title: A Bayesian approach to sequential meta-analysis.
abstract: As evidence accumulates within a meta-analysis, it is desirable to determine when the results could be considered conclusive to guide systematic review updates and future trial designs. Adapting sequential testing methodology from clinical trials for application to pooled meta-analytic effect size estimates appears well suited for this objective. In this paper, we describe a Bayesian sequential meta-analysis method, in which an informative heterogeneity prior is employed and stopping rule criteria are applied directly to the posterior distribution for the treatment effect parameter. Using simulation studies, we examine how well this approach performs under different parameter combinations by monitoring the proportion of sequential meta-analyses that reach incorrect conclusions (to yield error rates), the number of studies required to reach conclusion, and the resulting parameter estimates. By adjusting the stopping rule thresholds, the overall error rates can be controlled within the target levels and are no higher than those of alternative frequentist and semi-Bayes methods for the majority of the simulation scenarios. To illustrate the potential application of this method, we consider two contrasting meta-analyses using data from the Cochrane Library and compare the results of employing different sequential methods while examining the effect of the heterogeneity prior in the proposed Bayesian approach. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, U.K.
authors.forename: Graeme T
authors.initials: GT
authors.lastname: Spence
authors.affiliation: Department of Statistics, University of Oxford, Oxford, U.K.
authors.forename: David
authors.initials: D
authors.lastname: Steinsaltz
authors.affiliation: Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, U.K.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Fanshawe
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D015201:Meta-Analysis as Topic
keywords: Bayesian inference; Markov chain Monte Carlo methods; cumulative meta-analysis; meta-analysis; sequential methods