Causal mediation analysis with multiple mediators.
Causal mediation analysis with multiple mediators.
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DOI：
10.1111/biom.12248
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发表时间：
2015-03
期刊：
影响因子：
1.9
通讯作者：
Vansteelandt S
中科院分区：
文献类型：
--
作者：
Daniel RM;De Stavola BL;Cousens SN;Vansteelandt S
在不同的实证研究领域，包括许多生物科学领域，人们试图通过许多不同的途径将暴露对结果的影响分解为其影响。例如，我们可能希望将大量饮酒对收缩压（SBP）的影响分为通过体重指数（BMI），γ-谷氨酰转肽酶（GGT）和其他途径的影响。主要由于因果推理领域的贡献，在理解捕获这种直观效应的统计被估量的确切性质，可以识别它们的假设以及这样做的统计方法方面取得了很大进展。这些贡献几乎完全集中在设置一个单一的调解人，或一组调解人被认为是整体，但在许多应用中，研究人员试图通过许多调解人到许多路径特异性的影响更雄心勃勃的分解。在这篇文章中，我们给出了反事实的定义，这样的路径特定的被估量在设置多个介质，当较早的介质可能会影响到后来的，显示有很多方法可以分解。我们讨论了强有力的假设下，确定的影响，建议敏感性分析方法时，一个特定的子集的假设不能被证明是合理的。这些想法是用伊热夫斯克家庭研究的饮酒量、收缩压、体重指数和谷氨酰转肽酶的数据来说明的。我们的目标是弥合从“单一调解人理论”到“多调解人实践”的差距，突出这一奋进的雄心勃勃的性质，并就如何进行提出切实可行的建议。
In diverse fields of empirical research—including many in the biological sciences—attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects via body mass index (BMI), via gamma-glutamyl transpeptidase (GGT), and via other pathways. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of statistical estimands that capture such intuitive effects, the assumptions under which they can be identified, and statistical methods for doing so. These contributions have focused almost entirely on settings with a single mediator, or a set of mediators considered en bloc; in many applications, however, researchers attempt a much more ambitious decomposition into numerous path-specific effects through many mediators. In this article, we give counterfactual definitions of such path-specific estimands in settings with multiple mediators, when earlier mediators may affect later ones, showing that there are many ways in which decomposition can be done. We discuss the strong assumptions under which the effects are identified, suggesting a sensitivity analysis approach when a particular subset of the assumptions cannot be justified. These ideas are illustrated using data on alcohol consumption, SBP, BMI, and GGT from the Izhevsk Family Study. We aim to bridge the gap from “single mediator theory” to “multiple mediator practice,” highlighting the ambitious nature of this endeavor and giving practical suggestions on how to proceed.
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