Generate Kaplan-Meier curves with prediction intervals using simulated survival time

Usage

calc_ave_km_pi(
  sim,
  trt = NULL,
  group = NULL,
  pi.range = 0.95,
  calc.obs = TRUE,
  simtimelast = NULL,
  trt.assign = c("default", "reverse"),
  boot.subj = TRUE,
  calc.median.surv = FALSE
)

calc_km_pi(
  sim,
  trt = NULL,
  group = NULL,
  pi.range = 0.95,
  calc.obs = TRUE,
  simtimelast = NULL,
  trt.assign = c("default", "reverse")
)

Arguments

sim: A survparamsim class object generated by surv_param_sim() function.
trt: An optional string to specify which column define treatment status. You will have survival curves with different colors in plot_km_pi() function.
group: Optional string(s) to specify grouping variable(s). You will have faceted survival curves for these variables in plot_km_pi() function.
pi.range: Prediction interval for simulated survival curves.
calc.obs: A logical to specify whether KM estimates will be performed for the observed data. Need be set as FALSE if survival information in the newdata is dummy.
simtimelast: An optional numeric to specify last simulation time for survival curve. If NULL (default), the last observation time in the newdata will be used.
trt.assign: Specify which of the categories of trt need to be considered as control group. See details below if you have more than two categories. Only applicable if you will use extract_medsurv_delta_pi() to extract delta of median survival times.
boot.subj: Boolean to specify whether bootstrapping of subjects are performed before calculating HR. Default TRUE.
calc.median.surv: Whether to calculate median survival time for calc_ave_km_pi(). Default FALSE as the calculation can be long. Currently median survival calculation not implemented yet.

Details

calc_km_pi() calculate survival profile using the simulated survival times with Kaplan-Meier estimates, while calc_ave_km_pi() calculate "average" survival using the mean survival function per treatment groups. calc_ave_km_pi() actually does not rely on Kaplan-Meier estimates as it directly uses the underlying parametric survival model, however the function has km for naming consistency.

If your trt has more than two categories/levels and want to specify which one to use as a reference group, you can convert the column into a factor in the newdata input for surv_param_sim(). The first level will be used as a reference group.