Restricted Win Probability with Bayesian Estimation for Implementing the Estimand Framework in Clinical Trials With a Time-to-Event Outcome

TL;DR

This paper introduces a Bayesian method for estimating a restricted win probability in clinical trials with time-to-event data, improving power and interpretability within the estimand framework.

Contribution

It proposes a novel Bayesian estimand and estimator for restricted win probability, enhancing handling of censoring and aligning with the estimand framework in survival analysis.

Findings

More power than log-rank test in early difference scenarios

Comparable power to win ratio across scenarios

High concordance with log-rank test in oncology datasets

Abstract

We propose a restricted win probability estimand for comparing treatments in a randomized trial with a time-to-event outcome. We also propose Bayesian estimators for this summary measure as well as the unrestricted win probability. Bayesian estimation is scalable and facilitates seamless handling of censoring mechanisms as compared to related non-parametric pairwise approaches like win ratios. Unlike the log-rank test, these measures effectuate the estimand framework as they reflect a clearly defined population quantity related to the probability of a later event time with the potential restriction that event times exceeding a pre-specified time are deemed equivalent. We compare efficacy with established methods using computer simulation and apply the proposed approach to 304 reconstructed datasets from oncology trials. We show that the proposed approach has more power than the log-rank test in early treatment difference scenarios, and at least as much power as the win ratio in all scenarios considered. We also find that the proposed approach's statistical significance is concordant with the log-rank test for the vast majority of the oncology datasets examined. The proposed approach offers an interpretable, efficient alternative for trials with time-to-event outcomes that aligns with the estimand framework.