Leveraging win time endpoint and combining moderators for personalized treatment recommendation in time-to-event clinical trials

Abstract

Abstract Background There has been increasing interest in randomized trials on the discovery and identification of treatment moderators on an outcome. This reflects the stratified medicine paradigm, which moves beyond average treatment effects to focus on patient subgroups with heterogeneous responses. Baseline biomarkers and clinical variables are commonly evaluated as candidate moderators and standard subgroup analyses are typically used to identify single moderators. Recent research has sought to improve the detection of effect modification by optimally combining multiple moderators into a single composite measure. This composite moderator can offer greater power and sensitivity to detect effect modification over any single moderator alone and can be used in medical applications to derive personalized treatment recommendations (PTRs). Methods Kraemer applied this concept to continuous outcomes, and we extended this approach to time-to-event settings using the expected win time against reference (EWTR), an estimand that measures time spent in better or worse clinical states. We compared the composite moderator with single moderators and explored its utility by evaluating whether there were improved treatment recommendations in simulations that varied (1) effect modification scenarios (quantitative vs qualitative), (2) interaction-to-main-effect ratio magnitudes, and (3) configurations of the moderator correlations with the endpoint. We illustrated this approach using randomized trial data investigating two management strategies for participants with atrial fibrillation. Results Simulations demonstrated that in the presence of strong effect modification, the composite moderator matched or outperformed the best single moderator. However, its advantage was attenuated by weak interactions, correlated moderators, or inclusion of a null endpoint. Conclusions We extended Kraemer’s composite-moderator framework to time-to-event outcomes using EWTR. The composite moderator may serve as a pragmatic tool for detecting effect modification and informing personalized treatment recommendations, particularly when effect modification is moderate to strong.