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summarize_coxreg.R
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summarize_coxreg.R
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#' Cox proportional hazards regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Fits a Cox regression model and estimates hazard ratio to describe the effect size in a survival analysis.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table. Run `get_stats("summarize_coxreg")`
#' to see available statistics for this function.
#'
#' @details Cox models are the most commonly used methods to estimate the magnitude of
#' the effect in survival analysis. It assumes proportional hazards: the ratio
#' of the hazards between groups (e.g., two arms) is constant over time.
#' This ratio is referred to as the "hazard ratio" (HR) and is one of the
#' most commonly reported metrics to describe the effect size in survival
#' analysis (NEST Team, 2020).
#'
#' @seealso [fit_coxreg] for relevant fitting functions, [h_cox_regression] for relevant
#' helper functions, and [tidy_coxreg] for custom tidy methods.
#'
#' @examples
#' library(survival)
#'
#' # Testing dataset [survival::bladder].
#' set.seed(1, kind = "Mersenne-Twister")
#' dta_bladder <- with(
#' data = bladder[bladder$enum < 5, ],
#' tibble::tibble(
#' TIME = stop,
#' STATUS = event,
#' ARM = as.factor(rx),
#' COVAR1 = as.factor(enum) %>% formatters::with_label("A Covariate Label"),
#' COVAR2 = factor(
#' sample(as.factor(enum)),
#' levels = 1:4, labels = c("F", "F", "M", "M")
#' ) %>% formatters::with_label("Sex (F/M)")
#' )
#' )
#' dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#' dta_bladder$STUDYID <- factor("X")
#'
#' u1_variables <- list(
#' time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")
#' )
#'
#' u2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))
#'
#' m1_variables <- list(
#' time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")
#' )
#'
#' m2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))
#'
#' @name cox_regression
#' @order 1
NULL
#' @describeIn cox_regression Statistics function that transforms results tabulated
#' from [fit_coxreg_univar()] or [fit_coxreg_multivar()] into a list.
#'
#' @param model_df (`data.frame`)\cr contains the resulting model fit from a [fit_coxreg]
#' function with tidying applied via [broom::tidy()].
#' @param .stats (`character`)\cr the names of statistics to be reported among:
#' * `n`: number of observations (univariate only)
#' * `hr`: hazard ratio
#' * `ci`: confidence interval
#' * `pval`: p-value of the treatment effect
#' * `pval_inter`: p-value of the interaction effect between the treatment and the covariate (univariate only)
#' @param .which_vars (`character`)\cr which rows should statistics be returned for from the given model.
#' Defaults to `"all"`. Other options include `"var_main"` for main effects, `"inter"` for interaction effects,
#' and `"multi_lvl"` for multivariate model covariate level rows. When `.which_vars` is `"all"`, specific
#' variables can be selected by specifying `.var_nms`.
#' @param .var_nms (`character`)\cr the `term` value of rows in `df` for which `.stats` should be returned. Typically
#' this is the name of a variable. If using variable labels, `var` should be a vector of both the desired
#' variable name and the variable label in that order to see all `.stats` related to that variable. When `.which_vars`
#' is `"var_main"`, `.var_nms` should be only the variable name.
#'
#' @return
#' * `s_coxreg()` returns the selected statistic for from the Cox regression model for the selected variable(s).
#'
#' @examples
#' # s_coxreg
#'
#' # Univariate
#' univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder)
#' df1 <- broom::tidy(univar_model)
#'
#' s_coxreg(model_df = df1, .stats = "hr")
#'
#' # Univariate with interactions
#' univar_model_inter <- fit_coxreg_univar(
#' variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder
#' )
#' df1_inter <- broom::tidy(univar_model_inter)
#'
#' s_coxreg(model_df = df1_inter, .stats = "hr", .which_vars = "inter", .var_nms = "COVAR1")
#'
#' # Univariate without treatment arm - only "COVAR2" covariate effects
#' univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder)
#' df1_covs <- broom::tidy(univar_covs_model)
#'
#' s_coxreg(model_df = df1_covs, .stats = "hr", .var_nms = c("COVAR2", "Sex (F/M)"))
#'
#' # Multivariate.
#' multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder)
#' df2 <- broom::tidy(multivar_model)
#'
#' s_coxreg(model_df = df2, .stats = "pval", .which_vars = "var_main", .var_nms = "COVAR1")
#' s_coxreg(
#' model_df = df2, .stats = "pval", .which_vars = "multi_lvl",
#' .var_nms = c("COVAR1", "A Covariate Label")
#' )
#'
#' # Multivariate without treatment arm - only "COVAR1" main effect
#' multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder)
#' df2_covs <- broom::tidy(multivar_covs_model)
#'
#' s_coxreg(model_df = df2_covs, .stats = "hr")
#'
#' @export
s_coxreg <- function(model_df, .stats, .which_vars = "all", .var_nms = NULL) {
assert_df_with_variables(model_df, list(term = "term", stat = .stats))
checkmate::assert_multi_class(model_df$term, classes = c("factor", "character"))
model_df$term <- as.character(model_df$term)
.var_nms <- .var_nms[!is.na(.var_nms)]
if (length(.var_nms) > 0) model_df <- model_df[model_df$term %in% .var_nms, ]
if (.which_vars == "multi_lvl") model_df$term <- tail(.var_nms, 1)
# We need a list with names corresponding to the stats to display of equal length to the list of stats.
y <- split(model_df, f = model_df$term, drop = FALSE)
y <- stats::setNames(y, nm = rep(.stats, length(y)))
if (.which_vars == "var_main") {
y <- lapply(y, function(x) x[1, ]) # only main effect
} else if (.which_vars %in% c("inter", "multi_lvl")) {
y <- lapply(y, function(x) if (nrow(y[[1]]) > 1) x[-1, ] else x) # exclude main effect
}
lapply(
X = y,
FUN = function(x) {
z <- as.list(x[[.stats]])
stats::setNames(z, nm = x$term_label)
}
)
}
#' @describeIn cox_regression Analysis function which is used as `afun` in [rtables::analyze()]
#' and `cfun` in [rtables::summarize_row_groups()] within `summarize_coxreg()`.
#'
#' @param eff (`flag`)\cr whether treatment effect should be calculated. Defaults to `FALSE`.
#' @param var_main (`flag`)\cr whether main effects should be calculated. Defaults to `FALSE`.
#' @param na_str (`string`)\cr custom string to replace all `NA` values with. Defaults to `""`.
#' @param cache_env (`environment`)\cr an environment object used to cache the regression model in order to
#' avoid repeatedly fitting the same model for every row in the table. Defaults to `NULL` (no caching).
#' @param varlabels (`list`)\cr a named list corresponds to the names of variables found in data, passed
#' as a named list and corresponding to time, event, arm, strata, and covariates terms. If arm is missing
#' from variables, then only Cox model(s) including the covariates will be fitted and the corresponding
#' effect estimates will be tabulated later.
#'
#' @return
#' * `a_coxreg()` returns formatted [rtables::CellValue()].
#'
#' @examples
#' a_coxreg(
#' df = dta_bladder,
#' labelstr = "Label 1",
#' variables = u1_variables,
#' .spl_context = list(value = "COVAR1"),
#' .stats = "n",
#' .formats = "xx"
#' )
#'
#' a_coxreg(
#' df = dta_bladder,
#' labelstr = "",
#' variables = u1_variables,
#' .spl_context = list(value = "COVAR2"),
#' .stats = "pval",
#' .formats = "xx.xxxx"
#' )
#'
#' @export
a_coxreg <- function(df,
labelstr,
eff = FALSE,
var_main = FALSE,
multivar = FALSE,
variables,
at = list(),
control = control_coxreg(),
.spl_context,
.stats,
.formats,
.indent_mods = NULL,
na_str = "",
cache_env = NULL) {
cov_no_arm <- !multivar && !"arm" %in% names(variables) && control$interaction # special case: univar no arm
cov <- tail(.spl_context$value, 1) # current variable/covariate
var_lbl <- formatters::var_labels(df)[cov] # check for df labels
if (length(labelstr) > 1) {
labelstr <- if (cov %in% names(labelstr)) labelstr[[cov]] else var_lbl # use df labels if none
} else if (!is.na(var_lbl) && labelstr == cov && cov %in% variables$covariates) {
labelstr <- var_lbl
}
if (eff || multivar || cov_no_arm) {
control$interaction <- FALSE
} else {
variables$covariates <- cov
if (var_main) control$interaction <- TRUE
}
if (is.null(cache_env[[cov]])) {
if (!multivar) {
model <- fit_coxreg_univar(variables = variables, data = df, at = at, control = control) %>% broom::tidy()
} else {
model <- fit_coxreg_multivar(variables = variables, data = df, control = control) %>% broom::tidy()
}
cache_env[[cov]] <- model
} else {
model <- cache_env[[cov]]
}
if (!multivar && !var_main) model[, "pval_inter"] <- NA_real_
if (cov_no_arm || (!cov_no_arm && !"arm" %in% names(variables) && is.numeric(df[[cov]]))) {
multivar <- TRUE
if (!cov_no_arm) var_main <- TRUE
}
vars_coxreg <- list(which_vars = "all", var_nms = NULL)
if (eff) {
if (multivar && !var_main) { # multivar treatment level
var_lbl_arm <- formatters::var_labels(df)[[variables$arm]]
vars_coxreg[c("var_nms", "which_vars")] <- list(c(variables$arm, var_lbl_arm), "multi_lvl")
} else { # treatment effect
vars_coxreg["var_nms"] <- variables$arm
if (var_main) vars_coxreg["which_vars"] <- "var_main"
}
} else {
if (!multivar || (multivar && var_main && !is.numeric(df[[cov]]))) { # covariate effect/level
vars_coxreg[c("var_nms", "which_vars")] <- list(cov, "var_main")
} else if (multivar) { # multivar covariate level
vars_coxreg[c("var_nms", "which_vars")] <- list(c(cov, var_lbl), "multi_lvl")
if (var_main) model[cov, .stats] <- NA_real_
}
if (!multivar && !var_main && control$interaction) vars_coxreg["which_vars"] <- "inter" # interaction effect
}
var_vals <- s_coxreg(model, .stats, .which_vars = vars_coxreg$which_vars, .var_nms = vars_coxreg$var_nms)[[1]]
var_names <- if (all(grepl("\\(reference = ", names(var_vals))) && labelstr != tail(.spl_context$value, 1)) {
paste(c(labelstr, tail(strsplit(names(var_vals), " ")[[1]], 3)), collapse = " ") # "reference" main effect labels
} else if ((!multivar && !eff && !(!var_main && control$interaction) && nchar(labelstr) > 0) ||
(multivar && var_main && is.numeric(df[[cov]]))) { # nolint
labelstr # other main effect labels
} else if (multivar && !eff && !var_main && is.numeric(df[[cov]])) {
"All" # multivar numeric covariate
} else {
names(var_vals)
}
in_rows(
.list = var_vals, .names = var_names, .labels = var_names, .indent_mods = .indent_mods,
.formats = stats::setNames(rep(.formats, length(var_names)), var_names),
.format_na_strs = stats::setNames(rep(na_str, length(var_names)), var_names)
)
}
#' @describeIn cox_regression Layout-creating function which creates a Cox regression summary table
#' layout. This function is a wrapper for several `rtables` layouting functions. This function
#' is a wrapper for [rtables::analyze_colvars()] and [rtables::summarize_row_groups()].
#'
#' @inheritParams fit_coxreg_univar
#' @param multivar (`flag`)\cr whether multivariate Cox regression should run (defaults to `FALSE`), otherwise
#' univariate Cox regression will run.
#' @param common_var (`string`)\cr the name of a factor variable in the dataset which takes the same value
#' for all rows. This should be created during pre-processing if no such variable currently exists.
#' @param .section_div (`string` or `NA`)\cr string which should be repeated as a section divider between sections.
#' Defaults to `NA` for no section divider. If a vector of two strings are given, the first will be used between
#' treatment and covariate sections and the second between different covariates.
#'
#' @return
#' * `summarize_coxreg()` 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 a Cox regression table
#' containing the chosen statistics to the table layout.
#'
#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()] which also take the `variables`, `data`,
#' `at` (univariate only), and `control` arguments but return unformatted univariate and multivariate
#' Cox regression models, respectively.
#'
#' @examples
#' # summarize_coxreg
#'
#' result_univar <- basic_table() %>%
#' summarize_coxreg(variables = u1_variables) %>%
#' build_table(dta_bladder)
#' result_univar
#'
#' result_univar_covs <- basic_table() %>%
#' summarize_coxreg(
#' variables = u2_variables,
#' ) %>%
#' build_table(dta_bladder)
#' result_univar_covs
#'
#' result_multivar <- basic_table() %>%
#' summarize_coxreg(
#' variables = m1_variables,
#' multivar = TRUE,
#' ) %>%
#' build_table(dta_bladder)
#' result_multivar
#'
#' result_multivar_covs <- basic_table() %>%
#' summarize_coxreg(
#' variables = m2_variables,
#' multivar = TRUE,
#' varlabels = c("Covariate 1", "Covariate 2") # custom labels
#' ) %>%
#' build_table(dta_bladder)
#' result_multivar_covs
#'
#' @export
#' @order 2
summarize_coxreg <- function(lyt,
variables,
control = control_coxreg(),
at = list(),
multivar = FALSE,
common_var = "STUDYID",
.stats = c("n", "hr", "ci", "pval", "pval_inter"),
.formats = c(
n = "xx", hr = "xx.xx", ci = "(xx.xx, xx.xx)",
pval = "x.xxxx | (<0.0001)", pval_inter = "x.xxxx | (<0.0001)"
),
varlabels = NULL,
.indent_mods = NULL,
na_str = "",
.section_div = NA_character_) {
if (multivar && control$interaction) {
warning(paste(
"Interactions are not available for multivariate cox regression using summarize_coxreg.",
"The model will be calculated without interaction effects."
))
}
if (control$interaction && !"arm" %in% names(variables)) {
stop("To include interactions please specify 'arm' in variables.")
}
.stats <- if (!"arm" %in% names(variables) || multivar) { # only valid statistics
intersect(c("hr", "ci", "pval"), .stats)
} else if (control$interaction) {
intersect(c("n", "hr", "ci", "pval", "pval_inter"), .stats)
} else {
intersect(c("n", "hr", "ci", "pval"), .stats)
}
stat_labels <- c(
n = "n", hr = "Hazard Ratio", ci = paste0(control$conf_level * 100, "% CI"),
pval = "p-value", pval_inter = "Interaction p-value"
)
stat_labels <- stat_labels[names(stat_labels) %in% .stats]
.formats <- .formats[names(.formats) %in% .stats]
env <- new.env() # create caching environment
lyt <- lyt %>%
split_cols_by_multivar(
vars = rep(common_var, length(.stats)),
varlabels = stat_labels,
extra_args = list(
.stats = .stats, .formats = .formats, .indent_mods = .indent_mods, na_str = rep(na_str, length(.stats)),
cache_env = replicate(length(.stats), list(env))
)
)
if ("arm" %in% names(variables)) { # treatment effect
lyt <- lyt %>%
split_rows_by(
common_var,
split_label = "Treatment:",
label_pos = "visible",
child_labels = "hidden",
section_div = head(.section_div, 1)
)
if (!multivar) {
lyt <- lyt %>%
analyze_colvars(
afun = a_coxreg,
na_str = na_str,
extra_args = list(
variables = variables, control = control, multivar = multivar, eff = TRUE, var_main = multivar,
labelstr = ""
)
)
} else { # treatment level effects
lyt <- lyt %>%
summarize_row_groups(
cfun = a_coxreg,
na_str = na_str,
extra_args = list(
variables = variables, control = control, multivar = multivar, eff = TRUE, var_main = multivar
)
) %>%
analyze_colvars(
afun = a_coxreg,
na_str = na_str,
extra_args = list(eff = TRUE, control = control, variables = variables, multivar = multivar, labelstr = "")
)
}
}
if ("covariates" %in% names(variables)) { # covariate main effects
lyt <- lyt %>%
split_rows_by_multivar(
vars = variables$covariates,
varlabels = varlabels,
split_label = "Covariate:",
nested = FALSE,
child_labels = if (multivar || control$interaction || !"arm" %in% names(variables)) "default" else "hidden",
section_div = tail(.section_div, 1)
)
if (multivar || control$interaction || !"arm" %in% names(variables)) {
lyt <- lyt %>%
summarize_row_groups(
cfun = a_coxreg,
na_str = na_str,
extra_args = list(
variables = variables, at = at, control = control, multivar = multivar,
var_main = if (multivar) multivar else control$interaction
)
)
} else {
if (!is.null(varlabels)) names(varlabels) <- variables$covariates
lyt <- lyt %>%
analyze_colvars(
afun = a_coxreg,
na_str = na_str,
extra_args = list(
variables = variables, at = at, control = control, multivar = multivar,
var_main = if (multivar) multivar else control$interaction,
labelstr = if (is.null(varlabels)) "" else varlabels
)
)
}
if (!"arm" %in% names(variables)) control$interaction <- TRUE # special case: univar no arm
if (multivar || control$interaction) { # covariate level effects
lyt <- lyt %>%
analyze_colvars(
afun = a_coxreg,
na_str = na_str,
extra_args = list(variables = variables, at = at, control = control, multivar = multivar, labelstr = ""),
indent_mod = if (!"arm" %in% names(variables) || multivar) 0L else -1L
)
}
}
lyt
}