A generalized pairwise modelling framework for network meta-analysis

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Abstract

Indirect comparison methods are used to measure the effect of two treatments that were each compared against a similar control group in a meta-analysis. The network meta-analysis method extends this to multiple treatments which are assessed simultaneously. Currently, there exist Bayesian and multivariate modelling approaches to these analyses, but these are computationally intensive and rely on assumptions that may not be valid in practice. Here we introduce a generalized pairwise modelling (GPM) framework for network meta-analysis, so named as it is based on the repeated application of adjusted indirect comparisons, also known as the Bucher method. The validity of this method hinges on the sufficient similarity of the common control node (transitivity), and for the application in the GPM framework this requirement extends to all common nodes used to make an indirect comparison estimate. Apart from the assumption of sufficient similarity, the GPM framework assumes only standard arithmetic and statistical rules making it more robust when compared with existing methods for network meta-analysis. A software program (MetaXL; www.epigear.com) is available to run this framework, so it is easily accessible to researchers.

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