A Bayesian approach to sequential meta-analysis
| Autor: | David Steinsaltz, Graeme T. Spence, Thomas R. Fanshawe |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Epidemiology Computer science Bayesian probability Posterior probability Bayes Theorem Cochrane Library Bayesian inference 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Sequential method Meta-Analysis as Topic Frequentist inference Sequential analysis Meta-analysis Statistics Humans 030212 general & internal medicine 0101 mathematics |
| Zdroj: | Statistics in Medicine. 35(29) |
| ISSN: | 1097-0258 |
| Popis: | Copyright © 2016 John Wiley & Sons, Ltd. 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. |
| Databáze: | OpenAIRE |
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