This study develops a systematic approach for evaluating the effect of a treatment on a time-to-event outcome in a matched-pair study. While most methods for paired right-censored outcomes allow determining an overall treatment effect over the course of follow-up, they generally lack in providing detailed insights into how the effect changes over time. To address this gap, we propose novel tests for paired right-censored outcomes using randomization inference. We further extend our tests to matched observational studies by developing corresponding sensitivity analysis methods to take into account departures from randomization. Simulations demonstrate the robustness of our approach against various non-proportional hazards alternatives, including a crossing survival curves scenario. We demonstrate the application of our methods using a matched observational study from the Korean Longitudinal Study of Aging (KLoSA) data, focusing on the effect of social engagement on survival.

翻译：本研究针对配对研究中的时间-事件结局，开发了一种评估治疗效应的系统性方法。尽管大多数针对配对右删失结局的方法能够确定整个随访期间的整体治疗效应，但它们通常无法提供关于效应随时间变化细节的深入分析。为填补这一空白，我们提出了基于随机化推断的配对右删失结局新型检验方法。我们进一步将检验扩展至匹配观察性研究，通过开发相应的敏感性分析方法来考量对随机化假设的偏离。模拟实验表明，我们的方法对包括生存曲线交叉场景在内的多种非比例风险替代方案具有稳健性。我们利用韩国老龄化纵向研究（KLoSA）数据中的一项匹配观察性研究，以社会参与对生存的影响为重点，展示了所提方法的应用。