Bayesian approaches to random-effects meta-analysis: a comparative study

Smith, Teresa C.; Spiegelhalter, David J.; Thomas, Andrew

doi:10.1002/sim.4780142408

Cited by 568Meta-analysis and systematic reviewsStatistical Methods and Bayesian InferenceStatistical Methods in Epidemiology

Bayesian approaches to random-effects meta-analysis: a comparative study

Smith, Teresa C., Spiegelhalter, David J., Thomas, Andrew·1995·Statistics in Medicine

DOI 10.1002/sim.4780142408

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Abstract

Current methods for meta-analysis still leave a number of unresolved issues, such as the choice between fixed- and random-effects models, the choice of population distribution in a random-effects analysis, the treatment of small studies and extreme results, and incorporation of study-specific covariates. We describe how a full Bayesian analysis can deal with these and other issues in a natural way, illustrated by a recent published example that displays a number of problems. Such analyses are now generally available using the BUGS implementation of Markov chain Monte Carlo numerical integration techniques. Appropriate proper prior distributions are derived, and sensitivity analysis to a variety of prior assumptions carried out. Current methods are briefly summarized and compared to the full Bayes analysis.

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Using an inverse gamma distribution for τ2 with parameters 0.001 and 0.001 produces a prediction interval from −0.008 to 0.73 (P(θnew<0)=0.973).
“Alternatively using an inverse gamma distribution for τ2 with parameters 0.001 and 0.001 (Smith et al., 1995) produces a prediction interval from −0.008 to 0.73 (P(θnew<0)=0.973)”
From A Re-Evaluation of Random-Effects Meta-Analysis (2009) · cites this paper as Smith et al., 1995
When 2×2 contingency tables are available for binary outcomes, meta-analyses are appropriately performed by using binomial likelihoods.
“For example, when 2×2 contingency tables are available for binary outcomes, meta-analyses are appropriately performed by using binomial likelihoods (Smith et al., 1995; Warn et al., 2002).”
From A Re-Evaluation of Random-Effects Meta-Analysis (2009) · cites this paper as Smith et al., 1995

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Authors: Smith, T. C., Spiegelhalter, D. J., & Thomas, A.
Affiliations: MRC Biostatistics Unit (GB)
ORCID: Smith, Teresa C.
Spiegelhalter, David J.
Venue: Statistics in Medicine
Publisher: Wiley
Published: 1995-12-30
Volume: 14
Issue: 24
Pages: 2685-2699
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.4780142408
OpenAlex: W2099880429
Cited by: 568
References: 21
Citations by year: 2026: 10 · 2025: 9 · 2024: 20 · 2023: 12 · 2022: 12 · 2021: 26 · 2020: 32 · 2019: 18 · 2018: 36 · 2017: 20 · 2016: 21 · 2015: 21 · 2014: 27 · 2013: 27 · 2012: 38
FWCI: 6.47
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 4078 d)
Locations: Statistics in Medicine (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: random-effects meta-analysis, Bayesian meta-analysis, fixed effects model, prior distributions, sensitivity analysis, small-study effects, bugs, Markov chain Monte Carlo, population distribution
MeSH terms: Bayes Theorem, Humans, Intensive Care Units, Markov Chains, Monte Carlo Method, Respiratory Tract Infections, Sensitivity and Specificity, Software, Stochastic Processes, Meta-Analysis as Topic, Binomial Distribution, Linear Models, Logistic Models, Odds Ratio

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Bayesian approaches to random-effects meta-analysis: a comparative study

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