Abstract

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 I 2 statistics to quantify the impact of the between‐study heterogeneity and the inconsistency. We apply our model to two examples. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

Keywords

Random effects modelComputer scienceMeta-analysisRanking (information retrieval)Rank (graph theory)Medical statisticsStatisticsEconometricsMachine learningMathematicsMedicine

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Year
2014
Type
article
Volume
33
Issue
21
Pages
3639-3654
Citations
281
Access
Closed

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Dan Jackson, Jessica Barrett, Stephen Rice et al. (2014). A design‐by‐treatment interaction model for network meta‐analysis with random inconsistency effects. Statistics in Medicine , 33 (21) , 3639-3654. https://doi.org/10.1002/sim.6188

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DOI
10.1002/sim.6188