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Stat Med. 2017 Apr 15;36(8):1210-1226. doi: 10.1002/sim.7223. Epub 2017 Jan 12.

Combining randomized and non-randomized evidence in network meta-analysis.

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Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Department of Primary Education, University of Ioannina, Ioannina, Greece.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Department of Psychiatry and Psychotherapy, Technische Universität München, München, Germany.
Eli Lilly and Company, Lilly Research Centre, Windlesham, U.K.
Department of Cardiology, Bern University Hospital, Bern, Switzerland.
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Berner Institut für Hausarztmedizin (BIHAM), University of Bern, Bern, Switzerland.


Non-randomized studies aim to reveal whether or not interventions are effective in real-life clinical practice, and there is a growing interest in including such evidence in the decision-making process. We evaluate existing methodologies and present new approaches to using non-randomized evidence in a network meta-analysis of randomized controlled trials (RCTs) when the aim is to assess relative treatment effects. We first discuss how to assess compatibility between the two types of evidence. We then present and compare an array of alternative methods that allow the inclusion of non-randomized studies in a network meta-analysis of RCTs: the naïve data synthesis, the design-adjusted synthesis, the use of non-randomized evidence as prior information and the use of three-level hierarchical models. We apply some of the methods in two previously published clinical examples comparing percutaneous interventions for the treatment of coronary in-stent restenosis and antipsychotics in patients with schizophrenia. We discuss in depth the advantages and limitations of each method, and we conclude that the inclusion of real-world evidence from non-randomized studies has the potential to corroborate findings from RCTs, increase precision and enhance the decision-making process.


cohort studies; mixed treatment comparison; multiple treatments meta-analysis; observational data; observational evidence; observational studies

[Indexed for MEDLINE]

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