Effect decomposition in the presence of an exposure-induced mediator-outcome confounder.
Effect decomposition in the presence of an exposure-induced mediator-outcome confounder.
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DOI：
10.1097/ede.0000000000000034
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发表时间：
2014-03
期刊：
影响因子：
--
通讯作者：
Robins JM
中科院分区：
文献类型：
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作者：
Vanderweele TJ;Vansteelandt S;Robins JM
因果中介分析的方法通过考虑相互作用和非线性，概括了流行病学和社会科学文献中直接和间接影响的传统方法。然而，从因果推理文献的方法本身受到一个重大的限制，即所谓的自然直接和间接的影响，是不确定的数据时，有一个变量，是受影响的曝光，这也混淆了中介和结果之间的关系。在本文中，我们描述了三种替代方法，效果分解，给出的数量，可以被解释为直接和间接的影响，并可以确定从数据中，即使在存在一个保险引起的中介结果混淆。我们描述了一个简单的加权为基础的估计方法，这三种方法，说明围产期流行病学的数据。这里所描述的方法可以深入了解调解的途径和问题，即使是当一个不确定性诱导的调解结果混杂因素。
Methods from causal mediation analysis have generalized the traditional approach to direct and indirect effects in the epidemiologic and social science literature by allowing for interaction and non-linearities. However, the methods from the causal inference literature have themselves been subject to a major limitation in that the so-called natural direct and indirect effects that are employed are not identified from data whenever there is a variable that is affected by the exposure, which also confounds the relationship between the mediator and the outcome. In this paper we describe three alternative approaches to effect decomposition that give quantities that can be interpreted as direct and indirect effects, and that can be identified from data even in the presence of an exposure-induced mediator-outcome confounder. We describe a simple weighting-based estimation method for each of these three approaches, illustrated with data from perinatal epidemiology. The methods described here can shed insight into pathways and questions of mediation even when an exposure-induced mediator-outcome confounder is present.