Ranking treatments in frequentist network meta-analysis works without resampling methods
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BACKGROUND: Network meta-analysis is used to compare three or more treatments for the same condition. Within a Bayesian framework, for each treatment the probability of being best, or, more general, the probability that it has a certain rank can be derived from the posterior distributions of all treatments. The treatments can then be ranked by the surface under the cumulative ranking curve (SUCRA). For comparing treatments in a network meta-analysis, we propose a frequentist analogue to SUCRA which we call P-score that works without resampling. METHODS: P-scores are based solely on the point estimates and standard errors of the frequentist network meta-analysis estimates under normality assumption and can easily be calculated as means of one-sided p-values. They measure the mean extent of certainty that a treatment is better than the competing treatments. RESULTS: Using case studies of network meta-analysis in diabetes and depression, we demonstrate that the numerical values of SUCRA and P-Score are nearly identical. CONCLUSIONS: Ranking treatments in frequentist network meta-analysis works without resampling. Like the SUCRA values, P-scores induce a ranking of all treatments that mostly follows that of the point estimates, but takes precision into account. However, neither SUCRA nor P-score offer a major advantage compared to looking at credible or confidence intervals.
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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 [57]
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- Authors
- Rücker, G., & Schwarzer, G.
- Affiliations
- University of Freiburg (DE)
- Corresponding author
- Rücker, Gerta
- Venue
- BMC Medical Research Methodology
- Published
- 2015-07-31
- Volume
- 15
- Issue
- 1
- Pages
- 58-58
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC4521472
- OpenAlex
- W2111999709
- Cited by
- 1,732
- References
- 32
- Citations by year
- 2026: 249 · 2025: 253 · 2024: 267 · 2023: 229 · 2022: 226 · 2021: 191 · 2020: 148 · 2019: 76 · 2018: 52 · 2017: 32 · 2016: 8 · 2015: 1
- FWCI
- 28.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Research Methodology (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (RU1747/1-1)
- 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
- treatment ranking, P-score, surface under the cumulative ranking curve, confidence intervals, diabetes mellitus, depression
- MeSH terms
- Algorithms, Bayes Theorem, Biometry, Data Interpretation, Statistical, Humans, Review Literature as Topic, Meta-Analysis as Topic, Reproducibility of Results, Biomedical Research
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