发表机构
Stanford University; University Medical Center Utrecht（斯坦福大学; 乌得勒支大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出分解分层复合终点获胜概率的方法，以区分生存和功能贡献，并通过ALS模拟验证，指导CAFS终点的解释。
AI 中文摘要
结合生存和纵向功能结局的分层复合终点在临床试验中日益常用，尤其是在死亡阻碍后续功能评估的情况下。Finkelstein--Schoenfeld策略通过优先配对比较分析此类终点，其中生存先于功能进行比较。在肌萎缩侧索硬化（ALS）中，该策略在功能与生存综合评估（CAFS）中得以实施，CAFS将生存与ALS功能评定量表修订版相结合。尽管此类终点提供了临床上有意义的总体治疗获益总结，研究者也可能希望了解治疗效应是由生存、功能结局还是两者共同驱动。受估计目标框架的启发，我们使用来自联合纵向-生存模型的ALS知情模拟，研究治疗对生存和功能的影响一致或冲突的情况。模拟表明，将复合终点获胜概率分解为生存和基于幸存者的功能贡献，可阐明其相对作用，并将复合治疗获益问题与功能导向问题（包括存活期间比较以及基于假设和始终幸存者估计目标的概念性替代方案）区分开来。当治疗影响生存时，存活期间比较所依据的功能获胜概率可能受幸存者选择偏倚影响，而在适当假设下，逆概率加权可减轻由测量预测因子引起的选择偏倚。基于主分层和多重稳健估计的简要支持性分析说明了始终幸存者估计目标。该框架为报告和解释分层复合终点提供了实用指导，其中ALS中的CAFS作为具体示例。
英文摘要
Hierarchical composite endpoints combining survival and longitudinal functional outcomes are increasingly used in clinical trials, especially when death precludes subsequent functional assessment. The Finkelstein--Schoenfeld strategy analyzes such endpoints through prioritized pairwise comparisons, with survival compared before function. In amyotrophic lateral sclerosis (ALS), this strategy is implemented in the Combined Assessment of Function and Survival (CAFS), which combines survival with the ALS Functional Rating Scale Revised. Although such endpoints provide a clinically meaningful summary of overall treatment benefit, investigators may also want to understand whether the treatment effect is driven by survival, functional outcome, or both. Motivated by the estimand framework, we use ALS-informed simulations from a joint longitudinal--survival model to study settings in which treatment effects on survival and function align or conflict. The simulations show that decomposing the composite win probability into survival and survivor-based functional contributions clarifies their relative roles and separates the composite treatment-benefit question from function-focused questions, including the while-alive comparison and conceptual alternatives based on hypothetical and always-survivor estimands. The functional win probability underlying the while-alive comparison can be subject to survivor-selection bias when treatment affects survival, and inverse-probability weighting can attenuate selection induced by measured predictors under appropriate assumptions. Brief supporting analyses based on principal stratification and multiply robust estimation illustrate the always-survivor estimand. This framework provides practical guidance for reporting and interpreting hierarchical composite endpoints, with CAFS in ALS serving as a concrete motivating example.
