Bayesian network meta-analysis

Network meta-analysis is a statistical approach to combining evidence from multiple studies comparing multiple treatments. It may be "two-stage", where treatment effects and their variances are estimated separately for each study and then combined using a normal approximation or "one stage", where summary statistics at treatment group level (for example, number of events and number of individuals) are directly analyzed. My network suite currently provides various tools for exploring network meta-analysis data and analyzing them in a two-stage frequentist approach (C. White 2015, The Stata Journal 15: 1–34). I will describe arguments for preferring a one-stage Bayesian approach and recent work implementing it. The one-stage approach amounts to fitting a generalized linear mixed model, but I was unable to achieve adequate mixing using bayes: meglm. I will describe my alternative approach of automating the writing and running of a WinBUGS program. This process is implemented in the new network bayes and allows substantial modelling flexibility, including normal or binomial data; various contrast-based and arm-based models; various heterogeneity structures; and the option to sample from the prior. Features not yet implemented are inconsistency models and meta-regression.