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-ap

SUBMITTER: Huh D 

PROVIDER: S-EPMC6989027 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature