TY - JOUR
T1 - A tutorial on individual participant data meta-analysis using Bayesian multilevel modeling to estimate alcohol intervention effects across heterogeneous studies
AU - Huh, David
AU - Mun, Eun Young
AU - Walters, Scott T.
AU - Zhou, Zhengyang
AU - Atkins, David C.
N1 - 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, 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; Helene R. White, Center of Alcohol Studies, Rutgers, The State University of New Jersey; and the late Mark D. Wood, Department of Psychology, The University of Rhode Island. We would also like to thank Todd Darlington at the University of Oregon for providing feedback on an earlier version of the R code.
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, 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; Helene R. White, Center of Alcohol Studies, Rutgers, The State University of New Jersey; and the late Mark D. Wood, Department of Psychology, The University of Rhode Island.We would also like to thank Todd Darlington at the University of Oregon for providing feedback on an earlier version of the R code.
Funding Information:
This research was supported by Award Number R01 AA019511 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). In addition, David C. Atkins' effort was supported in part by K02 AA023814. 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.
Publisher Copyright:
© 2019
PY - 2019/7
Y1 - 2019/7
N2 - This paper provides a tutorial companion for the methodological approach implemented in Huh et al. (2015)that overcame two major challenges for individual participant data (IPD)meta-analysis. Specifically, we show how to validly combine data from heterogeneous studies with varying numbers of treatment arms, and how to analyze highly-skewed count outcomes with many zeroes (e.g., alcohol and substance use outcomes)to estimate overall effect sizes. These issues have important implications for the feasibility, applicability, and interpretation of IPD meta-analysis but have received little attention thus far in the applied research literature. We present a Bayesian multilevel modeling approach for combining multi-arm trials (i.e., those with two or more treatment groups)in a distribution-appropriate IPD analysis. Illustrative data come from Project INTEGRATE, an IPD meta-analysis study of brief motivational interventions to reduce excessive alcohol use and related harm among college students. Our approach preserves the original random allocation within studies, combines within-study estimates across all studies, overcomes between-study heterogeneity in trial design (i.e., number of treatment arms)and/or study-level missing data, and derives two related treatment outcomes in a multivariate IPD meta-analysis. This methodological approach is a favorable alternative to collapsing or excluding intervention groups within multi-arm trials, making it possible to directly compare multiple treatment arms in a one-step IPD meta-analysis. To facilitate application of the method, we provide annotated computer code in R along with the example data used in this tutorial.
AB - This paper provides a tutorial companion for the methodological approach implemented in Huh et al. (2015)that overcame two major challenges for individual participant data (IPD)meta-analysis. Specifically, we show how to validly combine data from heterogeneous studies with varying numbers of treatment arms, and how to analyze highly-skewed count outcomes with many zeroes (e.g., alcohol and substance use outcomes)to estimate overall effect sizes. These issues have important implications for the feasibility, applicability, and interpretation of IPD meta-analysis but have received little attention thus far in the applied research literature. We present a Bayesian multilevel modeling approach for combining multi-arm trials (i.e., those with two or more treatment groups)in a distribution-appropriate IPD analysis. Illustrative data come from Project INTEGRATE, an IPD meta-analysis study of brief motivational interventions to reduce excessive alcohol use and related harm among college students. Our approach preserves the original random allocation within studies, combines within-study estimates across all studies, overcomes between-study heterogeneity in trial design (i.e., number of treatment arms)and/or study-level missing data, and derives two related treatment outcomes in a multivariate IPD meta-analysis. This methodological approach is a favorable alternative to collapsing or excluding intervention groups within multi-arm trials, making it possible to directly compare multiple treatment arms in a one-step IPD meta-analysis. To facilitate application of the method, we provide annotated computer code in R along with the example data used in this tutorial.
KW - Bayesian multilevel modeling
KW - Brief motivational intervention
KW - College drinking
KW - Individual participant data
KW - Meta-analysis
KW - Multivariate meta-analysis
UR - http://www.scopus.com/inward/record.url?scp=85061664259&partnerID=8YFLogxK
U2 - 10.1016/j.addbeh.2019.01.032
DO - 10.1016/j.addbeh.2019.01.032
M3 - Article
C2 - 30791977
AN - SCOPUS:85061664259
SN - 0306-4603
VL - 94
SP - 162
EP - 170
JO - Addictive Behaviors
JF - Addictive Behaviors
ER -