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A tutorial on individual participant data meta-analysis using Bayesian multilevel modeling to estimate alcohol
David Huh1, Eun-Young Mun2, Scott T Walters2
1University of Washington, School of Social Work, 4101 15th Ave. NE, Box 354900, Seattle, WA 98195-4900, USA.
Addictive Behaviors
|February 23, 2019
Summary
This tutorial introduces a Bayesian multilevel modeling approach for individual participant data (IPD) meta-analysis. It addresses challenges in combining heterogeneous multi-arm trials and analyzing skewed count data for accurate effect size estimation.
Area of Science:
- Biostatistics
- Meta-analysis methodology
- Health services research
Background:
- Individual participant data (IPD) meta-analysis faces challenges with heterogeneous study designs and skewed outcome data.
- Existing methods often struggle to incorporate multi-arm trials or analyze count data with many zeros effectively.
- These limitations impact the feasibility and interpretation of IPD meta-analyses.
Purpose of the Study:
- To provide a tutorial companion for a methodological approach to IPD meta-analysis.
- To demonstrate how to combine data from heterogeneous studies with varying numbers of treatment arms.
- To show how to analyze highly-skewed count outcomes with many zeroes to estimate overall effect sizes.
Main Methods:
- A Bayesian multilevel modeling approach is presented for combining multi-arm trials in IPD meta-analysis.
- The method handles distribution-appropriate analysis of skewed count data.
- Illustrative data from Project INTEGRATE, focusing on interventions for alcohol use, are used.
Main Results:
- The approach validly combines data from heterogeneous studies with varying numbers of treatment arms.
- It enables the analysis of highly-skewed count outcomes, common in substance use research.
- The method preserves random allocation and overcomes heterogeneity in trial design and missing data.
Conclusions:
- This Bayesian multilevel modeling approach offers a favorable alternative for IPD meta-analysis of multi-arm trials.
- It allows for direct comparison of multiple treatment arms in a one-step analysis.
- The provided R code and example data facilitate the application of this advanced methodology.
Keywords:
Bayesian multilevel modelingBrief motivational interventionCollege drinkingIndividual participant dataMeta-analysisMultivariate meta-analysisMore Related Videos
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