winratiosim: Simulate Hierarchical Win Ratio Trials

In winratiosim: Simulate Power for Hierarchical Win Ratio Endpoints

Simulate Hierarchical Win Ratio Trials

Description

Simulates replicated two-arm clinical trials and analyzes each trial using a three-layer hierarchical win ratio framework: time to death, annualized recurrent event count, and a continuous quality-of-life score.

Usage

winratiosim(
  nsim,
  N,
  Randomization.ratio,
  alpha.JFM,
  theta.JFM,
  lambda_trt,
  lambda_ctl,
  ann.icr_trt,
  ann.icr_ctl,
  xbase_trt,
  xfinal_trt,
  xbase_ctl,
  xfinal_ctl,
  sd.delta.x_trt,
  sd.delta.x_ctl,
  censorrate_trt,
  censorrate_ctl,
  nc = 1,
  seed = NULL
)

Arguments

nsim	Integer. Number of simulated trials.
N	Integer. Total number of subjects in each simulated trial.
Randomization.ratio	Numeric vector of length 2 giving the treatment and control allocation ratio, for example c(1, 1).
alpha.JFM	Numeric. Alpha parameter for the joint frailty model.
theta.JFM	Numeric. Frailty variance parameter for the joint frailty model. Must be positive.
lambda_trt, lambda_ctl	Numeric. Annual mortality probabilities for the treatment and control arms.
ann.icr_trt, ann.icr_ctl	Numeric. Annual recurrent event incidence rates for the treatment and control arms.
xbase_trt, xfinal_trt	Numeric. Baseline and expected final continuous outcome values in the treatment arm.
xbase_ctl, xfinal_ctl	Numeric. Baseline and expected final continuous outcome values in the control arm.
sd.delta.x_trt, sd.delta.x_ctl	Numeric. Standard deviations for the continuous outcome change in the treatment and control arms.
censorrate_trt, censorrate_ctl	Numeric. Annual censoring probabilities for the treatment and control arms.
nc	Integer. Number of worker processes to use. The default is 1.
seed	Optional integer seed. If supplied, results are reproducible across different values of nc.

Value

A named list with the following elements:

df_FS.analysis.summary

Finkelstein-Schoenfeld analysis summary for each simulation.

df_WR.analysis.summary

Win ratio analysis summary for each simulation.

df_sample.size.summary

Sample sizes used in each simulated trial.

df_Total_probability

Win, tie, loss, and total probabilities for each simulation.

df_Total_count

Win, tie, loss, and total counts for each simulation.

References

Lee, S. Y. (2025). A note on the sample size formula for a win ratio endpoint. Statistics in Medicine, 44, e70165. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.70165")}

Examples

result <- winratiosim(
  nsim = 1,
  N = 20,
  Randomization.ratio = c(1, 1),
  alpha.JFM = 0,
  theta.JFM = 1,
  lambda_trt = 0.13,
  lambda_ctl = 0.15,
  ann.icr_trt = 0.32,
  ann.icr_ctl = 0.55,
  xbase_trt = 45,
  xfinal_trt = 52.5,
  xbase_ctl = 45,
  xfinal_ctl = 45,
  sd.delta.x_trt = 20,
  sd.delta.x_ctl = 20,
  censorrate_trt = 0.2,
  censorrate_ctl = 0.2,
  nc = 1,
  seed = 2025
)
result$df_WR.analysis.summary

winratiosim documentation built on July 7, 2026, 1:07 a.m.