Simulate Power for Hierarchical Win Ratio Endpoints.

winratiosim

winratiosim provides simulation tools for two-arm clinical trials with a hierarchical win ratio endpoint. It simulates time-to-event, recurrent event, and continuous outcomes; applies prioritized pairwise win/loss scoring; and summarizes operating characteristics for the win ratio and Finkelstein-Schoenfeld tests.

The package implements the simulation workflow used in:

Lee, S. Y. (2025). A note on the sample size formula for a win ratio endpoint. Statistics in Medicine, 44, e70165. https://doi.org/10.1002/sim.70165

Installation

After CRAN acceptance, install with:

install.packages("winratiosim")

The development version can be installed from GitHub:

# install.packages("remotes")
remotes::install_github("yain22/winratiosim")

Example

library(winratiosim)

result <- winratiosim(
  nsim = 10,
  N = 400,
  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 = 20250518
)

result$df_WR.analysis.summary

For publication-scale operating characteristics, increase nsim substantially and set nc to the number of worker processes you want to use.

After installation, open the package vignette for a longer worked example:

vignette("winratiosim", package = "winratiosim")

Power and Type I Error Summaries

power_fs <- mean(result$df_FS.analysis.summary$p_value_FS < 0.025,
                 na.rm = TRUE)
power_wr <- mean(result$df_WR.analysis.summary$LB_R_w > 1,
                 na.rm = TRUE)

binom.conf.exact(
  x = sum(result$df_WR.analysis.summary$LB_R_w > 1, na.rm = TRUE),
  n = sum(!is.na(result$df_WR.analysis.summary$LB_R_w))
)

References

1. Lee, S. Y. (2025). A note on the sample size formula for a win ratio endpoint. Statistics in Medicine, 44, e70165. https://doi.org/10.1002/sim.70165
2. Yu, R. X., and Ganju, J. (2022). Sample size formula for a win ratio endpoint. Statistics in Medicine, 41(6), 950-963.
3. Finkelstein, D. M., and Schoenfeld, D. A. (1999). Combining mortality and longitudinal measures in clinical trials. Statistics in Medicine, 18(11), 1341-1354.
4. Pocock, S. J., Ariti, C. A., Collier, T. J., and Wang, D. (2012). The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. European Heart Journal, 33(2), 176-182.