Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/106018
Title: Meta-analysis with few studies and binary data: a bayesian model averaging approach
Authors: Vázquez Polo, Francisco José 
Negrín Hernández, Miguel Ángel 
Martel Escobar, María Carmen 
UNESCO Clasification: 530202 Modelos econométricos
Keywords: Estadística bayesiana
Modelos económetricos
Datos
Issue Date: 2020
Project: Aportaciones A la Toma de Decisiones Bayesianas Óptimas: Aplicaciones Al Coste-Efectividad Con Datos Clínicos y Al Análisis de Riestos Con Datos Acturiales. 
Journal: Mathematics 
Abstract: In meta-analysis, the existence of between-sample heterogeneity introduces model uncertainty, which must be incorporated into the inference. We argue that an alternative way to measure this heterogeneity is by clustering the samples and then determining the posterior probability of the cluster models. The meta-inference is obtained as a mixture of all the meta-inferences for the cluster models, where the mixing distribution is the posterior model probabilities. When there are few studies, the number of cluster configurations is manageable, and the meta-inferences can be drawn with BMA techniques. Although this topic has been relatively neglected in the meta-analysis literature, the inference thus obtained accurately reflects the cluster structure of the samples used. In this paper, illustrative examples are given and analysed, using real binary data
URI: http://hdl.handle.net/10553/106018
ISSN: 2227-7390
DOI: 10.3390/math8122159
Source: Mathematics [2227-7390], v. 8(12), 2159
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