ArticleResearch synthesis methods2021
Bayesian meta-analysis using SAS PROC BGLIMM.
Article in Research synthesis methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 8 citations in OpenAlex.
- Choice of Link Functions for Generalized Linear Mixed Models in Meta-Analyses of Proportions.Research methods in medicine & health sciences · 2025Article
- Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis.BMC medical research methodology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.