Find Sample Size for a Linear RMST Model via Simulation

Performs an iterative sample size search to achieve a target power based on the direct linear regression model for RMST, using bootstrap simulation.

Usage

linear.ss.boot(
  pilot_data,
  time_var,
  status_var,
  arm_var,
  target_power,
  linear_terms = NULL,
  L,
  n_sim = 1000,
  alpha = 0.05,
  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.

target_power

A single numeric value for the target power (e.g., 0.80).

linear_terms

Optional character vector of other covariates for the linear model.

L

The numeric truncation time for RMST.

n_sim

The number of bootstrap simulations per search step.

alpha

The significance level.

patience

The number of consecutive non-improving steps in the search before terminating.

n_start

The starting sample size per arm for the search.

n_step

The increment in sample size at each step of the search.

max_n_per_arm

The maximum sample size per arm to search up to.

Value

A list containing:

results_data

A data.frame with the target power and the final required sample size per arm.

results_plot

A ggplot object showing the search path.

results_summary

A data.frame with summary statistics for the estimated treatment effect from the final simulation.

Details

This function iteratively searches for the required sample size to achieve a specified target_power. At each step of the search, it runs a full bootstrap simulation (n_sim iterations), as described in linear.power.boot, to estimate the power for the current sample size. The search stops when the target power is achieved or other stopping criteria (e.g., patience) are met. Due to the nested simulation structure, this function can be very time-consuming.

Note

status_var should be 1 for an event, 0 for censored. arm_var should be 1 for treatment, 0 for control.

Examples

if (FALSE) { # \dontrun{
pilot_df_effect <- data.frame(
  time = c(rexp(50, 0.1), rexp(50, 0.05)), # Effect present
  status = rbinom(100, 1, 0.8),
  arm = rep(0:1, each = 50)
)
ss_results <- linear.ss.boot(
  pilot_data = pilot_df_effect,
  time_var = "time",
  status_var = "status",
  arm_var = "arm",
  target_power = 0.80,
  L = 10,
  n_sim = 200, # Low n_sim for example
  patience = 2,
  n_start = 100,
  n_step = 50,
  max_n_per_arm = 500
)
print(ss_results$results_data)
print(ss_results$results_plot)
} # }