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Combining randomized and non‐randomized evidence in network meta‐analysis
Author(s) -
Efthimiou Orestis,
Mavridis Dimitris,
Debray Thomas P. A.,
Samara Myrto,
Belger Mark,
Siontis George C. M.,
Leucht Stefan,
Salanti Georgia
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7223
Subject(s) - randomized controlled trial , meta analysis , computer science , randomized experiment , statistics , econometrics , medicine , mathematics
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. Copyright © 2017 John Wiley & Sons, Ltd.