Approximate Bayesian inference for random effects meta-analysis.
Whilst meta-analysis is becoming a more commonplace statistical technique, Bayesian inference in meta-analysis requires complex computational techniques to be routinely applied. We consider simple approximations for the first and second moments of the parameters of a Bayesian random effects model for meta-analysis. These computationally inexpensive methods are based on simple analytical formulae that provide an efficient tool for a qualitative analysis and a quick numerical estimation of posterior quantities. They are shown to lead to sensible approximations in two examples of meta-analyses and to be in broad agreement with the more computationally intensive Gibbs sampling.
Abrams
K
K
Department of Epidemiology and Public Health, University of Leicester, U.K. kral@le.ac.uk
Sansó
B
B
D019072:Antibiotic Prophylaxis; D001499:Bayes Theorem; D003731:Dental Caries / Q000517:prevention & control; D003802:Dentifrices / Q000627:therapeutic use; D004064:Digestive System / Q000382:microbiology; D006801:Humans; D016015:Logistic Models; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D016017:Odds Ratio
D003802:Dentifrices