Approaches to interpreting and choosing the best treatments in network meta-analyses
Open accessAbstract
When randomized trials have addressed multiple interventions for the same health problem, network meta-analyses (NMAs) permit researchers to statistically pool data from individual studies including evidence from both direct and indirect comparisons. Grasping the significance of the results of NMAs may be very challenging. Authors may present the findings from such analyses in several numerical and graphical ways. In this paper, we discuss ranking strategies and visual depictions of rank, including the surface under the cumulative ranking (SUCRA) curve method. We present ranking approaches' merits and limitations and provide an example of how to apply the results of a NMA to clinical practice.
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Using SUCRA rankings to identify the most promising interventions is often misleading because it considers only the point estimate of effect and not the associated precision or certainty of evidence and ignores absolute differences between treatment alternatives.
“This approach is often misleading, as it considers only the point estimate of effect and not the associated precision or certainty of evidence and ignores absolute differences between treatment alternatives.[56][57][58]”
From Management of chronic pain secondary to temporomandibular disorders: a systematic review and network meta-analysis of randomised trials (2023) · cites this paper as [56]
The section “Five reasons why these rankings may mislead if not interpreted correctly” says SUCRA rankings can be misleading because they do not capture certainty, the magnitude of differences between treatments, or whether apparent differences are due to chance. But “Ranking treatments” describes SUCRA as a measure of the likelihood that a treatment occupies a high rank, not as considering only a point estimate; that part of the claim is not supported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe ranking analysis suggests that cognitive behavioural therapy following the French and Morrison protocol, currently the most widely adopted intervention, may not be the best candidate treatment.
“it suggests that CBT-F, which currently represents the most widely adopted intervention, may not be the best candidate”
From Lack of evidence to favor specific preventive interventions in psychosis: a network meta‐analysis (2018) · cites this paper as [66]
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- Authors
- Mbuagbaw, L., Rochwerg, B., Jaeschke, R., Heels-Andsell, D., Alhazzani, W., Thabane, L., & Guyatt, G. H.
- Affiliations
- Central Hospital of Yaoundé (CM)
- McMaster University (CA)
- Population Health Research Institute (CA)
- Corresponding author
- Guyatt, Gordon H.
- Venue
- Systematic Reviews
- Published
- 2017-04-12
- Volume
- 6
- Issue
- 1
- Pages
- 79-79
- ISSN
- 2046-4053 (ISSN-L)
- PMC
- PMC5389085
- OpenAlex
- W2606304149
- Cited by
- 693
- References
- 14
- Citations by year
- 2026: 98 · 2025: 138 · 2024: 103 · 2023: 94 · 2022: 94 · 2021: 69 · 2020: 43 · 2019: 36 · 2018: 17 · 2017: 1
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Systematic Reviews (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Keywords
- network meta-analysis, treatment ranking, surface under the cumulative ranking curve, indirect comparison, head-to-head comparison, clinical decision-making, treatment selection
- MeSH terms
- Data Interpretation, Statistical, Humans, Meta-Analysis as Topic, Randomized Controlled Trials as Topic, Treatment Outcome, Sepsis
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