Adaptive Designs in Trials With Time-to-Event Endpoints and Covariate Adjustment

We investigate adaptive designs that can be used to take advantage of increases in efficiency from the use of the covariate-adjusted log-rank test in trials with time-to-event endpoints. These adaptive designs are intended to address a key practical challenge in taking advantage of efficiency gains from this test, which is that the actual efficiency gain attained in a trial may differ from estimates of the efficiency gain at the design stage. We evaluate information-based interim monitoring and blinded event target adjustment (BETA) as tools for improving efficiency, focusing on statistical and operational trade-offs between these approaches. We show using two different data-generating processes that regression coefficients used in the construction of the covariate-adjusted log-rank test may increase over time. As a result, variance reductions from adjustment with the covariate-adjusted log-rank test may also increase with additional follow-up in the trial. This means that BETA, which estimates variance reductions at an interim time-point in order to decide the timing of interim and final analyses, may not fully take advantage of the attainable efficiency gains with covariate adjustment. Simulations that incorporate repeated testing compare trial designs in terms of power, duration, and sample size reduction. While information-based monitoring enables faster analyses when covariates are highly prognostic, it may be operationally burdensome. BETA offers logistical simplicity but may not fully realize the potential efficiency gains from covariate adjustment. Reducing sample size with either adaptive design is potentially risky, as losses from a longer trial duration arising from over-optimistic estimates of efficiency gains at the design stage may be much greater than savings from a smaller trial.