#' Competing risks multivariable regression: \code{finalfit} model wrapper

#'

#' Using \code{finalfit} conventions, produces multivariable Competing Risks

#' Regression models for a set of explanatory variables.

#'

#' Uses \code{\link[cmprsk]{crr}} with \code{finalfit} modelling conventions.

#' Output can be passed to \code{\link{fit2df}}.

#'

#' @param .data Data frame or tibble.

#' @param dependent Character vector of length 1: name of survival object in

#'   form \code{Surv(time, status)}. \code{Status} default values should be 0

#'   censored (e.g. alive), 1 event of interest (e.g. died of disease of

#'   interest), 2 competing event (e.g. died of other cause).

#' @param explanatory Character vector of any length: name(s) of explanatory

#'   variables.

#' @param ... Other arguments to \code{\link[cmprsk]{crr}}

#' @return A multivariable \code{\link[cmprsk]{crr}} fitted model

#'   class \code{crr}.

#'

#' @seealso \code{\link{fit2df}, \link{finalfit_merge}}

#' @family finalfit model wrappers

#' @export

#'

#' @examples

#' library(dplyr)

#' melanoma = boot::melanoma

#' melanoma = melanoma %>%

#'   mutate(

#'     # Cox PH to determine cause-specific hazards

#'     status_coxph = ifelse(status == 2, 0, # "still alive"

#'       ifelse(status == 1, 1, # "died of melanoma"

#'         0)), # "died of other causes is censored"

#'         

#'     # Fine and Gray to determine subdistribution hazards

#'     status_crr = ifelse(status == 2, 0, # "still alive"

#'       ifelse(status == 1, 1, # "died of melanoma"

#'         2)), # "died of other causes"

#'     sex = factor(sex),

#'     ulcer = factor(ulcer)

#'   )

#'

#' dependent_coxph = c("Surv(time, status_coxph)")

#' dependent_crr = c("Surv(time, status_crr)")

#' explanatory = c("sex", "age", "ulcer")

#' 

#' # Create single well-formatted table

#' melanoma %>%

#'   summary_factorlist(dependent_crr, explanatory, column = TRUE, fit_id = TRUE) %>%

#'   ff_merge(

#'     melanoma %>%

#'       coxphmulti(dependent_coxph, explanatory) %>%

#'       fit2df(estimate_suffix = " (Cox PH multivariable)")

#'     ) %>%

#'   ff_merge(

#'     melanoma %>%

#'       crrmulti(dependent_crr, explanatory) %>%

#'       fit2df(estimate_suffix = " (competing risks multivariable)")

#'     ) %>%

#'   select(-fit_id, -index) %>%

#'   dependent_label(melanoma, dependent_crr)

crrmulti <- function(.data, dependent, explanatory, ...){

  # Keep survival object grammar, split into terms

}