Censoring-robust estimation in fixed sample time-to-event clinical trials with adaptive randomization

Adaptive randomization is a clinical trial design feature used to modify treatment allocation probabilities during accrual. In time-to-event trials, the impact of adaptive randomization is less well understood for estimating treatment efficacy in the presence of time-varying effects [e.g., relative risk of progression to acquired immunodeficiency syndrome (AIDS) or death changes over time]. Here, we focus on time-to-event trials where the scientific estimand is a marginal hazard ratio in the absence of intermittent censoring over the support of observed times. We analytically show that adaptive randomization alters censoring patterns and illustrate via Monte Carlo simulations that the Cox proportional hazards estimator can yield biased estimates. As a remedy, we propose a censoring-robust estimator based on reweighting the partial likelihood score by treatment-specific censoring distributions that account for adaptive randomization. We derive the asymptotic properties of the proposed estimator and evaluate its finite sample operating characteristics via simulation. Finally, we apply our proposed method using data from the Community Programs for Clinical Research on AIDS Trial 002.