Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve

Biometrika

Volumn 96, Issue 2, 2009, Pages 371-382

  Janes, Holly ^a   Pepe, Margaret S ^a  

^a Fred Hutchinson Cancer Research Center   (United States)

Author keywords

Classification accuracy; Covariate effect; Receiver operating characteristic curve; Sensitivity; Specificity

Indexed keywords

EID: 66249139548     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asp002     Document Type: Article

References (20)

1. ALONZO, T. A. & PEPE, M. S. (2002). Distribution-free ROC analysis using binary regression techniques. Biostatistics 3,421-32.
2. BAKER, S. (2003). The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer. J. Nat. Cancer Inst. 95, 511-5.
3. CAI, T. & PEPE, M. S. (2002). Semi-parametric ROC analysis to evaluate biomarkers for disease. J. Am. Statist. Assoc. 97, 1099-107.
4. COLE, T. J. (1990). The LMS method for constructing normalized growth standards. Eur. J. Clin. Nutr. 44, 45-60.
5. COLE, T. J. & GREEN, P. J. (1992). Smoothing reference centile curves: the LMS method and penalized likelihood. Statist. Med. 11, 1305-19.
6. ETZIONI, R., FALCON, S., GANN, P. H., KOOPERBERG, C. L., PENSON, D. E & STAMPFER, M. J. (2004). Prostate-specific antigen and free prostate-specific antigen in the early detection of prostate cancer: do combination tests improve detection? Cancer Epidemiol. Biomark. Prev. 13, 1640-5.
7. FERGUSON, T. S. (1996). A Course in Large Sample Theory. London: Chapman and Hall.
8. GANN, P. H., HENNEKENS, C. H. & STAMPFER, M. J. (1995). A prospective evaluation of plasma prostate-specific antigen for detection of prostatic cancer. J. Am. Med. Assoc. 273, 289-94.
9. HANLEY, J. A. (1988). The robustness of the 'binormal' assumptions used in fitting ROC curves. Med. Decis. Making 8, 197-203.
10. HANLEY, J. A. (1996). The use of the 'binormal' model for parametric ROC analysis of quantitative diagnostic tests. Statist. Med. 15, 1575-85.
11. HEAGERTY, P. & PEPE, M. S. (1999). Semi-parametric estimation of regression quantiles with application to standardizing weight for height and age in U.S. children. Appl. Statist. 48, 533-51.
12. JANES, H. & PEPE, M. S. (2008). Matching in studies of classification accuracy: implications for bias, efficiency, and assessment of incremental value. Biometrics 64, 1-9.
13. LIN, Y. & JEON, Y. (2003). Discriminant analysis through a semi-parametric model. Biometrika 90, 379-92.
14. PEPE, M. S. (2000). An interpretation for the ROC curve and inference using GLM procedures. Biometrics 56, 352-9.
15. PEPE, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford: Oxford University Press.
16. PEPE, M. S. & CAI, T. (2004). The analysis of placement values for evaluating discriminatory measures. Biometrics 60, 528-35.
17. PEPE, M. S., ETZIONI, R., FENG, Z., POTTER, J. D., THOMPSON, M. L., THORNQUIST, M., WINGET, M. & YATSUI, Y. (2001). Phases of biomarker development for early detection of cancer. J. Nat. Cancer Inst. 93, 1054-61.
18. SILVERMAN, B. W. (1986). Density Estimation for Statistics and Data Analysis. London: Chapman and Hall.
19. SWETS, J. A. (1986). Indices of discrimination or diagnostic accuracy: their ROCs and implied methods. Psychol. Bull. 99, 100-17.
20. ZHENG Y. & HEAGERTY P. J. (2004). Semiparametric estimation of time-dependent ROC curves for longitudinal marker data. Biostatistics 5, 615-32.