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

Abstract

Summary 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.

The authors' abstract, as published at the source. Biometrika, 2026 · DOI ↗