2))
# 
# sample_fct <- function(x, N, ...) { # nolint: object_name.
#   checkmate::assert_number(N)
#   factor(sample(x, N, replace = TRUE, ...), levels = if (is.factor(x)) levels(x) else x)
# }
# 
# retain <- function(df, value_var, event, outside = NA) {
#   indices <- c(1, which(event == TRUE), nrow(df) + 1)
#   values <- c(outside, value_var[event == TRUE])
#   rep(values, diff(indices))
# }
# 
# relvar_init <- function(relvar1, relvar2) {
#   if (length(relvar1) != length(relvar2)) {
#     message(simpleError(
#       "The argument value length of relvar1 and relvar2 differ. They must contain the same number of elements."
#     ))
#     return(NA)
#   }
#   List("relvar1" = relvar1, "relvar2" = relvar2)
# }
# 
# rel_var <- function(df = NULL, var_name = NULL, var_values = NULL, related_var = NULL) {
#   if (is.null(df)) {
#     message("Missing data frame argument value.")
#     return(NA)
#   } else {
#     n_relvar1 <- length(unique(df[, related_var, drop = TRUE]))
#     n_relvar2 <- length(var_values)
#     if (n_relvar1 != n_relvar2) {
#       message(paste("Unequal vector lengths for", related_var, "and", var_name))
#       return(NA)
#     } else {
#       relvar1 <- unique(df[, related_var, drop = TRUE])
#       relvar2_values <- rep(NA, nrow(df))
#       for (r in seq_len(length(relvar1))) {
#         matched <- which(df[, related_var, drop = TRUE] == relvar1[r])
#         relvar2_values[matched] <- var_values[r]
#       }
#       return(relvar2_values)
#     }
#   }
# }
# 
# visit_schedule <- function(visit_format = "WEEK",
#                            n_assessments = 10L,
#                            n_days = 5L) {
#   if (!(toupper(visit_format) %in% c("WEEK", "CYCLE"))) {
#     message("Visit format value must either be: WEEK or CYCLE")
#     return(NA)
#   }
#   if (toupper(visit_format) == "WEEK") {
#     assessments <- 1:n_assessments
#     assessments_ord <- -1:n_assessments
#     visit_values <- c("SCREENING", "BASELINE", paste(toupper(visit_format), assessments, "DAY", (assessments * 7) + 1))
#   } else if (toupper(visit_format) == "CYCLE") {
#     cycles <- sort(rep(1:n_assessments, times = 1, each = n_days))
#     days <- rep(seq(1:n_days), times = n_assessments, each = 1)
#     assessments_ord <- 0:(n_assessments * n_days)
#     visit_values <- c("SCREENING", paste(toupper(visit_format), cycles, "DAY", days))
#   }
#   visit_values <- stats::reorder(factor(visit_values), assessments_ord)
# }
# 
# rtpois <- function(n, lambda) stats::qpois(stats::runif(n, stats::dpois(0, lambda), 1), lambda)
# 
# rtexp <- function(n, rate, l = NULL, r = NULL) {
#   if (!is.null(l)) {
#     l - log(1 - stats::runif(n)) / rate
#   } else if (!is.null(r)) {
#     -log(1 - stats::runif(n) * (1 - exp(-r * rate))) / rate
#   } else {
#     stats::rexp(n, rate)
#   }
# }
# 
# str_extract <- function(string, pattern) regmatches(string, gregexpr(pattern, string))
# 
# with_label <- function(x, label) {
#   attr(x, "label") <- as.vector(label)
#   x
# }
# 
# common_var_labels <- c(
#   USUBJID = "Unique Subject Identifier",
#   STUDYID = "Study Identifier",
#   PARAM = "Parameter",
#   PARAMCD = "Parameter Code",
#   AVISIT = "Analysis Visit",
#   AVISITN = "Analysis Visit (N)",
#   AVAL = "Analysis Value",
#   AVALU = "Analysis Value Unit",
#   AVALC = "Character Result/Finding",
#   BASE = "Baseline Value",
#   BASE2 = "Screening Value",
#   ABLFL = "Baseline Record Flag",
#   ABLFL2 = "Screening Record Flag",
#   CHG = "Absolute Change from Baseline",
#   PCHG = "Percentage Change from Baseline",
#   ANRIND = "Analysis Reference Range Indicator",
#   BNRIND = "Baseline Reference Range Indicator",
#   ANRLO = "Analysis Normal Range Lower Limit",
#   ANRHI = "Analysis Normal Range Upper Limit",
#   CNSR = "Censor",
#   ADTM = "Analysis Datetime",
#   ADY = "Analysis Relative Day",
#   ASTDY = "Analysis Start Relative Day",
#   AENDY = "Analysis End Relative Day",
#   ASTDTM = "Analysis Start Datetime",
#   AENDTM = "Analysis End Datetime",
#   VISITDY = "Planned Study Day of Visit",
#   EVNTDESC = "Event or Censoring Description",
#   CNSDTDSC = "Censor Date Description",
#   BASETYPE = "Baseline Type",
#   DTYPE = "Derivation Type",
#   ONTRTFL = "On Treatment Record Flag",
#   WORS01FL = "Worst Observation in Window Flag 01",
#   WORS02FL = "Worst Post-Baseline Observation"
# )

## ----adsl, eval=FALSE, opts.label=c("remove_linter_comments")-----------------
# generate_adsl <- function(N = 200) { # nolint: object_name.
#   set.seed(1)
#   sys_dtm <- lubridate::fast_strptime("20/2/2019 11:16:16.683", "%d/%m/%Y %H:%M:%OS", tz = "UTC")
#   country_site_prob <- c(.5, .121, .077, .077, .075, .052, .046, .025, .014, .003)
# 
#   adsl <- tibble::tibble(
#     STUDYID = rep("AB12345", N) %>% with_label("Study Identifier"),
#     COUNTRY = sample_fct(
#       c("CHN", "USA", "BRA", "PAK", "NGA", "RUS", "JPN", "GBR", "CAN", "CHE"),
#       N,
#       prob = country_site_prob
#     ) %>% with_label("Country"),
#     SITEID = sample_fct(1:20, N, prob = rep(country_site_prob, times = 2)),
#     SUBJID = paste("id", seq_len(N), sep = "-") %>% with_label("Subject Identifier for the Study"),
#     AGE = (sapply(stats::rchisq(N, df = 5, ncp = 10), max, 0) + 20) %>% with_label("Age"),
#     SEX = c("F", "M") %>% sample_fct(N, prob = c(.52, .48)) %>% with_label("Sex"),
#     ARMCD = c("ARM A", "ARM B", "ARM C") %>% sample_fct(N) %>% with_label("Planned Arm Code"),
#     ARM = dplyr::recode(
#       .data$ARMCD,
#       "ARM A" = "A: Drug X", "ARM B" = "B: Placebo", "ARM C" = "C: Combination"
#     ) %>% with_label("Description of Planned Arm"),
#     ACTARMCD = .data$ARMCD %>% with_label("Actual Arm Code"),
#     ACTARM = .data$ARM %>% with_label("Description of Actual Arm"),
#     RACE = c(
#       "ASIAN", "BLACK OR AFRICAN AMERICAN", "WHITE", "AMERICAN INDIAN OR ALASKA NATIVE",
#       "MULTIPLE", "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER", "OTHER", "UNKNOWN"
#     ) %>%
#       sample_fct(N, prob = c(.55, .23, .16, .05, .004, .003, .002, .002)) %>%
#       with_label("Race"),
#     TRTSDTM = sys_dtm + sample(seq(0, study_duration_secs), size = N, replace = TRUE) %>%
#       with_label("Datetime of First Exposure to Treatment"),
#     TRTEDTM = c(TRTSDTM + study_duration_secs) %>%
#       with_label("Datetime of Last Exposure to Treatment"),
#     EOSDY = ceiling(as.numeric(difftime(TRTEDTM, TRTSDTM, units = "days"))) %>%
#       with_label("End of Study Relative Day"),
#     EOSDT = lubridate::date(TRTEDTM) %>% with_label("End of Study Date"),
#     STRATA1 = c("A", "B", "C") %>% sample_fct(N) %>% with_label("Stratification Factor 1"),
#     STRATA2 = c("S1", "S2") %>% sample_fct(N) %>% with_label("Stratification Factor 2"),
#     BMRKR1 = stats::rchisq(N, 6) %>% with_label("Continuous Level Biomarker 1"),
#     BMRKR2 = sample_fct(c("LOW", "MEDIUM", "HIGH"), N) %>% with_label("Continuous Level Biomarker 2")
#   )
# 
#   # associate sites with countries and regions
#   adsl <- adsl %>%
#     dplyr::mutate(
#       SITEID = paste0(.data$COUNTRY, "-", .data$SITEID) %>% with_label("Study Site Identifier"),
#       REGION1 = factor(dplyr::case_when(
#         COUNTRY %in% c("NGA") ~ "Africa",
#         COUNTRY %in% c("CHN", "JPN", "PAK") ~ "Asia",
#         COUNTRY %in% c("RUS") ~ "Eurasia",
#         COUNTRY %in% c("GBR") ~ "Europe",
#         COUNTRY %in% c("CAN", "USA") ~ "North America",
#         COUNTRY %in% c("BRA") ~ "South America",
#         TRUE ~ as.character(NA)
#       )) %>% with_label("Geographic Region 1"),
#       SAFFL = factor("Y") %>% with_label("Safety Population Flag")
#     ) %>%
#     dplyr::mutate(
#       USUBJID = paste(.data$STUDYID, .data$SITEID, .data$SUBJID, sep = "-") %>%
#         with_label("Unique Subject Identifier")
#     )
# 
#   # disposition related variables
#   # using probability of 1 for the "DEATH" level to ensure at least one death record exists
#   l_dcsreas <- list(
#     choices = c(
#       "ADVERSE EVENT", "DEATH", "LACK OF EFFICACY", "PHYSICIAN DECISION",
#       "PROTOCOL VIOLATION", "WITHDRAWAL BY PARENT/GUARDIAN", "WITHDRAWAL BY SUBJECT"
#     ),
#     prob = c(.2, 1, .1, .1, .2, .1, .1)
#   )
#   l_dthcat_other <- list(
#     choices = c(
#       "Post-study reporting of death", "LOST TO FOLLOW UP", "MISSING", "SUICIDE", "UNKNOWN"
#     ),
#     prob = c(.1, .3, .3, .2, .1)
#   )
# 
#   adsl <- adsl %>%
#     dplyr::mutate(
#       EOSSTT = dplyr::case_when(
#         EOSDY == max(EOSDY, na.rm = TRUE) ~ "COMPLETED",
#         EOSDY < max(EOSDY, na.rm = TRUE) ~ "DISCONTINUED",
#         is.na(TRTEDTM) ~ "ONGOING"
#       ) %>% with_label("End of Study Status")
#     ) %>%
#     dplyr::mutate(
#       EOTSTT = .data$EOSSTT %>% with_label("End of Treatment Status")
#     ) %>%
#     dplyr::mutate(
#       DCSREAS = ifelse(
#         .data$EOSSTT == "DISCONTINUED",
#         sample(x = l_dcsreas$choices, size = N, replace = TRUE, prob = l_dcsreas$prob),
#         as.character(NA)
#       ) %>% with_label("Reason for Discontinuation from Study")
#     )
# 
#   tmc_ex_adsl <- adsl %>%
#     dplyr::mutate(DTHDT = dplyr::case_when(
#       DCSREAS == "DEATH" ~ lubridate::date(TRTEDTM + lubridate::days(sample(0:50, size = N, replace = TRUE)))
#     ) %>% with_label("Date of Death"))
# 
#   save(tmc_ex_adsl, file = "data/tmc_ex_adsl.rda", compress = "xz")
# }

## ----adae, eval=FALSE---------------------------------------------------------
# generate_adae <- function(adsl = tmc_ex_adsl,
#                           max_n_aes = 5) {
#   set.seed(1)
#   lookup_ae <- tibble::tribble(
#     ~AEBODSYS, ~AELLT, ~AEDECOD, ~AEHLT, ~AEHLGT, ~AETOXGR, ~AESOC, ~AESER, ~AEREL,
#     "cl A.1", "llt A.1.1.1.1", "dcd A.1.1.1.1", "hlt A.1.1.1", "hlgt A.1.1", "1", "cl A", "N", "N",
#     "cl A.1", "llt A.1.1.1.2", "dcd A.1.1.1.2", "hlt A.1.1.1", "hlgt A.1.1", "2", "cl A", "Y", "N",
#     "cl B.1", "llt B.1.1.1.1", "dcd B.1.1.1.1", "hlt B.1.1.1", "hlgt B.1.1", "5", "cl B", "Y", "Y",
#     "cl B.2", "llt B.2.1.2.1", "dcd B.2.1.2.1", "hlt B.2.1.2", "hlgt B.2.1", "3", "cl B", "N", "N",
#     "cl B.2", "llt B.2.2.3.1", "dcd B.2.2.3.1", "hlt B.2.2.3", "hlgt B.2.2", "1", "cl B", "Y", "N",
#     "cl C.1", "llt C.1.1.1.3", "dcd C.1.1.1.3", "hlt C.1.1.1", "hlgt C.1.1", "4", "cl C", "N", "Y",
#     "cl C.2", "llt C.2.1.2.1", "dcd C.2.1.2.1", "hlt C.2.1.2", "hlgt C.2.1", "2", "cl C", "N", "Y",
#     "cl D.1", "llt D.1.1.1.1", "dcd D.1.1.1.1", "hlt D.1.1.1", "hlgt D.1.1", "5", "cl D", "Y", "Y",
#     "cl D.1", "llt D.1.1.4.2", "dcd D.1.1.4.2", "hlt D.1.1.4", "hlgt D.1.1", "3", "cl D", "N", "N",
#     "cl D.2", "llt D.2.1.5.3", "dcd D.2.1.5.3", "hlt D.2.1.5", "hlgt D.2.1", "1", "cl D", "N", "Y"
#   )
# 
#   aag <- utils::read.table(
#     sep = ",", header = TRUE,
#     text = paste(
#       "NAMVAR,SRCVAR,GRPTYPE,REFNAME,REFTERM,SCOPE",
#       "CQ01NAM,AEDECOD,CUSTOM,D.2.1.5.3/A.1.1.1.1 aesi,dcd D.2.1.5.3,",
#       "CQ01NAM,AEDECOD,CUSTOM,D.2.1.5.3/A.1.1.1.1 aesi,dcd A.1.1.1.1,",
#       "SMQ01NAM,AEDECOD,SMQ,C.1.1.1.3/B.2.2.3.1 aesi,dcd C.1.1.1.3,BROAD",
#       "SMQ01NAM,AEDECOD,SMQ,C.1.1.1.3/B.2.2.3.1 aesi,dcd B.2.2.3.1,BROAD",
#       "SMQ02NAM,AEDECOD,SMQ,Y.9.9.9.9/Z.9.9.9.9 aesi,dcd Y.9.9.9.9,NARROW",
#       "SMQ02NAM,AEDECOD,SMQ,Y.9.9.9.9/Z.9.9.9.9 aesi,dcd Z.9.9.9.9,NARROW",
#       sep = "\n"
#     ), stringsAsFactors = FALSE
#   )
# 
#   adae <- Map(
#     function(id, sid) {
#       n_aes <- sample(c(0, seq_len(max_n_aes)), 1)
#       i <- sample(seq_len(nrow(lookup_ae)), n_aes, TRUE)
#       dplyr::mutate(
#         lookup_ae[i, ],
#         USUBJID = id,
#         STUDYID = sid
#       )
#     },
#     adsl$USUBJID,
#     adsl$STUDYID
#   ) %>%
#     Reduce(rbind, .) %>%
#     `[`(c(10, 11, 1, 2, 3, 4, 5, 6, 7, 8, 9)) %>%
#     dplyr::mutate(
#       AETERM = gsub("dcd", "trm", .data$AEDECOD) %>% with_label("Reported Term for the Adverse Event"),
#       AESEV = dplyr::case_when(
#         AETOXGR == 1 ~ "MILD",
#         AETOXGR %in% c(2, 3) ~ "MODERATE",
#         AETOXGR %in% c(4, 5) ~ "SEVERE"
#       ) %>% with_label("Severity/Intensity")
#     )
# 
#   # merge adsl to be able to add AE date and study day variables
#   adae <- dplyr::inner_join(adae, adsl, by = c("STUDYID", "USUBJID"), multiple = "all") %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     dplyr::mutate(ASTDTM = sample(
#       seq(lubridate::as_datetime(TRTSDTM), lubridate::as_datetime(TRTENDT), by = "day"),
#       size = 1
#     )) %>%
#     dplyr::mutate(ASTDY = ceiling(difftime(ASTDTM, TRTSDTM, units = "days"))) %>%
#     # add 1 to end of range incase both values passed to sample() are the same
#     dplyr::mutate(AENDTM = sample(
#       seq(lubridate::as_datetime(ASTDTM), lubridate::as_datetime(TRTENDT + 1), by = "day"),
#       size = 1
#     )) %>%
#     dplyr::mutate(AENDY = ceiling(difftime(AENDTM, TRTSDTM, units = "days"))) %>%
#     dplyr::mutate(LDOSEDTM = dplyr::case_when(
#       TRTSDTM < ASTDTM ~ lubridate::as_datetime(stats::runif(1, TRTSDTM, ASTDTM)),
#       TRUE ~ ASTDTM
#     )) %>%
#     dplyr::select(-TRTENDT) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ASTDTM, .data$AETERM)
# 
#   adae <- adae %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::mutate(AESEQ = seq_len(dplyr::n())) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$ASTDTM,
#       .data$AETERM,
#       .data$AESEQ
#     )
# 
#   outcomes <- c(
#     "UNKNOWN",
#     "NOT RECOVERED/NOT RESOLVED",
#     "RECOVERED/RESOLVED WITH SEQUELAE",
#     "RECOVERING/RESOLVING",
#     "RECOVERED/RESOLVED"
#   )
# 
#   adae <- adae %>%
#     dplyr::mutate(
#       AEOUT = factor(ifelse(
#         .data$AETOXGR == "5",
#         "FATAL",
#         as.character(sample_fct(outcomes, nrow(adae), prob = c(0.1, 0.2, 0.1, 0.3, 0.3)))
#       )) %>% with_label("Outcome of Adverse Event"),
#       TRTEMFL = ifelse(.data$ASTDTM >= .data$TRTSDTM, "Y", "") %>%
#         with_label("Treatment Emergent Analysis Flag")
#     )
# 
#   l_aag <- split(aag, interaction(aag$NAMVAR, aag$SRCVAR, aag$GRPTYPE, drop = TRUE))
# 
#   # Create aesi flags
#   l_aesi <- lapply(l_aag, function(d_adag, d_adae) {
#     names(d_adag)[names(d_adag) == "REFTERM"] <- d_adag$SRCVAR[1]
#     names(d_adag)[names(d_adag) == "REFNAME"] <- d_adag$NAMVAR[1]
# 
#     if (d_adag$GRPTYPE[1] == "CUSTOM") {
#       d_adag <- d_adag[-which(names(d_adag) == "SCOPE")]
#     } else if (d_adag$GRPTYPE[1] == "SMQ") {
#       names(d_adag)[names(d_adag) == "SCOPE"] <- paste0(substr(d_adag$NAMVAR[1], 1, 5), "SC")
#     }
# 
#     d_adag <- d_adag[-which(names(d_adag) %in% c("NAMVAR", "SRCVAR", "GRPTYPE"))]
#     d_new <- dplyr::left_join(x = d_adae, y = d_adag, by = intersect(names(d_adae), names(d_adag)))
#     d_new[, dplyr::setdiff(names(d_new), names(d_adae)), drop = FALSE]
#   }, adae)
#   adae <- dplyr::bind_cols(adae, l_aesi)
# 
#   actions <- c(
#     "DOSE RATE REDUCED",
#     "UNKNOWN",
#     "NOT APPLICABLE",
#     "DRUG INTERRUPTED",
#     "DRUG WITHDRAWN",
#     "DOSE INCREASED",
#     "DOSE NOT CHANGED",
#     "DOSE REDUCED",
#     "NOT EVALUABLE"
#   )
# 
#   tmc_ex_adae <- adae %>%
#     dplyr::mutate(
#       AEACN = factor(ifelse(
#         .data$AETOXGR == "5",
#         "NOT EVALUABLE",
#         as.character(sample_fct(actions, nrow(adae), prob = c(0.05, 0.05, 0.05, 0.01, 0.05, 0.1, 0.45, 0.1, 0.05)))
#       )) %>% with_label("Action Taken With Study Treatment")
#     ) %>%
#     col_relabel(
#       AEBODSYS = "Body System or Organ Class",
#       AELLT = "Lowest Level Term",
#       AEDECOD = "Dictionary-Derived Term",
#       AEHLT = "High Level Term",
#       AEHLGT = "High Level Group Term",
#       AETOXGR = "Analysis Toxicity Grade",
#       AESOC = "Primary System Organ Class",
#       AESER = "Serious Event",
#       AEREL = "Analysis Causality",
#       AESEQ = "Sponsor-Defined Identifier",
#       LDOSEDTM = "End Time/Time of Last Dose",
#       CQ01NAM = "CQ 01 Reference Name",
#       SMQ01NAM = "SMQ 01 Reference Name",
#       SMQ01SC = "SMQ 01 Scope",
#       SMQ02NAM = "SMQ 02 Reference Name",
#       SMQ02SC = "SMQ 02 Scope"
#     )
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adae)[is.na(col_labels(tmc_ex_adae))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adae[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adae, file = "data/tmc_ex_adae.rda", compress = "xz")
# }

## ----adaette, eval=FALSE------------------------------------------------------
# generate_adaette <- function(adsl = tmc_ex_adsl) {
#   set.seed(1)
#   lookup_adaette <- tibble::tribble(
#     ~ARM, ~CATCD, ~CAT, ~LAMBDA, ~CNSR_P,
#     "ARM A", "1", "any adverse event", 1 / 80, 0.4,
#     "ARM B", "1", "any adverse event", 1 / 100, 0.2,
#     "ARM C", "1", "any adverse event", 1 / 60, 0.42,
#     "ARM A", "2", "any serious adverse event", 1 / 100, 0.3,
#     "ARM B", "2", "any serious adverse event", 1 / 150, 0.1,
#     "ARM C", "2", "any serious adverse event", 1 / 80, 0.32,
#     "ARM A", "3", "a grade 3-5 adverse event", 1 / 80, 0.2,
#     "ARM B", "3", "a grade 3-5 adverse event", 1 / 100, 0.08,
#     "ARM C", "3", "a grade 3-5 adverse event", 1 / 60, 0.23
#   )
#   evntdescr_sel <- "Preferred Term"
#   cnsdtdscr_sel <- c(
#     "Clinical Cut Off",
#     "Completion or Discontinuation",
#     "End of AE Reporting Period"
#   )
# 
#   random_patient_data <- function(patient_info) {
#     startdt <- lubridate::date(patient_info$TRTSDTM)
#     trtedtm <- lubridate::floor_date(dplyr::case_when(
#       is.na(patient_info$TRTEDTM) ~ lubridate::date(patient_info$TRTSDTM) + study_duration_secs,
#       TRUE ~ lubridate::date(patient_info$TRTEDTM)
#     ), unit = "day")
#     enddts <- c(patient_info$EOSDT, lubridate::date(trtedtm))
#     enddts_min_index <- which.min(enddts)
#     adt <- enddts[enddts_min_index]
#     adtm <- lubridate::as_datetime(adt)
#     ady <- as.numeric(adt - startdt + 1)
#     data.frame(
#       ARM = patient_info$ARM,
#       STUDYID = patient_info$STUDYID,
#       SITEID = patient_info$SITEID,
#       USUBJID = patient_info$USUBJID,
#       PARAMCD = "AEREPTTE",
#       PARAM = "Time to end of AE reporting period",
#       CNSR = 0,
#       AVAL = lubridate::days(ady) / lubridate::years(1),
#       AVALU = "YEARS",
#       EVNTDESC = ifelse(enddts_min_index == 1, "Completion or Discontinuation", "End of AE Reporting Period"),
#       CNSDTDSC = NA,
#       ADTM = adtm,
#       ADY = ady,
#       stringsAsFactors = FALSE
#     )
#   }
# 
#   paramcd_hy <- c("HYSTTEUL", "HYSTTEBL")
#   param_hy <- c("Time to Hy's Law Elevation in relation to ULN", "Time to Hy's Law Elevation in relation to Baseline")
#   param_init_list <- relvar_init(param_hy, paramcd_hy)
#   adsl_hy <- dplyr::select(adsl, "STUDYID", "USUBJID", "TRTSDTM", "SITEID", "ARM")
#   adaette_hy <- expand.grid(
#     STUDYID = unique(adsl$STUDYID),
#     USUBJID = adsl$USUBJID,
#     PARAM = as.factor(param_init_list$relvar1),
#     stringsAsFactors = FALSE
#   )
# 
#   adaette_hy <- dplyr::left_join(adaette_hy, adsl_hy, by = c("STUDYID", "USUBJID"), multiple = "all") %>%
#     dplyr::mutate(
#       PARAMCD = factor(rel_var(
#         df = as.data.frame(adaette_hy),
#         var_values = param_init_list$relvar2,
#         related_var = "PARAM"
#       ))
#     ) %>%
#     dplyr::mutate(
#       CNSR = sample(c(0, 1), prob = c(0.1, 0.9), size = dplyr::n(), replace = TRUE),
#       EVNTDESC = dplyr::if_else(
#         .data$CNSR == 0,
#         "First Post-Baseline Raised ALT or AST Elevation Result",
#         NA_character_
#       ),
#       CNSDTDSC = dplyr::if_else(.data$CNSR == 0, NA_character_,
#         sample(c("Last Post-Baseline ALT or AST Result", "Treatment Start"),
#           prob = c(0.9, 0.1),
#           size = dplyr::n(), replace = TRUE
#         )
#       )
#     ) %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(ADTM = dplyr::case_when(
#       CNSDTDSC == "Treatment Start" ~ TRTSDTM,
#       TRUE ~ TRTSDTM + sample(seq(0, study_duration_secs), size = dplyr::n(), replace = TRUE)
#     )) %>%
#     dplyr::mutate(
#       ADY_int = lubridate::date(ADTM) - lubridate::date(TRTSDTM) + 1,
#       ADY = as.numeric(ADY_int),
#       AVAL = lubridate::days(ADY_int) / lubridate::weeks(1),
#       AVALU = "WEEKS"
#     ) %>%
#     dplyr::select(-TRTSDTM, -ADY_int)
# 
#   random_ae_data <- function(lookup_info, patient_info, patient_data) {
#     cnsr <- sample(c(0, 1), 1, prob = c(1 - lookup_info$CNSR_P, lookup_info$CNSR_P))
#     ae_rep_tte <- patient_data$AVAL[patient_data$PARAMCD == "AEREPTTE"]
#     data.frame(
#       ARM = rep(patient_data$ARM, 2),
#       STUDYID = rep(patient_data$STUDYID, 2),
#       SITEID = rep(patient_data$SITEID, 2),
#       USUBJID = rep(patient_data$USUBJID, 2),
#       PARAMCD = c(
#         paste0("AETTE", lookup_info$CATCD),
#         paste0("AETOT", lookup_info$CATCD)
#       ),
#       PARAM = c(
#         paste("Time to first occurrence of", lookup_info$CAT),
#         paste("Number of occurrences of", lookup_info$CAT)
#       ),
#       CNSR = c(cnsr, NA),
#       AVAL = c(
#         ifelse(cnsr == 1, ae_rep_tte, rtexp(1, lookup_info$LAMBDA * 365.25, r = ae_rep_tte)),
#         ifelse(cnsr == 1, 0, rtpois(1, lookup_info$LAMBDA * 365.25))
#       ),
#       AVALU = c("YEARS", NA),
#       EVNTDESC = c(ifelse(cnsr == 0, sample(evntdescr_sel, 1), ""), NA),
#       CNSDTDSC = c(ifelse(cnsr == 1, sample(cnsdtdscr_sel, 1), ""), NA),
#       stringsAsFactors = FALSE
#     ) %>% dplyr::mutate(
#       ADY = dplyr::if_else(is.na(AVALU), NA_real_, ceiling(as.numeric(lubridate::dyears(AVAL), "days"))),
#       ADTM = dplyr::if_else(
#         is.na(AVALU),
#         lubridate::as_datetime(NA),
#         patient_info$TRTSDTM + lubridate::days(ADY)
#       )
#     )
#   }
# 
#   adaette <- split(adsl, adsl$USUBJID) %>%
#     lapply(function(patient_info) {
#       patient_data <- random_patient_data(patient_info)
#       lookup_arm <- lookup_adaette %>%
#         dplyr::filter(.data$ARM == as.character(patient_info$ARMCD))
#       ae_data <- split(lookup_arm, lookup_arm$CATCD) %>%
#         lapply(random_ae_data, patient_data = patient_data, patient_info = patient_info) %>%
#         Reduce(rbind, .)
#       dplyr::bind_rows(patient_data, ae_data)
#     }) %>%
#     Reduce(rbind, .)
#   adaette <- rbind(adaette, adaette_hy)
# 
#   tmc_ex_adaette <- adsl %>%
#     dplyr::inner_join(
#       dplyr::select(adaette, -"SITEID", -"ARM"),
#       by = c("STUDYID", "USUBJID"),
#       multiple = "all"
#     ) %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::arrange(.data$ADTM) %>%
#     dplyr::mutate(PARAM = as.factor(.data$PARAM)) %>%
#     dplyr::mutate(PARAMCD = as.factor(.data$PARAMCD)) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$ADTM
#     )
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adaette)[is.na(col_labels(tmc_ex_adaette))]),
#     function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adaette[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adaette, file = "data/tmc_ex_adaette.rda", compress = "xz")
# }

## ----adcm, eval=FALSE---------------------------------------------------------
# generate_adcm <- function(adsl = tmc_ex_adsl,
#                           max_n_cms = 5L) {
#   set.seed(1)
#   lookup_cm <- tibble::tribble(
#     ~CMCLAS, ~CMDECOD, ~ATIREL,
#     "medcl A", "medname A_1/3", "PRIOR",
#     "medcl A", "medname A_2/3", "CONCOMITANT",
#     "medcl A", "medname A_3/3", "CONCOMITANT",
#     "medcl B", "medname B_1/4", "CONCOMITANT",
#     "medcl B", "medname B_2/4", "PRIOR",
#     "medcl B", "medname B_3/4", "PRIOR",
#     "medcl B", "medname B_4/4", "CONCOMITANT",
#     "medcl C", "medname C_1/2", "CONCOMITANT",
#     "medcl C", "medname C_2/2", "CONCOMITANT"
#   )
# 
#   adcm <- Map(function(id, sid) {
#     n_cms <- sample(c(0, seq_len(max_n_cms)), 1)
#     i <- sample(seq_len(nrow(lookup_cm)), n_cms, TRUE)
#     dplyr::mutate(
#       lookup_cm[i, ],
#       USUBJID = id,
#       STUDYID = sid
#     )
#   }, adsl$USUBJID, adsl$STUDYID) %>%
#     Reduce(rbind, .) %>%
#     `[`(c(4, 5, 1, 2, 3)) %>%
#     dplyr::mutate(CMCAT = .data$CMCLAS %>% with_label("Category for Medication"))
# 
#   # merge adsl to be able to add CM date and study day variables
#   adcm <- dplyr::inner_join(
#     adcm,
#     adsl,
#     by = c("STUDYID", "USUBJID"),
#     multiple = "all"
#   ) %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     dplyr::mutate(ASTDTM = sample(
#       seq(lubridate::as_datetime(TRTSDTM), lubridate::as_datetime(TRTENDT), by = "day"),
#       size = 1
#     )) %>%
#     dplyr::mutate(ASTDY = ceiling(difftime(ASTDTM, TRTSDTM, units = "days"))) %>%
#     # add 1 to end of range incase both values passed to sample() are the same
#     dplyr::mutate(AENDTM = sample(
#       seq(lubridate::as_datetime(ASTDTM), lubridate::as_datetime(TRTENDT + 1), by = "day"),
#       size = 1
#     )) %>%
#     dplyr::mutate(AENDY = ceiling(difftime(AENDTM, TRTSDTM, units = "days"))) %>%
#     dplyr::select(-TRTENDT) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(STUDYID, USUBJID, ASTDTM)
# 
#   tmc_ex_adcm <- adcm %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::mutate(CMSEQ = seq_len(dplyr::n())) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ASTDTM, .data$CMSEQ) %>%
#     dplyr::mutate(
#       ATC1 = paste("ATCCLAS1", substr(.data$CMDECOD, 9, 9)) %>% with_label("ATC Level 1 Text"),
#       ATC2 = paste("ATCCLAS2", substr(.data$CMDECOD, 9, 9)) %>% with_label("ATC Level 2 Text"),
#       ATC3 = paste("ATCCLAS3", substr(.data$CMDECOD, 9, 9)) %>% with_label("ATC Level 3 Text"),
#       ATC4 = paste("ATCCLAS4", substr(.data$CMDECOD, 9, 9)) %>% with_label("ATC Level 4 Text")
#     ) %>%
#     dplyr::mutate(
#       CMINDC = sample(c(
#         "Nausea", "Hypertension", "Urticaria", "Fever",
#         "Asthma", "Infection", "Diabete", "Diarrhea", "Pneumonia"
#       ), dplyr::n(), replace = TRUE) %>% with_label("Indication"),
#       CMDOSE = sample(1:99, dplyr::n(), replace = TRUE) %>% with_label("Dose per Administration"),
#       CMTRT = substr(.data$CMDECOD, 9, 13) %>% with_label("Reported Name of Drug, Med, or Therapy"),
#       CMDOSU = sample(c(
#         "ug/mL", "ug/kg/day", "%", "uL", "DROP",
#         "umol/L", "mg", "mg/breath", "ug"
#       ), dplyr::n(), replace = TRUE) %>% with_label("Dose Units")
#     ) %>%
#     dplyr::mutate(
#       CMROUTE = sample(c(
#         "INTRAVENOUS", "ORAL", "NASAL",
#         "INTRAMUSCULAR", "SUBCUTANEOUS", "INHALED", "RECTAL", "UNKNOWN"
#       ), dplyr::n(), replace = TRUE) %>% with_label("Route of Administration"),
#       CMDOSFRQ = sample(c(
#         "Q4W", "QN", "Q4H", "UNKNOWN", "TWICE",
#         "Q4H", "QD", "TID", "4 TIMES PER MONTH"
#       ), dplyr::n(), replace = TRUE) %>% with_label("Dosing Frequency per Interval")
#     ) %>%
#     col_relabel(
#       CMCLAS = "Medication Class",
#       CMDECOD = "Standardized Medication Name",
#       ATIREL = "Time Relation of Medication",
#       CMSEQ = "Sponsor-Defined Identifier"
#     )
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adcm)[is.na(col_labels(tmc_ex_adcm))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adcm[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adcm, file = "data/tmc_ex_adcm.rda", compress = "xz")
# }

## ----adeg, eval=FALSE---------------------------------------------------------
# generate_adeg <- function(adsl = tmc_ex_adsl,
#                           n_assessments = 3L,
#                           n_days = 3L,
#                           max_n_eg = 3L) {
#   set.seed(1)
#   param <- c("QT Duration", "RR Duration", "Heart Rate", "ECG Interpretation")
#   paramcd <- c("QT", "RR", "HR", "ECGINTP")
#   paramu <- c("msec", "msec", "beats/min", "")
#   visit_format <- "WEEK"
# 
#   param_init_list <- relvar_init(param, paramcd)
#   unit_init_list <- relvar_init(param, paramu)
# 
#   adeg <- expand.grid(
#     STUDYID = unique(adsl$STUDYID),
#     USUBJID = adsl$USUBJID,
#     PARAM = as.factor(param_init_list$relvar1),
#     AVISIT = visit_schedule(visit_format = visit_format, n_assessments = n_assessments, n_days = n_days),
#     stringsAsFactors = FALSE
#   )
# 
#   adeg$PARAMCD <- as.factor(rel_var(
#     df = adeg,
#     var_name = "PARAMCD",
#     var_values = param_init_list$relvar2,
#     related_var = "PARAM"
#   ))
# 
#   adeg <- adeg %>% dplyr::mutate(AVAL = dplyr::case_when(
#     .data$PARAMCD == "QT" ~ stats::rnorm(nrow(adeg), mean = 350, sd = 100),
#     .data$PARAMCD == "RR" ~ stats::rnorm(nrow(adeg), mean = 1050, sd = 300),
#     .data$PARAMCD == "HR" ~ stats::rnorm(nrow(adeg), mean = 70, sd = 20),
#     .data$PARAMCD == "ECGINTP" ~ NA_real_
#   ))
# 
#   adeg <- adeg %>% dplyr::mutate(AVISITN = dplyr::case_when(
#     AVISIT == "SCREENING" ~ -1,
#     AVISIT == "BASELINE" ~ 0,
#     (grepl("^WEEK", AVISIT) | grepl("^CYCLE", AVISIT)) ~ as.numeric(AVISIT) - 2,
#     TRUE ~ NA_real_
#   ))
# 
#   adeg$AVALU <- as.factor(rel_var(
#     df = adeg,
#     var_name = "AVALU",
#     var_values = unit_init_list$relvar2,
#     related_var = "PARAM"
#   ))
# 
#   adeg <- adeg[order(adeg$STUDYID, adeg$USUBJID, adeg$PARAMCD, adeg$AVISITN), ]
#   adeg <- Reduce(rbind, lapply(split(adeg, adeg$USUBJID), function(x) {
#     x$STUDYID <- adsl$STUDYID[which(adsl$USUBJID == x$USUBJID[1])]
#     x$ABLFL <- ifelse(toupper(visit_format) == "WEEK" & x$AVISIT == "BASELINE",
#       "Y",
#       ifelse(toupper(visit_format) == "CYCLE" & x$AVISIT == "CYCLE 1 DAY 1", "Y", "")
#     )
#     x
#   }))
# 
#   adeg$BASE <- ifelse(adeg$AVISITN >= 0, retain(adeg, adeg$AVAL, adeg$ABLFL == "Y"), adeg$AVAL)
#   adeg <- adeg %>%
#     dplyr::mutate(ANRLO = dplyr::case_when(
#       .data$PARAMCD == "QT" ~ 200,
#       .data$PARAMCD == "RR" ~ 600,
#       .data$PARAMCD == "HR" ~ 40,
#       .data$PARAMCD == "ECGINTP" ~ NA_real_
#     )) %>%
#     dplyr::mutate(ANRHI = dplyr::case_when(
#       .data$PARAMCD == "QT" ~ 500,
#       .data$PARAMCD == "RR" ~ 1500,
#       .data$PARAMCD == "HR" ~ 100,
#       .data$PARAMCD == "ECGINTP" ~ NA_real_
#     )) %>%
#     dplyr::mutate(ANRIND = factor(dplyr::case_when(
#       .data$AVAL < .data$ANRLO ~ "LOW",
#       .data$AVAL >= .data$ANRLO & .data$AVAL <= .data$ANRHI ~ "NORMAL",
#       .data$AVAL > .data$ANRHI ~ "HIGH"
#     )))
# 
#   adeg <- adeg %>%
#     dplyr::mutate(CHG = ifelse(.data$AVISITN > 0, .data$AVAL - .data$BASE, NA)) %>%
#     dplyr::mutate(PCHG = ifelse(.data$AVISITN > 0, 100 * (.data$CHG / .data$BASE), NA)) %>%
#     dplyr::mutate(BASETYPE = "LAST") %>%
#     dplyr::group_by(.data$USUBJID, .data$PARAMCD, .data$BASETYPE) %>%
#     dplyr::mutate(BNRIND = .data$ANRIND[.data$ABLFL == "Y"]) %>%
#     dplyr::ungroup() %>%
#     dplyr::mutate(DTYPE = NA)
# 
#   adeg$ANRIND <- factor(adeg$ANRIND, levels = c("LOW", "NORMAL", "HIGH"))
#   adeg$BNRIND <- factor(adeg$BNRIND, levels = c("LOW", "NORMAL", "HIGH"))
# 
#   adeg <- dplyr::inner_join(
#     adsl,
#     adeg,
#     by = c("STUDYID", "USUBJID"),
#     multiple = "all"
#   ) %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     dplyr::ungroup() %>%
#     dplyr::group_by(USUBJID) %>%
#     dplyr::arrange(USUBJID, AVISITN) %>%
#     dplyr::mutate(ADTM = rep(
#       sort(sample(
#         seq(lubridate::as_datetime(TRTSDTM[1]), lubridate::as_datetime(TRTENDT[1]), by = "day"),
#         size = nlevels(AVISIT)
#       )),
#       each = n() / nlevels(AVISIT)
#     )) %>%
#     dplyr::ungroup() %>%
#     dplyr::select(-TRTENDT) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ADTM)
# 
#   adeg <- adeg %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$BASETYPE,
#       .data$AVISITN,
#       .data$DTYPE,
#       .data$ADTM
#     )
# 
#   adeg <- adeg %>%
#     dplyr::mutate(ONTRTFL = factor(dplyr::case_when(
#       is.na(.data$TRTSDTM) ~ "",
#       is.na(.data$ADTM) ~ "Y",
#       (.data$ADTM < .data$TRTSDTM) ~ "",
#       (.data$ADTM > .data$TRTEDTM) ~ "",
#       TRUE ~ "Y"
#     ))) %>%
#     dplyr::mutate(AVALC = ifelse(
#       .data$PARAMCD == "ECGINTP",
#       as.character(sample_fct(c("ABNORMAL", "NORMAL"), nrow(adeg), prob = c(0.25, 0.75))),
#       as.character(.data$AVAL)
#     ))
# 
#   adeg <- adeg %>% dplyr::mutate(row_check = seq_len(nrow(adeg)))
#   get_groups <- function(data, minimum) {
#     data <- data %>%
#       dplyr::group_by(.data$USUBJID, .data$PARAMCD, .data$BASETYPE) %>%
#       dplyr::arrange(.data$ADTM) %>%
#       dplyr::filter(
#         (.data$AVISIT != "BASELINE" & .data$AVISIT != "SCREENING") &
#           (.data$ONTRTFL == "Y" | .data$ADTM <= .data$TRTSDTM)
#       ) %>%
#       {
#         if (minimum == TRUE) {
#           dplyr::filter(., .data$AVAL == min(.data$AVAL)) %>%
#             dplyr::mutate(., DTYPE = "MINIMUM", AVISIT = "POST-BASELINE MINIMUM")
#         } else {
#           dplyr::filter(., .data$AVAL == max(.data$AVAL)) %>%
#             dplyr::mutate(., DTYPE = "MAXIMUM", AVISIT = "POST-BASELINE MAXIMUM")
#         }
#       } %>%
#       dplyr::slice(1) %>%
#       dplyr::ungroup()
#     data
#   }
# 
#   lbls <- col_labels(adeg)
#   adeg <- rbind(adeg, get_groups(adeg, TRUE), get_groups(adeg, FALSE)) %>%
#     dplyr::arrange(.data$row_check) %>%
#     dplyr::group_by(.data$USUBJID, .data$PARAMCD, .data$BASETYPE) %>%
#     dplyr::arrange(.data$AVISIT, .by_group = TRUE) %>%
#     dplyr::ungroup()
#   col_labels(adeg) <- lbls
# 
#   adeg <- adeg[, -which(names(adeg) %in% c("row_check"))]
#   flag_variables <- function(data, worst_obs) {
#     data_compare <- data %>%
#       dplyr::mutate(row_check = seq_len(nrow(data)))
#     data <- data_compare %>%
#       {
#         if (worst_obs == FALSE) {
#           dplyr::group_by(., .data$USUBJID, .data$PARAMCD, .data$BASETYPE, .data$AVISIT) %>%
#             dplyr::arrange(., .data$ADTM)
#         } else {
#           dplyr::group_by(., .data$USUBJID, .data$PARAMCD, .data$BASETYPE)
#         }
#       } %>%
#       dplyr::filter(
#         .data$AVISITN > 0 & (.data$ONTRTFL == "Y" | .data$ADTM <= .data$TRTSDTM) &
#           is.na(.data$DTYPE)
#       ) %>%
#       {
#         if (worst_obs == TRUE) {
#           dplyr::arrange(., .data$AVALC) %>% dplyr::filter(., ifelse(
#             .data$PARAMCD == "ECGINTP",
#             ifelse(.data$AVALC == "ABNORMAL", .data$AVALC == "ABNORMAL", .data$AVALC == "NORMAL"),
#             .data$AVAL == min(.data$AVAL)
#           ))
#         } else {
#           dplyr::filter(., ifelse(
#             .data$PARAMCD == "ECGINTP",
#             .data$AVALC == "ABNORMAL" | .data$AVALC == "NORMAL",
#             .data$AVAL == min(.data$AVAL)
#           ))
#         }
#       } %>%
#       dplyr::slice(1) %>%
#       {
#         if (worst_obs == TRUE) {
#           dplyr::mutate(., new_var = dplyr::case_when(
#             (.data$AVALC == "ABNORMAL" | .data$AVALC == "NORMAL") ~ "Y",
#             (!is.na(.data$AVAL) & is.na(.data$DTYPE)) ~ "Y",
#             TRUE ~ ""
#           ))
#         } else {
#           dplyr::mutate(., new_var = dplyr::case_when(
#             (.data$AVALC == "ABNORMAL" | .data$AVALC == "NORMAL") ~ "Y",
#             (!is.na(.data$AVAL) & is.na(.data$DTYPE)) ~ "Y",
#             TRUE ~ ""
#           ))
#         }
#       } %>%
#       dplyr::ungroup()
# 
#     data_compare$new_var <- ifelse(data_compare$row_check %in% data$row_check, "Y", "")
#     data_compare <- data_compare[, -which(names(data_compare) %in% c("row_check"))]
# 
#     data_compare
#   }
#   adeg <- flag_variables(adeg, FALSE) %>% dplyr::rename(WORS01FL = "new_var")
#   adeg <- flag_variables(adeg, TRUE) %>% dplyr::rename(WORS02FL = "new_var")
# 
#   tmc_ex_adeg <- adeg %>%
#     dplyr::group_by(.data$USUBJID, .data$PARAMCD, .data$BASETYPE) %>%
#     dplyr::mutate(BASEC = ifelse(
#       .data$PARAMCD == "ECGINTP",
#       .data$AVALC[.data$AVISIT == "BASELINE"],
#       as.character(.data$BASE)
#     )) %>%
#     dplyr::ungroup() %>%
#     col_relabel(BASEC = "Baseline Character Value")
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adeg)[is.na(col_labels(tmc_ex_adeg))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adeg[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adeg, file = "data/tmc_ex_adeg.rda", compress = "xz")
# }

## ----adex, eval=FALSE---------------------------------------------------------
# generate_adex <- function(adsl = tmc_ex_adsl,
#                           n_assessments = 3L,
#                           n_days = 3L,
#                           max_n_exs = 3L) {
#   set.seed(1)
#   param <- c(
#     "Dose administered during constant dosing interval",
#     "Number of doses administered during constant dosing interval",
#     "Total dose administered",
#     "Total number of doses administered"
#   )
#   paramcd <- c("DOSE", "NDOSE", "TDOSE", "TNDOSE")
#   paramu <- c("mg", " ", "mg", " ")
#   parcat1 <- c("INDIVIDUAL", "OVERALL")
#   parcat2 <- c("Drug A", "Drug B")
#   visit_format <- "WEEK"
# 
#   param_init_list <- relvar_init(param, paramcd)
#   unit_init_list <- relvar_init(param, paramu)
# 
#   adex <- expand.grid(
#     STUDYID = unique(adsl$STUDYID),
#     USUBJID = adsl$USUBJID,
#     PARAM = c(
#       rep(
#         param_init_list$relvar1[1],
#         length(levels(visit_schedule(visit_format = visit_format, n_assessments = n_assessments, n_days = n_days)))
#       ),
#       rep(
#         param_init_list$relvar1[2],
#         length(levels(visit_schedule(visit_format = visit_format, n_assessments = n_assessments, n_days = n_days)))
#       ),
#       param_init_list$relvar1[3:4]
#     ),
#     stringsAsFactors = FALSE
#   )
# 
#   adex$PARAMCD <- as.factor(rel_var(
#     df = adex,
#     var_name = "PARAMCD",
#     var_values = param_init_list$relvar2,
#     related_var = "PARAM"
#   ))
# 
#   adex$AVALU <- as.factor(rel_var(
#     df = adex,
#     var_name = "AVALU",
#     var_values = unit_init_list$relvar2,
#     related_var = "PARAM"
#   ))
# 
#   adex <- adex %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::mutate(PARCAT_ind = sample(c(1, 2), size = 1)) %>%
#     dplyr::mutate(PARCAT2 = ifelse(.data$PARCAT_ind == 1, parcat2[1], parcat2[2])) %>%
#     dplyr::select(-"PARCAT_ind")
# 
#   adex <- adex %>% dplyr::mutate(PARCAT1 = dplyr::case_when(
#     (.data$PARAMCD == "TNDOSE" | .data$PARAMCD == "TDOSE") ~ "OVERALL",
#     .data$PARAMCD == "DOSE" | .data$PARAMCD == "NDOSE" ~ "INDIVIDUAL"
#   ))
# 
#   adex_visit <- adex %>%
#     dplyr::filter(.data$PARAMCD == "DOSE" | .data$PARAMCD == "NDOSE") %>%
#     dplyr::mutate(
#       AVISIT = rep(visit_schedule(visit_format = visit_format, n_assessments = n_assessments, n_days = n_days), 2)
#     )
# 
#   adex <- dplyr::left_join(
#     adex %>%
#       dplyr::group_by(
#         .data$USUBJID,
#         .data$STUDYID,
#         .data$PARAM,
#         .data$PARAMCD,
#         .data$AVALU,
#         .data$PARCAT1,
#         .data$PARCAT2
#       ) %>%
#       dplyr::mutate(id = dplyr::row_number()),
#     adex_visit %>%
#       dplyr::group_by(
#         .data$USUBJID,
#         .data$STUDYID,
#         .data$PARAM,
#         .data$PARAMCD,
#         .data$AVALU,
#         .data$PARCAT1,
#         .data$PARCAT2
#       ) %>%
#       dplyr::mutate(id = dplyr::row_number()),
#     by = c("USUBJID", "STUDYID", "PARCAT1", "PARCAT2", "id", "PARAMCD", "PARAM", "AVALU")
#   ) %>%
#     dplyr::select(-"id")
# 
#   adex <- adex %>% dplyr::mutate(AVISITN = dplyr::case_when(
#     AVISIT == "SCREENING" ~ -1,
#     AVISIT == "BASELINE" ~ 0,
#     (grepl("^WEEK", AVISIT) | grepl("^CYCLE", AVISIT)) ~ as.numeric(AVISIT) - 2,
#     TRUE ~ 999000
#   ))
# 
#   adex2 <- split(adex, adex$USUBJID) %>%
#     lapply(function(pinfo) {
#       pinfo %>%
#         dplyr::filter(.data$PARAMCD == "DOSE") %>%
#         dplyr::group_by(.data$USUBJID, .data$PARCAT2, .data$AVISIT) %>%
#         dplyr::mutate(changeind = dplyr::case_when(
#           .data$AVISIT == "SCREENING" ~ 0,
#           .data$AVISIT != "SCREENING" ~ sample(c(-1, 0, 1),
#             size = 1,
#             prob = c(0.25, 0.5, 0.25),
#             replace = TRUE
#           )
#         )) %>%
#         dplyr::ungroup() %>%
#         dplyr::group_by(.data$USUBJID, .data$PARCAT2) %>%
#         dplyr::mutate(
#           csum = cumsum(.data$changeind),
#           changeind = dplyr::case_when(
#             .data$csum <= -3 ~ sample(c(0, 1), size = 1, prob = c(0.5, 0.5)),
#             .data$csum >= 3 ~ sample(c(0, -1), size = 1, prob = c(0.5, 0.5)),
#             TRUE ~ .data$changeind
#           )
#         ) %>%
#         dplyr::mutate(csum = cumsum(.data$changeind)) %>%
#         dplyr::ungroup() %>%
#         dplyr::group_by(.data$USUBJID, .data$PARCAT2, .data$AVISIT) %>%
#         dplyr::mutate(AVAL = dplyr::case_when(
#           .data$csum == -2 ~ 480,
#           .data$csum == -1 ~ 720,
#           .data$csum == 0 ~ 960,
#           .data$csum == 1 ~ 1200,
#           .data$csum == 2 ~ 1440
#         )) %>%
#         dplyr::select(-c("csum", "changeind")) %>%
#         dplyr::ungroup()
#     }) %>%
#     Reduce(rbind, .)
# 
#   adextmp <- dplyr::full_join(adex2, adex, by = names(adex))
#   adex <- adextmp %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::mutate(AVAL = ifelse(.data$PARAMCD == "NDOSE", 1, .data$AVAL)) %>%
#     dplyr::mutate(AVAL = ifelse(
#       .data$PARAMCD == "TNDOSE",
#       sum(.data$AVAL[.data$PARAMCD == "NDOSE"]),
#       .data$AVAL
#     )) %>%
#     dplyr::ungroup() %>%
#     dplyr::group_by(.data$USUBJID, .data$STUDYID, .data$PARCAT2) %>%
#     dplyr::mutate(AVAL = ifelse(
#       .data$PARAMCD == "TDOSE",
#       sum(.data$AVAL[.data$PARAMCD == "DOSE"]),
#       .data$AVAL
#     ))
# 
#   adex <- dplyr::inner_join(adsl, adex, by = c("STUDYID", "USUBJID"), multiple = "all") %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     dplyr::mutate(ASTDTM = sample(
#       seq(lubridate::as_datetime(TRTSDTM), lubridate::as_datetime(TRTENDT), by = "day"),
#       size = 1
#     )) %>%
#     dplyr::select(-TRTENDT) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ASTDTM)
# 
#   adex <- adex %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::mutate(EXSEQ = seq_len(dplyr::n())) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$ASTDTM,
#       .data$AVISITN
#     ) %>%
#     col_relabel(
#       PARCAT1 = "Parameter Category (Individual/Overall)",
#       PARCAT2 = "Parameter Category (Drug A/Drug B)",
#       EXSEQ = "Analysis Sequence Number"
#     )
# 
#   visit_levels <- str_extract(levels(adex$AVISIT), pattern = "[0-9]+")
#   vl_extracted <- vapply(visit_levels, function(x) as.numeric(x[2]), numeric(1))
#   vl_extracted <- c(-1, 1, vl_extracted[!is.na(vl_extracted)])
# 
#   tmc_ex_adex <- adex %>%
#     dplyr::mutate(VISITDY = as.numeric(as.character(factor(AVISIT, labels = vl_extracted)))) %>%
#     dplyr::mutate(ASTDTM = lubridate::as_datetime(TRTSDTM) + lubridate::days(VISITDY)) %>%
#     dplyr::distinct(USUBJID, .keep_all = TRUE)
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adex)[is.na(col_labels(tmc_ex_adex))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adex[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adex, file = "data/tmc_ex_adex.rda", compress = "xz")
# }

## ----adlb, eval=FALSE---------------------------------------------------------
# generate_adlb <- function(adsl = tmc_ex_adsl,
#                           n_assessments = 3L,
#                           n_days = 3L,
#                           max_n_lbs = 3L) {
#   set.seed(1)
#   lbcat <- c("CHEMISTRY", "CHEMISTRY", "IMMUNOLOGY")
#   param <- c(
#     "Alanine Aminotransferase Measurement",
#     "C-Reactive Protein Measurement",
#     "Immunoglobulin A Measurement"
#   )
#   paramcd <- c("ALT", "CRP", "IGA")
#   paramu <- c("U/L", "mg/L", "g/L")
#   aval_mean <- c(20, 1, 2)
#   visit_format <- "WEEK"
# 
#   # validate and initialize related variables
#   lbcat_init_list <- relvar_init(param, lbcat)
#   param_init_list <- relvar_init(param, paramcd)
#   unit_init_list <- relvar_init(param, paramu)
# 
#   adlb <- expand.grid(
#     STUDYID = unique(adsl$STUDYID),
#     USUBJID = adsl$USUBJID,
#     PARAM = as.factor(param_init_list$relvar1),
#     AVISIT = visit_schedule(visit_format = visit_format, n_assessments = n_assessments, n_days = n_days),
#     stringsAsFactors = FALSE
#   )
# 
#   # assign AVAL based on different test
#   adlb <- adlb %>%
#     dplyr::mutate(AVAL = stats::rnorm(nrow(adlb), mean = 1, sd = 0.2)) %>%
#     dplyr::left_join(data.frame(PARAM = param, ADJUST = aval_mean), by = "PARAM") %>%
#     dplyr::mutate(AVAL = .data$AVAL * .data$ADJUST) %>%
#     dplyr::select(-"ADJUST")
# 
#   # assign related variable values: PARAMxLBCAT are related
#   adlb$LBCAT <- as.factor(rel_var(
#     df = adlb,
#     var_name = "LBCAT",
#     var_values = lbcat_init_list$relvar2,
#     related_var = "PARAM"
#   ))
# 
#   # assign related variable values: PARAMxPARAMCD are related
#   adlb$PARAMCD <- as.factor(rel_var(
#     df = adlb,
#     var_name = "PARAMCD",
#     var_values = param_init_list$relvar2,
#     related_var = "PARAM"
#   ))
# 
#   adlb$AVALU <- as.factor(rel_var(
#     df = adlb,
#     var_name = "AVALU",
#     var_values = unit_init_list$relvar2,
#     related_var = "PARAM"
#   ))
# 
#   adlb <- adlb %>% dplyr::mutate(AVISITN = dplyr::case_when(
#     AVISIT == "SCREENING" ~ -1,
#     AVISIT == "BASELINE" ~ 0,
#     (grepl("^WEEK", AVISIT) | grepl("^CYCLE", AVISIT)) ~ as.numeric(AVISIT) - 2,
#     TRUE ~ NA_real_
#   ))
# 
#   adlb <- adlb %>%
#     dplyr::mutate(AVISITN = dplyr::case_when(
#       AVISIT == "SCREENING" ~ -1,
#       AVISIT == "BASELINE" ~ 0,
#       (grepl("^WEEK", AVISIT) | grepl("^CYCLE", AVISIT)) ~ as.numeric(AVISIT) - 2,
#       TRUE ~ NA_real_
#     ))
# 
#   # order to prepare for change from screening and baseline values
#   adlb <- adlb[order(adlb$STUDYID, adlb$USUBJID, adlb$PARAMCD, adlb$AVISITN), ]
# 
#   adlb <- Reduce(rbind, lapply(split(adlb, adlb$USUBJID), function(x) {
#     x$STUDYID <- adsl$STUDYID[which(adsl$USUBJID == x$USUBJID[1])]
#     x$ABLFL2 <- ifelse(x$AVISIT == "SCREENING", "Y", "")
#     x$ABLFL <- ifelse(toupper(visit_format) == "WEEK" & x$AVISIT == "BASELINE",
#       "Y",
#       ifelse(toupper(visit_format) == "CYCLE" & x$AVISIT == "CYCLE 1 DAY 1", "Y", "")
#     )
#     x
#   }))
# 
#   adlb$BASE <- ifelse(adlb$ABLFL2 != "Y", retain(adlb, adlb$AVAL, adlb$ABLFL == "Y"), NA)
#   anrind_choices <- c("HIGH", "LOW", "NORMAL")
#   adlb <- adlb %>%
#     dplyr::mutate(BASETYPE = "LAST") %>%
#     dplyr::mutate(ANRIND = sample_fct(anrind_choices, nrow(adlb), prob = c(0.1, 0.1, 0.8))) %>%
#     dplyr::mutate(ANRLO = dplyr::case_when(
#       .data$PARAMCD == "ALT" ~ 7,
#       .data$PARAMCD == "CRP" ~ 8,
#       .data$PARAMCD == "IGA" ~ 0.8
#     )) %>%
#     dplyr::mutate(ANRHI = dplyr::case_when(
#       .data$PARAMCD == "ALT" ~ 55,
#       .data$PARAMCD == "CRP" ~ 10,
#       .data$PARAMCD == "IGA" ~ 3
#     )) %>%
#     dplyr::mutate(DTYPE = NA) %>%
#     dplyr::mutate(
#       ATOXGR = factor(dplyr::case_when(
#         .data$ANRIND == "LOW" ~ sample(
#           c("-1", "-2", "-3", "-4", "-5"),
#           nrow(adlb),
#           replace = TRUE,
#           prob = c(0.30, 0.25, 0.20, 0.15, 0)
#         ),
#         .data$ANRIND == "HIGH" ~ sample(
#           c("1", "2", "3", "4", "5"),
#           nrow(adlb),
#           replace = TRUE,
#           prob = c(0.30, 0.25, 0.20, 0.15, 0)
#         ),
#         .data$ANRIND == "NORMAL" ~ "0"
#       )) %>% with_label("Analysis Toxicity Grade")
#     ) %>%
#     dplyr::group_by(.data$USUBJID, .data$PARAMCD, .data$BASETYPE) %>%
#     dplyr::mutate(BTOXGR = .data$ATOXGR[.data$ABLFL == "Y"]) %>%
#     dplyr::ungroup() %>%
#     col_relabel(BTOXGR = "Baseline Toxicity Grade")
# 
#   # High and low descriptions of the different PARAMCD values
#   # This is currently hard coded as the GDSR does not have these descriptions yet
#   grade_lookup <- tibble::tribble(
#     ~PARAMCD, ~ATOXDSCL, ~ATOXDSCH,
#     "ALB", "Hypoalbuminemia", NA_character_,
#     "ALKPH", NA_character_, "Alkaline phosphatase increased",
#     "ALT", NA_character_, "Alanine aminotransferase increased",
#     "AST", NA_character_, "Aspartate aminotransferase increased",
#     "BILI", NA_character_, "Blood bilirubin increased",
#     "CA", "Hypocalcemia", "Hypercalcemia",
#     "CHOLES", NA_character_, "Cholesterol high",
#     "CK", NA_character_, "CPK increased",
#     "CREAT", NA_character_, "Creatinine increased",
#     "CRP", NA_character_, "C reactive protein increased",
#     "GGT", NA_character_, "GGT increased",
#     "GLUC", "Hypoglycemia", "Hyperglycemia",
#     "HGB", "Anemia", "Hemoglobin increased",
#     "IGA", NA_character_, "Immunoglobulin A increased",
#     "POTAS", "Hypokalemia", "Hyperkalemia",
#     "LYMPH", "CD4 lymphocytes decreased", NA_character_,
#     "PHOS", "Hypophosphatemia", NA_character_,
#     "PLAT", "Platelet count decreased", NA_character_,
#     "SODIUM", "Hyponatremia", "Hypernatremia",
#     "WBC", "White blood cell decreased", "Leukocytosis",
#   )
# 
#   # merge grade_lookup onto adlb
#   adlb <- dplyr::left_join(adlb, grade_lookup, by = "PARAMCD")
# 
#   # merge adsl to be able to add LB date and study day variables
#   adlb <- dplyr::inner_join(
#     adsl,
#     adlb,
#     by = c("STUDYID", "USUBJID"),
#     multiple = "all"
#   ) %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     dplyr::ungroup() %>%
#     dplyr::group_by(USUBJID) %>%
#     dplyr::arrange(USUBJID, AVISITN) %>%
#     dplyr::mutate(ADTM = rep(
#       sort(sample(
#         seq(lubridate::as_datetime(TRTSDTM[1]), lubridate::as_datetime(TRTENDT[1]), by = "day"),
#         size = nlevels(AVISIT)
#       )),
#       each = n() / nlevels(AVISIT)
#     )) %>%
#     dplyr::ungroup() %>%
#     dplyr::select(-TRTENDT) %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ADTM)
# 
#   adlb <- adlb %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::mutate(LBSEQ = seq_len(dplyr::n())) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$BASETYPE,
#       .data$AVISITN,
#       .data$DTYPE,
#       .data$ADTM,
#       .data$LBSEQ
#     ) %>%
#     col_relabel(LBSEQ = "Lab Test or Examination Sequence Number")
# 
#   adlb <- adlb %>% dplyr::mutate(ONTRTFL = factor(dplyr::case_when(
#     is.na(.data$TRTSDTM) ~ "",
#     is.na(.data$ADTM) ~ "Y",
#     (.data$ADTM < .data$TRTSDTM) ~ "",
#     (.data$ADTM > .data$TRTEDTM) ~ "",
#     TRUE ~ "Y"
#   )))
# 
#   flag_variables <- function(data,
#                              apply_grouping,
#                              apply_filter,
#                              apply_mutate) {
#     data_compare <- data %>% dplyr::mutate(row_check = seq_len(nrow(data)))
#     data <- data_compare %>%
#       {
#         if (apply_grouping == TRUE) {
#           dplyr::group_by(., .data$USUBJID, .data$PARAMCD, .data$BASETYPE, .data$AVISIT)
#         } else {
#           dplyr::group_by(., .data$USUBJID, .data$PARAMCD, .data$BASETYPE)
#         }
#       } %>%
#       dplyr::arrange(.data$ADTM, .data$LBSEQ) %>%
#       {
#         if (apply_filter == TRUE) {
#           dplyr::filter(
#             .,
#             (.data$AVISIT != "BASELINE" & .data$AVISIT != "SCREENING") &
#               (.data$ONTRTFL == "Y" | .data$ADTM <= .data$TRTSDTM)
#           ) %>%
#             dplyr::filter(.data$ATOXGR == max(as.numeric(as.character(.data$ATOXGR))))
#         } else if (apply_filter == FALSE) {
#           dplyr::filter(
#             .,
#             (.data$AVISIT != "BASELINE" & .data$AVISIT != "SCREENING") &
#               (.data$ONTRTFL == "Y" | .data$ADTM <= .data$TRTSDTM)
#           ) %>%
#             dplyr::filter(.data$ATOXGR == min(as.numeric(as.character(.data$ATOXGR))))
#         } else {
#           dplyr::filter(
#             .,
#             .data$AVAL == min(.data$AVAL) &
#               (.data$AVISIT != "BASELINE" & .data$AVISIT != "SCREENING") &
#               (.data$ONTRTFL == "Y" | .data$ADTM <= .data$TRTSDTM)
#           )
#         }
#       } %>%
#       dplyr::slice(1) %>%
#       {
#         if (apply_mutate == TRUE) {
#           dplyr::mutate(., new_var = ifelse(is.na(.data$DTYPE), "Y", ""))
#         } else {
#           dplyr::mutate(., new_var = ifelse(is.na(.data$AVAL) == FALSE & is.na(.data$DTYPE), "Y", ""))
#         }
#       } %>%
#       dplyr::ungroup()
# 
#     data_compare$new_var <- ifelse(data_compare$row_check %in% data$row_check, "Y", "")
#     data_compare <- data_compare[, -which(names(data_compare) %in% c("row_check"))]
#     data_compare
#   }
#   adlb <- flag_variables(adlb, TRUE, "ELSE", FALSE) %>% dplyr::rename(WORS01FL = "new_var")
#   adlb <- flag_variables(adlb, FALSE, TRUE, TRUE) %>% dplyr::rename(WGRHIFL = "new_var")
#   adlb <- flag_variables(adlb, FALSE, FALSE, TRUE) %>% dplyr::rename(WGRLOFL = "new_var")
#   adlb <- flag_variables(adlb, TRUE, TRUE, TRUE) %>% dplyr::rename(WGRHIVFL = "new_var")
#   adlb <- flag_variables(adlb, TRUE, FALSE, TRUE) %>% dplyr::rename(WGRLOVFL = "new_var")
# 
#   tmc_ex_adlb <- adlb %>% dplyr::mutate(
#     ANL01FL = ifelse(
#       (.data$ABLFL == "Y" | (.data$WORS01FL == "Y" & is.na(.data$DTYPE))) &
#         (.data$AVISIT != "SCREENING"),
#       "Y",
#       ""
#     ) %>% with_label("Analysis Flag 01 Baseline Post-Baseline"),
#     PARAM = as.factor(.data$PARAM)
#   )
# 
#   tmc_ex_adlb <- tmc_ex_adlb %>%
#     group_by(.data$USUBJID, .data$PARAMCD, .data$BASETYPE) %>%
#     mutate(BNRIND = .data$ANRIND[.data$ABLFL == "Y"]) %>%
#     ungroup() %>%
#     dplyr::mutate(ADY = ceiling(as.numeric(difftime(.data$ADTM, .data$TRTSDTM, units = "days"))))
# 
#   tmc_ex_adlb$PARAMCD <- as.factor(tmc_ex_adlb$PARAMCD)
#   tmc_ex_adlb <- tmc_ex_adlb %>%
#     dplyr::mutate(CHG = .data$AVAL - .data$BASE) %>%
#     dplyr::mutate(PCHG = 100 * (.data$CHG / .data$BASE)) %>%
#     col_relabel(
#       LBCAT = "Category for Lab Test",
#       ATOXDSCL = "Analysis Toxicity Description Low",
#       ATOXDSCH = "Analysis Toxicity Description High",
#       WGRHIFL = "Worst High Grade per Patient",
#       WGRLOFL = "Worst Low Grade per Patient",
#       WGRHIVFL = "Worst High Grade per Patient per Visit",
#       WGRLOVFL = "Worst Low Grade per Patient per Visit"
#     )
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adlb)[is.na(col_labels(tmc_ex_adlb))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adlb[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adlb, file = "data/tmc_ex_adlb.rda", compress = "xz")
# }

## ----admh, eval=FALSE---------------------------------------------------------
# generate_admh <- function(adsl = tmc_ex_adsl,
#                           max_n_mhs = 10L) {
#   set.seed(1)
#   lookup_mh <- tibble::tribble(
#     ~MHBODSYS, ~MHDECOD, ~MHSOC,
#     "cl A", "trm A_1/2", "cl A",
#     "cl A", "trm A_2/2", "cl A",
#     "cl B", "trm B_1/3", "cl B",
#     "cl B", "trm B_2/3", "cl B",
#     "cl B", "trm B_3/3", "cl B",
#     "cl C", "trm C_1/2", "cl C",
#     "cl C", "trm C_2/2", "cl C",
#     "cl D", "trm D_1/3", "cl D",
#     "cl D", "trm D_2/3", "cl D",
#     "cl D", "trm D_3/3", "cl D"
#   )
# 
#   admh <- Map(
#     function(id, sid) {
#       n_mhs <- sample(0:max_n_mhs, 1)
#       i <- sample(seq_len(nrow(lookup_mh)), n_mhs, TRUE)
#       dplyr::mutate(
#         lookup_mh[i, ],
#         USUBJID = id,
#         STUDYID = sid
#       )
#     },
#     adsl$USUBJID,
#     adsl$STUDYID
#   ) %>%
#     Reduce(rbind, .) %>%
#     `[`(c(4, 5, 1, 2, 3)) %>%
#     dplyr::mutate(MHTERM = .data$MHDECOD %>% with_label("Reported Term for the Medical History"))
# 
#   admh <- dplyr::inner_join(
#     adsl,
#     admh,
#     by = c("STUDYID", "USUBJID"),
#     multiple = "all"
#   ) %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     dplyr::mutate(ASTDTM = sample(
#       seq(lubridate::as_datetime(TRTSDTM), lubridate::as_datetime(TRTENDT), by = "day"),
#       size = 1
#     )) %>%
#     select(-TRTENDT) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ASTDTM, .data$MHTERM) %>%
#     dplyr::mutate(MHDISTAT = sample(
#       x = c("Resolved", "Ongoing with treatment", "Ongoing without treatment"),
#       prob = c(0.6, 0.2, 0.2),
#       size = dplyr::n(),
#       replace = TRUE
#     ) %>% with_label("Status of Disease"))
# 
#   tmc_ex_admh <- admh %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::mutate(MHSEQ = seq_len(dplyr::n())) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ASTDTM) %>%
#     col_relabel(
#       MHBODSYS = "Body System or Organ Class",
#       MHDECOD = "Dictionary-Derived Term",
#       MHSOC = "Primary System Organ Class",
#       MHSEQ = "Sponsor-Defined Identifier"
#     )
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_admh)[is.na(col_labels(tmc_ex_admh))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_admh[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_admh, file = "data/tmc_ex_admh.rda", compress = "xz")
# }

## ----adqs, eval=FALSE---------------------------------------------------------
# generate_adqs <- function(adsl = tmc_ex_adsl,
#                           n_assessments = 5L,
#                           n_days = 5L) {
#   set.seed(1)
#   param <- c(
#     "BFI All Questions",
#     "Fatigue Interference",
#     "Function/Well-Being (GF1,GF3,GF7)",
#     "Treatment Side Effects (GP2,C5,GP5)",
#     "FKSI-19 All Questions"
#   )
#   paramcd <- c("BFIALL", "FATIGI", "FKSI-FWB", "FKSI-TSE", "FKSIALL")
#   visit_format <- "WEEK"
# 
#   param_init_list <- relvar_init(param, paramcd)
# 
#   adqs <- expand.grid(
#     STUDYID = unique(adsl$STUDYID),
#     USUBJID = adsl$USUBJID,
#     PARAM = param_init_list$relvar1,
#     AVISIT = visit_schedule(visit_format = visit_format, n_assessments = n_assessments, n_days = n_days),
#     stringsAsFactors = FALSE
#   )
# 
#   adqs <- dplyr::mutate(
#     adqs,
#     AVISITN = dplyr::case_when(
#       AVISIT == "SCREENING" ~ -1,
#       AVISIT == "BASELINE" ~ 0,
#       (grepl("^WEEK", AVISIT) | grepl("^CYCLE", AVISIT)) ~ as.numeric(AVISIT) - 2,
#       TRUE ~ NA_real_
#     )
#   )
# 
#   adqs$PARAMCD <- rel_var(df = adqs, var_name = "PARAMCD", var_values = param_init_list$relvar2, related_var = "PARAM")
#   adqs$AVAL <- stats::rnorm(nrow(adqs), mean = 50, sd = 8) + adqs$AVISITN * stats::rnorm(nrow(adqs), mean = 5, sd = 2)
#   adqs <- adqs[order(adqs$STUDYID, adqs$USUBJID, adqs$PARAMCD, adqs$AVISITN), ]
# 
#   adqs <- Reduce(
#     rbind,
#     lapply(
#       split(adqs, adqs$USUBJID),
#       function(x) {
#         x$STUDYID <- adsl$STUDYID[which(adsl$USUBJID == x$USUBJID[1])]
#         x$ABLFL2 <- ifelse(x$AVISIT == "SCREENING", "Y", "")
#         x$ABLFL <- ifelse(
#           toupper(visit_format) == "WEEK" & x$AVISIT == "BASELINE",
#           "Y",
#           ifelse(
#             toupper(visit_format) == "CYCLE" & x$AVISIT == "CYCLE 1 DAY 1",
#             "Y",
#             ""
#           )
#         )
#         x
#       }
#     )
#   )
# 
#   adqs$BASE <- ifelse(adqs$ABLFL2 != "Y", retain(adqs, adqs$AVAL, adqs$ABLFL == "Y"), NA)
#   adqs <- adqs %>% dplyr::mutate(CHG = .data$AVAL - .data$BASE)
# 
#   adqs <- dplyr::inner_join(
#     adsl,
#     adqs,
#     by = c("STUDYID", "USUBJID"),
#     multiple = "all"
#   ) %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     ungroup() %>%
#     group_by(USUBJID) %>%
#     arrange(USUBJID, AVISITN) %>%
#     dplyr::mutate(ADTM = rep(
#       sort(sample(
#         seq(lubridate::as_datetime(TRTSDTM[1]), lubridate::as_datetime(TRTENDT[1]), by = "day"),
#         size = nlevels(AVISIT)
#       )),
#       each = n() / nlevels(AVISIT)
#     )) %>%
#     dplyr::ungroup() %>%
#     dplyr::select(-TRTENDT) %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ADTM)
# 
#   tmc_ex_adqs <- adqs %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$AVISITN,
#       .data$ADTM
#     )
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adqs)[is.na(col_labels(tmc_ex_adqs))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adqs[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adqs, file = "data/tmc_ex_adqs.rda", compress = "xz")
# }

## ----adrs, eval=FALSE, opts.label=c("remove_linter_comments")-----------------
# generate_adrs <- function(adsl = tmc_ex_adsl) {
#   set.seed(1)
#   param_codes <- stats::setNames(1:5, c("CR", "PR", "SD", "PD", "NE"))
# 
#   lookup_ars <- expand.grid(
#     ARM = c("A: Drug X", "B: Placebo", "C: Combination"),
#     AVALC = names(param_codes)
#   ) %>% dplyr::mutate(
#     AVAL = param_codes[.data$AVALC],
#     p_scr = c(rep(0, 3), rep(0, 3), c(1, 1, 1), c(0, 0, 0), c(0, 0, 0)),
#     p_bsl = c(rep(0, 3), rep(0, 3), c(1, 1, 1), c(0, 0, 0), c(0, 0, 0)),
#     p_cycle = c(c(.35, .25, .4), c(.30, .20, .20), c(.2, .25, .3), c(.14, 0.20, 0.18), c(.01, 0.1, 0.02)),
#     p_eoi = c(c(.35, .25, .4), c(.30, .20, .20), c(.2, .25, .3), c(.14, 0.20, 0.18), c(.01, 0.1, 0.02)),
#     p_fu = c(c(.25, .15, .3), c(.15, .05, .25), c(.3, .25, .3), c(.3, .55, .25), rep(0, 3))
#   )
# 
#   adrs <- split(adsl, adsl$USUBJID) %>%
#     lapply(function(pinfo) {
#       probs <- dplyr::filter(lookup_ars, .data$ARM == as.character(pinfo$ACTARM))
#       # screening
#       rsp_screen <- sample(probs$AVALC, 1, prob = probs$p_scr) %>% as.character()
#       # baseline
#       rsp_bsl <- sample(probs$AVALC, 1, prob = probs$p_bsl) %>% as.character()
#       # cycle
#       rsp_c2d1 <- sample(probs$AVALC, 1, prob = probs$p_cycle) %>% as.character()
#       rsp_c4d1 <- sample(probs$AVALC, 1, prob = probs$p_cycle) %>% as.character()
#       # end of induction
#       rsp_eoi <- sample(probs$AVALC, 1, prob = probs$p_eoi) %>% as.character()
#       # follow up
#       rsp_fu <- sample(probs$AVALC, 1, prob = probs$p_fu) %>% as.character()
# 
#       best_rsp <- min(param_codes[c(rsp_screen, rsp_bsl, rsp_eoi, rsp_fu, rsp_c2d1, rsp_c4d1)])
#       best_rsp_i <- which.min(param_codes[c(rsp_screen, rsp_bsl, rsp_eoi, rsp_fu, rsp_c2d1, rsp_c4d1)])
# 
#       avisit <- c("SCREENING", "BASELINE", "CYCLE 2 DAY 1", "CYCLE 4 DAY 1", "END OF INDUCTION", "FOLLOW UP")
# 
#       # meaningful date information
#       TRTSTDT <- lubridate::date(pinfo$TRTSDTM) # nolint: object_name.
#       TRTENDT <- lubridate::date(dplyr::if_else( # nolint: object_name.
#         !is.na(pinfo$TRTEDTM), pinfo$TRTEDTM,
#         lubridate::floor_date(TRTSTDT + study_duration_secs, unit = "day")
#       ))
#       scr_date <- TRTSTDT - lubridate::days(100)
#       bs_date <- TRTSTDT
#       flu_date <- sample(seq(lubridate::as_datetime(TRTSTDT), lubridate::as_datetime(TRTENDT), by = "day"), size = 1)
#       eoi_date <- sample(seq(lubridate::as_datetime(TRTSTDT), lubridate::as_datetime(TRTENDT), by = "day"), size = 1)
#       c2d1_date <- sample(seq(lubridate::as_datetime(TRTSTDT), lubridate::as_datetime(TRTENDT), by = "day"), size = 1)
#       c4d1_date <- min(lubridate::date(c2d1_date + lubridate::days(60)), TRTENDT)
# 
#       tibble::tibble(
#         STUDYID = pinfo$STUDYID,
#         USUBJID = pinfo$USUBJID,
#         PARAMCD = as.factor(c(rep("OVRINV", 6), "BESRSPI", "INVET")),
#         PARAM = as.factor(dplyr::recode(
#           .data$PARAMCD,
#           OVRINV = "Overall Response by Investigator - by visit",
#           OVRSPI = "Best Overall Response by Investigator (no confirmation required)",
#           BESRSPI = "Best Confirmed Overall Response by Investigator",
#           INVET = "Investigator End Of Induction Response"
#         )),
#         AVALC = c(
#           rsp_screen, rsp_bsl, rsp_c2d1, rsp_c4d1, rsp_eoi, rsp_fu,
#           names(param_codes)[best_rsp],
#           rsp_eoi
#         ),
#         AVAL = param_codes[.data$AVALC],
#         AVISIT = factor(c(avisit, avisit[best_rsp_i], avisit[5]), levels = avisit)
#       ) %>%
#         merge(
#           tibble::tibble(
#             AVISIT = avisit,
#             ADTM = c(scr_date, bs_date, c2d1_date, c4d1_date, eoi_date, flu_date),
#             AVISITN = c(-1, 0, 2, 4, 999, 999),
#             TRTSDTM = pinfo$TRTSDTM
#           ) %>%
#             dplyr::select(-"TRTSDTM"),
#           by = "AVISIT"
#         )
#     }) %>%
#     Reduce(rbind, .) %>%
#     dplyr::mutate(
#       AVALC = factor(.data$AVALC, levels = names(param_codes)),
#       DTHFL = factor(sample(c("Y", "N"), nrow(.), replace = TRUE, prob = c(1, 0.8))) %>%
#         with_label("Death Flag")
#     )
# 
#   # merge ADSL to be able to add RS date and study day variables
#   adrs <- dplyr::inner_join(
#     adsl,
#     adrs,
#     by = c("STUDYID", "USUBJID"),
#     multiple = "all"
#   )
# 
#   tmc_ex_adrs <- adrs %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$AVISITN,
#       .data$ADTM
#     )
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adrs)[is.na(col_labels(tmc_ex_adrs))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adrs[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adrs, file = "data/tmc_ex_adrs.rda", compress = "xz")
# }

## ----adtte, eval=FALSE--------------------------------------------------------
# generate_adtte <- function(adsl = tmc_ex_adsl) {
#   set.seed(1)
#   lookup_tte <- tibble::tribble(
#     ~ARM, ~PARAMCD, ~PARAM, ~LAMBDA, ~CNSR_P,
#     "ARM A", "OS", "Overall Survival", log(2) / 610, 0.4,
#     "ARM B", "OS", "Overall Survival", log(2) / 490, 0.3,
#     "ARM C", "OS", "Overall Survival", log(2) / 365, 0.2,
#     "ARM A", "PFS", "Progression Free Survival", log(2) / 365, 0.4,
#     "ARM B", "PFS", "Progression Free Survival", log(2) / 305, 0.3,
#     "ARM C", "PFS", "Progression Free Survival", log(2) / 243, 0.2,
#     "ARM A", "EFS", "Event Free Survival", log(2) / 365, 0.4,
#     "ARM B", "EFS", "Event Free Survival", log(2) / 305, 0.3,
#     "ARM C", "EFS", "Event Free Survival", log(2) / 243, 0.2,
#     "ARM A", "CRSD", "Duration of Confirmed Response", log(2) / 305, 0.4,
#     "ARM B", "CRSD", "Duration of Confirmed Response", log(2) / 243, 0.3,
#     "ARM C", "CRSD", "Duration of Confirmed Response", log(2) / 182, 0.2
#   )
# 
#   evntdescr_sel <- c(
#     "Death",
#     "Disease Progression",
#     "Last Tumor Assessment",
#     "Adverse Event",
#     "Last Date Known To Be Alive"
#   )
# 
#   cnsdtdscr_sel <- c(
#     "Preferred Term",
#     "Clinical Cut Off",
#     "Completion or Discontinuation",
#     "End of AE Reporting Period"
#   )
# 
#   adtte <- split(adsl, adsl$USUBJID) %>%
#     lapply(FUN = function(pinfo) {
#       lookup_tte %>%
#         dplyr::filter(.data$ARM == as.character(pinfo$ACTARMCD)) %>%
#         dplyr::rowwise() %>%
#         dplyr::mutate(
#           STUDYID = pinfo$STUDYID,
#           USUBJID = pinfo$USUBJID,
#           CNSR = sample(c(0, 1), 1, prob = c(1 - .data$CNSR_P, .data$CNSR_P)),
#           AVAL = stats::rexp(1, .data$LAMBDA),
#           AVALU = "DAYS",
#           EVNTDESC = if (.data$CNSR == 1) {
#             sample(evntdescr_sel[-c(1:2)], 1)
#           } else {
#             ifelse(.data$PARAMCD == "OS",
#               sample(evntdescr_sel[1], 1),
#               sample(evntdescr_sel[c(1:2)], 1)
#             )
#           }
#         ) %>%
#         dplyr::select(-"LAMBDA", -"CNSR_P")
#     }) %>%
#     Reduce(rbind, .)
# 
#   # merge ADSL to be able to add TTE date and study day variables
#   adtte <- dplyr::inner_join(
#     adsl,
#     dplyr::select(adtte, -"ARM"),
#     by = c("STUDYID", "USUBJID"),
#     multiple = "all"
#   ) %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     dplyr::mutate(ADTM = sample(
#       seq(lubridate::as_datetime(TRTSDTM), lubridate::as_datetime(TRTENDT), by = "day"),
#       size = 1
#     )) %>%
#     dplyr::select(-TRTENDT) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ADTM)
# 
#   adtte <- adtte %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::mutate(PARAM = as.factor(.data$PARAM)) %>%
#     dplyr::mutate(PARAMCD = as.factor(.data$PARAMCD)) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$ADTM
#     )
#   lbls <- col_labels(adtte)
# 
#   # adding adverse event counts and log follow-up time
#   tmc_ex_adtte <- dplyr::bind_rows(
#     adtte,
#     data.frame(adtte %>%
#       dplyr::group_by(.data$USUBJID) %>%
#       dplyr::slice_head(n = 1) %>%
#       dplyr::mutate(
#         PARAMCD = "TNE",
#         PARAM = "Total Number of Exacerbations",
#         AVAL = stats::rpois(1, 3),
#         AVALU = "COUNT",
#         lgTMATRSK = log(stats::rexp(1, rate = 3)),
#         dplyr::across(c("ADTM", "EVNTDESC"), ~NA)
#       ))
#   ) %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$ADTM
#     )
#   col_labels(tmc_ex_adtte) <- c(lbls, lgTMATRSK = "Log Time At Risk")
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_adtte)[is.na(col_labels(tmc_ex_adtte))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_adtte[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_adtte, file = "data/tmc_ex_adtte.rda", compress = "xz")
# }

## ----advs, eval=FALSE---------------------------------------------------------
# generate_advs <- function(adsl = tmc_ex_adsl,
#                           n_assessments = 5L,
#                           n_days = 5L) {
#   set.seed(1)
#   param <- c(
#     "Diastolic Blood Pressure",
#     "Pulse Rate",
#     "Respiratory Rate",
#     "Systolic Blood Pressure",
#     "Temperature", "Weight"
#   )
#   paramcd <- c("DIABP", "PULSE", "RESP", "SYSBP", "TEMP", "WEIGHT")
#   paramu <- c("Pa", "beats/min", "breaths/min", "Pa", "C", "Kg")
#   visit_format <- "WEEK"
# 
#   param_init_list <- relvar_init(param, paramcd)
#   unit_init_list <- relvar_init(param, paramu)
# 
#   advs <- expand.grid(
#     STUDYID = unique(adsl$STUDYID),
#     USUBJID = adsl$USUBJID,
#     PARAM = as.factor(param_init_list$relvar1),
#     AVISIT = visit_schedule(visit_format = visit_format, n_assessments = n_assessments),
#     stringsAsFactors = FALSE
#   )
# 
#   advs <- dplyr::mutate(
#     advs,
#     AVISITN = dplyr::case_when(
#       AVISIT == "SCREENING" ~ -1,
#       AVISIT == "BASELINE" ~ 0,
#       (grepl("^WEEK", AVISIT) | grepl("^CYCLE", AVISIT)) ~ as.numeric(AVISIT) - 2,
#       TRUE ~ NA_real_
#     )
#   )
# 
#   advs$PARAMCD <- as.factor(rel_var(
#     df = advs,
#     var_name = "PARAMCD",
#     var_values = param_init_list$relvar2,
#     related_var = "PARAM"
#   ))
#   advs$AVALU <- as.factor(rel_var(
#     df = advs,
#     var_name = "AVALU",
#     var_values = unit_init_list$relvar2,
#     related_var = "PARAM"
#   ))
# 
#   advs$AVAL <- stats::rnorm(nrow(advs), mean = 50, sd = 8)
#   advs <- advs[order(advs$STUDYID, advs$USUBJID, advs$PARAMCD, advs$AVISITN), ]
# 
#   advs <- dplyr::inner_join(
#     adsl,
#     advs,
#     by = c("STUDYID", "USUBJID"),
#     multiple = "all"
#   ) %>%
#     dplyr::rowwise() %>%
#     dplyr::mutate(TRTENDT = lubridate::date(dplyr::case_when(
#       is.na(TRTEDTM) ~ lubridate::floor_date(lubridate::date(TRTSDTM) + study_duration_secs, unit = "day"),
#       TRUE ~ TRTEDTM
#     ))) %>%
#     dplyr::mutate(ADTM = sample(
#       seq(lubridate::as_datetime(TRTSDTM), lubridate::as_datetime(TRTENDT), by = "day"),
#       size = 1
#     )) %>%
#     dplyr::mutate(ADY = ceiling(difftime(ADTM, TRTSDTM, units = "days"))) %>%
#     dplyr::select(-TRTENDT) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(.data$STUDYID, .data$USUBJID, .data$ADTM)
# 
#   tmc_ex_advs <- advs %>%
#     dplyr::group_by(.data$USUBJID) %>%
#     dplyr::ungroup() %>%
#     dplyr::arrange(
#       .data$STUDYID,
#       .data$USUBJID,
#       .data$PARAMCD,
#       .data$AVISITN,
#       .data$ADTM
#     )
# 
#   i_lbls <- sapply(
#     names(col_labels(tmc_ex_advs)[is.na(col_labels(tmc_ex_advs))]), function(x) which(names(common_var_labels) == x)
#   )
#   col_labels(tmc_ex_advs[names(i_lbls)]) <- common_var_labels[i_lbls]
# 
#   save(tmc_ex_advs, file = "data/tmc_ex_advs.rda", compress = "xz")
# }

## ----generate data, eval=FALSE------------------------------------------------
# # Generate & load adsl
# tmp_fol <- getwd()
# setwd(dirname(tmp_fol))
# generate_adsl()
# load("data/tmc_ex_adsl.rda")
# 
# # Generate other datasets
# generate_adae()
# generate_adaette()
# generate_adcm()
# generate_adeg()
# generate_adex()
# generate_adlb()
# generate_admh()
# generate_adqs()
# generate_adrs()
# generate_adtte()
# generate_advs()
# 
# setwd(tmp_fol)