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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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The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Basic principles of ROC analysis
Metz, C. E. (1978) · Seminars in Nuclear Medicine · doi:10.1016/s0001-2998(78)80014-2 · Cited by 6,122
A Class of Statistics with Asymptotically Normal Distribution
Hoeffding, W. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-0919-5_20 · Cited by 1,898
Bamber, D. (1975) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(75)90001-2 · Cited by 1,452
(1982) · Cited by 1,288
Dorfman, D. D., & Alf, E. F. (1969) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(69)90019-4 · Cited by 878
Metz, C. E., Wang, P.-L., & Kronman, H. B. (1984) · Information Processing in Medical Imaging · doi:10.1007/978-94-009-6045-9_25 · Cited by 359
Arvesen, J. N. (1969) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177697287 · Cited by 286
On Some Convergence Properties of UStatistics
Sen, P. K. (1960) · Calcutta Statistical Association Bulletin · doi:10.1177/0008068319600101 · Cited by 182
Surgical management of bowel obstruction in advanced ovarian carcinoma.
Krebs, H. B., & Goplerud, D. R. (1983) · PubMed · Cited by 132
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Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.03.026 · Cited by 15,396
FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009490 · Cited by 14,894
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data
Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., Saunders, L. D., Beck, C. A., Feasby, T. E., & Ghali, W. A. (2005) · Medical Care · doi:10.1097/01.mlr.0000182534.19832.83 · Cited by 10,669
Acute Respiratory Distress Syndrome
Ards Definition Task Force (2012) · JAMA · doi:10.1001/jama.2012.5669 · Cited by 9,066
Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine
ZWEIG, M. H., & Campbell, G. (1993) · Clinical Chemistry · doi:10.1093/clinchem/39.4.561 · Cited by 6,595
Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
Levey, A. S., Coresh, J., Greene, T., Stevens, L. A., Zhang, Y., Hendriksen, S. M., Kusek, J. W., & Lente, F. V. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-145-4-200608150-00004 · Cited by 5,726
FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix
Price, M. N., Dehal, P. S., & Arkin, A. P. (2009) · Molecular Biology and Evolution · doi:10.1093/molbev/msp077 · Cited by 5,602
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection
Kroenke, K., Spitzer, R. L., Williams, J. B. W., Monahan, P. O., & Löwe, B. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-146-5-200703060-00004 · Cited by 4,741
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McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287
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Ioannidis, N. M., Rothstein, J. H., Pejaver, V., Middha, S., McDonnell, S. K., Baheti, S., Musolf, A., Li, Q., Holzinger, E., Karyadi, D., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.08.016 · Cited by 2,908
Yusuf, S., Hawken, S., Ôunpuu, S., Bautista, L., Franzosi, M. G., Commerford, P., Lang, C. C., Rumboldt, Z., Onen, C. L., Lisheng, L., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67663-5 · Cited by 2,903
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Melo, S. A., Luecke, L. B., Kahlert, C., Fernandez, A. F., Gammon, S. T., Kaye, J., LeBleu, V. S., Mittendorf, E. A., Weitz, J., Rahbari, N., et al. (2015) · Nature · doi:10.1038/nature14581 · Cited by 2,880
Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
Heagerty, P. J., Lumley, T., & PEPE, M. S. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00337.x · Cited by 2,870
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Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799
Frailty as a predictor of surgical outcomes in older patients
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Logistic regression and Artificial Neural Network Classification models: A methodology review
Dreiseitl, S., & Ohno-Machado, L. (2002) · Journal of Biomedical Informatics · doi:10.1016/s1532-0464(03)00034-0 · Cited by 2,177
Lauretani, F., Russo, C. R., Bandinelli, S., Bartali, B., Cavazzini, C., Di Iorio, A., Corsi, A. M., Rantanen, T., Guralnik, J. M., & Ferrucci, L. (2003) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00246.2003 · Cited by 1,946
Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening
Pisano, E. D., Gatsonis, C. A., Hendrick, E. R., Yaffe, M. J., Baum, J. K., Acharyya, S., Conant, E. F., Fajardo, L. L., Bassett, L. W., D'Orsi, C. J., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052911 · Cited by 1,937
Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020) · Radiology · doi:10.1148/radiol.2020200905 · Cited by 1,927
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Mattace-Raso, F. U. S., van der Cammen, T. J. M., Hofman, A., van Popele, N. M., Bos, M. L., Schalekamp, M. A. D. H., Asmar, R., Reneman, R. S., Hoeks, A. P. G., Breteler, M. M. B., et al. (2006) · Circulation · doi:10.1161/circulationaha.105.555235 · Cited by 1,914
Simplified criteria for the diagnosis of autoimmune hepatitis
Hennes, E. M., Zeniya, M., Czaja, A. J., Parés, A., Dalekos, G. N., Krawitt, E. L., Bittencourt, P. L., Porta, G., Boberg, K. M., Hofer, H., et al. (2008) · Hepatology · doi:10.1002/hep.22322 · Cited by 1,901
Blanche, P., Dartigues, J., & Jacqmin‐Gadda, H. (2013) · Statistics in Medicine · doi:10.1002/sim.5958 · Cited by 1,917
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Evaluating presence–absence models in ecology: the need to account for prevalence
Manel, S., Williams, H. C., & Ormerod, S. J. (2001) · Journal of Applied Ecology · doi:10.1046/j.1365-2664.2001.00647.x · Cited by 1,759
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Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719
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Nakamura, A., Kaneko, N., Villemagne, V. L., Kato, T., Doecke, J., Doré, V., Fowler, C., Li, Q.-X., Martins, R., Rowe, C., et al. (2018) · Nature · doi:10.1038/nature25456 · Cited by 1,681
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Reichlin, T., Hochholzer, W., Bassetti, S., Steuer, S., Stelzig, C., Hartwiger, S., Biedert, S., Schaub, N., Buerge, C., Potocki, M., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0900428 · Cited by 1,678
Serdar, C. C., Cihan, M., Yücel, D., & Serdar, M. A. (2020) · Biochemia Medica · doi:10.11613/bm.2021.010502 · Cited by 1,868
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Communication, S., Been-chian Chien, A., Jung Yi Lin, A., Tzung-pei Hong, B., & Com/locate/eswa, W. E. (2004) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.10.010 · Cited by 1,670
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Bradley, K. A., Rubinsky, A. D., Volk, R. J., Williams, E. C., Frank, D., & Kivlahan, D. R. (2007) · Alcoholism Clinical and Experimental Research · doi:10.1111/j.1530-0277.2007.00403.x · Cited by 1,634
Palmqvist, S., Janelidze, S., Quiroz, Y. T., Zetterberg, H., Lopera, F., Stomrud, E., Su, Y., Chen, Y., Serrano, G. E., Leuzy, A., et al. (2020) · JAMA · doi:10.1001/jama.2020.12134 · Cited by 1,603
Receiver operating characteristic curve: overview and practical use for clinicians
Nahm, F. S. (2022) · Korean Journal of Anesthesiology · doi:10.4097/kja.21209 · Cited by 1,680
Berg, W. A. (2008) · JAMA · doi:10.1001/jama.299.18.2151 · Cited by 1,527
Nørgaard, B. L., Leipsic, J. A., Gaur, S., SENEVIRATNE, S. K., Ko, B. S. H., Ito, H., Jensen, J. M., Mauri, L., Bruyne, B. D., Bezerra, H. G., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.11.043 · Cited by 1,511
Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models
Zou, K. H., O’Malley, A. J., & Mauri, L. (2007) · Circulation · doi:10.1161/circulationaha.105.594929 · Cited by 1,473
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Zoungas, S., Patel, A., Chalmers, J., de Galan, B. E., Li, Q., Billot, L., Woodward, M., Ninomiya, T., Neal, B., MacMahon, S., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1003795 · Cited by 1,471
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Chew, L. D., Griffin, J. M., Partin, M. R., Noorbaloochi, S., Grill, J. P., Snyder, A., Bradley, K. A., Nugent, S. M., Simon, A. B., & van Ryn, M. (2008) · Journal of General Internal Medicine · doi:10.1007/s11606-008-0520-5 · Cited by 1,432
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Lam, C. S. P., Roger, V. L., Rodeheffer, R. J., Borlaug, B. A., Enders, F., & Redfield, M. M. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2008.11.051 · Cited by 1,345
Musso, G., Gambino, R., Cassader, M., & Pagano, G. (2010) · Annals of Medicine · doi:10.3109/07853890.2010.518623 · Cited by 1,304
Pepe, M. S. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh101 · Cited by 1,276
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Iglesias, J. E., Augustinack, J. C., Nguyen, K., Player, C. M., Player, A., Wright, M., Roy, N., Frosch, M. P., McKee, A. C., Wald, L. L., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.042 · Cited by 1,267
Bruderer, R., Bernhardt, O. M., Gandhi, T., Miladinović, S. M., Cheng, L.-Y., Messner, S., Ehrenberger, T., Zanotelli, V., Butscheid, Y., Escher, C., et al. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.044305 · Cited by 1,238
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Vasan, R. S. (2006) · Circulation · doi:10.1161/circulationaha.104.482570 · Cited by 1,212
Fisher, A., Rudin, C., & Dominici, F. (2018) · arXiv · doi:10.48550/arxiv.1801.01489 · Cited by 1,165
Tsang, T. S. M., Barnes, M. E., Gersh, B. J., Bailey, K. R., & Seward, J. B. (2002) · The American Journal of Cardiology · doi:10.1016/s0002-9149(02)02864-3 · Cited by 1,159
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Min, J. K., Leipsic, J. A., Pencina, M. J., Berman, D. S., Koo, B., van Mieghem, C. A. G., Ērglis, A., Lin, F. Y.-H., Dunning, A. M., Apruzzese, P. K., et al. (2012) · JAMA · doi:10.1001/2012.jama.11274 · Cited by 1,157
Yeboah, J., McClelland, R. L., Polonsky, T. S., Burke, G. L., Sibley, C. T., O’Leary, D., Carr, J. J., Goff, D. C., Greenland, P., & Herrington, D. M. (2012) · JAMA · doi:10.1001/jama.2012.9624 · Cited by 1,122
Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., & Suvas, A. (2016) · Journal of International Financial Management and Accounting · doi:10.1111/jifm.12053 · Cited by 1,111
Webster, L. R., & Webster, R. M. (2005) · Pain Medicine · doi:10.1111/j.1526-4637.2005.00072.x · Cited by 1,096
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
Mei, X., Lee, H.-C., Diao, K.-Y., Huang, M., Lin, B., Liu, C., Xie, Z., Ma, Y., Robson, P. M., Chung, M., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0931-3 · Cited by 1,084
LOWE, B. (2004) · Journal of Affective Disorders · doi:10.1016/s0165-0327(02)00237-9 · Cited by 1,060
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Hajian‐Tilaki, K. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.02.013 · Cited by 1,035
Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.034338 · Cited by 1,006
“Left atrial volume, mL 4800 53 (40–71) 9 (16) 16 (29) 33 (66)”
Leach, M. O., Boggis, C. R. M., Dixon, A. K., Easton, D. F., Eeles, R. A., Evans, D. G., Gilbert, F. J., Griebsch, I., Hoff, R., Kessar, P., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)66481-1 · Cited by 1,005
Floegel, A., Stefan, N., Yu, Z., Mühlenbruch, K., Drogan, D., Joost, H.-G., Fritsche, A., Häring, H.-U., Hrabě de Angelis, M., Peters, A., et al. (2013) · Diabetes · doi:10.2337/db12-0495 · Cited by 988
Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent
Peterson, A. T., Papeş, M., & Eaton, M. D. (2007) · Ecography · doi:10.1111/j.0906-7590.2007.05102.x · Cited by 966
Pickhardt, P. J., Pooler, B. D., Lauder, T., del Rio, A. M., Bruce, R. J., & Binkley, N. (2013) · Annals of Internal Medicine · doi:10.7326/0003-4819-158-8-201304160-00003 · Cited by 954
Ma, X.-J., Wang, Z., Ryan, P. D., Isakoff, S. J., Barmettler, A., Fuller, A. P., Muir, B., Mohapatra, G., Salunga, R. C., Tuggle, J. T., et al. (2004) · Cancer Cell · doi:10.1016/j.ccr.2004.05.015 · Cited by 959
The early prediction of mortality in acute pancreatitis: a large population-based study
Wu, B. U., Johannes, R. S., Sun, X., Tabak, Y. P., Conwell, D. L., & Banks, P. A. (2008) · Gut · doi:10.1136/gut.2008.152702 · Cited by 920
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Berg, T., Burg, V., Gombović, A., & Puri, M. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhz099 · Cited by 919
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Park, S. H., Goo, J. M., & Jo, C.-H. (2004) · Korean Journal of Radiology · doi:10.3348/kjr.2004.5.1.11 · Cited by 909
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Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601545 · Cited by 904
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Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S., & Goldstein, D. B. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003709 · Cited by 901
Zemek, R., Barrowman, N., Freedman, S. B., Gravel, J., Gagnon, I., McGahern, C., Aglipay, M., Sangha, G., Boutis, K., Beer, D., et al. (2016) · JAMA · doi:10.1001/jama.2016.1203 · Cited by 900
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Yeboah, J., Crouse, J. R., Hsu, F., Burke, G. L., & Herrington, D. M. (2007) · Circulation · doi:10.1161/circulationaha.106.678276 · Cited by 902
Davis, P. H., Dawson, J. D., Riley, W. A., & Lauer, R. M. (2001) · Circulation · doi:10.1161/hc4601.099486 · Cited by 881
Orlando, J. I., Fu, H., Barbosa‐Breda, J., Keer, K. V., Bathula, D. R., Diaz‐Pinto, A., Fang, R., Heng, P., Kim, J., Lee, J., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101570 · Cited by 910
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McCullough, P. A., Nowak, R. M., McCord, J., Hollander, J. E., Herrmann, H. C., Steg, P. G., Duc, P., Westheim, A. S., Omland, T., Knudsen, C. W., et al. (2002) · Circulation · doi:10.1161/01.cir.0000025242.79963.4c · Cited by 875
Receiver operating characteristic (ROC) curve for medical researchers
Kumar, R., & Indrayan, A. (2011) · Indian Pediatrics · doi:10.1007/s13312-011-0055-4 · Cited by 872
McQueen, M. J., Hawken, S., Wang, X., Ounpuu, S., Sniderman, A., Probstfield, J., Steyn, K., Sanderson, J. E., Hasani, M., Volkova, E., et al. (2008) · The Lancet · doi:10.1016/s0140-6736(08)61076-4 · Cited by 864
DISOPRED3: precise disordered region predictions with annotated protein-binding activity
Jones, D. T., & Cozzetto, D. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu744 · Cited by 877
Mason, S. J., & Graham, N. E. (2002) · Quarterly Journal of the Royal Meteorological Society · doi:10.1256/003590002320603584 · Cited by 860
Receiver Operating Characteristic Curves and Their Use in Radiology
Obuchowski, N. A. (2003) · Radiology · doi:10.1148/radiol.2291010898 · Cited by 843
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Lasko, T. A., Bhagwat, J. G., Zou, K. H., & Lucila Ohno-machado, A. (2005) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2005.02.008 · Cited by 821
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Klein, J., Wood, J., Jaycox, J. R., Dhodapkar, R. M., Lu, P., Gehlhausen, J. R., Tabachnikova, A., Greene, K., TABACOF, L. R., Malik, A. A., et al. (2023) · Nature · doi:10.1038/s41586-023-06651-y · Cited by 852
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Norton, M. E., Jacobsson, B., Swamy, G. K., Laurent, L. C., Ranzini, A. C., Brar, H., Tomlinson, M. W., Pereira, L., Spitz, J. L., Hollemon, D., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1407349 · Cited by 817
Imajo, K., Kessoku, T., Honda, Y., Tomeno, W., Ogawa, Y., Mawatari, H., Fujita, K., Yoneda, M., Taguri, M., Hyogo, H., et al. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.11.048 · Cited by 815
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Roca, O., Caralt, B., Messika, J., Samper, M., Sztrymf, B., Hernández, G., García-de-Acilu, M., Frat, J., Masclans, J. R., & Ricard, J.-D. (2018) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201803-0589oc · Cited by 834
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Lorenzo, C., Okoloise, M., Williams, K., Stern, M. P., & Haffner, S. M. (2003) · Diabetes Care · doi:10.2337/diacare.26.11.3153 · Cited by 800
Erbel, R. M., Möhlenkamp, S., Moebus, S., Schmermund, A., Lehmann, N., Stang, A., Dragano, N., Grönemeyer, D. H. W., Seibel, R. M. M., Kälsch, H., et al. (2010) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2010.06.030 · Cited by 783
Parental History of Myopia, Sports and Outdoor Activities, and Future Myopia
Jones, L. A., Sinnott, L. T., Mutti, D. O., Mitchell, G. L., Moeschberger, M. L., & Zadnik, K. (2007) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.06-1118 · Cited by 769
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
Brown, I., & Mues, C. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.033 · Cited by 813
Postoperative Biomarkers Predict Acute Kidney Injury and Poor Outcomes after Adult Cardiac Surgery
Parikh, C. R., Coca, S. G., Thiessen‐Philbrook, H., Shlipak, M. G., Koyner, J. L., Wang, Z., Edelstein, C. L., Devarajan, P., Patel, U. D., Zappitelli, M., et al. (2011) · Journal of the American Society of Nephrology · doi:10.1681/asn.2010121302 · Cited by 772
Genotype score in addition to common risk factors for prediction of type 2 diabetes
Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., Manning, A. K., Florez, J. C., Wilson, P. W. F., D'Agostino, R. B., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0804742 · Cited by 759
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Yala, A., Lehman, C., Schuster, T., Portnoi, T., & Barzilay, R. (2019) · Radiology · doi:10.1148/radiol.2019182716 · Cited by 758
Stern, M. P., Williams, K., González-Villalpando, C., Hunt, K. J., & Haffner, S. M. (2004) · Diabetes Care · doi:10.2337/diacare.27.11.2676 · Cited by 735
Bien, N., Rajpurkar, P., Ball, R. L., Irvin, J., Park, A., Jones, E., Bereket, M., Patel, B. N., Yeom, K. W., Shpanskaya, K., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002699 · Cited by 734
Cardiovascular Mortality Risk in Chronic Kidney Disease
Shlipak, M. G., Fried, L. F., Cushman, M., Manolio, T. A., Peterson, D., Stehman‐Breen, C. O., Bleyer, A. J., Newman, A. B., Siscovick, D. S., & Psaty, B. M. (2005) · JAMA · doi:10.1001/jama.293.14.1737 · Cited by 728
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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