This article is published in Research Synthesis Methods as a special issue on Network Meta-analysis, edited by Georgia Salanti, University of Ioannina, Greece.
Special Issue Paper
Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies†
Article first published online: 20 JUL 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Research Synthesis Methods
Special Issue: Special issue on Network Meta-analysis
Volume 3, Issue 2, pages 98–110, June 2012
How to Cite
Higgins, J. P. T., Jackson, D., Barrett, J. K., Lu, G., Ades, A. E. and White, I. R. (2012), Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res. Synth. Method, 3: 98–110. doi: 10.1002/jrsm.1044
- Issue published online: 20 JUL 2012
- Article first published online: 20 JUL 2012
- Manuscript Accepted: 15 MAY 2012
- Manuscript Revised: 11 MAY 2012
- Manuscript Received: 22 JUN 2011
- Medical Research Council. Grant Numbers: G0902100, G0600650
- National Science Foundation. Grant Number: DMS-0635449
- network meta-analysis;
- multiple treatments meta-analysis;
- mixed treatment comparisons;
Meta-analyses that simultaneously compare multiple treatments (usually referred to as network meta-analyses or mixed treatment comparisons) are becoming increasingly common. An important component of a network meta-analysis is an assessment of the extent to which different sources of evidence are compatible, both substantively and statistically. A simple indirect comparison may be confounded if the studies involving one of the treatments of interest are fundamentally different from the studies involving the other treatment of interest. Here, we discuss methods for addressing inconsistency of evidence from comparative studies of different treatments. We define and review basic concepts of heterogeneity and inconsistency, and attempt to introduce a distinction between ‘loop inconsistency’ and ‘design inconsistency’. We then propose that the notion of design-by-treatment interaction provides a useful general framework for investigating inconsistency. In particular, using design-by-treatment interactions successfully addresses complications that arise from the presence of multi-arm trials in an evidence network. We show how the inconsistency model proposed by Lu and Ades is a restricted version of our full design-by-treatment interaction model and that there may be several distinct Lu–Ades models for any particular data set. We introduce novel graphical methods for depicting networks of evidence, clearly depicting multi-arm trials and illustrating where there is potential for inconsistency to arise. We apply various inconsistency models to data from trials of different comparisons among four smoking cessation interventions and show that models seeking to address loop inconsistency alone can run into problems. Copyright © 2012 John Wiley & Sons, Ltd.