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How vague is vague? How informative is informative? Reference analysis for Bayesian meta‐analysis
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Ott, Manuela, Plummer, Martyn and Roos, Małgorzata (2021) How vague is vague? How informative is informative? Reference analysis for Bayesian meta‐analysis. Statistics in Medicine, 40 (2). pp. 4505-4521. doi:10.1002/sim.9076 ISSN 0277-6715.
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WRAP-how-vague-is-vague-how-informative-informative-reference-analysis-Bayesian-meta‐analysis-Plummer-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1572Kb) | Preview |
Official URL: http://dx.doi.org/10.1002/sim.9076
Abstract
Meta-analysis provides important insights for evidence-based medicine by synthesizing evidence from multiple studies which address the same research question. Within the Bayesian framework, meta-analysis is frequently expressed by a Bayesian normal-normal hierarchical model (NNHM). Recently, several publications have discussed the choice of the prior distribution for the between-study heterogeneity in the Bayesian NNHM and used several “vague” priors. However, no approach exists to quantify the informativeness of such priors, and thus, we develop a principled reference analysis framework for the Bayesian NNHM acting at the posterior level. The posterior reference analysis (post-RA) is based on two posterior benchmarks: one induced by the improper reference prior, which is minimally informative for the data, and the other induced by a highly anticonservative proper prior. This approach applies the Hellinger distance to quantify the informativeness of a heterogeneity prior of interest by comparing the corresponding marginal posteriors with both posterior benchmarks. The post-RA is implemented in the freely accessible R package ra4bayesmeta and is applied to two medical case studies. Our findings show that anticonservative heterogeneity priors produce platykurtic posteriors compared with the reference posterior, and they produce shorter 95% credible intervals (CrI) and optimistic inference compared with the reference prior. Conservative heterogeneity priors produce leptokurtic posteriors, longer 95% CrI and cautious inference. The novel post-RA framework could support numerous Bayesian meta-analyses in many research fields, as it determines how informative a heterogeneity prior is for the actual data as compared with the minimally informative reference prior.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics R Medicine > R Medicine (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory , Medicine -- Research -- Statistical methods, Biometry | ||||||||
Journal or Publication Title: | Statistics in Medicine | ||||||||
Publisher: | John Wiley & Sons Ltd. | ||||||||
ISSN: | 0277-6715 | ||||||||
Official Date: | 10 September 2021 | ||||||||
Dates: |
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Volume: | 40 | ||||||||
Number: | 2 | ||||||||
Page Range: | pp. 4505-4521 | ||||||||
DOI: | 10.1002/sim.9076 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 24 June 2021 | ||||||||
Date of first compliant Open Access: | 24 June 2021 | ||||||||
RIOXX Funder/Project Grant: |
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