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Research Methods & Reporting

Demystifying trial networks and network meta-analysis

BMJ 2013; 346 doi: https://doi.org/10.1136/bmj.f2914 (Published 14 May 2013) Cite this as: BMJ 2013;346:f2914
  1. Edward J Mills, associate professor12,
  2. Kristian Thorlund, associate professor23,
  3. John P A Ioannidis, professor24
  1. 1Faculty of Health Sciences, University of Ottawa, 35 University Drive, Ottawa, ON, Canada, K1N 6N5
  2. 2Stanford Prevention Research Center, Department of Medicine, Stanford University School of Humanities and Sciences, Stanford, CA, USA
  3. 3Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada
  4. 4Department of Health Research and Policy, Stanford University School of Medicine, and Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA, USA
  1. Correspondence to: E J Mills Edward.mills{at}uottawa.ca.
  • Accepted 13 March 2013

Networks of randomized clinical trials can be evaluated in the context of a network meta-analysis, a procedure that permits inferences into the comparative effectiveness of interventions that may or may not have been evaluated directly against each other. This approach is quickly gaining popularity among clinicians and guideline decision makers. However, certain methodological aspects are poorly understood. Here, we explain the geometry of a network, statistical and conceptual heterogeneity and incoherence, and challenges in the application and interpretation of data synthesis. These concepts are essential to make sense of a network meta-analysis.

Introduction

When multiple interventions have been used and compared for the same disease and outcomes, network meta-analysis (also commonly referred to as a multiple treatment comparison meta-analysis or mixed treatment meta-analysis) offers a set of methods to visualize and interpret the wider picture of the evidence and to understand the relative merits of these multiple interventions.1 Network meta-analysis has advantages over conventional pairwise meta-analysis, as the technique borrows strength from indirect evidence to gain certainty about all treatment comparisons and allows for estimation of comparative effects that have not been investigated head to head in randomized clinical trials.2 For this reason, network meta-analysis is quickly gaining popularity among clinicians, guideline developers, and health technology agencies as new evidence on new interventions continues to surface and needs to be placed in the context of all available evidence for appraisals.3 For example, over the past two decades more than 20 randomized clinical trials have investigated the long term (>12 months) effects of several variants of warfarin and aspirin as well as other drug treatments for the prevention of stroke in patients with non-rheumatic atrial fibrillation. This accumulation of evidence on multiple treatments has resulted in a network of interventions and comparisons (such as the resulting treatment network, fig …

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