A simple, step-by-step guide to interpreting decision curve analysis
Open accessAbstract
Abstract Background Decision curve analysis is a method to evaluate prediction models and diagnostic tests that was introduced in a 2006 publication. Decision curves are now commonly reported in the literature, but there remains widespread misunderstanding of and confusion about what they mean. Summary of commentary In this paper, we present a didactic, step-by-step introduction to interpreting a decision curve analysis and answer some common questions about the method. We argue that many of the difficulties with interpreting decision curves can be solved by relabeling the y -axis as “benefit” and the x -axis as “preference.” A model or test can be recommended for clinical use if it has the highest level of benefit across a range of clinically reasonable preferences. Conclusion Decision curves are readily interpretable if readers and authors follow a few simple guidelines.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Setting misclassification costs is not straightforward, and disagreements can occur about what the costs should be.
“Setting misclassification costs is not straightforward, and disagreements can occur about what the costs should be (appendix p 13).”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [85]
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 [85]
Vickers and colleagues provide a step-by-step guide to interpreting decision curve analysis, and a website with a software tutorial and other resources.
“Vickers and colleagues provide a helpful step-by-step guide to interpreting decision curve analysis, and a website with a software tutorial and other resources.107”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [107]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341 · see citing works
Self-citationVickers, A. J., Cronin, A. M., Elkin, E. B., & Gönen, M. (2008) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-8-53 · Cited by 1,377 · see citing works
Self-citationThe Threshold Approach to Clinical Decision Making
Pauker, S. G., & Kassirer, J. P. (1980) · New England Journal of Medicine · doi:10.1056/nejm198005153022003 · Cited by 1,371 · see citing works
Vickers, A. J., Van Calster, B., & Steyerberg, E. W. (2016) · BMJ · doi:10.1136/bmj.i6 · Cited by 1,163 · see citing works
Self-citationStinnett, A. A., & Mullahy, J. (1998) · Medical Decision Making · doi:10.1177/0272989x98018002s09 · Cited by 1,103 · see citing works
Fitzgerald, M., Saville, B. R., & Lewis, R. (2015) · JAMA · doi:10.1001/jama.2015.37 · Cited by 789 · see citing works
Kerr, K. F., Brown, M. D., Zhu, K., & Janes, H. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2015.65.5654 · Cited by 697 · see citing works
Decision Making in Health and Medicine: Integrating Evidence and Values
Hunink, M. G. M., Weinstein, M. C., Wittenberg, E., Drummond, M. F., Pliskin, J. S., Wong, J. B., & Glasziou, P. P. (2014) · doi:10.1017/cbo9781139506779 · Cited by 425 · see citing works
Calibration of Risk Prediction Models: Impact on Decision-Analytic Performance
Van Calster, B., & Vickers, A. J. (2015) · Medical Decision Making · doi:10.1177/0272989x14547233 · Cited by 371 · see citing works
Using Relative Utility Curves to Evaluate Risk Prediction
Baker, S. G., Cook, N. R., Vickers, A., & Kramer, B. S. (2009) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2009.00592.x · Cited by 142 · see citing works
Self-citationBeyond the Usual Prediction Accuracy Metrics: Reporting Results for Clinical Decision Making
Localio, A. R., & Goodman, S. N. (2012) · Annals of Internal Medicine · doi:10.7326/0003-4819-157-4-201208210-00014 · Cited by 139 · see citing works
Vickers, A. J., Kattan, M. W., & Sargent, D. J. (2007) · Trials · doi:10.1186/1745-6215-8-14 · Cited by 105 · see citing works
Self-citationProspective Multi-Institutional Study Evaluating the Performance of Prostate Cancer Risk Calculators
Nam, R. K., Kattan, M. W., Chin, J. L., Trachtenberg, J., Singal, R. K., Rendon, R. A., Klotz, L. H., Sugar, L., Sherman, C., Izawa, J. I., et al. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2010.32.6371 · Cited by 94 · see citing works
Evaluation of Prediction Models for Decision-Making: Beyond Calibration and Discrimination
Holmberg, L., & Vickers, A. J. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001491 · Cited by 51 · see citing works
Self-citationThe summary test tradeoff: a new measure of the value of an additional risk prediction marker
Baker, S. G. (2017) · Statistics in Medicine · doi:10.1002/sim.7132 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Developing clinical prediction models: a step-by-step guide
Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544 · see citing works
Binuya, M. A. E., Engelhardt, E. G., Schats, W., Schmidt, M. K., & Steyerberg, E. W. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01801-8 · Cited by 246 · see citing works
Self-citationVan Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100916 · Cited by 112 · see citing works
Self-citationProteomic Biomarkers of Survival in Idiopathic Pulmonary Fibrosis
Oldham, J. M., Huang, Y., Bose, S., Ma, S.-F., Kim, J. S., Schwab, A., Ting, C., Mou, K., Lee, C. T., Adegunsoye, A., et al. (2024) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202301-0117oc · Cited by 72 · see citing works
Osawa, I., Goto, T., Yamamoto, Y., & Tsugawa, Y. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00354-8 · Cited by 66 · see citing works
Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs
Weiss, J., Raghu, V. K., Paruchuri, K., Zinzuwadia, A., Natarajan, P., Aerts, H. J. W. L., & Lu, M. T. (2024) · Annals of Internal Medicine · doi:10.7326/m23-1898 · Cited by 50 · see citing works
Eriksson, L. S. E., Epstein, E., Testa, A. C., Fischerova, D., Valentin, L., Sladkevicius, P., Franchi, D., Frühauf, F., Fruscio, R., Haak, L. A., et al. (2020) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.21950 · Cited by 24 · see citing works
Self-citationCabanillas Silva, P., Sun, H., Rezk, M., Roccaro-Waldmeyer, D. M., Fliegenschmidt, J., Hulde, N., von Dossow, V., Meesseman, L., Depraetere, K., Stieg, J., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/51409 · Cited by 20 · see citing works
Levy, T. J., Coppa, K., Cang, J., Barnaby, D. P., Paradis, M. D., Cohen, S. L., Makhnevich, A., van Klaveren, D., Kent, D. M., Davidson, K. W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34646-2 · Cited by 14 · see citing works
Landolfo, C., Ceusters, J., Valentin, L., Froyman, W., Van Gorp, T., Heremans, R., Baert, T., Wouters, R., Vankerckhoven, A., Van Rompuy, A.-S., et al. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02578-x · Cited by 14 · see citing works
Self-citation
- Authors
- Vickers, A. J., van Calster, B., & Steyerberg, E. W.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Leiden University Medical Center (NL)
- Corresponding author
- Vickers, Andrew J.
- Venue
- Diagnostic and Prognostic Research
- Published
- 2019-10-04
- Volume
- 3
- Issue
- 1
- Pages
- 18-18
- ISSN
- 2397-7523 (ISSN-L)
- PMC
- PMC6777022
- OpenAlex
- W2977262858
- Cited by
- 1,174
- References
- 15
- Citations by year
- 2026: 309 · 2025: 229 · 2024: 203 · 2023: 145 · 2022: 148 · 2021: 93 · 2020: 45 · 2019: 2
- FWCI
- 56.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Diagnostic and Prognostic Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Cancer Institute (P50-CA92629)
- National Institutes of Health (P30-CA008748, U01 NS086294)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- decision curve analysis, diagnostic test evaluation, prediction model evaluation, clinical decision-making
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
