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Characteristics and methods of incorporating randomized and nonrandomized evidence in network meta-analyses: a
Kathryn Zhang1, Paul Arora2, Neha Sati3
1Department of Public Health, Dalla Lana School of Public Health, University of Toronto, 6th floor, 155 College Street, Toronto, Ontario M5T 3M7, Canada.
Objectives:
The objective of the study was to conduct a scoping review of the published literature on methods used to combine randomized and nonrandomized evidence (NRE) in network meta-analyses (NMAs) and their respective characteristics.
Study Design And Setting:
We conducted a scoping review using a list of NMAs which incorporated NRE that were identified from a previous review. All NMAs that included NRE in the analysis of main outcomes or sensitivity analyses were eligible for inclusion. Two reviewers independently screened studies for inclusion and performed data abstraction. Data analysis involved quantitative (frequencies and percentages) and qualitative (narrative synthesis) methods.
Results:
A total of 23 NMAs met the predefined inclusion criteria, of which 74% (n = 17) used naïve pooling, 0% used NRE as informative priors, 9% (n = 2) used the 3-level Bayesian hierarchical model, 9% (n = 2) used all methods, and 9% (n = 2) used other methods. Most NMAs were supplemented with additional analyses to investigate the effect estimates when only randomized evidence was included.
Conclusion:
Although most studies provided justification for the inclusion of NRE, transparent reporting of the method used to combine randomized evidence and NRE was unclear in most published networks. Most NMAs used naïve pooling for combining randomized evidence and NRE.
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