title: A Bayesian sensitivity study of risk difference in the meta-analysis of binary outcomes from sparse data.
abstract: In most cases, including those of discrete random variables, statistical meta-analysis is carried out using the normal random effect model. The authors argue that normal approximation does not always properly reflect the underlying uncertainty of the original discrete data. Furthermore, in the presence of rare events the results from this approximation can be very poor. This review proposes a Bayesian meta-analysis to address binary outcomes from sparse data and also introduces a simple way to examine the sensitivity of the quantities of interest in the meta-analysis with respect to the structure dependence selected. The findings suggest that for binary outcomes data it is possible to develop a Bayesian procedure, which can be directly applied to sparse data without ad hoc corrections. By choosing a suitable class of linking distributions, the authors found that a Bayesian robustness study can be easily implemented. For illustrative purposes, an example with real data is analyzed using the proposed Bayesian meta-analysis for binomial sparse data.
authors: Department of Quantitative Methods and TiDES Institute, University of Las Palmas de GC, 35017-Las Palmas de Gran Canaria, Spain.
authors: Francisco-Jose
authors: FJ
authors: Vázquez-Polo
authors: Elías
authors: E
authors: Moreno
authors: Miguel A
authors: MA
authors: Negrín
authors: Maria
authors: M
authors: Martel
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D012306:Risk
keywords: Bayesian inference; intrinsic link distribution; meta-predictive distribution; testing on meta-parameters