Moerkerke Beatrijs, Loeys Tom, Vansteelandt Stijn
Department of Data Analysis, Ghent University.
Department of Applied Mathematics, Computer Science and Statistics, Ghent University.
Psychol Methods. 2015 Jun;20(2):204-20. doi: 10.1037/a0036368. Epub 2015 Mar 9.
Inverse probability weighting for marginal structural models has been suggested as a strategy to estimate the direct effect of a treatment or exposure on an outcome in studies where the effect of mediator on outcome is subject to posttreatment confounding. This type of confounding, whereby confounders of the effect of mediator on outcome are themselves affected by the exposure, complicates mediation analyses and necessitates apt analysis strategies. In this article, we contrast the inverse probability weighting approach with the traditional path analysis approach to mediation analysis. We show that in a particular class of linear models, adjustment for posttreatment confounding can be realized via a fairly standard modification of the traditional path analysis approach. The resulting approach is simpler; by avoiding inverse probability weighting, it moreover results in direct effect estimators with smaller finite sample bias and greater precision. We further show that a particular variant of the G-estimation approach from the causal inference literature is equivalent with the path analysis approach in simple linear settings but is more generally applicable in settings with interactions and/or noncontinuous mediators and confounders. We conclude that the use of inverse probability weighting for marginal structural models to adjust for posttreatment confounding in mediation analysis is primarily indicated in nonlinear models for the outcome.
在中介变量对结局的影响存在治疗后混杂因素的研究中，边际结构模型的逆概率加权法已被提议作为一种估计治疗或暴露对结局直接效应的策略。这种类型的混杂，即中介变量对结局的影响的混杂因素本身受到暴露的影响，使中介分析变得复杂，因此需要合适的分析策略。在本文中，我们将逆概率加权法与传统的路径分析中介分析方法进行了对比。我们表明，在一类特定的线性模型中，通过对传统路径分析方法进行相当标准的修改，可以实现对治疗后混杂因素的调整。由此产生的方法更简单；通过避免逆概率加权，它还能得到有限样本偏差更小、精度更高的直接效应估计量。我们进一步表明，因果推断文献中G估计方法的一个特定变体在简单线性设置中与路径分析方法等效，但在存在交互作用和/或非连续中介变量及混杂因素的设置中更普遍适用。我们得出结论，在中介分析中使用边际结构模型的逆概率加权法来调整治疗后混杂因素主要适用于结局的非线性模型。
