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A Bayesian approach to sequential meta-analysis.

Graeme T Spence1, David Steinsaltz2, Thomas R Fanshawe1

  • 1Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, U.K.

Statistics in Medicine
|August 3, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian sequential meta-analysis method for determining conclusive evidence. The approach controls error rates effectively, offering a valuable tool for systematic reviews and trial design.

Keywords:
Bayesian inferenceMarkov chain Monte Carlo methodscumulative meta-analysismeta-analysissequential methods

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Research Methodology

Background:

  • Meta-analyses require methods to determine conclusive results for efficient systematic review updates and future trial design.
  • Sequential testing, common in clinical trials, can be adapted for meta-analytic effect size estimates.

Purpose of the Study:

  • To describe and evaluate a Bayesian sequential meta-analysis method.
  • To assess the performance of this method under various parameter combinations using simulation studies.

Main Methods:

  • A Bayesian sequential meta-analysis approach is detailed, incorporating an informative heterogeneity prior.
  • Stopping rule criteria are applied directly to the posterior distribution of the treatment effect parameter.
  • Simulation studies monitor error rates, required study numbers, and parameter estimates.

Main Results:

  • The Bayesian sequential method allows control of overall error rates by adjusting stopping rule thresholds.
  • Error rates are comparable to or lower than alternative frequentist and semi-Bayes methods in most scenarios.
  • Illustrative examples using Cochrane Library data demonstrate the method's application and the impact of heterogeneity priors.

Conclusions:

  • The proposed Bayesian sequential meta-analysis method is a viable approach for determining conclusive evidence in meta-analyses.
  • It offers robust control over error rates and performs competitively against existing methods.
  • This method can inform systematic review updates and the design of future clinical trials.