Stratified Restricted Mean Survival Time Model for Marginal Causal Effect in Observational Survival Data.
Stratified Restricted Mean Survival Time Model for Marginal Causal Effect in Observational Survival Data.
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DOI：
10.1016/j.annepidem.2021.09.016
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发表时间：
2021-12
影响因子：
5.6
通讯作者：
Lu B
中科院分区：
文献类型：
--
作者：
Ni A;Lin Z;Lu B
事件发生时间结局在流行病学研究中很常见。多篇论文讨论了使用风险比作为因果效应度量的不足，因为其不可解释性和时变性质。在本文中，我们进一步澄清了风险比可以用作条件因果效应度量，但它通常不是有效的边际效应度量，即使在随机设计下也是如此。我们建议使用限制性平均生存时间（RMST）差异作为因果效应度量，因为它基本上测量了指定时间范围内的平均差异，并且简单地解释为生存曲线下的面积。对于观察性研究，可以使用RMST估计进行倾向评分调整，以消除观察到的混杂偏倚。我们提出了一个倾向得分分层RMST估计策略，它表现良好，在我们的模拟评估，是相对容易实现的流行病学家在实践中。我们的分层RMST估计包括两个不同版本的实现，这取决于研究人员是否希望涉及回归建模调整，这为观察生存数据提供了一个强大的工具来检查边际因果效应。
Time to event outcomes are commonly encountered in epidemiologic research. Multiple papers have discussed the inadequacy of using the hazard ratio as a causal effect measure due to its noncollapsibility and the time-varying nature. In this paper, we further clarified that the hazard ratio might be used as a conditional causal effect measure, but it is generally not a valid marginal effect measure, even under randomized design. We proposed to use the restricted mean survival time (RMST) difference as a causal effect measure, since it essentially measures the mean difference over a specified time horizon and has a simple interpretation as the area under survival curves. For observational studies, propensity score adjustment can be implemented with RMST estimation to remove observed confounding bias. We proposed a propensity score stratified RMST estimation strategy, which performs well in our simulation evaluation and is relatively easy to implement for epidemiologists in practice. Our stratified RMST estimation includes two different versions of implementation, depending on whether researchers want to involve regression modeling adjustment, which provides a powerful tool to examine the marginal causal effect with observational survival data.
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