Plot IPCW Kaplan-Meier curves with bootstrap percentile confidence intervals

Combines bootstrap survival curves from get_ipcw_boot_se() with point estimates from the original IPCW-weighted data to produce a Kaplan-Meier plot with 95% percentile confidence intervals.

Usage

plot_ipcw_km_boot_ci_se(
  boot_data,
  orig_data,
  pre_times = seq(0, 50, 1),
  covariate = "x",
  weight_var = "wgt",
  event_var = "delta"
)

Arguments

boot_data

A list of long-format data frames as returned by get_ipcw_boot_se().

orig_data

A single long-format data frame for the original (non-bootstrapped) dataset, as returned by get_ipcw_wgt_se(). Used for the point estimates.

pre_times

Numeric vector of times at which to evaluate survival probabilities. Default is seq(0, 50, 1).

covariate

Character string. Name of the stratification covariate column. Default is "x".

weight_var

Character string. Name of the IPCW weight column. Default is "wgt".

event_var

Character string. Name of the event indicator column in the long-format data. Default is "delta".

Value

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

Examples

if (FALSE) { # \dontrun{
# Not run due to computational time. 
# Use a smaller n and/or smaller B for testing.
set.seed(1)
dat      <- sim_data_se(n = 500)
dat_long <- get_ipcw_wgt_se(dat)
boot_list <- get_ipcw_boot_se(dat, B = 500)
plot_ipcw_km_boot_ci_se(boot_list, dat_long, pre_times = seq(0, 500, 10))
} # }