The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the performance of clinical prediction models
Open accessAbstract
OBJECTIVES: Receiver operating characteristic (ROC) curves show how well a risk prediction model discriminates between patients with and without a condition. We aim to investigate how ROC curves are presented in the literature and discuss and illustrate their potential limitations. STUDY DESIGN AND SETTING: We conducted a pragmatic literature review of contemporary publications that externally validated clinical prediction models. We illustrated limitations of ROC curves using a testicular cancer case study and simulated data. RESULTS: Of 86 identified prediction modeling studies, 52 (60%) presented ROC curves without thresholds and one (1%) presented an ROC curve with only a few thresholds. We illustrate that ROC curves in their standard form withhold threshold information have an unstable shape even for the same area under the curve (AUC) and are problematic for comparing model performance conditional on threshold. We compare ROC curves with classification plots, which show sensitivity and specificity conditional on risk thresholds. CONCLUSION: ROC curves do not offer more information than the AUC to indicate discriminative ability. To assess the model's performance for decision-making, results should be provided conditional on risk thresholds. Therefore, if discriminatory ability must be visualized, classification plots are attractive.
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“For binary outcomes, the C-statistic is equal to area under the receiver operating characteristic curve (AUROC). 37,38”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [38]
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“A limitation of these plots is that the thresholds are not easily visible (figure 1).”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [38]
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Evaluation of clinical prediction models (part 2): how to undertake an external validation study
Riley, R. D., Archer, L., Snell, K. I. E., Ensor, J., Dhiman, P., Martin, G. P., Bonnett, L. J., & Collins, P. G. S. (2024) · BMJ · doi:10.1136/bmj-2023-074820 · Cited by 565
Rosati, D., Palmieri, M., Brunelli, G., Morrione, A., Iannelli, F., Frullanti, E., & Giordano, A. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.02.018 · Cited by 216
Evidence of questionable research practices in clinical prediction models
White, N. M., Parsons, R., Collins, P. G. S., & Barnett, A. G. (2023) · BMC Medicine · doi:10.1186/s12916-023-03048-6 · Cited by 149
Van 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
Self-citationBaquero, J. P. H., Chumillas, M. R., Perez, A. G., Escámez, P. S. F., Samper, A. A., Iguaz, A., Esnoz, A., & Gómez, A. P. (2013) · Foods · doi:10.3390/foods · Cited by 101
Metadata
- Authors
- Verbakel, J. Y., Steyerberg, E. W., Uno, H., De Cock, B., Wynants, L., Collins, G. S., & Van Calster, B.
- Affiliations
- University of Oxford (GB)
- Leiden University Medical Center (NL)
- Dana-Farber Cancer Institute (US)
- KU Leuven (BE)
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 126
- Pages
- 207-216
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W3045258609
- Cited by
- 138
- References
- 48
- Citations by year
- 2026: 26 · 2025: 34 · 2024: 29 · 2023: 21 · 2022: 15 · 2021: 9 · 2020: 3
- FWCI
- 8.46
- Type
- journal article
- Language
- en
- Update notices
- erratum (2021-02-01)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Clinical Epidemiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion, cc-by-nc-nd)
- Lirias (KU Leuven) (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
- Funders
- Cancer Research UK
- Patient-Centered Outcomes Research Institute
- Fonds Wetenschappelijk Onderzoek
- NIHR Oxford Biomedical Research Centre
- Seventh Framework Programme
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- ROC curve, area under the curve, clinical prediction model, risk prediction model, sensitivity and specificity, risk thresholds, model discrimination, external validation, testicular cancer
- MeSH terms
- Clinical Decision Rules, Computer Simulation, Decision Making, Humans, Lymph Nodes, Male, Models, Theoretical, Predictive Value of Tests, Retroperitoneal Neoplasms, ROC Curve, Sensitivity and Specificity, Testicular Neoplasms, Models, Statistical, Risk Assessment, Area Under Curve
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