Extending DerSimonian and Laird's methodology to perform network meta‐analyses with random inconsistency effects

Dan Jackson · Martin Wai-Ming Law · Jessica Barrett · Rebecca Turner · Julian P. T. Higgins · Georgia Salanti +1 more

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Meta-analysis and systematic reviews Statistical Methods in Clinical Trials Statistical Methods and Bayesian Inference

Abstract

Network meta-analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta-analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi-parametric, non-iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples.

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References

1986Meta-analysis in clinical trials 2002Quantifying heterogeneity in a meta‐analysis 2010Checking consistency in mixed treatment comparison meta‐analysis 2012Consistency and inconsistency in network meta‐analysis: concepts and models for multi‐arm studies 2012Indirect and mixed‐treatment comparison, network, or multiple‐treatments meta‐analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool 2002Network meta‐analysis for indirect treatment comparisons 2012Consistency and inconsistency in network meta‐analysis: model estimation using multivariate meta‐regression 2007Evaluation of networks of randomized trials

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