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Multiple treatment comparison meta-analyses: a step forward into complexity

Authors Mills E, Bansback N, Ghement, Tholund, Kelly S, Puhan, Wright G

Published 27 May 2011 Volume 2011:3(1) Pages 193—202

DOI https://doi.org/10.2147/CLEP.S16526

Review by Single-blind

Peer reviewer comments 4


Edward J Mills1, Nick Bansback2,8, Isabella Ghement3, Kristian Thorlund4, Steven Kelly5, Milo A Puhan6, James Wright7
1Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada; 2Centre for Health Evaluation and Outcomes Sciences (CHEOS), University of British Columbia, Vancouver, BC, Canada; 3Ghement Statistical Consulting Company, Richmond, BC, Canada; 4Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; 5Department of Outcomes Research and Evidence Based Medicine, Pfizer Ltd, Walton Oaks, UK; 6Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; 7Department of Oncology and Medicine, McMaster University, Hamilton, ON, Canada; 8School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada

Abstract: The use of meta-analysis has become increasingly useful for clinical and policy decision making. A recent development in meta-analysis, multiple treatment comparison (MTC) meta-analysis, provides inferences on the comparative effectiveness of interventions that may have never been directly evaluated in clinical trials. This new approach may be confusing for clinicians and methodologists and raises specific challenges relevant to certain areas of medicine. This article addresses the methodological concepts of MTC meta-analysis, including issues of heterogeneity, choice of model, and adequacy of sample sizes. We address domain-specific challenges relevant to disciplines of medicine, including baseline risks of patient populations. We conclude that MTC meta-analysis is a useful tool in the context of comparative effectiveness and requires further study, as its utility and transparency will likely predict its uptake by the research and clinical community.

Keywords: network, multiple treatment comparison, mixed treatment comparison, meta-analysis

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