Discrimination measures for survival outcomes: Connection between the AUC and the predictiveness curve

Biometrical Journal

Volumn 53, Issue 2, 2011, Pages 217-236

  Viallon, Vivian ^a,b   Latouche, Aurélien ^c  

^a UNIVERSITÉ DE LYON   (France)

^b University Lyon   (France)

^c Île de France Ouest University Paris   (France)

Author keywords

AUC; Discrimination; Predictiveness curve; Prognostic models; ROC curve

Indexed keywords

CURVE FITTING; FUNCTION EVALUATION; RISK ASSESSMENT;

AUC; BUILDING RISK; DISCRIMINATION; PREDICTIVENESS CURVE; PROGNOSTIC MODELING; RECEIVER OPERATING CURVES; RISK FUNCTION; RISK SCORE; ROC CURVES; TIME DEPENDENT;

RISK PERCEPTION;

EID: 79952263869     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201000153     Document Type: Article

References (27)

1. Aalen, O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine 8, 907-925.
2. Aalen, O., Andersen, P. K., Borgan, Ø., Gill, R. D. and Keiding, N. (2009). History of applications of martingales in survival analysis. Electronic Journal for History of Probability and Statistics 5.
3. Akritas, M. (1994). Nearest neighbor estimation of a bivariate distribution under random censoring. Annals of Statistics 22, 1299-1327.
4. Beran, R. (1981). Nonparametric regression with randomly censored survival data. Technical Report, University of California, Berkley.
5. Chambless, L. E. and Diao, G. (2006). Estimation of time-dependent area under the ROC curve for long-term risk prediction. Statistics in Medicine 25, 3474-3486.
6. Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, Series B 34, 187-220.
7. Cox, D. R. and Snell, E. (1968). A general definition of residuals. Journal of the Royal Statistical Society, Series B 30, 248-275.
8. Dodd, L. E. and Pepe, M. S. (2003). Partial AUC estimation and regression. Biometrics 59, 614-623.
9. Fleming, T. R. and Harrington, D. P. (1991). Counting Processes and Survival Analysis. Wiley, New York.
10. Gail, G. H. and Pfeiffer, R. M. (2005). On criteria for evaluating models of absolute risk. Biostatistics 6, 227-239.
11. Gönen, M. and Heller, G. (2005). Concordance probability and discriminatory power in proportional hazards regression. Biometrika 92, 965-970.
12. Gu, W. and Pepe, M. S. (2009). Measures to summarize and compare the predictive capacity of markers. International Journal of Biostatistics 5, Art. 27, 49.
13. Härdle, W. and Mammen, E. (1993). Comparing nonparametric versus parametric regression fits. The Annals of Statistics 21, 1926-1947.
14. Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L. and Rosati, R. A. (1982). Evaluating the yield of medical tests. Journal of the American Medical Association 247, 2543-2546.
15. Harrell, F. E., Lee, K. L. and Mark, D. B. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 15, 361-387.
16. Heagerty, P. J., Lumley, T. and Pepe, M. S. (2000). Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337-344.
17. Heagerty, P. J. and Zheng, Y. (2005). Survival model predictive accuracy and ROC curves. Biometrics 61, 92-105.
18. Huang, Y., Pepe, M. S. and Feng, Z. (2007). Evaluating the predictiveness of a continuous marker. Biometrics 63, 1181-1188.
19. Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data. Wiley, Hoboken.
20. Leemis, L. M., Shih, L.-H. and Reynertson, K. (1990). Variate generation for accelerated life and proportional hazards models with time dependent covariates. Statistics and Probability Letters 10, 335-339.
21. Lemeshow, S. and Hosmer, D. W. (1982). A review of goodness of fit statistics for use in the development of logistic regression models. American Journal of Epidemiology 115, 92-106.
22. Pencina, M. J., Larson, M. G. and D'Agostino, R. B. (2007). 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.
23. Pepe, M. S., Feng, Z., Huang, Y., Longton, G., Prentice, R., Thompson, I. M. and Zheng, Y. (2008a). Integrating the predictiveness of a marker with its performance as a classifier. American Journal of Epidemiology 167, 362-368.
24. Pepe, M. S., Zheng, Y., Jin, Y., Huang, Y., Parikh, C. R. and Levy, W. C. (2008b). Evaluating the ROC performance of markers for future events. Lifetime Data Analysis 14, 86-113.
25. Schemper, M. (2003). Predictive accuracy and explained variation. Statistics in Medicine 22, 2299-2308.
26. Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J. and Kattan, M. W. (2010). Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128-138.
27. Viallon, V., Ragusa, S., Clavel-Chapelon, F. and Bénichou, J. (2009). How to evaluate the calibration of a disease risk prediction tool. Statistics in Medicine 28, 901-916.