TY - JOUR
T1 - A Structural Equation Modeling Approach to Meta-analytic Mediation Analysis Using Individual Participant Data
T2 - Testing Protective Behavioral Strategies as a Mediator of Brief Motivational Intervention Effects on Alcohol-Related Problems
AU - Huh, David
AU - Li, Xiaoyin
AU - Zhou, Zhengyang
AU - Walters, Scott T.
AU - Baldwin, Scott A.
AU - Tan, Zhengqi
AU - Larimer, Mary E.
AU - Mun, Eun Young
N1 - Funding Information:
The project described was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) grants R01 AA019511 and K02 AA028630. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAAA or the National Institutes of Health.
Funding Information:
We would like to thank the following contributors to Project INTEGRATE in alphabetical order: John S. Baer, Department of Psychology, The University of Washington, and Veterans? Affairs Puget Sound Health Care System; Nancy P. Barnett, Center for Alcohol and Addiction Studies, Brown University; M. Dolores Cimini, University Counseling Center, The University at Albany, State University of New York; William R. Corbin, Department of Psychology, Arizona State University; Kim Fromme, Department of Psychology, The University of Texas at Austin; Joseph W. LaBrie, Department of Psychology, Loyola Marymount University; Mary E. Larimer, Department of Psychiatry and Behavioral Sciences, The University of Washington; Matthew P. Martens, Department of Educational, School, and Counseling Psychology, The University of Missouri; James G. Murphy, Department of Psychology, The University of Memphis; Scott T. Walters, Department of Health Behavior and Health Systems, The University of North Texas Health Science Center; Helene R. White, Center of Alcohol and Substance Use Studies, Rutgers, The State University of New Jersey; and the late Mark D. Wood, Department of Psychology, The University of Rhode Island. We would like to thank Minge Xie, Department of Statistics, Rutgers University, and Jae-kwang Kim, Department of Statistics, Iowa State University, for their suggestions on statistical issues. We also thank Nickeisha Clarke, Yang Jiao, Su-Young Kim, and Anne E. Ray for their earlier work on coding and harmonizing interventions and outcomes, and Jimmy de la Torre and Yan Huo for their work on measurement. Finally, we thank Helene R. White for her valuable conceptual and methodological contributions in the early years of Project INTEGRATE.
Publisher Copyright:
© 2021, The Author(s).
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Bootstrap inference with multiple imputation
KW - Brief alcohol intervention
KW - Complex synthesis
KW - Indirect effect
KW - Integrative data analysis
UR - http://www.scopus.com/inward/record.url?scp=85118981645&partnerID=8YFLogxK
U2 - 10.1007/s11121-021-01318-4
DO - 10.1007/s11121-021-01318-4
M3 - Article
C2 - 34767159
AN - SCOPUS:85118981645
SN - 1389-4986
VL - 23
SP - 390
EP - 402
JO - Prevention Science
JF - Prevention Science
IS - 3
ER -