Causal inference in the face of competing events.
Causal inference in the face of competing events.
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DOI：
10.1007/s40471-020-00240-7
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发表时间：
2020-09
影响因子：
3.3
通讯作者：
Naimi AI
中科院分区：
文献类型：
--
作者：
Rudolph JE;Lesko CR;Naimi AI
流行病学家经常必须处理竞争事件，这会阻止感兴趣的事件发生。我们审查的考虑因素处理竞争事件时，解释结果的因果关系。当将统计关联解释为因果效应时，我们建议遵循因果推断“路线图”，就像在没有竞争事件的分析中一样。然而，在选择最能回答感兴趣的问题的因果被估量、选择将以该因果被估量为目标的统计被估量（例如，原因特异性或子分布）以及评估因果识别条件（例如，条件交换性、积极性和一致性）得到充分满足。在竞争事件背景下进行因果推理时，首先确定相关问题和最佳答案的因果被估量是至关重要的，选择通常是在消除或不消除竞争事件的被估量之间进行。
Epidemiologists frequently must handle competing events, which prevent the event of interest from occurring. We review considerations for handling competing events when interpreting results causally. When interpreting statistical associations as causal effects, we recommend following a causal inference “roadmap” as one would in an analysis without competing events. There are, however, special considerations to be made for competing events when choosing the causal estimand that best answers the question of interest, selecting the statistical estimand (e.g. the cause-specific or subdistribution) that will target that causal estimand, and assessing whether causal identification conditions (e.g., conditional exchangeability, positivity, and consistency) have been sufficiently met. When doing causal inference in the competing events setting, it is critical to first ascertain the relevant question and the causal estimand that best answers it, with the choice often being between estimands that do and do not eliminate competing events.