Adaptive Designs for Two Candidate Primary Time-to-Event Endpoints

In clinical trials, the choice of an adequate primary endpoint is often difficult. Besides its clinical relevance, the endpoint must be measurable within reasonable time and must allow differentiating between the treatments. Often, the most relevant endpoint is ‘’time-to-death,” but if the overall survival prognosis is good, only a few deaths are observed during the study duration. A possible solution is to use surrogate endpoints instead. However, various examples from the literature demonstrate that surrogates do not always perform as intended. Sometimes, the surrogate effect is smaller than for the original endpoint, or the latter shows a higher effect than anticipated so using the surrogate is not reasonable. In this work, different adaptive design strategies for two candidate endpoints are proposed to solve these problems. The idea is to base the efficacy proof on the significance of at least one endpoint. At an interim analysis, both candidates are evaluated. If it is not possible to stop the study early, the sample size is recalculated based on the more promising endpoint. The new methods are illustrated by a clinical study example and compared in terms of power and sample size using Monte Carlo simulations. The software code is provided as supplementary material.

在临床试验（clinical trials）中，选择合适的主要终点（primary endpoint）往往颇具挑战。该终点不仅需具备临床相关性，还需能够在合理时限内完成测量，并可区分不同治疗方案的疗效差异。通常，最具临床价值的终点为死亡时间（time-to-death），但倘若受试人群的总生存期（overall survival）预后良好，研究周期内观测到的死亡事件将极为稀少。此时可考虑采用替代终点（surrogate endpoints）作为解决方案。然而，现有文献中的诸多案例表明，替代终点未必总能达到预期效果：有时替代终点的效应量小于原终点，抑或原终点的实际效应超出预期，此时使用替代终点并不合理。本研究针对两类候选终点提出多种自适应设计（adaptive design）策略以解决上述问题。其核心思路为以至少一个终点的统计学显著性作为疗效验证的依据。在中期分析（interim analysis）阶段，对两类候选终点进行评估；若无法提前终止研究，则基于疗效更具前景的终点重新计算样本量（sample size）。本文通过一则临床研究实例对所提新方法进行阐释，并借助蒙特卡洛模拟（Monte Carlo simulations）从检验效能（power）与样本量两方面对其展开比较。相关软件代码已作为补充材料（supplementary material）随文附出。