R/derive_param_tte.R

In epijim/admiral: ADaM in R Asset Library

#' Derive a Time-to-Event Parameter
#'
#' Add a time-to-event parameter to the input dataset.
#'
#' @param dataset Input dataset
#'
#'   The `PARAMCD` variable is expected.
#'
#' @param dataset_adsl ADSL input dataset
#'
#'   The variables specified for `start_date`, `start_imputation_flag`, and
#'   `subject_keys` are expected.
#'
#' @param source_datasets Source datasets
#'
#'   A named list of datasets is expected. The `dataset_name` field of
#'   `tte_source()` refers to the dataset provided in the list.
#'
#' @param by_vars By variables
#'
#'   If the parameter is specified, separate time to event parameters are
#'   derived for each by group.
#'
#'   The by variables must be in at least one of the source datasets. Each
#'   source dataset must contain either all by variables or none of the by
#'   variables.
#'
#'   The by variables are not included in the output dataset.
#'
#' @param start_date Time to event origin date
#'
#'   The variable `STARTDT` is set to the specified date. The value is taken
#'   from the ADSL dataset.
#'
#'   If the event or censoring date is before the origin date, `ADT` is set to
#'   the origin date.
#'
#'   If the specified variable is imputed, the corresponding date imputation
#'   flag must specified for `start_imputation_flag`.
#'
#' @param start_date_imputation_flag Date imputation flag for start date
#'
#'   If the start date is imputed, the corresponding date imputation flag must
#'   be specified. The variable `STARTDTF` is set to the specified variable.
#'
#' @param start_time_imputation_flag Time imputation flag for start date
#'
#'   If the start time is imputed, the corresponding time imputation flag must
#'   be specified. The variable `STARTTMF` is set to the specified variable.
#'
#' @param event_conditions Sources and conditions defining events
#'
#'   A list of `event_source()` objects is expected.
#'
#' @param censor_conditions Sources and conditions defining censorings
#'
#'   A list of `censor_source()` objects is expected.
#'
#' @param create_datetime Create datetime variables?
#'
#'   If set to `TRUE`, variables `ADTM` and `STARTDTM` are created. Otherwise,
#'   variables `ADT` and `STARTDT` are created.
#'
#' @param set_values_to Variables to set
#'
#'   A named list returned by `vars()` defining the variables to be set for the
#'   new parameter, e.g. `vars(PARAMCD = "OS", PARAM = "Overall Survival")` is
#'   expected. The values must be symbols, character strings, numeric values,
#'   expressions, or `NA`.
#'
#' @param subject_keys Variables to uniquely identify a subject
#'
#'   A list of symbols created using `vars()` is expected.
#'
#' @details The following steps are performed to create the observations of the
#'   new parameter:
#'
#'   **Deriving the events:**
#'
#'   \enumerate{ \item For each event source dataset the observations as
#'   specified by the `filter` element are selected. Then for each patient the
#'   first observation (with respect to `date`) is selected.
#'
#'   \item The `ADT` variable is set to the variable specified by the
#'   \code{date} element. If the date variable is a datetime variable, only
#'   the datepart is copied. If the source variable is a character variable, it
#'   is converted to a date. If the date is incomplete, it is imputed as the
#'   first possible date.
#'
#'   \item The `CNSR` variable is added and set to the \code{censor} element.
#'
#'   \item The variables specified by the \code{set_values_to} element are
#'   added.
#'
#'   \item The selected observations of all event source datasets are combined into a
#'   single dataset.
#'
#'   \item For each patient the first observation (with respect to the `ADT`
#'   variable) from the single dataset is selected. }
#'
#'   **Deriving the censoring observations:**
#'
#'   \enumerate{ \item For each censoring source dataset the observations as
#'   specified by the `filter` element are selected. Then for each patient the
#'   last observation (with respect to `date`) is selected.
#'
#'   \item The `ADT` variable is set to the variable specified by the
#'   \code{date} element. If the date variable is a datetime variable, only
#'   the datepart is copied. If the source variable is a character variable, it
#'   is converted to a date. If the date is incomplete, it is imputed as the
#'   first possible date.
#'
#'   \item The `CNSR` variable is added and set to the \code{censor} element.
#'
#'   \item The variables specified by the \code{set_values_to} element are
#'   added.
#'
#'   \item The selected observations of all censoring source datasets are
#'   combined into a single dataset.
#'
#'   \item For each patient the last observation (with respect to the `ADT`
#'   variable) from the single dataset is selected. }
#'
#'   For each subject (as defined by the `subject_keys` parameter) an
#'   observation is selected. If an event is available, the event observation is
#'   selected. Otherwise the censoring observation is selected.
#'
#'   Finally
#'   1. the variables specified for `start_date` and `start_imputation_flag` are
#'   joined from the ADSL dataset,
#'   1. the variables as defined by the `set_values_to` parameter are added,
#'   1. the `ADT`/`ADTM` variable is set to the maximum of `ADT`/`ADTM` and
#'   `STARTDT`/`STARTDTM` (depending on the `create_datetime` parameter), and
#'   1. the new observations are added to the output dataset.
#'
#' @author Stefan Bundfuss
#'
#' @return The input dataset with the new parameter added
#'
#' @keywords derivation bds
#'
#' @export
#'
#' @examples
#' library(dplyr, warn.conflicts = FALSE)
#' library(lubridate, warn.conflicts = FALSE)
#' data("adsl")
#'
#' death <- event_source(
#'   dataset_name = "adsl",
#'   filter = DTHFL == "Y",
#'   date = DTHDT,
#'   set_values_to = vars(
#'     EVNTDESC = "DEATH",
#'     SRCDOM = "ADSL",
#'     SRCVAR = "DTHDT"
#'   )
#' )
#'
#' last_alive_dt <- censor_source(
#'   dataset_name = "adsl",
#'   date = LSTALVDT,
#'   set_values_to = vars(
#'     EVNTDESC = "LAST DATE KNOWN ALIVE",
#'     SRCDOM = "ADSL",
#'     SRCVAR = "LSTALVDT"
#'   )
#' )
#'
#' derive_param_tte(
#'   dataset_adsl = adsl,
#'   event_conditions = list(death),
#'   censor_conditions = list(last_alive_dt),
#'   source_datasets = list(adsl = adsl),
#'   set_values_to = vars(
#'     PARAMCD = "OS",
#'     PARAM = "Overall Survival"
#'   )
#' ) %>%
#'   select(-STUDYID) %>%
#'   filter(row_number() %in% 20:30)
#'
#' # derive time to adverse event for each preferred term #
#' adsl <- tibble::tribble(
#'   ~USUBJID, ~TRTSDT,           ~EOSDT,
#'   "01",     ymd("2020-12-06"), ymd("2021-03-06"),
#'   "02",     ymd("2021-01-16"), ymd("2021-02-03")
#' ) %>%
#'   mutate(STUDYID = "AB42")
#'
#' ae <- tibble::tribble(
#'   ~USUBJID, ~AESTDTC,           ~AESEQ, ~AEDECOD,
#'   "01",     "2021-01-03T10:56", 1,      "Flu",
#'   "01",     "2021-03-04",       2,      "Cough",
#'   "01",     "2021",             3,      "Flu"
#' ) %>%
#'   mutate(STUDYID = "AB42")
#'
#' ttae <- event_source(
#'   dataset_name = "ae",
#'   date = AESTDTC,
#'   set_values_to = vars(
#'     EVNTDESC = "AE",
#'     SRCDOM = "AE",
#'     SRCVAR = "AESTDTC",
#'     SRCSEQ = AESEQ
#'   )
#' )
#'
#' eos <- censor_source(
#'   dataset_name = "adsl",
#'   date = EOSDT,
#'   set_values_to = vars(
#'     EVNTDESC = "END OF STUDY",
#'     SRCDOM = "ADSL",
#'     SRCVAR = "EOSDT"
#'   )
#' )
#'
#' derive_param_tte(
#'   dataset_adsl = adsl,
#'   by_vars = vars(AEDECOD),
#'   start_date = TRTSDT,
#'   event_conditions = list(ttae),
#'   censor_conditions = list(eos),
#'   source_datasets = list(adsl = adsl, ae = ae),
#'   set_values_to = vars(
#'     PARAMCD = paste0("TTAE", as.numeric(as.factor(AEDECOD))),
#'     PARAM = paste("Time to First", AEDECOD, "Adverse Event"),
#'     PARCAT1 = "TTAE",
#'     PARCAT2 = AEDECOD
#'   )
#' ) %>%
#'   select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
derive_param_tte <- function(dataset = NULL,
                             dataset_adsl,
                             source_datasets,
                             by_vars = NULL,
                             start_date = TRTSDT,
                             start_date_imputation_flag = NULL,
                             start_time_imputation_flag = NULL,
                             event_conditions,
                             censor_conditions,
                             create_datetime = FALSE,
                             set_values_to,
                             subject_keys = vars(STUDYID, USUBJID)) {
  # checking and quoting #
  assert_data_frame(dataset, optional = TRUE)
  assert_vars(by_vars, optional = TRUE)
  start_date <- assert_symbol(enquo(start_date))
  start_date_imputation_flag <- assert_symbol(
    enquo(start_date_imputation_flag),
    optional = TRUE
  )
  start_time_imputation_flag <- assert_symbol(
    enquo(start_time_imputation_flag),
    optional = TRUE
  )
  assert_data_frame(
    dataset_adsl,
    required_vars = quo_c(
      start_date,
      start_date_imputation_flag,
      start_time_imputation_flag
    )
  )
  assert_vars(subject_keys)
  assert_list_of(event_conditions, "event_source")
  assert_list_of(censor_conditions, "censor_source")
  assert_list_of(source_datasets, "data.frame")
  source_names <- names(source_datasets)
  assert_list_element(
    list = event_conditions,
    element = "dataset_name",
    condition = dataset_name %in% source_names,
    source_names = source_names,
    message_text = paste0(
      "The dataset names must be included in the list specified for the ",
      "`source_datasets` parameter.\n",
      "Following names were provided by `source_datasets`:\n",
      enumerate(source_names, quote_fun = squote)
    )
  )
  assert_list_element(
    list = censor_conditions,
    element = "dataset_name",
    condition = dataset_name %in% source_names,
    source_names = source_names,
    message_text = paste0(
      "The dataset names must be included in the list specified for the ",
      "`source_datasets` parameter.\n",
      "Following names were provided by `source_datasets`:\n",
      enumerate(source_names, quote_fun = squote)
    )
  )
  assert_logical_scalar(create_datetime)
  assert_varval_list(set_values_to, accept_expr = TRUE, optional = TRUE)
  if (!is.null(set_values_to$PARAMCD) & !is.null(dataset)) {
    assert_param_does_not_exist(dataset, quo_get_expr(set_values_to$PARAMCD))
  }
  if (!is.null(by_vars)) {
    source_datasets <-
      extend_source_datasets(
        source_datasets = source_datasets,
        by_vars = by_vars
      )
  }

  # determine events #
  event_data <- filter_date_sources(
    sources = event_conditions,
    source_datasets = source_datasets,
    by_vars = by_vars,
    create_datetime = create_datetime,
    subject_keys = subject_keys,
    mode = "first"
  ) %>%
    mutate(temp_event = 1)

  # determine censoring observations #
  censor_data <- filter_date_sources(
    sources = censor_conditions,
    source_datasets = source_datasets,
    by_vars = by_vars,
    create_datetime = create_datetime,
    subject_keys = subject_keys,
    mode = "last"
  ) %>%
    mutate(temp_event = 0)

  # determine variable to add from ADSL #
  if (create_datetime) {
    date_var <- sym("ADTM")
    start_var <- sym("STARTDTM")
  }
  else {
    date_var <- sym("ADT")
    start_var <- sym("STARTDT")
  }
  adsl_vars <- vars(
    !!!subject_keys,
    !!start_var := !!start_date
  )
  if (!quo_is_null(start_date_imputation_flag)) {
    adsl_vars <- vars(
      !!!adsl_vars,
      STARTDTF = !!start_date_imputation_flag
    )
  }
  if (!quo_is_null(start_time_imputation_flag)) {
    adsl_vars <- vars(
      !!!adsl_vars,
      STARTTMF = !!start_time_imputation_flag
    )
  }
  adsl <- dataset_adsl %>%
    select(!!!adsl_vars)

  # create observations for new parameter #
  new_param <- filter_extreme(
    bind_rows(event_data, censor_data),
    by_vars = quo_c(subject_keys, by_vars),
    order = vars(temp_event),
    mode = "last"
  ) %>%
    left_join(
      adsl,
      by = vars2chr(subject_keys)
    )
  tryCatch(
    new_param <- mutate(new_param, !!!set_values_to),
    error = function(cnd) {
      abort(
        paste0(
          "Assigning new variables failed!\n",
          "set_values_to = (\n",
          paste(
            " ",
            names(set_values_to),
            "=",
            lapply(set_values_to, quo_get_expr),
            collapse = "\n"
          ),
          "\n)\nError message:\n  ",
          cnd
        )
      )
    }
  )

  new_param <-
    mutate(new_param, !!date_var := pmax(!!date_var, !!start_var)) %>%
    select(-starts_with("temp_"))

  if (!is.null(by_vars)) {
    if (!is.null(set_values_to$PARAMCD)) {
      assert_one_to_one(new_param, vars(PARAMCD), by_vars)
    }

    # -vars2chr(by_vars) does not work for 3.5 #
    new_param <- select(new_param, !!!negate_vars(by_vars))
  }

  # add new parameter to input dataset #
  bind_rows(dataset, new_param)
}

#' Select the First or Last Date from Several Sources
#'
#' Select for each subject the first or last observation with respect to a date
#' from a list of sources.
#'
#' @param sources Sources
#'
#'    A list of `tte_source()` objects is expected.
#'
#' @param source_datasets Source datasets
#'
#'   A named list of datasets is expected. The `dataset_name` field of
#'   `tte_source()` refers to the dataset provided in the list.
#'
#' @param by_vars By variables
#'
#'   If the parameter is specified, for each by group the observations are
#'   selected separately.
#'
#' @param create_datetime Create datetime variable?
#'
#'   If set to `TRUE`, variables `ADTM` is created. Otherwise, variables `ADT`
#'   is created.
#'
#' @param subject_keys Variables to uniquely identify a subject
#'
#'   A list of symbols created using `vars()` is expected.
#'
#' @param mode Selection mode (first or last)
#'
#'   If `"first"` is specified, for each subject the first observation with
#'   respect to the date is included in the output dataset. If `"last"` is
#'   specified, the last observation is included in the output dataset.
#'
#'   Permitted Values:  `"first"`, `"last"`
#'
#' @details The following steps are performed to create the output dataset:
#'
#'   \enumerate{ \item For each source dataset the observations as specified by
#'   the `filter` element are selected. Then for each patient the first or last
#'   observation (with respect to `date`) is selected.
#'
#'   \item The `ADT` variable is set to the variable specified by the
#'   \code{date} element. If the date variable is a datetime variable, only
#'   the datepart is copied. If the source variable is a character variable, it
#'   is converted to a date. If the date is incomplete, it is imputed as
#'   the first possible date.
#'
#'   \item The `CNSR` is added and set to the value of the \code{censor}
#'   element.
#'
#'   \item The selected observations of all source datasets are combined into a
#'   single dataset.
#'
#'   \item For each patient the first or last observation (with respect to the
#'   `ADT` variable) from the single dataset is selected. }
#'
#' @return A dataset with one observation per subject as described in the
#'   "Details" section.
#'
#' @author Stefan Bundfuss
#'
#' @keywords dev_utility
#'
#' @export
filter_date_sources <- function(sources,
                                source_datasets,
                                by_vars,
                                create_datetime = FALSE,
                                subject_keys,
                                mode) {
  assert_list_of(sources, "tte_source")
  assert_list_of(source_datasets, "data.frame")
  assert_logical_scalar(create_datetime)
  assert_vars(subject_keys)
  assert_character_scalar(
    mode,
    values = c("first", "last"),
    case_sensitive = FALSE
  )

  if (create_datetime) {
    date_var <- sym("ADTM")
  } else {
    date_var <- sym("ADT")
  }

  data <- vector("list", length(sources))
  for (i in seq_along(sources)) {
    date <- sources[[i]]$date
    data[[i]] <- source_datasets[[sources[[i]]$dataset_name]] %>%
      filter_if(sources[[i]]$filter) %>%
      filter_extreme(
        order = vars(!!date),
        by_vars = quo_c(subject_keys, by_vars),
        mode = mode,
        check_type = "none"
      )
    # add date variable and accompanying variables
    if (is.instant(pull(data[[i]], !!date))) {
      if (create_datetime) {
        date_derv <- vars(!!date_var := as_datetime(!!date))
      } else {
        date_derv <- vars(!!date_var := date(!!date))
      }
    } else {
      if (create_datetime) {
        date_derv <- vars(
          !!date_var := convert_dtc_to_dtm(
            !!date,
            date_imputation = "first",
            time_imputation = "first"
          )
        )
      } else {
        date_derv <- vars(
          !!date_var := convert_dtc_to_dt(
            !!date,
            date_imputation = "first"
        ))
      }
    }

    data[[i]] <- transmute(
      data[[i]],
      !!!by_vars,
      !!!subject_keys,
      !!!sources[[i]]$set_values_to,
      CNSR = sources[[i]]$censor,
      !!!date_derv
    )
  }

  # put all source data into one dataset and select first or last date per subject
  data %>%
    bind_rows() %>%
    filter(!is.na(!!date_var)) %>%
    filter_extreme(
      by_vars = quo_c(subject_keys, by_vars),
      order = vars(!!date_var),
      mode = mode,
      check_type = "none"
    )
}

#' Add By Groups to All Datasets if Necessary
#'
#' The function ensures that the by variables are contained in all source
#' datasets.
#'
#' @details
#'   1. The by groups are determined as the union of the by groups occuring in
#'   the source datasets.
#'   1. For all source datasets which do not contain the by variables the source
#'   dataset is replaced by the cartesian product of the source dataset and the
#'   by groups.
#'
#' @param source_datasets Source datasets
#'
#'   A named list of datasets is expected. Each dataset must contain either all
#'   by variables or none of the by variables.
#'
#' @param by_vars By variables
#'
#'
#' @return The list of extended source datasets
#'
#' @author Stefan Bundfuss
#'
#' @keywords dev_utility
#'
#' @export
extend_source_datasets <- function(source_datasets,
                                   by_vars) {
  assert_list_of(source_datasets, "data.frame")
  assert_vars(by_vars)
  # ensure that the by variables are contained in all source datasets #
  by_vars_chr <- vars2chr(by_vars)
  # determine which source datasets need to be extended #
  by_groups <- list()
  extend <- vector("list", length(source_datasets))
  for (i in seq_along(source_datasets)) {
    missing_by_vars <- setdiff(by_vars_chr, names(source_datasets[[i]]))
    if (length(missing_by_vars) == 0) {
      # all by variables are included in the source dataset #
      extend[[i]] <- FALSE
      by_groups <-
        append(
          by_groups,
          list(unique(select(
            source_datasets[[i]], !!!by_vars
          )))
        )
    }
    else if (!setequal(by_vars_chr, missing_by_vars)) {
      # only some of the by variables are included in the source dataset #
      abort(paste(
        "Only",
        paste(setdiff(by_vars_chr, missing_by_vars), collapse = ", "),
        "are included in source dataset",
        names(source_datasets)[[i]],
        ".\n The source dataset must include all or none of the by variables."
      ))
    }
    else {
      extend[[i]] <- TRUE
    }
  }
  if (length(by_groups) == 0) {
    abort(paste0(
      "The by variables (",
      paste(by_vars_chr, collapse = ", "),
      ") are not contained in any of the source datasets."
    ))
  }
  # extend source datasets #
  by_groups <- unique(bind_rows(by_groups))
  for (i in seq_along(source_datasets)) {
    if (extend[[i]]) {
      source_datasets[[i]] <-
        full_join(
          mutate(by_groups, temp_dummy = 1),
          mutate(source_datasets[[i]], temp_dummy = 1),
          by = "temp_dummy"
        ) %>%
        select(-temp_dummy)
    }
  }
  source_datasets
}

#' Create a `tte_source` Object
#'
#' The `tte_source` object is used to define events and possible censorings.
#'
#' @param dataset_name The name of the source dataset
#'
#'   The name refers to the dataset provided by the `source_datasets` parameter
#'   of `derive_param_tte()`.
#'
#' @param filter An unquoted condition for selecting the observations from
#'   `dataset` which are events or possible censoring time points.
#'
#' @param date A variable providing the date of the event or censoring. A date,
#'   a datetime, or a character variable containing ISO 8601 dates can be
#'   specified. An unquoted symbol is expected.
#'
#' @param censor Censoring value
#'
#'   CDISC strongly recommends using `0` for events and positive integers for
#'   censoring.
#'
#' @param set_values_to A named list returned by `vars()` defining the variables
#'   to be set for the event or censoring, e.g. `vars(EVENTDESC = "DEATH",
#'   SRCDOM = "ADSL", SRCVAR = "DTHDT")`. The values must be a symbol, a
#'   character string, a numeric value, or `NA`.
#'
#' @author Stefan Bundfuss
#'
#' @keywords dev_utility
#'
#' @seealso [derive_param_tte()], [censor_source()], [event_source()]
#'
#' @return An object of class `tte_source`
#'
#' @examples
#' # Death event
#' admiral:::tte_source(
#'   dataset_name = "adsl",
#'   filter = DTHFL == "Y",
#'   date = DTHDT,
#'   censor = 0,
#'   set_values_to = vars(
#'     EVNTDESC = "DEATH",
#'     SRCDOM = "ADSL",
#'     SRCVAR = "DTHDT"
#'   )
#' )
#'
#' # Last study date known alive censor
#' admiral:::tte_source(
#'   dataset_name = "adsl",
#'   date = LSTALVDT,
#'   censor = 1,
#'   set_values_to = vars(
#'     EVNTDESC = "ALIVE",
#'     SRCDOM = "ADSL",
#'     SRCVAR = "LSTALVDT"
#'   )
#' )
tte_source <- function(dataset_name,
                       filter = NULL,
                       date,
                       censor = 0,
                       set_values_to = NULL) {
  out <- list(
    dataset_name = assert_character_scalar(dataset_name),
    filter = assert_filter_cond(enquo(filter), optional = TRUE),
    date = assert_symbol(enquo(date)),
    censor = assert_integer_scalar(censor),
    set_values_to = assert_varval_list(
      set_values_to,
      optional = TRUE
    )
  )
  class(out) <- c("tte_source", "source", "list")
  out
}

#' Create an `event_source` Object
#'
#' `event_source` objects are used to define events as input for the
#' `derive_param_tte()` function.
#'
#' @inheritParams tte_source
#'
#' @author Stefan Bundfuss
#'
#' @keywords source_specifications
#'
#' @seealso [derive_param_tte()], [censor_source()]
#'
#' @export
#'
#' @return An object of class `event_source`, inheriting from class `tte_source`
#'
#' @examples
#' # Death event
#' event_source(
#'   dataset_name = "adsl",
#'   filter = DTHFL == "Y",
#'   date = DTHDT,
#'   set_values_to = vars(
#'     EVNTDESC = "DEATH",
#'     SRCDOM = "ADSL",
#'     SRCVAR = "DTHDT"
#'   )
#' )
event_source <- function(dataset_name,
                         filter = NULL,
                         date,
                         set_values_to = NULL) {
  out <- tte_source(
    dataset_name = assert_character_scalar(dataset_name),
    filter = !!enquo(filter),
    date = !!assert_symbol(enquo(date)),
    censor = 0,
    set_values_to = set_values_to
  )
  class(out) <- c("event_source", class(out))
  out
}

#' Create a `censor_source` Object
#'
#' `censor_source` objects are used to define censorings as input for the
#' `derive_param_tte()` function.
#'
#' @inheritParams tte_source
#'
#' @author Stefan Bundfuss
#'
#' @keywords source_specifications
#'
#' @seealso [derive_param_tte()], [event_source()]
#'
#' @export
#'
#' @return An object of class `censor_source`, inheriting from class `tte_source`
#'
#' @examples
#' # Last study date known alive censor
#' censor_source(
#'   dataset_name = "adsl",
#'   date = LSTALVDT,
#'   set_values_to = vars(
#'     EVNTDESC = "ALIVE",
#'     SRCDOM = "ADSL",
#'     SRCVAR = "LSTALVDT"
#'   )
#' )
censor_source <- function(dataset_name,
                          filter = NULL,
                          date,
                          censor = 1,
                          set_values_to = NULL) {
  out <- tte_source(
    dataset_name = assert_character_scalar(dataset_name),
    filter = !!enquo(filter),
    date = !!assert_symbol(enquo(date)),
    censor = assert_integer_scalar(censor, subset = "positive"),
    set_values_to = set_values_to
  )
  class(out) <- c("censor_source", class(out))
  out
}

#' Print `tte_source` Objects
#'
#' @param x A `tte_source` object
#' @param ... Not used
#'
#' @export
#'
#' @seealso [tte_source()], [censor_source()], [event_source()]
#'
#' @examples
#' print(death_event)
print.tte_source <- function(x, ...) {
  cat <- function(...) base::cat(..., sep = "")
  cat("<tte_source> object\n")
  cat("dataset_name: \"", x$dataset_name, "\"\n")
  cat("filter: ", quo_text(x$filter), "\n")
  cat("date: ", quo_text(x$date), "\n")
  cat("censor: ", x$censor, "\n")
  cat("set_values_to:\n")
  for (name in names(x$set_values_to)) {
    cat("  ", name, ": ", quo_text(x$set_values_to[[name]]), "\n")
  }
}

list_tte_source_objects <- function() {
  objects <- grep("_(event|censor)$", getNamespaceExports("admiral"), value = TRUE)

  rows <- lapply(objects, function(obj_name) {
    obj <- get(obj_name)
    data.frame(
      object = obj_name,
      dataset_name = obj$dataset_name,
      filter = rlang::quo_text(obj$filter),
      date = rlang::quo_text(obj$date),
      censor = obj$censor,
      set_values_to = paste(
        paste(
          names(obj$set_values_to),
          purrr::map_chr(obj$set_values_to, rlang::quo_text, width = 100),
          sep = ": "
        ),
        collapse = "<br>"
      ),
      stringsAsFactors = FALSE
    )
  })

  do.call(rbind, rows)
}