Valente Matthew J, Rijnhart Judith J M, Smyth Heather L, Muniz Felix B, MacKinnon David P
Center for Children and Families, Department of Psychology, Florida International University, Miami, FL.
Amsterdam UMC, location VU University Medical Center, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
Struct Equ Modeling. 2020;27(6):975-984. doi: 10.1080/10705511.2020.1777133. Epub 2020 Aug 3.
Mediation analysis is a methodology used to understand how and why an independent variable () transmits its effect to an outcome () through a mediator (). New causal mediation methods based on the potential outcomes framework and counterfactual framework are a seminal advancement for mediation analysis, because they focus on the causal basis of mediation analysis. There are several programs available to estimate causal mediation effects, but these programs differ substantially in data set up, estimation, output, and software platform. To compare these programs, an empirical example is presented, and a single mediator model with interaction was estimated with a continuous mediator and a continuous outcome in each program. Even though the software packages employ different estimation methods, they do provide similar causal effect estimates for mediation models with a continuous mediator and outcome. A detailed explanation of program similarities, unique features, and recommendations are discussed.
中介分析是一种用于理解自变量()如何以及为何通过中介变量()将其效应传递给结果变量()的方法。基于潜在结果框架和反事实框架的新因果中介方法是中介分析的一项重大进展,因为它们关注中介分析的因果基础。有几个程序可用于估计因果中介效应,但这些程序在数据集设置、估计、输出和软件平台方面存在很大差异。为了比较这些程序,给出了一个实证例子,并在每个程序中用连续中介变量和连续结果估计了一个具有交互作用的单中介模型。尽管软件包采用了不同的估计方法,但它们确实为具有连续中介变量和结果的中介模型提供了相似的因果效应估计。文中讨论了程序的相似性、独特特征及建议的详细解释。