A causal framework for classical statistical estimands in failure-time settings with competing events.
A causal framework for classical statistical estimands in failure-time settings with competing events.
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DOI：
10.1002/sim.8471
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发表时间：
2020-04-15
影响因子：
2
通讯作者：
Hernán MA
中科院分区：
文献类型：
--
作者：
Young JG;Stensrud MJ;Tchetgen Tchetgen EJ;Hernán MA
在故障时间设置中，竞争事件是使感兴趣的事件不可能发生的任何事件。例如，心血管疾病死亡是前列腺癌死亡的竞争事件，因为一个人一旦死于心血管疾病，就不会死于前列腺癌。在经典的竞争风险文献中，各种统计被估量被定义为可能的推断目标。许多评论都描述了这些统计估计及其估计程序，并对其使用提出了建议。然而，以前的工作没有使用一个正式的框架来描述因果效应及其识别条件，这使得很难解释效应估计和评估有关分析选择的建议。在这里，我们使用一个反事实的框架来明确定义这些经典的被估量。我们阐明，根据竞争事件是否被定义为删失事件，风险对比可以定义治疗对关注事件的总体影响，或治疗对关注事件的直接影响（不通过竞争事件介导）。相反，无论竞争事件是否被定义为删失事件，反事实危险对比通常不能被解释为因果效应。我们说明了如何识别所有这些反事实的被估量的假设可以表示在因果图中，竞争事件被描绘为随时间变化的协变量。我们提出了一个应用这些想法的数据，从随机试验设计，以估计雌激素治疗对前列腺癌死亡率的影响。
In failure-time settings, a competing event is any event that makes it impossible for the event of interest to occur. For example, cardiovascular disease death is a competing event for prostate cancer death because an individual cannot die of prostate cancer once he has died of cardiovascular disease. Various statistical estimands have been defined as possible targets of inference in the classical competing risks literature. Many reviews have described these statistical estimands and their estimating procedures with recommendations about their use. However, this previous work has not used a formal framework for characterizing causal effects and their identifying conditions, which makes it difficult to interpret effect estimates and assess recommendations regarding analytic choices. Here we use a counterfactual framework to explicitly define each of these classical estimands. We clarify that, depending on whether competing events are defined as censoring events, contrasts of risks can define a total effect of the treatment on the event of interest, or a direct effect of the treatment on the event of interest not mediated through the competing event. In contrast, regardless of whether competing events are defined as censoring events, counterfactual hazard contrasts cannot generally be interpreted as causal effects. We illustrate how identifying assumptions for all of these counterfactual estimands can be represented in causal diagrams in which competing events are depicted as time-varying covariates. We present an application of these ideas to data from a randomized trial designed to estimate the effect of estrogen therapy on prostate cancer mortality.
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