A design-by-treatment interaction model for network meta-analysis with random inconsistency effects

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 model, using these random effects to model inconsistency and estimate the parameters of primary interest. Our proposal is a generalisation of the model proposed by Lumley and allows trials with three or more arms to be included in the analysis. Our methods also facilitate the ranking of treatments under inconsistency. We derive R and I2 statistics to quantify the impact of the between-study heterogeneity and the inconsistency. We apply our model to two examples. (c) 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.