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Cited by 277Meta-analysis and systematic reviewsStatistical Methods and Bayesian InferenceStatistical Methods in Clinical Trials

Bayesian random effects meta-analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales

Warn, D. E., Thompson, S. G., Spiegelhalter, D. J.·2002·Statistics in Medicine
DOI 10.1002/sim.1189
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Abstract

When conducting a meta-analysis of clinical trials with binary outcomes, a normal approximation for the summary treatment effect measure in each trial is inappropriate in the common situation where some of the trials in the meta-analysis are small, or the observed risks are close to 0 or 1. This problem can be avoided by making direct use of the binomial distribution within trials. A fully Bayesian method has already been developed for random effects meta-analysis on the log-odds scale using the BUGS implementation of Gibbs sampling. In this paper we demonstrate how this method can be extended to perform analyses on both the absolute and relative risk scales. Within each approach we exemplify how trial-level covariates, including underlying risk, can be considered. Data from 46 trials of the effect of single-dose ibuprofen on post-operative pain are analysed and the results contrasted with those derived from classical and Bayesian summary statistic methods. The clinical interpretation of the odds ratio scale is not straightforward. The advantages and flexibility of a fully Bayesian approach to meta-analysis of binary outcome data, considered on an absolute risk or relative risk scale, are now available.

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  • 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 Warn et al., 2002

  • Common fixed-effect approaches are Mantel-Haenszel and inverse variance; random-effects usually use DerSimonian and Laird, though other methods exist including Bayesian.

    “Common fixed-effect approaches are Mantel-Haenszel and inverse variance, whereas random-effects analyses usually use the DerSimonian and Laird approach, although other methods exist, including Bayesian meta-analysis [201].”

    From The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration (2009) · cites this paper as [201]

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Authors
Warn, D. E., Thompson, S. G., & Spiegelhalter, D. J.
Affiliations
MRC Biostatistics Unit (GB)
Corresponding author
Warn, D. E.
ORCID
Venue
Statistics in Medicine
Publisher
Wiley
Published
2002-05-21
Volume
21
Issue
11
Pages
1601-1623
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.1189
OpenAlex
W2143109076
Cited by
277
References
49
Citations by year
2026: 5 · 2025: 10 · 2024: 6 · 2023: 12 · 2022: 11 · 2021: 12 · 2020: 14 · 2019: 19 · 2018: 12 · 2017: 13 · 2016: 12 · 2015: 13 · 2014: 11 · 2013: 12 · 2012: 25
FWCI
13.37
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
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 Clinical Trials
Keywords
binary outcomes, risk difference, relative risk, binomial distribution, ibuprofen, postoperative pain, odds ratio interpretation, small trials
MeSH terms
Anti-Inflammatory Agents, Non-Steroidal, Bayes Theorem, Clinical Trials as Topic, Humans, Ibuprofen, Pain, Risk, Meta-Analysis as Topic

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