Bayesian meta-analysis using SAS PROC BGLIMM

Kollin W. Rott, Lifeng Lin, James S. Hodges, Lianne Siegel, Amy Shi, Yong Chen, Haitao Chu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Meta-analysis is commonly used to compare two treatments. Network meta-analysis (NMA) is a powerful extension for comparing and contrasting multiple treatments simultaneously in a systematic review of multiple clinical trials. Although the practical utility of meta-analysis is apparent, it is not always straightforward to implement, especially for those interested in a Bayesian approach. This paper demonstrates that the recently-developed SAS procedure BGLIMM provides an intuitive and computationally efficient means for conducting Bayesian meta-analysis in SAS, using a worked example of a smoking cessation NMA data set. BGLIMM gives practitioners an effective and simple way to implement Bayesian meta-analysis (pairwise and network, either contrast-based or arm-based) without requiring significant background in coding or statistical modeling. Those familiar with generalized linear mixed models, and especially the SAS procedure GLIMMIX, will find this tutorial a useful introduction to Bayesian meta-analysis in SAS.

Original languageEnglish (US)
Pages (from-to)692-700
Number of pages9
JournalResearch Synthesis Methods
Issue number6
Early online dateJun 10 2021
StatePublished - Nov 2021

Bibliographical note

Funding Information:
U.S. National Library of Medicine, Grant/Award Number: R01LM012982

Publisher Copyright:
© 2021 John Wiley & Sons Ltd.


  • Bayesian methods
  • SAS
  • multiple treatment comparisons
  • network meta-analysis
  • Meta-Analysis as Topic
  • Bayes Theorem
  • Linear Models
  • Models, Statistical
  • Network Meta-Analysis
  • Smoking Cessation

PubMed: MeSH publication types

  • Journal Article


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