Partial AUC Estimation and Regression
Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test ac...
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| Veröffentlicht in: | Biometrics Jg. 59; H. 3; S. 614 - 623 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
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350 Main Street , Malden , MA 02148 , U.S.A , and P.O. Box 1354, 9600 Garsington Road , Oxford OX4 2DQ , U.K
Blackwell Publishing
01.09.2003
International Biometric Society |
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| ISSN: | 0006-341X, 1541-0420 |
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| Abstract | Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate-specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer. |
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| AbstractList | Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate-specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer. Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate-specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer.Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate-specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer. Summary . Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate‐specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer. |
| Author | Pepe, Margaret S. Dodd, Lori E. |
| Author_xml | – sequence: 1 givenname: Lori E. surname: Dodd fullname: Dodd, Lori E. – sequence: 2 givenname: Margaret S. surname: Pepe fullname: Pepe, Margaret S. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/14601762$$D View this record in MEDLINE/PubMed |
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| References | Wieand, S., Gail, M., James, B., and James, K. (1989). A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data. Biometrika 76, 585 - 592. Baker, S. (2000). Identifying combinations of cancer markers for further study as triggers of early intervention. Biometrics 56, 1082 - 1087. Hanley, J. and Hajian-Tilaki, K. (1997). Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: An update. Academic Radiology 17, 49 - 58. Heinonen, O., Albanes, D., Vitarmo, J., and Taylor, P. (1998). Prostate cancer and supplementation with α-tocopherol and β-carotene: Incidence and mortality in a controlled trial. Journal of the National Cancer Institute 90, 440 - 447. Dodd, L. and Pepe, M. (2003). Semi-parametric regression for the area under the receiver operating characteristic curve. Journal of the American Statistical Association 98, 409 - 417. Dorfman, D., Berbaum, K., and Metz, C. (1992). Receiver operating characteristic analysis: Generalization to the population of readers and patients with the jackknife method. Investigative Radiology 27, 723 - 731. Obuchowski, N. (1997). Testing for equivalence of diagnostic tests. American Journal of Roentgenology 168, 13 - 17. Barry, M. (2001). Prostate-specific-antigen testing for early diagnosis of prostate cancer. New England Journal of Medicine 344, 1373 - 1377. Cai, T. and Pepe, M. (2003). Semi-parametric ROC analysis to evaluate biomarkers for disease. Journal of the American Statistical Association 97, 1099 - 1107. McClish, D. (1989). Analyzing a portion of the ROC curve. Medical Decision Making 9, 190 - 195. Lehmann, E. (1997). Testing Statistical Hypotheses, Chapter 6, 314 - 321. New York : Springer. Thompson, M. L. and Zucchini, W. (1989). On the statistical analysis of ROC curves. Statistics in Medicine 8, 1277 - 1290. Tanguay, S., Begin, L., Elhilali, H., Karakiewicz, P., and Aprikian, A. (2002). Comparative evaluation of total PSA, free/total PSA, and complexed PSA in prostate cancer detection. Adult Urology 59, 261 - 265. Pepe, M. (2000). An interpretation for the ROC curve and inference using GLM procedures. Biometrics 56, 352 - 359. DeLong, E. R., DeLong, D., and Clarke-Pearson, D. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44, 837 - 845. Heagerty, P. and Pepe, M. (1999). Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children. Applied Statistics 48, 533 - 551. Metz, C., Herman, B., and Shen, J.-H. (1998). Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. Statistics in Medicine 17, 1033 - 1053. Etzioni, R., Pepe, M., Longton, G., Hu, C., and Goodman, G. (1999). Incorporating the time dimension in receiver operating characteristic curves. Medical Decision Making 19, 242 - 251. Jiang, Y., Metz, C., and Nishikawa, R. (1996). A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology 201, 745 - 750. Hanley, J. (1996). The use of the "binormal" model for parametric ROC analysis of quantitative diagnostic tests. Statistics of Medicine 15, 1575 - 1585. Pepe, M. (2003). Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford: Oxford University Press. Baker, S. and Pinsky, P. (2001). A proposed design and analysis for comparing digital and analog mammography: Special receiver operating characteristic methods for cancer screening. Journal of the American Statistical Association 96, 421 - 428. 1997; 168 2001; 344 1998; 17 2002; 59 1989; 76 2001 2000; 56 1999; 19 1999; 48 1989; 9 1989; 8 1988; 44 1997 1997; 17 1998; 90 2003 1992; 27 1996; 201 1996; 15 2003; 97 2001; 96 2003; 98 e_1_2_10_12_1 e_1_2_10_23_1 e_1_2_10_9_1 e_1_2_10_13_1 e_1_2_10_24_1 e_1_2_10_10_1 e_1_2_10_11_1 e_1_2_10_22_1 e_1_2_10_20_1 Lehmann E. (e_1_2_10_16_1) 1997 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_18_1 e_1_2_10_3_1 Heinonen O. (e_1_2_10_14_1) 1998; 90 e_1_2_10_19_1 e_1_2_10_6_1 e_1_2_10_5_1 e_1_2_10_17_1 e_1_2_10_8_1 Pepe M. (e_1_2_10_21_1) 2003 e_1_2_10_7_1 e_1_2_10_15_1 |
| References_xml | – reference: Dodd, L. and Pepe, M. (2003). Semi-parametric regression for the area under the receiver operating characteristic curve. Journal of the American Statistical Association 98, 409 - 417. – reference: Pepe, M. (2000). An interpretation for the ROC curve and inference using GLM procedures. Biometrics 56, 352 - 359. – reference: Baker, S. (2000). Identifying combinations of cancer markers for further study as triggers of early intervention. Biometrics 56, 1082 - 1087. – reference: Pepe, M. (2003). Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford: Oxford University Press. – reference: Jiang, Y., Metz, C., and Nishikawa, R. (1996). A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology 201, 745 - 750. – reference: McClish, D. (1989). Analyzing a portion of the ROC curve. Medical Decision Making 9, 190 - 195. – reference: Tanguay, S., Begin, L., Elhilali, H., Karakiewicz, P., and Aprikian, A. (2002). Comparative evaluation of total PSA, free/total PSA, and complexed PSA in prostate cancer detection. Adult Urology 59, 261 - 265. – reference: Metz, C., Herman, B., and Shen, J.-H. (1998). Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. Statistics in Medicine 17, 1033 - 1053. – reference: Heagerty, P. and Pepe, M. (1999). Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children. Applied Statistics 48, 533 - 551. – reference: Lehmann, E. (1997). Testing Statistical Hypotheses, Chapter 6, 314 - 321. New York : Springer. – reference: Baker, S. and Pinsky, P. (2001). A proposed design and analysis for comparing digital and analog mammography: Special receiver operating characteristic methods for cancer screening. Journal of the American Statistical Association 96, 421 - 428. – reference: DeLong, E. R., DeLong, D., and Clarke-Pearson, D. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44, 837 - 845. – reference: Hanley, J. and Hajian-Tilaki, K. (1997). Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: An update. Academic Radiology 17, 49 - 58. – reference: Hanley, J. (1996). The use of the "binormal" model for parametric ROC analysis of quantitative diagnostic tests. Statistics of Medicine 15, 1575 - 1585. – reference: Heinonen, O., Albanes, D., Vitarmo, J., and Taylor, P. (1998). Prostate cancer and supplementation with α-tocopherol and β-carotene: Incidence and mortality in a controlled trial. Journal of the National Cancer Institute 90, 440 - 447. – reference: Cai, T. and Pepe, M. (2003). Semi-parametric ROC analysis to evaluate biomarkers for disease. Journal of the American Statistical Association 97, 1099 - 1107. – reference: Thompson, M. L. and Zucchini, W. (1989). On the statistical analysis of ROC curves. Statistics in Medicine 8, 1277 - 1290. – reference: Barry, M. (2001). Prostate-specific-antigen testing for early diagnosis of prostate cancer. New England Journal of Medicine 344, 1373 - 1377. – reference: Etzioni, R., Pepe, M., Longton, G., Hu, C., and Goodman, G. (1999). Incorporating the time dimension in receiver operating characteristic curves. Medical Decision Making 19, 242 - 251. – reference: Wieand, S., Gail, M., James, B., and James, K. (1989). A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data. Biometrika 76, 585 - 592. – reference: Dorfman, D., Berbaum, K., and Metz, C. (1992). Receiver operating characteristic analysis: Generalization to the population of readers and patients with the jackknife method. Investigative Radiology 27, 723 - 731. – reference: Obuchowski, N. (1997). Testing for equivalence of diagnostic tests. American Journal of Roentgenology 168, 13 - 17. – volume: 201 start-page: 745 year: 1996 end-page: 750 article-title: A receiver operating characteristic partial area index for highly sensitive diagnostic tests publication-title: Radiology – volume: 344 start-page: 1373 year: 2001 end-page: 1377 article-title: Prostate‐specific‐antigen testing for early diagnosis of prostate cancer publication-title: New England Journal of Medicine – volume: 59 start-page: 261 year: 2002 end-page: 265 article-title: Comparative evaluation of total PSA, free/total PSA, and complexed PSA in prostate cancer detection publication-title: Adult Urology – volume: 19 start-page: 242 year: 1999 end-page: 251 article-title: Incorporating the time dimension in receiver operating characteristic curves publication-title: Medical Decision Making – volume: 56 start-page: 1082 year: 2000 end-page: 1087 article-title: Identifying combinations of cancer markers for further study as triggers of early intervention publication-title: Biometrics – year: 2001 – volume: 17 start-page: 1033 year: 1998 end-page: 1053 article-title: Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously‐distributed data publication-title: Statistics in Medicine – volume: 98 start-page: 409 year: 2003 end-page: 417 article-title: Semi‐parametric regression for the area under the receiver operating characteristic curve publication-title: Journal of the American Statistical Association – volume: 168 start-page: 13 year: 1997 end-page: 17 article-title: Testing for equivalence of diagnostic tests publication-title: American Journal of Roentgenology – year: 2003 – volume: 96 start-page: 421 year: 2001 end-page: 428 article-title: A proposed design and analysis for comparing digital and analog mammography: Special receiver operating characteristic methods for cancer screening publication-title: Journal of the American Statistical Association – volume: 9 start-page: 190 year: 1989 end-page: 195 article-title: Analyzing a portion of the ROC curve publication-title: Medical Decision Making – volume: 44 start-page: 837 year: 1988 end-page: 845 article-title: Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach publication-title: Biometrics – volume: 56 start-page: 352 year: 2000 end-page: 359 article-title: An interpretation for the ROC curve and inference using GLM procedures publication-title: Biometrics – volume: 15 start-page: 1575 year: 1996 end-page: 1585 article-title: The use of the “binormal” model for parametric ROC analysis of quantitative diagnostic tests publication-title: Statistics of Medicine – volume: 76 start-page: 585 year: 1989 end-page: 592 article-title: A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data publication-title: Biometrika – volume: 97 start-page: 1099 year: 2003 end-page: 1107 article-title: Semi‐parametric ROC analysis to evaluate biomarkers for disease publication-title: Journal of the American Statistical Association – volume: 17 start-page: 49 year: 1997 end-page: 58 article-title: Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: An update publication-title: Academic Radiology – volume: 48 start-page: 533 year: 1999 end-page: 551 article-title: Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children publication-title: Applied Statistics – volume: 90 start-page: 440 year: 1998 end-page: 447 article-title: Prostate cancer and supplementation with α‐tocopherol and β‐carotene: Incidence and mortality in a controlled trial publication-title: Journal of the National Cancer Institute – volume: 8 start-page: 1277 year: 1989 end-page: 1290 article-title: On the statistical analysis of ROC curves publication-title: Statistics in Medicine – volume: 27 start-page: 723 year: 1992 end-page: 731 article-title: Receiver operating characteristic analysis: Generalization to the population of readers and patients with the jackknife method publication-title: Investigative Radiology – start-page: 314 year: 1997 end-page: 321 – ident: e_1_2_10_19_1 doi: 10.2214/ajr.168.1.8976911 – volume: 90 start-page: 440 year: 1998 ident: e_1_2_10_14_1 article-title: Prostate cancer and supplementation with α‐tocopherol and β‐carotene: Incidence and mortality in a controlled trial publication-title: Journal of the National Cancer Institute doi: 10.1093/jnci/90.6.440 – start-page: 314 volume-title: Testing Statistical Hypotheses year: 1997 ident: e_1_2_10_16_1 – ident: e_1_2_10_23_1 doi: 10.1002/sim.4780081011 – ident: e_1_2_10_12_1 doi: 10.1016/S1076-6332(97)80161-4 – ident: e_1_2_10_20_1 doi: 10.1111/j.0006-341X.2000.00352.x – ident: e_1_2_10_2_1 doi: 10.1111/j.0006-341X.2000.01082.x – ident: e_1_2_10_8_1 doi: 10.1198/016214503000198 – ident: e_1_2_10_3_1 doi: 10.1198/016214501753168136 – ident: e_1_2_10_9_1 doi: 10.1097/00004424-199209000-00015 – ident: e_1_2_10_4_1 doi: 10.1056/NEJM200105033441806 – ident: e_1_2_10_7_1 – ident: e_1_2_10_5_1 doi: 10.1198/016214502388618915 – ident: e_1_2_10_13_1 doi: 10.1111/1467-9876.00170 – volume-title: Statistical Evaluation of Medical Tests for Classification and Prediction year: 2003 ident: e_1_2_10_21_1 doi: 10.1093/oso/9780198509844.001.0001 – ident: e_1_2_10_24_1 doi: 10.1093/biomet/76.3.585 – ident: e_1_2_10_6_1 doi: 10.2307/2531595 – ident: e_1_2_10_15_1 doi: 10.1148/radiology.201.3.8939225 – ident: e_1_2_10_11_1 doi: 10.1002/(SICI)1097-0258(19960730)15:14<1575::AID-SIM283>3.0.CO;2-2 – ident: e_1_2_10_17_1 doi: 10.1177/0272989X8900900307 – ident: e_1_2_10_22_1 doi: 10.1016/S0090-4295(01)01497-2 – ident: e_1_2_10_10_1 doi: 10.1177/0272989X9901900303 – ident: e_1_2_10_18_1 doi: 10.1002/(SICI)1097-0258(19980515)17:9<1033::AID-SIM784>3.0.CO;2-Z |
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| Snippet | Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate... Summary . Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to... |
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| SubjectTerms | Area Under Curve Biometrics Biometry Diagnostic testing Estimation bias Estimation methods Estimators Humans Male Mann-Whitney U-statistic Maximum likelihood estimation Modeling Models, Statistical Parametric models Prostate cancer Prostate-Specific Antigen - blood Prostatic Neoplasms - blood Prostatic Neoplasms - diagnosis Receiver operating characteristic curve Regression Regression Analysis Standard error Statistics, Nonparametric |
| Title | Partial AUC Estimation and Regression |
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