'Arm-based' parameterization for network meta-analysis

Abstract

We present an alternative to the contrast-based parameterization used in a number of publications for network meta-analysis. This alternative "arm-based" parameterization offers a number of advantages: it allows for a "long" normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi-arm trials is straightforward and avoids the need to generate a multivariate distribution describing treatment effects; there is a direct mapping between the parameterization and the analysis script in languages such as WinBUGS and finally, the arm-based parameterization allows simple extension to treatment-specific random treatment effect variances. We validated the parameterization using a published smoking cessation dataset. Network meta-analysis using arm- and contrast-based parameterizations produced comparable results (with means and standard deviations being within +/- 0.01) for both fixed and random effects models. We recommend that analysts consider using arm-based parameterization when carrying out network meta-analyses. © 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

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