Plot IPCW cumulative incidence with bootstrap percentile confidence intervals

Combines bootstrap cumulative incidence curves from get_ipcw_boot_cr() with the point estimate from the original IPCW-weighted data to produce a cumulative incidence plot with 95% percentile confidence intervals, mirroring plot_ipcw_km_boot_ci_se() for the single-event case.

Usage

plot_ipcw_cuminc_boot_ci_cr(
  boot_data,
  orig_data,
  esttimes = seq(from = 0, to = 10, length.out = 100),
  extend = TRUE,
  covariate = "z1"
)

Arguments

boot_data

A list of long-format data frames with IPCW weights, as returned by get_ipcw_boot_cr().

orig_data

A single long-format data frame with IPCW weights for the original (non-bootstrapped) dataset, as returned by add_ipcw_weights_cr(). Used for the point estimate.

esttimes

Numeric vector of times at which to evaluate cumulative incidence. Defaults to 100 equally spaced points from 0 to 10.

extend

Logical. Passed to cuminc_ipcw_cr(). Default is TRUE.

covariate

Character string. Name of the covariate column, used only when extend = FALSE to compute the truncation time. Default is "z1".

Value

A ggplot2::ggplot() object. Add layers or themes to customise.

Examples

if (FALSE) { # \dontrun{
set.seed(42)
dat <- sim_data_cr(n = 200, censoring = "baseline")
dat_long  <- add_ipcw_weights_cr(wide_to_long_cr(dat), strat = "no")
boot_list <- get_ipcw_boot_cr(dat, B = 50)
plot_ipcw_cuminc_boot_ci_cr(boot_list, dat_long, esttimes = seq(0, 5, 0.1))
} # }