AI 中文总结
研究竞争风险下事件负担的度量问题，提出平均特定病因风险（ACSH），开发其非参数估计和推断方法及两组比较的差异与比率，通过模拟研究和实例分析展示该方法性能及应用。
AI 中文摘要
在临床和流行病学研究中，竞争事件很常见，包括半竞争风险，即死亡等终末事件可能在非致命事件之后发生，但也会与其竞争。标准总结包括累积发病率函数（CIF）和发病率（IR）。在竞争事件中，除非强度恒定，否则朴素的IR通常取决于删失时间分布。我们提出了平均特定病因风险（ACSH），它是每个无事件发生个体时间的生存加权率，保留了发病率的解释，且仅由事件时间分布定义，不涉及删失时间分布。我们为ACSH开发了非参数估计和推断方法，对于两组比较，引入了ACSH差异和比率，无需两组之间有强模型假设就能提供可解释的对比。模拟研究检验了有限样本性能，对CANVAS试验的分析说明了所提出的方法。
英文摘要
Competing events are common in clinical and epidemiologic studies, including semi-competing risks in which a terminal event such as death may follow a nonfatal event but also competes with it beforehand. Standard summaries include the cumulative incidence function (CIF) and the incidence rate (IR), defined as the number of observed events divided by observed event-free person-time. With competing events, the naive IR generally depends on the censoring-time distribution unless intensities are constant. We propose the Average Cause-Specific Hazard (ACSH), a survival-weighted rate per event-free person-time that preserves the interpretation of an incidence rate and is defined purely from the event-time distribution, without involving the censoring-time distribution. We develop nonparametric estimation and inference for ACSH and, for two-sample comparisons, introduce ACSH differences and ratios that provide interpretable contrasts without requiring a strong model assumption between two groups. Simulation studies examine the finite-sample performance, and an analysis of the CANVAS trial illustrates the proposed methods.
