A Structural Equation Modeling Approach to Meta-analytic Mediation Analysis Using Individual Participant Data: Testing Protective Behavioral Strategies as a Mediator of Brief Motivational Intervention Effects on Alcohol-Related Problems

Abstract

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.

Do brief motivational interventions increase motivation for change in drinking among college students? A two‐step meta‐analysis of individual participant data

“…Although BMIs tend to share many of the same intervention components (Ray et al, 2014) PF was more effective in improving the use of protective behavioral strategies, which then helps reduce alcohol-related problems (Huh et al, 2022). PF interventions often require fewer resources to deliver and thus may be easier to scale up in their implementation, especially with newer technological adaptations.…”

“…We included key covariates, such as baseline covariates, in the analysis so that the resulting inferences account for these variables, assuming that missing responses were related to observed data but not unobserved data (i.e., missing at random [MAR]). In alcohol intervention research, MAR has been assumed reasonable in prior synthesis studies (Huh et al, 2022;Mun et al, 2009). Data preparation was conducted using SAS 9.4…”

“…These interventions are aimed at reducing alcohol consumption and alcohol-related negative consequences by changing students' alcohol-related knowledge, attitudes, and behaviors. Among these individually-focused interventions, brief motivational interventions (BMIs) have shown effectiveness in reducing young adults' alcohol use (Cronce et al, 2018;Mun, Zhou, et al, 2022), alcohol-related problems (Huh et al, 2015(Huh et al, , 2022Jiao et al, 2020), and driving after drinking . Many BMIs have been modeled after the Brief Alcohol Screening and Intervention for College Students (Dimeff et al, 1999), which incorporates personalized feedback (PF) with motivational interviewing (Miller & Rollnick, 2002).…”

Brief Alcohol Interventions are Effective through 6 Months: Findings from Marginalized Zero-inflated Poisson and Negative Binomial Models in a Two-step IPD Meta-analysis

“…A major thrust of complex data integration and synthesis such as IDA is to identify mechanisms of behavior change. Therefore, there is a need to continue to develop and implement new models that probe mechanistic mediational paths (e.g., Huh et al, 2022b), subgroups (e.g., Tan et al, 2022a), and person-specific intervention effects.…”

Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue

“…Nevertheless, IPD meta-analytic data can offer unique benefits above and beyond aggregate data, including the development of clinical prediction models (Riley et al, 2021), providing causally interpretable results (Barker et al, 2021; in this issue), and overcoming risk of ecological bias when assessing patient level variability in effects (Cooper & Patall, 2009). For example, Huh et al (2021; in this issue) use IPD and MASEM methods to investigate the potential mediating role of protective behavioral strategies in the effects of brief motivational interventions on college students' alcohol-related problems. This IPD meta-analysis was the result of an impressive collaborative project that obtained and harmonized data from 24 studies, but which was nonetheless a laborious and time-intensive process given attitudinal and structural barriers to data sharing across investigators (Mun et al, 2015).…”