Speaker
Description
The win ratio statistic has gained prominence as an interpretable method for analyzing composite endpoints in clinical trials, typically with a superiority objective. The use of the win ratio requires simulation to estimate the necessary sample size (1). Adapting win statistics to non-inferiority trials and incorporating covariate adjustment remain unresolved methodological challenges (2).
We use the hierarchical endpoints from a non-inferiority trial in the field of neurology, evaluating de-prescription of anti-seizure treatment, with the hierarchy: (i) survival, (ii) functional outcome (iii) freedom from unprovoked seizures, and (iv) quality of life.
In non-inferiority trials, a substantial proportion of ties is expected and should ideally inform the treatment effect, as proposed in the win odds statistic (3). Varying the proportion of ties, we compare power to detect non-inferiority between the win ratio, win odds, and alternative U-statistic kernels (4). Ties are induced by coarsening outcomes 2 and 4. Power is plotted against the non-inferiority margin, described in terms of the minimum accepted probability of the experimental treatment winning. Furthermore, using the full hierarchical endpoint, we compare the power of unstratified and stratified win statistics. As a reference, we use only a single continous outcome to compare the stratified win statistics to a standard linear regression model with the stratification variable as a covariate. Simulations follow the ADEMP framework (5).
Our simulation results quantify how the win ratio, win odds, and related U-statistics perform as the proportion of ties increases and how the stratification affects analysis results. The findings inform the design of future non-inferiority trials and clarify when stratification-based approaches can improve efficiency.
References
1. Kronthaler D, Schwenkglenks M, Beuschlein F, Held U. The win ratio at the design stage of clinical trials. 2025;
2. Pocock SJ, Gregson J, Collier TJ, Ferreira JP, Stone GW. The win ratio in cardiology trials: Lessons learnt, new developments, and wise future use. European Heart Journal. 2024;45(44).
3. Dong G, Hoaglin DC, Qiu J, Matsouaka RA, Chang YW, Wang J, et al. The win ratio: On interpretation and handling of ties. Statistics in Biopharmaceutical Research. 2020;12(1).
4. Buyse M. Generalized pairwise comparisons of prioritized outcomes in the two-sample problem. Statistics in Medicine. 2010;29.
5. Morris TP, White IR, Crowther MJ. Using simulation studies to evaluate statistical methods. Statistics in Medicine. 2019;38.
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