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#' Count number of patients with missed doses by thresholds
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function creates a layout element to calculate cumulative counts of patients with number of missed
#' doses at least equal to user-specified threshold values.
#'
#' This function analyzes numeric variable `vars`, a variable with numbers of missed doses,
#' against the threshold values supplied to the `thresholds` argument as a numeric vector. This function
#' assumes that every row of the given data frame corresponds to a unique patient.
#'
#' @inheritParams s_count_cumulative
#' @inheritParams argument_convention
#' @param thresholds (`numeric`)\cr minimum number of missed doses the patients had.
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("count_missed_doses")`
#' to see available statistics for this function.
#'
#' @seealso
#' * Relevant description function [d_count_missed_doses()] which generates labels for [count_missed_doses()].
#' * Similar analyze function [count_cumulative()] which more generally counts cumulative values and has more
#' options for threshold handling, but uses different labels.
#'
#' @name count_missed_doses
#' @order 1
NULL
#' @describeIn count_missed_doses Statistics function to count non-missing values.
#'
#' @return
#' * `s_count_nonmissing()` returns the statistic `n` which is the count of non-missing values in `x`.
#'
#' @keywords internal
s_count_nonmissing <- function(x) {
list(n = n_available(x))
}
#' Description function that calculates labels for `s_count_missed_doses()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams s_count_missed_doses
#'
#' @return [d_count_missed_doses()] returns a named `character` vector with the labels.
#'
#' @seealso [s_count_missed_doses()]
#'
#' @export
d_count_missed_doses <- function(thresholds) {
paste0("At least ", thresholds, " missed dose", ifelse(thresholds > 1, "s", ""))
}
#' @describeIn count_missed_doses Statistics function to count patients with missed doses.
#'
#' @return
#' * `s_count_missed_doses()` returns the statistics `n` and `count_fraction` with one element for each threshold.
#'
#' @keywords internal
s_count_missed_doses <- function(x,
thresholds,
.N_col) { # nolint
stat <- s_count_cumulative(
x = x,
thresholds = thresholds,
lower_tail = FALSE,
include_eq = TRUE,
.N_col = .N_col
)
labels <- d_count_missed_doses(thresholds)
for (i in seq_along(stat$count_fraction)) {
stat$count_fraction[[i]] <- formatters::with_label(stat$count_fraction[[i]], label = labels[i])
}
n_stat <- s_count_nonmissing(x)
c(n_stat, stat)
}
#' @describeIn count_missed_doses Formatted analysis function which is used as `afun`
#' in `count_missed_doses()`.
#'
#' @return
#' * `a_count_missed_doses()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_missed_doses <- make_afun(
s_count_missed_doses,
.formats = c(n = "xx", count_fraction = format_count_fraction)
)
#' @describeIn count_missed_doses Layout-creating function which can take statistics function arguments
#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_missed_doses()` returns a layout object suitable for passing to further layouting functions,
#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#' the statistics from `s_count_missed_doses()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' anl <- tern_ex_adsl %>%
#' distinct(STUDYID, USUBJID, ARM) %>%
#' mutate(
#' PARAMCD = "TNDOSMIS",
#' PARAM = "Total number of missed doses during study",
#' AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE),
#' AVALC = ""
#' )
#'
#' basic_table() %>%
#' split_cols_by("ARM") %>%
#' add_colcounts() %>%
#' count_missed_doses("AVAL", thresholds = c(1, 5, 10, 15), var_labels = "Missed Doses") %>%
#' build_table(anl, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_missed_doses <- function(lyt,
vars,
thresholds,
var_labels = vars,
show_labels = "visible",
na_str = default_na_str(),
nested = TRUE,
...,
table_names = vars,
.stats = NULL,
.formats = NULL,
.labels = NULL,
.indent_mods = NULL) {
extra_args <- list(thresholds = thresholds, ...)
afun <- make_afun(
a_count_missed_doses,
.stats = .stats,
.formats = .formats,
.labels = .labels,
.indent_mods = .indent_mods,
.ungroup_stats = "count_fraction"
)
analyze(
lyt = lyt,
vars = vars,
afun = afun,
var_labels = var_labels,
table_names = table_names,
show_labels = show_labels,
na_str = na_str,
nested = nested,
extra_args = extra_args
)
}
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