Rijnhart Judith J M, Twisk Jos W R, Chinapaw Mai J M, de Boer Michiel R, Heymans Martijn W
Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands.
Department of Public and Occupational Health, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands.
Contemp Clin Trials Commun. 2017 Jun 22;7:130-135. doi: 10.1016/j.conctc.2017.06.005. eCollection 2017 Sep.
BACKGROUND/AIMS: Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction.
Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework.
OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction.
Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.
背景/目的：统计中介分析是试验中常用的方法，用于揭示干预对特定结果变量产生影响的潜在途径。多年来，已经提出了几种方法，如普通最小二乘法（OLS）回归、结构方程模型（SEM）和潜在结果框架。大多数应用研究人员并不知道，当应用于具有连续中介变量和结果变量的中介模型时，这些方法在数学上是等效的。因此，本文的目的是在三种中介模型中展示OLS回归、SEM和潜在结果框架之间的相似性：1）简单模型；2）混杂因素调整模型；3）具有暴露-中介变量交互作用交互项的模型。
对一项纳入546名学童的随机对照试验进行二次数据分析。在我们的数据示例中，中介变量和结果变量均为连续变量。我们比较了OLS回归、SEM和潜在结果框架在总效应、直接效应和间接效应估计、中介比例以及间接效应的95%置信区间（CI）方面的差异。
在简单中介模型、混杂因素调整中介模型以及具有暴露-中介变量交互作用交互项的中介模型中，OLS回归、SEM和潜在结果框架得出了相同的效应估计值。
由于OLS回归、SEM和潜在结果框架在具有连续中介变量和结果变量的三种中介模型中得出相同的结果，研究人员可以继续使用对他们来说最方便的方法。
