协变量依赖删失下半竞争风险数据的双重稳健估计
Doubly Robust Estimation under Covariate Dependent Censoring in Semi-Competing Risks Data
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego（加州大学圣地亚哥分校赫伯特·韦特海姆公共卫生与人类长寿科学学院）
- Department of Mathematics, University of California, San Diego（加州大学圣地亚哥分校数学系）
- Halicioglu Data Science Institute, University of California, San Diego（加州大学圣地亚哥分校哈利奥格鲁数据科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对半竞争风险数据在协变量依赖删失下的估计问题，提出AIPCW框架并结合AIPTW，实现双重稳健的因果效应估计，应用于饮酒对认知与死亡影响研究。
AI中文摘要：
半竞争风险发生在个体可能经历非终端事件和终端事件时，其中终端事件会删失非终端事件，但反之则不然。在存在协变量依赖删失的情况下，增强逆概率删失加权（AIPCW）框架尚未针对半竞争风险的特殊设置进行开发，与竞争风险不同，半竞争风险具有不对称的事件时间结构和复杂的估计量。利用粗化数据上的半参数理论，我们仔细地为半竞争风险开发了一个AIPCW框架。当关注处理效应时，我们进一步将该方法与增强逆概率处理加权（AIPTW）相结合，从而产生一个在协变量依赖删失下估计因果估计量的框架。所得到的估计量被证明是双重稳健的。使用所提出的框架，我们估计处理特定的风险：i）非终端事件，ii）无非终端事件的终端事件，以及iii）非终端事件之后的终端事件。我们通过模拟评估有限样本性能，并将该方法应用于檀香山亚洲老龄化研究的数据，以评估中年重度饮酒对晚年认知障碍和死亡率的因果效应。
英文摘要：
Semi-competing risks occur when individuals may experience a non-terminal event and a terminal event, where the terminal event censors the non-terminal event but not vice versa. In the presence of covariate-dependent censoring, augmented inverse probability of censoring weighting (AIPCW) framework has not been developed for the special setting of semi-competing risks which, unlike competing risks, have asymmetric event time structure and complex estimands. Using semiparametric theory on coarsened data, we carefully develop an AIPCW framework for semi-competing risks. When treatment effects are of interest, we further integrate this approach with augmented inverse probability of treatment weighting (AIPTW), yielding a framework for estimating causal estimands under covariate-dependent censoring. The resulting estimators are shown to be doubly robust. Using the proposed framework, we estimate treatment specific risks of: i) non-terminal event, ii) terminal event without the non-terminal event, and iii) terminal event following the non-terminal event. We evaluate the finite sample performance through simulations, and apply the method to data from the Honolulu Asia Aging Study to assess the causal effects of midlife heavy drinking on late life cognitive impairment and mortality.
