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An informed reference prior for between-study heterogeneity in meta-analyses of binary outcomes.
1Department of Clinical Epidemiologyand Biostatistics, McMaster University, Hamilton, ON, Canada. pullena@mcmaster.ca
Statistics in Medicine
|October 25, 2011
Summary
Bayesian meta-analysis with few studies needs informed priors for between-study variance. This study analyzes Cochrane data to propose realistic priors, improving inference calibration for binary outcomes.
Area of Science:
- Biostatistics
- Medical Informatics
Background:
- Bayesian meta-analysis inference is sensitive to prior choice for between-study variance, especially with few studies.
- Vague priors do not resolve sensitivity issues and can yield unrealistic posterior inferences for heterogeneity.
Purpose of the Study:
- To describe the distribution of between-study variance in published meta-analyses.
- To propose realistic, informed priors for between-study variance in Bayesian meta-analyses of binary outcomes.
Main Methods:
- Analysis of data from the Cochrane Database of Systematic Reviews.
- Characterization of between-study variance distributions.
Main Results:
- Identified patterns in between-study variance across published meta-analyses.
- Developed a set of realistic prior distributions for between-study variance.
Conclusions:
- Informed priors are preferable to vague priors in Bayesian meta-analysis.
- The proposed priors can enhance the calibration and reliability of meta-analytic inferences for binary data.
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