Res Synth Methods - A Bayesian nonparametric meta-analysis model.

Tópicos

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Resumo

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.

Resumo Limpo

metaanalysi import specifi model adequ describ effects distribut under popul studi convent normal fixedeffect normal randomeffect model assum normal effects popul distribut condit paramet covari estim mean overal effect size model may adequ predict sure effects distribut exhibit nonnorm behavior address issu propos bayesian nonparametr metaanalysi model can describ wider rang effects distribut includ unimod symmetr distribut well skew multimod distribut demonstr model analysi real metaanalyt data aris behavioralgenet research compar predict perform bayesian nonparametr model various convent modern normal fixedeffect randomeffect model

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