Beyond Fixed Restriction Time: Adaptive Restricted Mean Survival Time Methods in Clinical Trials

Abstract

Restricted mean survival time offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-event data, particularly when the proportional hazards assumption is violated. By capturing the total event-free time over a specified horizon, it provides an intuitive and clinically meaningful measure of absolute treatment benefit. Nonetheless, selecting the restriction time poses challenges: choosing a small restriction time may overlook late-emerging benefits, while a large one can inflate variance and reduce power, an issue whose impact on the precision of inference is often underappreciated. We propose a novel data-driven, adaptive procedure that identifies the optimal restriction time from a continuous range by maximizing a criterion balancing effect size and estimation precision. Consequently, our procedure is particularly powerful when the pattern of the treatment effect is unknown at the design stage. We provide a rigorous theoretical foundation that accounts for the additional variability introduced by adaptive selection. To address nonregular estimation under the null, we develop two complementary strategies: a convex-hull-based estimator, and a penalized approach that regularizes restriction-time selection. Additionally, when restriction time candidates are pre-specified on a discrete grid, our procedure has the same first-order distribution as an oracle estimator evaluated at the penalized population optimizer, with no additional first-order variance from selection. Extensive simulations across realistic survival scenarios demonstrate that our method outperforms traditional restricted mean survival time analyses and the log-rank test, achieving superior power while maintaining approximately nominal type I error rates. In a phase III pancreatic cancer trial with transient treatment effects, our procedure uncovers clinically meaningful benefits that standard methods overlook. Software implementing the methods is available in the package AdaRMST.