Background/Objectives:Time-to-event endpoints such as Overall Survival (OS), Progression-Free Survival (PFS), and Event-Free Survival (EFS) are central in phase III oncology trials. Hazard ratios from Cox proportional hazards models and log-rank tests are the standard analytic tools, supplemented by Kaplan–Meier estimates. However, these methods depend on proportional hazards to deliver unbiased estimates of treatment effects and large-sample assumptions, and may perform poorly under heavy censoring or non-proportional hazards. We introduce the univariate martingale residual (UMR) as a new endpoint and summary measure that enables exact inference through randomization testing.Methods:The UMR reflects the difference between observed and expected events at the subject level. Average UMRs per treatment arm provide an absolute measure of excess events. A randomization-based testing framework is used to compare treatment arms and compute exactp-values without proportional hazards or asymptotic assumptions. Performance is assessed through simulations and demonstrated using real oncology trial data.Results:UMRs offered robust and interpretable treatment summaries under heavy censoring, non-proportional hazards, and quasi-complete separation, where Cox-based estimates were unstable or undefined. The exact UMR-based randomization test maintained Type I error control and was competitive or more powerful than the log-rank test when proportional hazards were violated.Conclusions:The UMR provides an intuitive, assumption-free summary of treatment effects and supports exact inference. It represents a practical and robust alternative to hazard-ratio-based methods in phase III oncology trials, especially in complex survival settings.
**背景/目的：** 在III期肿瘤学试验中，总生存期、无进展生存期和无事件生存期等时间-事件终点至关重要。Cox比例风险模型的风险比和对数秩检验是标准的分析工具，通常辅以Kaplan-Meier估计。然而，这些方法依赖于比例风险假设来提供无偏的治疗效应估计，并基于大样本假设，在重度删失或非比例风险情况下可能表现不佳。我们引入单变量鞅残差作为一种新的终点和汇总指标，它能够通过随机化检验实现精确推断。 **方法：** UMR反映了在受试者水平上观察到的事件与预期事件之间的差异。各治疗组的平均UMR提供了超额事件的绝对度量。我们采用基于随机化的检验框架来比较治疗组并计算精确的p值，无需依赖比例风险或渐近假设。通过模拟研究评估其性能，并使用真实的肿瘤学试验数据进行演示。 **结果：** 在重度删失、非比例风险以及拟完全分离的情况下，基于Cox模型的估计不稳定或无法定义，而UMR则能提供稳健且可解释的治疗效果汇总。基于UMR的精确随机化检验能控制I类错误，并且在违反比例风险假设时，其检验效能与对数秩检验相当或更优。 **结论：** UMR提供了一种直观、无需假设的治疗效果汇总方法，并支持精确推断。在III期肿瘤学试验中，尤其是在复杂的生存分析场景下，它代表了基于风险比方法的一种实用且稳健的替代方案。
An Alternative Treatment Effect Measure for Time-to-Event Oncology Randomized Trials
肿瘤(癌症)患者之家