发表机构
Intelligent Data Analytics (IDA) Lab, Department of Artificial Intelligence and Human Interfaces (AIHI), Paris Lodron University of Salzburg; School of Mathematical and Physical Sciences, Macquarie University; Division of Biostatistics, German Cancer Research Center（萨尔茨堡巴黎罗德大学; 麦考瑞大学数理科学学院; 德国癌症研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对生存曲线早期与晚期分歧难以用比例风险假设捕捉的问题，构建含Kaplan–Meier相关效应及新型时间对比的联合推断框架，经模拟和实际应用验证其性能优于log-rank检验，可揭示隐藏的早期治疗优势。
AI 中文摘要
临床试验中常出现治疗曲线早期分歧、晚期收敛，或反之的模式，这类模式难以用比例风险假设捕捉。我们为删失生存数据的两个非参数泛函构建联合推断框架：基于Kaplan–Meier的Mann–Whitney效应，以及区分早期与晚期差异的新型时间对比。该方法提供可解释的概率尺度效应度量，能在右删失下对全局和时间对比进行联合推断。模拟研究显示，该方法在非比例风险下优于log-rank检验，同时维持名义一类错误。实际应用表明，时间对比可揭示标准分析中被隐藏的临床有意义的早期治疗优势。
英文摘要
Clinical trials often show treatment curves that diverge early and converge later, or vice versa patterns that are poorly captured by the proportional-hazards assumption. We develop a joint inferential framework for two nonparametric functionals of censored survival data: the Kaplan--Meier-based Mann--Whitney effect and a novel temporal contrast separating early and late differences. The approach provides interpretable, probability-scale effect measures and enables joint inference for global and temporal contrasts under right censoring. In simulation studies, the method outperforms the log-rank test under non-proportional hazards while maintaining nominal type-I error. A real-world application illustrates how the temporal contrast reveals clinically meaningful early treatment advantages that remain hidden in standard analyses
