Abstract
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 language | English (US) |
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Pages (from-to) | 692-700 |
Number of pages | 9 |
Journal | Research Synthesis Methods |
Volume | 12 |
Issue number | 6 |
DOIs | |
State | Published - Nov 2021 |
Bibliographical note
Funding Information:U.S. National Library of Medicine, Grant/Award Number: R01LM012982
Publisher Copyright:
© 2021 John Wiley & Sons Ltd.
Keywords
- BGLIMM
- Bayesian methods
- SAS
- multiple treatment comparisons
- network meta-analysis