Find Sample Size for a Multiplicative Stratified RMST Model (Analytic)

Calculates the required sample size for a target power using the analytic (approximate) method from Wang et al. (2019).

Usage

MS.ss.analytical(
  pilot_data,
  time_var,
  status_var,
  arm_var,
  strata_var,
  target_power,
  linear_terms = NULL,
  L,
  alpha = 0.05,
  n_start = 50,
  n_step = 25,
  max_n_per_arm = 2000
)

Arguments

pilot_data

A data.frame containing pilot study data.

time_var

A character string for the time-to-event variable.

status_var

A character string for the event status variable (1=event, 0=censored).

arm_var

A character string for the treatment arm variable (1=treatment, 0=control).

strata_var

A character string for the stratification variable.

target_power

A single numeric value for the desired power.

linear_terms

An optional character vector of other covariate names.

L

The numeric value for the RMST truncation time.

alpha

The significance level (Type I error rate).

n_start

The starting sample size per stratum 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 stratum to search up to.

Value

A list containing:

results_data

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

results_plot

A ggplot object visualizing the search path.

results_summary

A data.frame summarizing the estimated log(RMST Ratio).

Details

This function performs an iterative search for the sample size required to achieve a specified target_power. It uses the same underlying theory and log-linear approximation as MS.power.analytical. It performs a one-time estimation of the log-RMST ratio and its asymptotic variance from the pilot data, then uses these parameters in an analytic formula to efficiently find the required sample size.

Examples

set.seed(456)
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"), each = 60))
)

ss_results <- MS.ss.analytical(
 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_start = 100, n_step = 50
)
#> --- Estimating parameters from pilot data (log-linear approximation)... ---
#> --- Searching for Sample Size (Method: Analytic/Approximation) ---
#>   N = 100/stratum, Calculated Power = 1
#> 
#> --- Calculation Summary ---
#> 
#> 
#> Table: Required Sample Size
#> 
#> | Target_Power| Required_N_per_Stratum|
#> |------------:|----------------------:|
#> |          0.8|                    100|
print(ss_results$results_data)
#>   Target_Power Required_N_per_Stratum
#> 1          0.8                    100