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Cited by 153Meta-analysis and systematic reviewsStatistical Methods and Bayesian InferenceStatistical Methods in Epidemiology

Multivariate meta‐analysis

Nam, In‐Sun, Mengersen, Kerrie, Garthwaite, Paul·2003·Statistics in Medicine
DOI 10.1002/sim.1410
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Abstract

Meta‐analysis is now a standard statistical tool for assessing the overall strength and interesting features of a relationship, on the basis of multiple independent studies. There is, however, recent acknowledgement of the fact that in many applications responses are rarely uniquely determined. Hence there has been some change of focus from a single response to the analysis of multiple outcomes. In this paper we propose and evaluate three Bayesian multivariate meta‐analysis models: two multivariate analogues of the traditional univariate random effects models which make different assumptions about the relationships between studies and estimates, and a multivariate random effects model which is a Bayesian adaptation of the mixed model approach. Our preferred method is then illustrated through an analysis of a new data set on parental smoking and two health outcomes (asthma and lower respiratory disease) in children. Copyright © 2003 John Wiley & Sons, Ltd.

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  • Multivariate meta-analysis enables the inclusion of multiple effect sizes reported within the same study and yields more robust pooled estimates by explicitly accounting for within-study dependence.

    “Compared with univariate models, multivariate meta-analysis enables the inclusion of multiple effect sizes reported within the same study and yields more robust pooled estimates by explicitly accounting for within-study dependence (e.g., multiple cognitive outcomes derived from the same participants)21.”

    From Meta-analysis of computerised working memory training: behavioural gains, training parameters, transfer mechanisms, and neural correlates (2026) · cites this paper as [21]

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Authors
Nam, I., Mengersen, K., & Garthwaite, P.
Affiliations
  • Queensland University of Technology (AU)
  • University of Newcastle Australia (AU)
  • The Open University (GB)
Corresponding author
Mengersen, Kerrie
ORCID
Mengersen, Kerrie
Venue
Statistics in Medicine
Publisher
Wiley
Published
2003-06-25
Volume
22
Issue
14
Pages
2309-2333
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.1410
OpenAlex
W2088746366
Cited by
153
References
34
Citations by year
2026: 5 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 3 · 2021: 10 · 2020: 14 · 2019: 6 · 2018: 4 · 2017: 2 · 2016: 3 · 2015: 8 · 2014: 13 · 2013: 13 · 2012: 10
FWCI
5.02
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Statistics in Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Open Research Online - ORO (The Open University) (acceptedVersion)
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
multivariate meta-analysis, Bayesian meta-analysis, random effects models, mixed models, multiple outcomes, parental smoking, childhood asthma, lower respiratory disease, child health outcomes
MeSH terms
Asthma, Australia, Bayes Theorem, Child, Female, Humans, Male, Respiratory Tract Infections, Tobacco Smoke Pollution, Meta-Analysis as Topic, Multivariate Analysis, Odds Ratio

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