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A Bayesian nonparametric meta‐analysis model
Author(s) -
Karabatsos George,
Talbott Elizabeth,
Walker Stephen G.
Publication year - 2015
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1117
Subject(s) - nonparametric statistics , random effects model , covariate , bayesian probability , statistics , econometrics , meta analysis , normal distribution , population , computer science , range (aeronautics) , fixed effects model , mathematics , panel data , medicine , demography , materials science , sociology , composite material
In a meta‐analysis, it is important to specify a model that adequately describes the effect‐size distribution of the underlying population of studies. The conventional normal fixed‐effect and normal random‐effects models assume a normal effect‐size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect‐size distribution exhibits non‐normal behavior. To address this issue, we propose a Bayesian nonparametric meta‐analysis model, which can describe a wider range of effect‐size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta‐analytic data arising from behavioral‐genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed‐effects and random‐effects models. Copyright © 2014 John Wiley & Sons, Ltd.