Meta-Analysis with Few Studies and Binary Data: A Bayesian Model Averaging Approach

Francisco-José Vázquez-Polo, Miguel-Ángel Negrín-Hernández and María Martel-Escobar
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Francisco-José Vázquez-Polo: Department of Quantitative Methods & TiDES Institute, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain
Miguel-Ángel Negrín-Hernández: Department of Quantitative Methods & TiDES Institute, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain
María Martel-Escobar: Department of Quantitative Methods & TiDES Institute, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain

Mathematics, 2020, vol. 8, issue 12, 1-13

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.

Keywords: Bayesian model averaging (BMA); binary data; clustering; few studies; heterogeneity; meta-analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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