Direct and Indirect Effects in a Survival Context
Direct and Indirect Effects in a Survival Context
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DOI：
10.1097/ede.0b013e31821c680c
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发表时间：
2011-07-01
期刊：
影响因子：
5.4
通讯作者：
Hansen, Jorgen V.
中科院分区：
文献类型：
--
作者：
Lange, Theis;Hansen, Jorgen V.
流行病学研究的一个基石是了解暴露于某种结果的因果路径。基于反事实的调解分析是解决这些问题的重要工具。然而，现有的正式中介分析技术都不能应用于生存数据。这是一个严重的缺陷，因为许多流行病学问题只能通过审查的生存数据来解决。一种解决方案是使用许多考克斯模型(有或没有潜在的中介)，但这种方法不允许因果解释，并且在数学上不一致。在这篇文章中，我们提出了一种在生存环境中进行调解的简单措施。该措施以反事实为基础，衡量了自然的直接和间接影响。该方法允许对中介效应进行因果解释(根据每单位时间的额外案例)，并且在数学上是一致的。通过分析社会经济状况、工作环境和长期病假来说明这项技术。在线电子附录(http://links.lww.com/EDE/A476).)中包含了详细的实施指南
A cornerstone of epidemiologic research is to understand the causal pathways from an exposure to an outcome. Mediation analysis based on counterfactuals is an important tool when addressing such questions. However, none of the existing techniques for formal mediation analysis can be applied to survival data. This is a severe shortcoming, as many epidemiologic questions can be addressed only with censored survival data. A solution has been to use a number of Cox models (with and without the potential mediator), but this approach does not allow a causal interpretation and is not mathematically consistent. In this paper, we propose a simple measure of mediation in a survival setting. The measure is based on counterfactuals, and measures the natural direct and indirect effects. The method allows a causal interpretation of the mediated effect (in terms of additional cases per unit of time) and is mathematically consistent. The technique is illustrated by analyzing socioeconomic status, work environment, and long-term sickness absence. A detailed implementation guide is included in an online eAppendix (http://links.lww.com/EDE/A476).