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estimate_multinomial_rsp.R
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estimate_multinomial_rsp.R
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#' Estimation of proportions per level of factor
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Estimate the proportion along with confidence interval of a proportion
#' regarding the level of a factor.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("estimate_multinomial_response")`
#' to see available statistics for this function.
#'
#' @seealso Relevant description function [d_onco_rsp_label()].
#'
#' @name estimate_multinomial_rsp
#' @order 1
NULL
#' Description of standard oncology response
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Describe the oncology response in a standard way.
#'
#' @param x (`character`)\cr the standard oncology codes to be described.
#'
#' @return Response labels.
#'
#' @seealso [estimate_multinomial_rsp()]
#'
#' @examples
#' d_onco_rsp_label(
#' c("CR", "PR", "SD", "NON CR/PD", "PD", "NE", "Missing", "<Missing>", "NE/Missing")
#' )
#'
#' # Adding some values not considered in d_onco_rsp_label
#'
#' d_onco_rsp_label(
#' c("CR", "PR", "hello", "hi")
#' )
#'
#' @export
d_onco_rsp_label <- function(x) {
x <- as.character(x)
desc <- c(
CR = "Complete Response (CR)",
PR = "Partial Response (PR)",
MR = "Minimal/Minor Response (MR)",
MRD = "Minimal Residual Disease (MRD)",
SD = "Stable Disease (SD)",
PD = "Progressive Disease (PD)",
`NON CR/PD` = "Non-CR or Non-PD (NON CR/PD)",
NE = "Not Evaluable (NE)",
`NE/Missing` = "Missing or unevaluable",
Missing = "Missing",
`NA` = "Not Applicable (NA)",
ND = "Not Done (ND)"
)
values_label <- vapply(
X = x,
FUN.VALUE = character(1),
function(val) {
if (val %in% names(desc)) desc[val] else val
}
)
return(factor(values_label, levels = c(intersect(desc, values_label), setdiff(values_label, desc))))
}
#' @describeIn estimate_multinomial_rsp Statistics function which feeds the length of `x` as number
#' of successes, and `.N_col` as total number of successes and failures into [s_proportion()].
#'
#' @return
#' * `s_length_proportion()` returns statistics from [s_proportion()].
#'
#' @examples
#' s_length_proportion(rep("CR", 10), .N_col = 100)
#' s_length_proportion(factor(character(0)), .N_col = 100)
#'
#' @export
s_length_proportion <- function(x,
.N_col, # nolint
...) {
checkmate::assert_multi_class(x, classes = c("factor", "character"))
checkmate::assert_vector(x, min.len = 0, max.len = .N_col)
checkmate::assert_vector(unique(x), min.len = 0, max.len = 1)
n_true <- length(x)
n_false <- .N_col - n_true
x_logical <- rep(c(TRUE, FALSE), c(n_true, n_false))
s_proportion(df = x_logical, ...)
}
#' @describeIn estimate_multinomial_rsp Formatted analysis function which is used as `afun`
#' in `estimate_multinomial_response()`.
#'
#' @return
#' * `a_length_proportion()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_length_proportion(rep("CR", 10), .N_col = 100)
#' a_length_proportion(factor(character(0)), .N_col = 100)
#'
#' @export
a_length_proportion <- make_afun(
s_length_proportion,
.formats = c(
n_prop = "xx (xx.x%)",
prop_ci = "(xx.xx, xx.xx)"
)
)
#' @describeIn estimate_multinomial_rsp Layout-creating function which can take statistics function arguments
#' and additional format arguments. This function is a wrapper for [rtables::analyze()] and
#' [rtables::summarize_row_groups()].
#'
#' @return
#' * `estimate_multinomial_response()` 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_length_proportion()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' # Use of the layout creating function.
#' dta_test <- data.frame(
#' USUBJID = paste0("S", 1:12),
#' ARM = factor(rep(LETTERS[1:3], each = 4)),
#' AVAL = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0))
#' ) %>% mutate(
#' AVALC = factor(AVAL,
#' levels = c(0, 1),
#' labels = c("Complete Response (CR)", "Partial Response (PR)")
#' )
#' )
#'
#' lyt <- basic_table() %>%
#' split_cols_by("ARM") %>%
#' estimate_multinomial_response(var = "AVALC")
#'
#' tbl <- build_table(lyt, dta_test)
#'
#' tbl
#'
#' @export
#' @order 2
estimate_multinomial_response <- function(lyt,
var,
na_str = default_na_str(),
nested = TRUE,
...,
show_labels = "hidden",
table_names = var,
.stats = "prop_ci",
.formats = NULL,
.labels = NULL,
.indent_mods = NULL) {
extra_args <- list(...)
afun <- make_afun(
a_length_proportion,
.stats = .stats,
.formats = .formats,
.labels = .labels,
.indent_mods = .indent_mods
)
lyt <- split_rows_by(lyt, var = var)
lyt <- summarize_row_groups(lyt, na_str = na_str)
analyze(
lyt,
vars = var,
afun = afun,
show_labels = show_labels,
table_names = table_names,
na_str = na_str,
nested = nested,
extra_args = extra_args
)
}