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GRADE approach to drawing conclusions from a network meta-analysis using a minimally contextualised framework
Open accessAbstract
This article describes GRADE (grading of recommendations assessment, development and evaluation) guidance on how to draw conclusions from a network meta-analysis of interventions that includes individual randomised controlled trials addressing a single outcome. The guidance uses a minimally contextualised approach that avoids value judgments regarding the magnitude of intervention effects. The framework is based on two principles: interventions should be grouped in categories, from the most to the least effective or harmful; and the judgments that place interventions in such categories should simultaneously consider the estimates of effect, the certainty of the evidence, and the rankings. The framework includes five steps, which we describe and illustrate using an example. The framework is simple, methodologically sound, and flexible, allowing for modifications to resolve situations in which additional complexity or value judgments might be appropriate.
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“We evaluated confidence in the evidence for the primary outcomes using CINeMA (Confidence-In-Network-Meta-Analysis), 14 and conclusions were drawn using a minimally contextualised framework of the GRADE approach.15”
From Comparative efficacy and tolerability of pharmacological interventions for acute bipolar depression in adults: a systematic review and network meta-analysis (2023) · cites this paper as [15]
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“We used the GRADE framework to assess the overall quality of evidence and presented our main findings according to GRADE guidance for NMA. 42 , 46”
From Exercise for sarcopenia in older people: A systematic review and network meta‐analysis (2023) · cites this paper as [42]
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Self-citationThe GRADE Working Group clarifies the construct of certainty of evidence
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Self-citationAdvances in the GRADE approach to rate the certainty in estimates from a network meta-analysis
Brignardello-Petersen, R., Bonner, A., Alexander, P. E., Siemieniuk, R. A., Furukawa, T. A., Rochwerg, B., Hazlewood, G. S., Alhazzani, W., Mustafa, R. A., Murad, M. H., et al. (2018) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2017.10.005 · Cited by 753
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Self-citationCompleting ‘Summary of findings’ tables and grading the certainty of the evidence
Schünemann, H. J., Higgins, J. P., Vist, G. E., Glasziou, P., Akl, E. A., Skoetz, N., Guyatt, G. H., & on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group (2019) · Cochrane Handbook for Systematic Reviews of Interventions · doi:10.1002/9781119536604.ch14 · Cited by 669
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Metadata
- Authors
- Brignardello-Petersen, R., Florez, I. D., Izcovich, A., Santesso, N., Hazlewood, G., Alhazanni, W., Yepes-Nuñez, J. J., Tomlinson, G., Schünemann, H. J., & Guyatt, G. H.
- Affiliations
- Hospital Alemán (AR)
- McMaster University (CA)
- University of Calgary (CA)
- University Health Network (CA)
- ORCID
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2020-11-11
- Volume
- 371
- Pages
- m3900
- ISSN
- 1756-1833 (ISSN-L)
- OpenAlex
- W3099906708
- Cited by
- 320
- References
- 35
- Citations by year
- 2026: 42 · 2025: 62 · 2024: 77 · 2023: 60 · 2022: 52 · 2021: 23 · 2020: 4
- FWCI
- 21.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.bmj.com/company/legal-information/terms-conditions/legal-information/tdm-licencepolicy (tdm)
- Locations
- BMJ (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Keywords
- GRADE, network meta-analysis, evidence synthesis, intervention ranking, quality of evidence, value judgments, evidence-based recommendations
- MeSH terms
- Network Meta-Analysis as Topic, Network Meta-Analysis, GRADE Approach, Humans, Randomized Controlled Trials as Topic
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