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Bayesian meta-analysis using SAS PROC BGLIMM.

Kollin W Rott1, Lifeng Lin2, James S Hodges1

  • 1Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA.

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This summary is machine-generated.

This study introduces SAS BGLIMM for Bayesian meta-analysis, simplifying complex comparisons of multiple treatments. It offers an accessible method for network meta-analysis (NMA) in SAS.

Keywords:
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Area of Science:

  • Biostatistics
  • Clinical Research Methodology
  • Statistical Software Applications

Background:

  • Meta-analysis is standard for comparing two treatments.
  • Network meta-analysis (NMA) extends this to multiple treatments simultaneously.
  • Implementing Bayesian meta-analysis can be complex.

Purpose of the Study:

  • To demonstrate the SAS procedure BGLIMM for Bayesian meta-analysis.
  • To provide an intuitive and efficient method for conducting pairwise and network meta-analyses in SAS.
  • To simplify Bayesian meta-analysis for practitioners without extensive coding or modeling expertise.

Main Methods:

  • Utilized the SAS procedure BGLIMM.
  • Applied BGLIMM to a smoking cessation network meta-analysis dataset.
  • Demonstrated both contrast-based and arm-based NMA.

Main Results:

  • SAS BGLIMM offers an intuitive and computationally efficient approach to Bayesian meta-analysis.
  • The procedure simplifies the implementation of pairwise and network meta-analyses.
  • Users familiar with SAS GLIMMIX can readily adopt BGLIMM.

Conclusions:

  • SAS BGLIMM is an effective tool for Bayesian meta-analysis.
  • It lowers the barrier to entry for complex meta-analytic techniques.
  • Facilitates robust comparative treatment effectiveness research.