A Bayesian approach to sequential meta-analysis
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 e...
| Main Authors: | , , |
|---|---|
| Format: | Journal article |
| Published: |
John Wiley and Sons, Ltd
2016
|
| _version_ | 1797102702293942272 |
|---|---|
| author | Spence, G Steinsaltz, D Fanshawe, T |
| author_facet | Spence, G Steinsaltz, D Fanshawe, T |
| author_sort | Spence, G |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T06:09:32Z |
| format | Journal article |
| id | oxford-uuid:ef0143ad-7f53-483c-a99f-2ffcbc054b76 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:09:32Z |
| publishDate | 2016 |
| publisher | John Wiley and Sons, Ltd |
| record_format | dspace |
| spelling | oxford-uuid:ef0143ad-7f53-483c-a99f-2ffcbc054b762022-03-27T11:37:01ZA Bayesian approach to sequential meta-analysisJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ef0143ad-7f53-483c-a99f-2ffcbc054b76Symplectic Elements at OxfordJohn Wiley and Sons, Ltd2016Spence, GSteinsaltz, DFanshawe, TAs 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. |
| spellingShingle | Spence, G Steinsaltz, D Fanshawe, T A Bayesian approach to sequential meta-analysis |
| title | A Bayesian approach to sequential meta-analysis |
| title_full | A Bayesian approach to sequential meta-analysis |
| title_fullStr | A Bayesian approach to sequential meta-analysis |
| title_full_unstemmed | A Bayesian approach to sequential meta-analysis |
| title_short | A Bayesian approach to sequential meta-analysis |
| title_sort | bayesian approach to sequential meta analysis |
| work_keys_str_mv | AT spenceg abayesianapproachtosequentialmetaanalysis AT steinsaltzd abayesianapproachtosequentialmetaanalysis AT fanshawet abayesianapproachtosequentialmetaanalysis AT spenceg bayesianapproachtosequentialmetaanalysis AT steinsaltzd bayesianapproachtosequentialmetaanalysis AT fanshawet bayesianapproachtosequentialmetaanalysis |