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Theory and practice of Bayesian and frequentist frameworks for network meta-analysis
  1. Behnam Sadeghirad1,2,3,
  2. Farid Foroutan2,4,
  3. Michael J Zoratti2,
  4. Jason W Busse1,2,3,
  5. Romina Brignardello-Petersen2,
  6. Gordon Guyatt2,
  7. Lehana Thabane1,2,5,6
  1. 1 Department of Anesthesia, McMaster University, Hamilton, Ontario, Canada
  2. 2 Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
  3. 3 Michael G. DeGroote National Pain Centre, McMaster University, Hamilton, Ontario, Canada
  4. 4 Ted Rogers Centre for Heart Research, University Health Network, Toronto, Ontario, Canada
  5. 5 Biostatistics Unit, St Joseph’s Healthcar - Hamilton, Hamilton, Ontario, Canada
  6. 6 Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
  1. Correspondence to Dr Behnam Sadeghirad, Department of Anesthesia, McMaster University, Hamilton, ON L8S 4k1, Canada; sadeghb{at}


Network meta-analysis (NMA) is an increasingly popular statistical method of synthesising evidence to assess the comparative benefits and harms of multiple treatments in a single analysis. Several automated software packages facilitate conducting NMA using either of two alternative approaches, Bayesian or frequentist frameworks. Researchers must choose a framework for conducting NMA (Bayesian or frequentist) and select appropriate model(s), and those conducting NMA need to understand the assumptions and limitations of different approaches. Bayesian models are more frequently used and can be more flexible but require checking additional assumptions and greater statistical expertise that are often ignored. The present paper describes the important theoretical aspects of Bayesian and frequentist models for NMA and the applications and considerations of contrast-synthesis and arm-synthesis NMAs. In addition, we present evidence from a limited number of simulation and empirical studies that compared different frequentist and Bayesian models and provide an overview of available automated software packages to perform NMA. We will conclude that when analysts choose appropriate models, there are seldom important differences in the results of Bayesian and frequentist approaches and that network meta-analysts should therefore focus on model features rather than the statistical framework.

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Data sharing not applicable as no datasets generated and/or analysed for this study.

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Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study.

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  • Contributors BS, FF, MJZ and LT did the planning and initiated the framework. All authors further developed the framework and participated in writing the article. BS is the guarantor of the article. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

  • Funding Dr JWB is supported, in part, by a CIHR Canada Research Chair in the prevention and management of chronic pain.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.