Evidence Synthesis for Decision Making 2 A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials

• Published in: Medical decision making Vol. 33; no. 5; pp. 607 - 617
• Main Authors: Dias, Sofia, Sutton, Alex J., Ades, A. E., Welton, Nicky J.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.07.2013
• Subjects: Decision Making; Evidence-Based Medicine; Likelihood Functions; Models, Theoretical; Network Meta-Analysis as Topic; Randomized Controlled Trials as Topic; generalized linear model; meta-analysis; network meta-analysis; indirect evidence
• ISSN: 0272-989X; 1552-681X
• DOI: 10.1177/0272989X12458724

We set out a generalized linear model framework for the synthesis of data from randomized controlled trials. A common model is described, taking the form of a linear regression for both fixed and random effects synthesis, which can be implemented with normal, binomial, Poisson, and multinomial data. The familiar logistic model for meta-analysis with binomial data is a generalized linear model with a logit link function, which is appropriate for probability outcomes. The same linear regression framework can be applied to continuous outcomes, rate models, competing risks, or ordered category outcomes by using other link functions, such as identity, log, complementary log-log, and probit link functions. The common core model for the linear predictor can be applied to pairwise meta-analysis, indirect comparisons, synthesis of multiarm trials, and mixed treatment comparisons, also known as network meta-analysis, without distinction. We take a Bayesian approach to estimation and provide WinBUGS program code for a Bayesian analysis using Markov chain Monte Carlo simulation. An advantage of this approach is that it is straightforward to extend to shared parameter models where different randomized controlled trials report outcomes in different formats but from a common underlying model. Use of the generalized linear model framework allows us to present a unified account of how models can be compared using the deviance information criterion and how goodness of fit can be assessed using the residual deviance. The approach is illustrated through a range of worked examples for commonly encountered evidence formats.