Trial Design with Win Statistics for Multiple Time-to-Event Endpoints with Hierarchy

Abstract

The conventional approach to the analysis of composite endpoints is time-to-first event analysis. However, this approach has been criticized because it ignores the differences in clinical severity and may end up emphasizing the less severe time-to-event. To overcome this limitation, win statistics (win ratio, win odds, or net benefit) have become popular in analysis of hierarchical time to event endpoints. However, design of randomized clinical trials using the win statistics is lagging behind. In this article, we derive formulas for the win statistics and probability of ties under specific assumptions that can be useful in practice. We also address two design issues: the selection of meaningful and justifiable design parameters and power calculations, when the win statistics method is the primary analysis method for multiple time-to-event endpoints for a pre-specified hierarchy. Finally, we identify patterns where the win statistics approach would have greater statistical power than time-to-first event analysis. Several examples are used to illustrate the usefulness of the formula-based power calculations.

Keywords:

• Net benefit
• Sample size and power
• Win odds
• Win ratio

Disclosure Statement

The authors report no competing interests to declare.

Additional information

Funding

This work was supported by the Duke Clinical Research Institute Biostatistics & Data Science Research fund.