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Objective Bayesian meta‐analysis for sparse discrete data
Author(s) -
Moreno E.,
VázquezPolo F.J.,
Negrín M.A.
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6163
Subject(s) - bivariate analysis , prior probability , computer science , bayesian probability , random effects model , binomial distribution , meta analysis , econometrics , marginal distribution , bayes' theorem , range (aeronautics) , statistics , mathematics , machine learning , artificial intelligence , random variable , medicine , materials science , composite material
This paper presents a Bayesian model for meta‐analysis of sparse discrete binomial data, which are out of the scope of the usual hierarchical normal random‐effect models. Treatment effectiveness data are often of this type. The crucial linking distribution between the effectiveness conditional on the healthcare center and the unconditional effectiveness is constructed from specific bivariate classes of distributions with given marginals. This assures coherency between the marginal and conditional prior distributions utilized in the analysis. Further, we impose a bivariate class of priors that is able to accommodate a wide range of heterogeneity degrees between the multicenter clinical trials involved. Applications to real multicenter data are given and compared with previous meta‐analysis. Copyright © 2014 John Wiley & Sons, Ltd.