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Power analysis for RMST-based models via formula interface

Source: R/rmst-interface.R
rmst.power.Rd

Routes a formula-based call to the matching RMST power routine.

Usage

rmst.power(
  formula,
  data,
  arm,
  sample_sizes,
  L,
  strata = NULL,
  strata_type = c("additive", "multiplicative"),
  dep_cens = FALSE,
  type = c("analytical", "boot"),
  alpha = 0.05,
  n_sim = 1000L,
  parallel.cores = 1L
)

Arguments

formula

A formula of the form Surv(time, status) ~ cov1 + cov2. Use s() wrapping (mgcv style) for smooth terms: Surv(time, status) ~ s(age).

data

A data.frame containing the reference (pilot) data.

arm

Character string naming the treatment arm column (binary 0/1).

sample_sizes

Integer vector of per-arm (or per-stratum) sample sizes to evaluate.

L

Numeric truncation time for RMST.

strata

Character column name, one-sided formula (~col), or NULL (default). Ignored when dep_cens = TRUE.

strata_type

One of "additive" (default) or "multiplicative". Only used when strata is non-NULL and dep_cens = FALSE.

dep_cens

Logical; use dependent-censoring model? Default FALSE.

type

One of "analytical" or "boot". Auto-switched with a message when the requested type is unavailable for the chosen model.

alpha

Significance level. Default 0.05.

n_sim

Number of bootstrap replicates (boot methods only). Default 1000.

parallel.cores

Number of cores for parallel processing. Default 1.

Value

An object of class c("rmst_power", "list") with elements results_data, results_plot, results_summary, model_output, and .meta.

See also

rmst.ss, print.rmst_power, summary.rmst_power, plot.rmst_power

Examples

data(aft_lognormal_L12_n150, package = "RMSTpowerBoost")
r <- rmst.power(Surv(time, status) ~ age,
                data = aft_lognormal_L12_n150,
                arm  = "arm",
                sample_sizes = c(50, 100, 150),
                L    = 12)
#> --- Estimating parameters from pilot data for analytic calculation... ---
#> Model: Y_rmst ~ factor(arm) + age
#> --- Calculating asymptotic variance... ---
#> --- Calculating power for specified sample sizes... ---
print(r)
#> ── RMST Power Analysis ──────────────────────────
#>   Model           : Linear IPCW (Analytical) 
#>   Formula         : Surv(time, status) ~ age 
#>   Arm             : arm 
#>   Truncation time : 12 
#>   Alpha           : 0.05 
#> 
#> Power Results:
#>  N_per_Arm      Power
#>         50 0.08365565
#>        100 0.12692736
#>        150 0.16928696
#> 
#> Treatment Effect (from reference data):
#>   Estimand : RMST Difference 
#>   Estimate : -0.3007 
s <- summary(r)
plot(r)