A Bayesian Nonparametric Meta-Analysis Model

Autor/inn/en	Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.
Titel	A Bayesian Nonparametric Meta-Analysis Model
Quelle	In: Research Synthesis Methods, 6 (2015) 1, S.28-44 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1117
Schlagwörter	Bayesian Statistics; Meta Analysis; Prediction; Nonparametric Statistics; Effect Size; Research Design; Genetics; Scientific Research; Comparative Analysis; Regression (Statistics) + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; Vorhersage; Forschungsdesign; Humangenetik; Regression; Regressionsanalyse
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01