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Research Methods & Reporting

Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples

BMJ 2017; 358 doi: https://doi.org/10.1136/bmj.j3932 (Published 13 September 2017) Cite this as: BMJ 2017;358:j3932
  1. Richard D Riley, professor of biostatistics1,
  2. Dan Jackson, senior statistician2,
  3. Georgia Salanti, associate professor of biostatistics and epidemiology3 4,
  4. Danielle L Burke, research associate in biostatistics1,
  5. Malcolm Price, lecturer in biostatistics5,
  6. Jamie Kirkham, senior lecturer in medical statistics6,
  7. Ian R White, professor of statistical methods for medicine2 7
  1. 1Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UK
  2. 2MRC Biostatistics Unit, Cambridge, UK
  3. 3Institute of Social and Preventive Medicine, University of Bern, Switzerland
  4. 4University of Ioannina School of Medicine, Ioannina, Greece
  5. 5Institute of Applied Health Research, University of Birmingham, UK
  6. 6MRC North West Hub for Trials Methodology Research, Department of Biostatistics, University of Liverpool, Liverpool, UK
  7. 7MRC Clinical Trials Unit at UCL, London, UK
  1. Correspondence to: R D Riley r.riley{at}keele.ac.uk

Organisations such as the National Institute for Health and Care Excellence require the synthesis of evidence from existing studies to inform their decisions—for example, about the best available treatments with respect to multiple efficacy and safety outcomes. However, relevant studies may not provide direct evidence about all the treatments or outcomes of interest. Multivariate and network meta-analysis methods provide a framework to address this, using correlated or indirect evidence from such studies alongside any direct evidence. In this article, the authors describe the key concepts and assumptions of these methods, outline how correlated and indirect evidence arises, and illustrate the contribution of such evidence in real clinical examples involving multiple outcomes and multiple treatments

Summary points

  • Meta-analysis methods combine quantitative evidence from related studies to produce results based on a whole body of research

  • Studies that do not provide direct evidence about a particular outcome or treatment comparison of interest are often discarded from a meta-analysis of that outcome or treatment comparison

  • Multivariate and network meta-analysis methods simultaneously analyse multiple outcomes and multiple treatments, respectively, which allows more studies to contribute towards each outcome and treatment comparison

  • Summary results for each outcome now depend on correlated results from other outcomes, and summary results for each treatment comparison now incorporate indirect evidence from related treatment comparisons, in addition to any direct evidence

  • This often leads to a gain in information, which can be quantified by the “borrowing of strength” statistic, BoS (the percentage reduction in the variance of a summary result that is due to correlated or indirect evidence)

  • Under a missing at random assumption, a multivariate meta-analysis of multiple outcomes is most beneficial when the outcomes are highly correlated and the percentage of studies with missing outcomes is large

  • Network meta-analyses gain information through a consistency assumption, which should be evaluated in …

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