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.
Highlights
Network meta-analysis combines evidence from trials comparing different sets of treatments in a single coherent analysis (Lu and Ades, 2004; Caldwell et al, 2005; Jansen et al, 2011) providing estimates of relative treatment effects that are informed by both direct and indirect evidence
This paper describes an alternative parameterization for network meta-analysis to that given in the National Institute for Health and Care Excellence Decision Support Unit Technical Support Document 2, TSD2 (Dias et al, 2014) and a number of other publications (e.g. Cooper et al, 2009)
When using arm-based parameterization, the variation of the estimated value of the trial-level response term can be used directly as a measure of heterogeneity in reference treatment risk. It has been suggested in the literature that the arm-based parameterization is ‘not identified’, we argue in this paper that it is, and we demonstrate empirically that this model produces equivalent results to the contrast-based model
Summary
Network meta-analysis combines evidence from trials comparing different sets of treatments in a single coherent analysis (Lu and Ades, 2004; Caldwell et al, 2005; Jansen et al, 2011) providing estimates of relative treatment effects that are informed by both direct and indirect evidence. Alternative parameterizations can give the same results, but may differ in their complexity and the difficulty with which specific analytic features can be incorporated. This paper describes an alternative parameterization for network meta-analysis to that given in the National Institute for Health and Care Excellence Decision Support Unit Technical Support Document 2, TSD2 (Dias et al, 2014) and a number of other publications Cooper et al, 2009) In keeping with these documents, we implement the arm-based parameterization within a Bayesian framework
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