Author
Spence, G
Steinsaltz, D
Fanshawe, T
Journal title
Statistics in Medicine
DOI
10.1002/sim.7052
Issue
29
Volume
35
Last updated
2022-03-06T21:37:40.47+00:00
Page
5356-5375
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.
Symplectic ID
631423
Publication type
Journal Article
Publication date
1 January 2016
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