Inverse odds ratio-weighted estimation for causal mediation analysis
Inverse odds ratio-weighted estimation for causal mediation analysis
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DOI：
10.1002/sim.5864
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发表时间：
2013-11-20
影响因子：
2
通讯作者：
Tchetgen, Eric J. Tchetgen
中科院分区：
文献类型：
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作者：
Tchetgen, Eric J. Tchetgen
健康和社会科学研究的一个重要科学目标是越来越多地确定在多大程度上点暴露的总效应是由暴露和结果之间因果路径上的中间变量介导的。最近提出了一个因果框架的中介分析，这引起了新的定义，正式的识别结果和新的估计的直接和间接的影响。在本文中，作者描述了一种新的逆优势比加权法来估计所谓的自然直接和间接效应。该方法，它使用作为一个权重的比值比函数相关的曝光和介质的估计的倒数，是通用的，因为它可以用来分解在实践中常用的回归模型的数量的总影响。具体而言，该方法可用于具有非线性链接函数的广义线性模型中的效应分解，以及许多其他常用模型，例如生存结果的考克斯比例风险回归。该方法很简单，可以在标准软件中实现，只要可以为每个观察指定权重。该方法的另一个优点是，它可以很容易地将多个介质的分类，离散或连续的性质。版权所有（c）2013约翰威利父子有限公司
An important scientific goal of studies in the health and social sciences is increasingly to determine to what extent the total effect of a point exposure is mediated by an intermediate variable on the causal pathway between the exposure and the outcome. A causal framework has recently been proposed for mediation analysis, which gives rise to new definitions, formal identification results and novel estimators of direct and indirect effects. In the present paper, the author describes a new inverse odds ratio-weighted approach to estimate so-called natural direct and indirect effects. The approach, which uses as a weight the inverse of an estimate of the odds ratio function relating the exposure and the mediator, is universal in that it can be used to decompose total effects in a number of regression models commonly used in practice. Specifically, the approach may be used for effect decomposition in generalized linear models with a nonlinear link function, and in a number of other commonly used models such as the Cox proportional hazards regression for a survival outcome. The approach is simple and can be implemented in standard software provided a weight can be specified for each observation. An additional advantage of the method is that it easily incorporates multiple mediators of a categorical, discrete or continuous nature. Copyright (c) 2013 John Wiley & Sons, Ltd.