Decision curve analysis: a novel method for evaluating prediction models
Open accessAbstract
BACKGROUND: Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. METHOD: The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. CONCLUSION: Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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Decision curve analysis may further help determine whether AI-assisted predictions provide meaningful net clinical benefit compared with conventional diagnostic strategies.
“Decision curve analysis may further help determine whether AI-assisted predictions provide meaningful net clinical benefit compared with conventional diagnostic strategies [75].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [75]
The most used measure for clinical utility in prediction studies in health care is net benefit.
“The most used measure for clinical utility in prediction studies in health care is net benefit.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [21]
Misclassification costs are informed by the nature and effect of the medical intervention at hand.
“Misclassification costs are informed by the nature and effect of the medical intervention at hand (eg, the decision to perform surgery or not).”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [21]
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Prognostic models for COVID-19 needed updating to warrant transportability over time and space
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- Authors
- Vickers, A. J., & Elkin, E. B.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Vickers, Andrew J.
- Venue
- Medical Decision Making
- Publisher
- SAGE Publications
- Published
- 2006-11
- Volume
- 26
- Issue
- 6
- Pages
- 565-574
- ISSN
- 0272-989X (ISSN-L)
- 1552-681X
- PMC
- PMC2577036
- OpenAlex
- W2045030989
- Cited by
- 6,341
- References
- 23
- Citations by year
- 2026: 1764 · 2025: 619 · 2024: 537 · 2023: 581 · 2022: 641 · 2021: 601 · 2020: 461 · 2019: 252 · 2018: 165 · 2017: 142 · 2016: 123 · 2015: 103 · 2014: 89 · 2013: 63 · 2012: 78
- FWCI
- 8.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Decision Making (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Cancer Institute (R21CA103169, P50 CA92629, P50CA092629)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- decision curve analysis, prediction model evaluation, net benefit, threshold probability, clinical consequences, prostate cancer, seminal vesicle invasion, model comparison
- MeSH terms
- Decision Support Techniques, Humans, Male, Neoplasm Invasiveness, Neoplasm Recurrence, Local, Probability, Prognosis, Prostatic Neoplasms, Seminal Vesicles, Outcome Assessment, Health Care, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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