The Win Ratio at the Design Stage of Clinical Trials

2025-07-21

AI Summary

In clinical trials with composite endpoints, conventional time-to-first-event (TTFE) or single-outcome analyses struggle to balance hierarchical outcome structures and statistical power. To address this, we propose a priority-based win-ratio framework and derive a novel sample-size formula optimized for confidence-interval width. Our method enables precision-oriented design in settings with multiple outcomes and non-dominant treatment effects. Simulation and empirical studies demonstrate that, when higher-priority outcomes exhibit strong treatment effects, the proposed approach achieves up to 50% greater statistical power than standard TTFE analysis; it retains substantial gains under moderate effects. This work establishes a new trial design paradigm for hierarchical composite endpoints—one that enhances interpretability, flexibility, and statistical efficiency simultaneously.

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Abstract

The win ratio offers a flexible approach to incorporate the hierarchy of clinical outcomes into the analysis of a composite endpoint, enabling simultaneous consideration of multiple outcome types, unlike traditional time-to-first-event (TTFE) analysis or focus on a single outcome. We examined the statistical power of the win ratio compared to single-endpoint analyses and TTFE analysis through a case study and simulation studies. Furthermore, we provide a novel formula to estimate the required sample size for win ratio analysis based on the desired width of its confidence interval, facilitating precision-based trial design. Our results indicate that win ratio analysis generally outperforms single-endpoint analyses when treatment effects on lower-ranked outcomes are moderate compared to those on higher-ranked outcomes. The win ratio can provide greater power than TTFE analysis, especially when the effect on the highest-ranked outcome is substantial, reaching increases in power up to 50%. Further, even for moderate treatment effects on the highest-ranked outcome, win ratio analysis achieved higher power. Future work should expand our simulations to additional data-generating mechanisms and outcome types, particularly ordinal outcomes, where the win ratio provides an alternative to existing non-parametric and parametric methods. Our findings highlight the potential of the win ratio to improve statistical efficiency in pharmaceutical and other clinical trial designs using composite endpoints, particularly when no single component dominates the treatment effect. However, when continuous outcomes occupy the top of the hierarchy, these tend to drive overall analysis, sidelining contributions of lower-ranked outcomes and limiting benefits of hierarchical win ratio analysis.

Problem

• Compares win ratio power to single-endpoint and TTFE analyses
• Provides sample size formula for win ratio confidence intervals
• Evaluates win ratio efficiency in hierarchical outcome trials

Innovation

• Hierarchical win ratio for composite endpoint analysis
• Novel sample size formula for precision-based design
• Enhanced power over traditional single-endpoint methods