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

Dan Jackson, Jessica K Barrett, Stephen Rice, Ian R White, Julian P T Higgins

Research output: Contribution to journalArticle (Academic Journal)peer-review

105 Citations (Scopus)

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.

Original languageEnglish
JournalStatistics in Medicine
DOIs
Publication statusPublished - 29 Apr 2014

Bibliographical note

© 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

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