# Example Data Generation

#### 2025-08-21

## Generating minimal data to test `teal.modules.clinical`

The following script is used to create and save cached synthetic `CDISC` data to the `data/` directory to use in examples and tests in the `teal.modules.clinical` package. This script/vignette was initialized by Emily de la Rua in `tern`.

Disclaimer: this vignette concerns mainly the development of minimal and stable test data and it is kept internal for feature tracking.

## Setup & Helper Functions

    library(dplyr)
    library(teal.data)

    study_duration_secs <- lubridate::seconds(lubridate::years(2))

    sample_fct <- function(x, N, ...) {
      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`

    generate_adsl <- function(N = 200) {
      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`

    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`

    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`

    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`

    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`

    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`

    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`

    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`

    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`

    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$TRTSDTM)
          TRTENDT <- lubridate::date(dplyr::if_else(
            !is.na(pinfo$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`

    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`

    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

    # 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)