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Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
Abstract
Methods of evaluating and comparing the performance of diagnostic tests are of increasing importance as new tests are developed and marketed. When a test is based on an observed variable that lies on a continuous or graded scale, an assessment of the overall value of the test can be made through the use of a receiver operating characteristic (ROC) curve. The curve is constructed by varying the cutpoint used to determine which values of the observed variable will be considered abnormal and then plotting the resulting sensitivities against the corresponding false positive rates. When two or more empirical curves are constructed based on tests performed on the same individuals, statistical analysis on differences between curves must take into account the correlated nature of the data. This paper presents a nonparametric approach to the analysis of areas under correlated ROC curves, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
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From AI-Driven Predictions of Readmission and Mortality for Improved Discharge Decisions in Critical Care: A Retrospective Study (2026) · cites this paper as [23]
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Metadata
- Authors
- DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L.
- Affiliations
- IQVIA (United States) (US)
- IQVIA (United Kingdom) (GB)
- ORCID
- DeLong, Elizabeth R.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1988-09
- Volume
- 44
- Issue
- 3
- Pages
- 837
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2328176404
- Cited by
- 22,649
- References
- 12
- Citations by year
- 2026: 1897 · 2025: 1546 · 2024: 1644 · 2023: 1628 · 2022: 1861 · 2021: 1946 · 2020: 1651 · 2019: 1364 · 2018: 1193 · 2017: 1076 · 2016: 1001 · 2015: 924 · 2014: 775 · 2013: 745 · 2012: 621
- FWCI
- 4.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Health Services Research and Development
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- receiver operating characteristic, area under the ROC curve, diagnostic test comparison, nonparametric covariance estimation, sensitivity and false positive rate
- MeSH terms
- Algorithms, Analysis of Variance, Female, Humans, Intestinal Obstruction, Ovarian Neoplasms, Predictive Value of Tests, ROC Curve, Models, Statistical
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