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在线性模型中评估调节中介效应比评估中介效应需要更少的混杂假设。

Assessing moderated mediation in linear models requires fewer confounding assumptions than assessing mediation.

作者信息

Loeys Tom, Talloen Wouter, Goubert Liesbet, Moerkerke Beatrijs, Vansteelandt Stijn

机构信息

Department of Data Analysis, Ghent University, Belgium.

Department of Experimental-Clinical and Health Psychology, Ghent University, Belgium.

出版信息

Br J Math Stat Psychol. 2016 Nov;69(3):352-374. doi: 10.1111/bmsp.12077.

DOI:10.1111/bmsp.12077
PMID:27711981
Abstract

It is well known from the mediation analysis literature that the identification of direct and indirect effects relies on strong no unmeasured confounding assumptions of no unmeasured confounding. Even in randomized studies the mediator may still be correlated with unobserved prognostic variables that affect the outcome, in which case the mediator's role in the causal process may not be inferred without bias. In the behavioural and social science literature very little attention has been given so far to the causal assumptions required for moderated mediation analysis. In this paper we focus on the index for moderated mediation, which measures by how much the mediated effect is larger or smaller for varying levels of the moderator. We show that in linear models this index can be estimated without bias in the presence of unmeasured common causes of the moderator, mediator and outcome under certain conditions. Importantly, one can thus use the test for moderated mediation to support evidence for mediation under less stringent confounding conditions. We illustrate our findings with data from a randomized experiment assessing the impact of being primed with social deception upon observer responses to others' pain, and from an observational study of individuals who ended a romantic relationship assessing the effect of attachment anxiety during the relationship on mental distress 2 years after the break-up.

摘要

从中介分析文献中可知,直接效应和间接效应的识别依赖于无未测量混杂这一强有力的假设。即使在随机研究中,中介变量仍可能与影响结果的未观察到的预后变量相关,在这种情况下,若不产生偏差,就无法推断中介变量在因果过程中的作用。在行为科学和社会科学文献中,到目前为止,对于调节中介分析所需的因果假设关注甚少。在本文中,我们聚焦于调节中介指标,该指标衡量的是对于调节变量的不同水平,中介效应会增大或减小多少。我们表明,在某些条件下,在线性模型中,在存在调节变量、中介变量和结果的未测量共同原因的情况下,该指标可以无偏差地估计。重要的是,因此人们可以在不那么严格的混杂条件下,使用调节中介检验来支持中介效应的证据。我们用一项随机实验的数据说明了我们的发现,该实验评估了社会欺骗启动对观察者对他人疼痛反应的影响,以及一项对结束浪漫关系的个体的观察性研究的数据,该研究评估了恋爱期间的依恋焦虑对分手后两年心理困扰的影响。

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