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Bayesian meta‐analysis using SAS PROC BGLIMM
Author(s) -
Rott Kollin W.,
Lin Lifeng,
Hodges James S.,
Siegel Lianne,
Shi Amy,
Chen Yong,
Chu Haitao
Publication year - 2021
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1513
Subject(s) - computer science , meta analysis , bayesian probability , pairwise comparison , bayesian network , set (abstract data type) , contrast (vision) , statistical analysis , artificial intelligence , statistics , mathematics , medicine , programming language
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.