Estimate Sample Size for a Multiplicative Stratified RMST Model via Simulation

Performs sample size estimation based on a multiplicative model for RMST for stratified trials, using iterative bootstrap simulations.

Usage

MS.ss.boot(
  pilot_data,
  time_var,
  status_var,
  arm_var,
  strata_var,
  target_power,
  linear_terms = NULL,
  L,
  n_sim = 1000,
  alpha = 0.05,
  parallel.cores = 1,
  patience = 5,
  n_start = 50,
  n_step = 25,
  max_n_per_arm = 2000
)

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.
target_power: A single numeric value for the target power (e.g., 0.80).
linear_terms: Optional vector of covariates for the model.
L: The numeric truncation time for RMST.
n_sim: Number of bootstrap simulations per search step.
alpha: The significance level.
parallel.cores: Number of cores for parallel processing.
patience: Number of consecutive non-improving steps in the search before terminating.
n_start: Starting sample size per stratum for the search.
n_step: Increment for the sample size search.
max_n_per_arm: Maximum sample size per stratum to try.

Value

A list containing:

results_data: A data.frame with the target power and required N.
results_plot: A ggplot object showing the search path.
results_summary: A data.frame with the estimated treatment effect.

Details

This function iteratively searches for the sample size required to achieve a target_power. At each step of the search, it runs a full bootstrap simulation (as described in MS.power.boot) to estimate the power for the current sample size. The search proceeds until the target power is met or other stopping criteria are satisfied. This process can be very computationally intensive.

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_effect <- data.frame(
 time = c(rexp(60, 0.15), rexp(60, 0.08)), # Effect
 status = rbinom(120, 1, 0.7),
 arm = rep(0:1, each = 60),
 region = factor(rep(c("A", "B", "C"), each = 40))
)
ss_results <- MS.ss.boot(
 pilot_data = pilot_df_strat_effect,
 time_var = "time", status_var = "status", arm_var = "arm", strata_var = "region",
 target_power = 0.80, L = 10,
 n_sim = 100, # Low n_sim for example
 n_start = 100,
 n_step = 50, patience = 2
)
print(ss_results$results_data)
} # }