School of Social Work, University of Washington, 4101 15th Ave NE, Box 354900, Seattle, WA, 98105-6299, USA.
Department of Health Behavior and Health Systems, University of North Texas Health Science Center, Fort Worth, TX, USA.
Prev Sci. 2022 Apr;23(3):390-402. doi: 10.1007/s11121-021-01318-4. Epub 2021 Nov 12.
This paper introduces a meta-analytic mediation analysis approach for individual participant data (IPD) from multiple studies. Mediation analysis evaluates whether the effectiveness of an intervention on health outcomes occurs because of change in a key behavior targeted by the intervention. However, individual trials are often statistically underpowered to test mediation hypotheses. Existing approaches for evaluating mediation in the meta-analytic context are limited by their reliance on aggregate data; thus, findings may be confounded with study-level differences unrelated to the pathway of interest. To overcome the limitations of existing meta-analytic mediation approaches, we used a one-stage estimation approach using structural equation modeling (SEM) to combine IPD from multiple studies for mediation analysis. This approach (1) accounts for the clustering of participants within studies, (2) accommodates missing data via multiple imputation, and (3) allows valid inferences about the indirect (i.e., mediated) effects via bootstrapped confidence intervals. We used data (N = 3691 from 10 studies) from Project INTEGRATE (Mun et al. Psychology of Addictive Behaviors, 29, 34-48, 2015) to illustrate the SEM approach to meta-analytic mediation analysis by testing whether improvements in the use of protective behavioral strategies mediate the effectiveness of brief motivational interventions for alcohol-related problems among college students. To facilitate the application of the methodology, we provide annotated computer code in R and data for replication. At a substantive level, stand-alone personalized feedback interventions reduced alcohol-related problems via greater use of protective behavioral strategies; however, the net-mediated effect across strategies was small in size, on average.
本文介绍了一种用于来自多个研究的个体参与者数据（IPD）的元分析中介分析方法。中介分析评估干预对健康结果的有效性是否是由于干预针对的关键行为的变化而发生的。然而，单个试验通常在统计学上不足以检验中介假设。元分析背景下现有的评估中介的方法受到其对汇总数据的依赖的限制；因此，研究结果可能与与感兴趣的途径无关的研究水平差异相混淆。为了克服现有元分析中介方法的局限性，我们使用结构方程建模（SEM）的单阶段估计方法来合并来自多个研究的 IPD 进行中介分析。这种方法 (1) 考虑了参与者在研究中的聚类，(2) 通过多次插补来处理缺失数据，以及 (3) 通过自举置信区间允许对间接（即中介）效应进行有效的推断。我们使用来自 Project INTEGRATE（Mun 等人，《成瘾行为心理学》，第 29 卷，第 34-48 页，2015 年）的数据（来自 10 项研究的 N = 3691）来说明 SEM 方法在元分析中介分析中的应用，通过测试在大学生中预防行为策略的使用改善是否可以中介简短动机干预对酒精相关问题的有效性。为了促进方法的应用，我们提供了在 R 中的注释计算机代码和可复制的数据。在实质性水平上，独立的个性化反馈干预通过更多地使用保护性行为策略来减少与酒精相关的问题；然而，策略之间的净中介效应平均来说很小。
