crruni {finalfit}

Competing risks univariable regression: finalfit model wrapper

Description

Using finalfit conventions, produces univariable Competing Risks Regression models for a set of explanatory variables.

Usage

crruni(.data, dependent, explanatory, ...)

Arguments

.data

Data frame or tibble.

dependent

Character vector of length 1: name of survival object in form Surv(time, status). 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).

explanatory

Character vector of any length: name(s) of explanatory variables.

...

Other arguments to crr

Details

Uses crr with finalfit modelling conventions. Output can be passed to fit2df.

Value

A list of univariable crr fitted models class crrlist.

See Also

fit2df, finalfit_merge

Other finalfit model wrappers: coxphmulti(), coxphuni(), crrmulti(), glmmixed(), glmmulti_boot(), glmmulti(), glmuni(), lmmixed(), lmmulti(), lmuni(), svyglmmulti(), svyglmuni()

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)