A tutorial on individual participant data meta-analysis using Bayesian multilevel modeling to estimate alcohol intervention effects across heterogeneous studies

David Huh, Eun-Young Mun, Scott T. Walters, Zhengyang Zhou, David C. Atkins

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Abstract

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.

Original languageEnglish
JournalAddictive Behaviors
DOIs
StatePublished - 1 Jan 2019

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Metadata
Meta-Analysis
Alcohols
Therapeutics
Random Allocation
Students
Research

Keywords

  • Bayesian multilevel modeling
  • Brief motivational intervention
  • College drinking
  • Individual participant data
  • Meta-analysis
  • Multivariate meta-analysis

Cite this

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title = "A tutorial on individual participant data meta-analysis using Bayesian multilevel modeling to estimate alcohol intervention effects across heterogeneous studies",
abstract = "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.",
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