Analyze Power for a Multiplicative Stratified RMST Model via Simulation

Performs power analysis based on a multiplicative model for RMST for stratified trials, using a bootstrap simulation approach.

Usage

MS.power.boot(
  pilot_data,
  time_var,
  status_var,
  arm_var,
  strata_var,
  sample_sizes,
  linear_terms = NULL,
  L,
  n_sim = 1000,
  alpha = 0.05,
  parallel.cores = 1
)

Arguments

pilot_data: A data.frame with pilot study data.
time_var: A character string for the time-to-event variable.
status_var: A character string for the event status variable.
arm_var: A character string for the treatment arm variable.
strata_var: A character string for the stratification variable.
sample_sizes: A numeric vector of sample sizes per stratum to calculate power for.
linear_terms: Optional character vector of covariates for the model.
L: The numeric truncation time for RMST.
n_sim: Number of bootstrap simulations.
alpha: The significance level.
parallel.cores: Number of cores for parallel processing.

Value

A list containing:

results_data: A data.frame of sample sizes and corresponding powers.
results_plot: A ggplot object visualizing the power curve.
results_summary: A data.frame with the estimated treatment effect (RMST Ratio).

Details

This function estimates power through bootstrap simulation, resampling within each stratum defined by strata_var. In each of the n_sim iterations:

Jackknife pseudo-observations for the RMST are calculated.
A log-linear model (stats::lm) is fitted to the log(pseudo_obs). This models the multiplicative relationship on the original RMST scale, i.e., \(\mu_{ij} = \mu_{0j} \exp\{\beta'Z_i\}\). The model formula includes stratum-specific intercepts and interactions with the treatment arm.
The p-value for the treatment effect is extracted from the model summary.

Power is determined as the proportion of simulations where the p-value is less than alpha.

Note

status_var should be 1/0. arm_var should be 1/0. strata_var is a mandatory argument.

Examples

if (FALSE) { # \dontrun{
pilot_df_strat <- data.frame(
 time = rexp(120, 0.15),
 status = rbinom(120, 1, 0.6),
 arm = rep(0:1, each = 60),
 region = factor(rep(c("A", "B", "C"), each = 40))
)
pilot_df_strat$time[pilot_df_strat$arm == 1] <- pilot_df_strat$time[pilot_df_strat$arm == 1] * 1.4

power_results <- MS.power.boot(
 pilot_data = pilot_df_strat,
 time_var = "time", status_var = "status", arm_var = "arm", strata_var = "region",
 sample_sizes = c(50, 75),
 L = 10,
 n_sim = 100 # Low n_sim for example
)
print(power_results$results_data)
} # }