Statistical issues in survival analysis (RMST adaptive sequential)
March 25, 2026 The goal of this article was to present a methodology to design and analyze an adaptive clinical trial with sample size recalculation at interim analysis, when the restricted mean survival time (RMST) is the primary endpoint. Especially, we focus on the situation of non-proportional hazards with a delayed treatment effect. Our aim is to provide trialists with new options to consider, to make better design choices, or to take better-informed decisions about design choices. They consider an adaptive “two-stage” feature design of the trial. The first part consists of further follow-up data from patients who entered the trial before the interim analysis, but for whom further follow-up data were collected after the interim analysis. The second part comes from patients who entered the trial after the interim analysis. To define p-value critical values, they suggested to use the O’Brien-Fleming alpha-spending function approach. Sample size adaptations are made at the interim analysis. They also had to define a relevant tau, calculate relevant first stage sample size n1, and pre-specify reasonable weights, w1, and w2. They ran simulation to compare their method to the log-rank test and a test comparing survival probabilities at tau years, studying the impact of timing of the interim analysis, and illustrating that the design can partly correct for wrong guesses during the initial planning. If the interim data suggested stopping early for efficacy or futility, the trial was stopped early but it if suggested continuing to the second stage, the sample size of the newly recruited patients for the second