Two Criteria for Evaluating Risk Prediction Models

Biometrics

Volumn 67, Issue 3, 2011, Pages 1057-1065

  Pfeiffer, R M ^a   Gail, M H ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Discrimination; Discriminatory accuracy; Lorenz curve; Risk models

Indexed keywords

DIAGNOSIS; DISCRIMINATORS; INVERSE PROBLEMS;

DISCRIMINATION; DISCRIMINATORY ACCURACY; DISEASE DIAGNOSIS; DISEASE INCIDENCE; FOLLOW UP; LORENZ CURVES; PROGNOSTIC MODELING; RISK MODELING; RISK PREDICTION MODELS; USEFULNESS OF MODELS;

RISK ASSESSMENT;

ACCURACY ASSESSMENT; DATA SET; DISEASE CONTROL; DISEASE INCIDENCE; FEASIBILITY STUDY; NUMERICAL MODEL; PREDICTION; RISK ASSESSMENT;

EID: 80052801788     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01523.x     Document Type: Article

References (25)

1. Albertsen, P. C., Hanley, J. A., and Fine, J. (2005). 20-year outcomes following conservative management of clinically localized prostate cancer. Journal of the American Medical Association 293, 2095-2101.
2. Bura, E. and Gastwirth, J. L. (2001). The binary regression quantile plot: Assessing the importance of predictors in binary regression visually. Biometrical Journal 43, 5-21.
3. Cook, N. R. (2007). Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115, 928-935.
4. Cox, D. R. (1958). Two further applications of a model for binary regression two further applications of a model for binary regression. Biometrika 45, 562-565.
5. Csörgö, M. and Yu, H. (1999). Weak approximations for empirical Lorenz curves and their Goldie inverses of stationary observations. Advances in Applied Probability 31, 698-719.
6. Dardanoni, V. and Forcina, A. (1999). Inference for the Lorenz curve ordering. Econometrics Journal 2, 48-74.
7. Freedman, A. N., Slattery, M. L., Ballard-Barbash, R., Willis, G., Cann, B. J., Pee, D., Gail, M. H., and Pfeiffer, R. M. (2009). Colorectal cancer risk prediction tool for white men and women without known susceptibility. Journal of Clinical Oncology 27, 686-693.
8. Gail, M. H. and Pfeiffer, R. M. (2005). On criteria for evaluating models of absolute risks. Biostatistics 6, 227-239.
9. Gail, M. H., Costantino, J. P., Bryant, J., Croyle, R., Freedman, L., Helzlsouer, K., and Vogel, V. (1999). Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. Journal of the National Cancer Institute 91, 1829-1846.
10. Gastwirth, J. L. (1971). A general definition of the Lorenz curve. Econometrica 39, 1037-1039.
11. Goldie, C. M. (1977). Convergence theorems for empirical Lorenz curves and their inverses. Advances in Applied Probability 9, 765-791.
12. Greenhouse, S. W. and Mantel, N. (1950). The evaluation of diagnostic tests. Biometrics 6, 399-412.
13. Hand, D. J. (1981). Discrimination and Classification. Chichester, U.K.: Wiley.
14. Hand, D. J. and Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society, Series A 160, 523-541.
15. Huang, Y. and Pepe, M. S. (2009). A parametric ROC model-based approach for evaluating the predictiveness of continuous markers in case-control studies. Biometrics 65, 1133-1144.
16. Park, Y., Freedman, A. N., Gail, M. H., Pee, D., Hollenbeck, A., Schatzkin, A., and Pfeiffer, R. M. (2009). Validation of a colorectal cancer risk prediction model among white patients age 50 years and older. Journal of Clinical Oncology 27, 694-698.
17. Pencina, M. J., D'Agostino, R. B., D'Agostino, R. B., and Vasan, R. S. (2008). Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Statistics in Medicine 27, 157-172.
18. Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford Statistical Science Series. New York: Oxford University Press.
19. Pepe, M. S., Feng, Z., Huang, Y., Longton, G., Prentice, R., Thompson, I. M., and Zheng, Y. (2008). Integrating the predictiveness of a marker with its performance as a classifier. American Journal of Epidemiology 167, 362-368.
20. Pharoah, P. D. P., Antoniou, A., Bobrow, M., Zimmern, R. L., Easton, D. F., and Ponder, B. A. J. (2002). Polygenic susceptibility to breast cancer and implications for prevention. Nature Genetics 31, 33-36.
21. Rose, G. (1992). The Strategy of Preventive Medicine. Oxford Medical Publications. New York: Oxford University Press.
22. Stephenson, A. J., Scardino, P. T., Eastham, J. A., Bianco, F. J., Dotan, Z. A., Fearn, P. A., and Kattan, M. W. (2006). Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. Journal of the National Cancer Institute 98, 715-717.
23. Vickers, A. J. and Elkin, E. B. (2006). Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making 26, 565-574.
24. Wilson, P. W. F., D'Agostino, R. B., Levy, D., Belanger, A. M., Silbershatz, H., and Kannel, W. B. (1998). Prediction of coronary heart disease using risk factor categories. Circulation 97, 1837-1847.
25. Zheng, B. H. and Cushing, B. J. (2001). Statistical inference for testing inequality indices with dependent samples. Journal of Econometrics 101, 315-335.