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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...
Network meta‐analysis is becoming more popular as a way to analyse multiple treatments simultaneously and, in the right circumstances, rank treatments. A difficulty in practice is the possibility of ‘inconsistency’ or ‘incoherence’, where direct evidence and indirect evidence are not in agreement. Here, we develop a random‐effects implementation of the recently proposed design‐by‐treatment interaction...
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