存在交错入组且效应随日历时间变化的因果推断
Causal inference with staggered entries and effects that change over calendar time
- Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对交错入组且效应随日历时间变化的因果推断问题，探讨了其对因果生存分析常用方法的影响，给出有效识别条件并引入敏感性分析，通过两个案例验证了方法的实用性。
AI中文摘要：
存在交错入组的研究（即个体在不同日历时间加入研究）在医学及相关学科中十分普遍。由于这类研究通常有固定的行政随访结束时间，目标估计量的识别依赖于对右删失机制的假设。这些假设通常被认为仅在协变量（包括个体入组时间E）的条件下才合理。然而，由于正性违反，以E为条件的删失假设是不适定的。本研究探讨了该问题对因果生存分析常用方法（如基于边际结构模型的方法）的影响，进一步给出了有效识别的条件，并引入敏感性分析以评估实际意义。我们通过两个案例研究说明所提方法：第一个基于Hernán等人（2000）关于齐多夫定治疗效应的开创性文章，第二个重新分析了一项关于mRNA疫苗在接受免疫检查点抑制剂治疗患者中效应的近期研究。
英文摘要：
Studies with staggered entry, in which individuals enroll at different calendar times, are ubiquitous in medicine and related disciplines. Because these studies usually have a fixed administrative end of follow-up, identification of the estimand of interest relies on assumptions about the right-censoring mechanism. The assumptions are often considered plausible only conditional on covariates, including the time an individual entered the study (E). Yet, censoring assumptions formulated conditional on E are ill-posed due to positivity violations. Here, we study the consequences of this issue for common procedures in causal survival analysis, such as those based on marginal structural models. We further give conditions for valid identification and introduce sensitivity analyses to assess practical implications. We illustrate our methodology through two case studies. The first builds on the seminal article by Hernán et al. (2000) on the effect of zidovudine treatment. The second reanalyzes a recent study on the effect of the mRNA vaccine in patients receiving immune checkpoint inhibitors.
