Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/116910
Título: Bayesian heterogeneity in a meta-analysis with two studies and binary data
Autores/as: Martel-Escobar, María 
Negrín, Miguel Ángel 
Vázquez Polo, Francisco José 
Palabras clave: Bayesian model averaging (BMA)
Binomial data
Heterogeneity
Meta-analysis
Sparse data, et al.
Fecha de publicación: 2022
Publicación seriada: Journal of Applied Statistics 
Resumen: The meta–analysis of two trials is valuable in many practical situations, such as studies of rare and/or orphan diseases focussed on a single intervention. In this context, additional concerns, like small sample size and/or heterogeneity in the results obtained, might make standard frequentist and Bayesian techniques inappropriate. In a meta–analysis, moreover, the presence of between–sample heterogeneity adds model uncertainty, which must be taken into consideration when drawing inferences. We suggest that the most appropriate 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 probability. We present a simple two–component form of Bayesian model averaging that is unaffected by characteristics such as small study size or zero–cell counts, and which is capable of incorporating uncertainties into the estimation process. Illustrative examples are given and analysed, using real sparse binomial data.
URI: http://hdl.handle.net/10553/116910
ISSN: 0266-4763
DOI: 10.1080/02664763.2022.2084719
Fuente: Journal of Applied Statistics [ISSN 0266-4763], Junio 2022
Colección:Artículos
Vista completa

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.