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

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Jackson, Dan 
Barrett, Jessica K 
Turner, Rebecca 
Higgins, Julian PT 

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.

method of moments, mixed treatment comparisons, multiple treatments meta-analysis, network meta-analysis, Anti-Bacterial Agents, Arthralgia, Bayes Theorem, Clinical Trials as Topic, Computer Simulation, Humans, Meta-Analysis as Topic, Models, Statistical, Osteoarthritis, Knee, Otitis Media with Effusion, Probability, Regression Analysis, Sample Size, Tympanic Membrane Perforation, Uncertainty
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Stat Med
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MRC (MR/L501566/1)
Medical Research Council (MR/K014811/1)
Medical Research Council (MR/L003120/1)
National Institute for Health Research (NIHR) (via Royal Brompton & Harefield NHS Foundation Trust) (unknown)
British Heart Foundation (None)
DJ, RT and IRW are employed by the UK Medical Research Council (code U105260558). JB is supported by the UK MRC grant numbers G0902100 and MR/K014811/1.