The impact of covariance priors on arm-based Bayesian network meta-analyses with binary outcomes

Zhenxun Wang, Lifeng Lin, James S. Hodges, Haitao Chu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Bayesian analyses with the arm-based (AB) network meta-analysis (NMA) model require researchers to specify a prior distribution for the covariance matrix of the treatment-specific event rates in a transformed scale, for example, the treatment-specific log-odds when a logit transformation is used. The commonly used conjugate prior for the covariance matrix, the inverse-Wishart (IW) distribution, has several limitations. For example, although the IW distribution is often described as noninformative or weakly informative, it may in fact provide strong information when some variance components are small (eg, when the standard deviation of study-specific log-odds of a treatment is smaller than 1/2), as is common in NMAs with binary outcomes. In addition, the IW prior generally leads to underestimation of correlations between treatment-specific log-odds, which are critical for borrowing strength across treatment arms to estimate treatment effects efficiently and to reduce potential bias. Alternatively, several separation strategies (ie, separate priors on variances and correlations) can be considered. To study the IW prior's impact on NMA results and compare it with separation strategies, we did simulation studies under different missing-treatment mechanisms. A separation strategy with appropriate priors for the correlation matrix and variances performs better than the IW prior, and should be recommended as the default vague prior in the AB NMA approach. Finally, we reanalyzed three case studies and illustrated the importance, when performing AB-NMA, of sensitivity analyses with different prior specifications on variances.

Original languageEnglish (US)
Pages (from-to)2883-2900
Number of pages18
JournalStatistics in Medicine
Volume39
Issue number22
DOIs
StatePublished - Sep 30 2020

Bibliographical note

Funding Information:
information U.S. National Library of Medicine, R01LM012982This research was supported in part by NIH NLM R01LM012982.

Funding Information:
This research was supported in part by NIH NLM R01LM012982.

Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Bayesian inference
  • covariance matrix
  • network meta-analysis
  • prior

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