A Bayesian network meta-analysis for binary outcome: how to do it

Stat Methods Med Res. 2016 Oct;25(5):1757-1773. doi: 10.1177/0962280213500185. Epub 2013 Aug 22.


This study presents an overview of conceptual and practical issues of a network meta-analysis (NMA), particularly focusing on its application to randomised controlled trials with a binary outcome of interest. We start from general considerations on NMA to specifically appraise how to collect study data, structure the analytical network and specify the requirements for different models and parameter interpretations, with the ultimate goal of providing physicians and clinician-investigators a practical tool to understand pros and cons of NMA. Specifically, we outline the key steps, from the literature search to sensitivity analysis, necessary to perform a valid NMA of binomial data, exploiting Markov Chain Monte Carlo approaches. We also apply this analytical approach to a case study on the beneficial effects of volatile agents compared to total intravenous anaesthetics for surgery to further clarify the statistical details of the models, diagnostics and computations. Finally, datasets and models for the freeware WinBUGS package are presented for the anaesthetic agent example.

Keywords: Bayesian; WinBUGS; anaesthetic agents; binary outcomes; hierarchical models; mixed treatment comparison; network meta-analysis.

MeSH terms

  • Bayes Theorem*
  • Humans
  • Markov Chains
  • Monte Carlo Method
  • Network Meta-Analysis*