Weighting-Based Sensitivity Analysis in Causal Mediation Studies
Weighting-Based Sensitivity Analysis in Causal Mediation Studies
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DOI：
10.3102/1076998617749561
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发表时间：
2018-02-01
影响因子：
2.4
通讯作者：
Yang, Fan
中科院分区：
文献类型：
--
作者：
Hong, Guanglei;Qin, Xu;Yang, Fan
通过敏感性分析，分析师试图确定因果推理的结论是否可以很容易地被识别假设的合理违反所逆转。更难被这种违反所改变的分析结论有望为关于因果关系的科学知识增加更高的价值。本文提出了一种基于权重的因果中介研究的敏感性分析方法。扩展了中介概率加权比（RMPW）方法用于识别自然间接效应和自然直接效应，新策略评估了存在遗漏的预处理或后处理协变量时的潜在偏倚。这种遗漏可能会破坏分析结论的因果有效性。敏感性分析的加权方法减少了对函数形式假设的依赖，并消除了对中介变量、结果和省略的协变量的测量尺度的限制。从本质上讲，调整遗漏的混杂因素的新权重与忽略混杂因素的初始权重之间的差异捕获了导致偏倚的混杂因素的作用。由于省略了中介-结果关系的混杂而导致的偏倚的效应量是两个敏感性参数的产物，一个与省略的混杂因素预测中介的程度相关，另一个与它们预测结果的程度相关。文章提供了一个应用实例，并讨论了这种新方法的广泛应用的敏感性分析的结论。在线补充材料包括用于实施拟议敏感性分析程序的R代码。
Through a sensitivity analysis, the analyst attempts to determine whether a conclusion of causal inference could be easily reversed by a plausible violation of an identification assumption. Analytic conclusions that are harder to alter by such a violation are expected to add a higher value to scientific knowledge about causality. This article presents a weighting-based approach to sensitivity analysis for causal mediation studies. Extending the ratio-of-mediator-probability weighting (RMPW) method for identifying natural indirect effect and natural direct effect, the new strategy assesses potential bias in the presence of omitted pretreatment or posttreatment covariates. Such omissions may undermine the causal validity of analytic conclusions. The weighting approach to sensitivity analysis reduces the reliance on functional form assumptions and removes constraints on the measurement scales for the mediator, the outcome, and the omitted covariates. In its essence, the discrepancy between a new weight that adjusts for an omitted confounder and an initial weight that omits the confounder captures the role of the confounder that contributes to the bias. The effect size of the bias due to omitted confounding of the mediator-outcome relationship is a product of two sensitivity parameters, one associated with the degree to which the omitted confounders predict the mediator and the other associated with the degree to which they predict the outcome. The article provides an application example and concludes with a discussion of broad applications of this new approach to sensitivity analysis. Online Supplemental Material includes R code for implementing the proposed sensitivity analysis procedure.