Comparing ROC curves derived from regression models

Statistics in Medicine

Volumn 32, Issue 9, 2013, Pages 1483-1493

  Seshan, Venkatraman E ^a   Gönen, Mithat ^a   Begg, Colin B ^a  

^a MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

Author keywords

Area under the ROC curve; Biomarker; Discrimination; Logistic regression; Predictive accuracy; Predictive model; Receiver operating characteristic curve

Indexed keywords

BIOLOGICAL MARKER;

AREA UNDER THE CURVE; ARTICLE; COMPARATIVE STUDY; GOLD STANDARD; LOGISTIC REGRESSION ANALYSIS; MATHEMATICAL COMPUTING; MAXIMUM LIKELIHOOD METHOD; NULL HYPOTHESIS; PREDICTIVE VALUE; RECEIVER OPERATING CHARACTERISTIC; REGRESSION ANALYSIS; VALIDATION PROCESS;

AREA UNDER CURVE; BIOLOGICAL MARKERS; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; REGRESSION ANALYSIS; ROC CURVE;

EID: 84876331767     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5648     Document Type: Article

References (21)

1. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44(3):837-845.
2. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983; 148(3):839-843.
3. Harrell FE, Califf RM, Pryor DB. Evaluating the yield of medical tests. Journal of the American Medical Association 1982; 247(18):2543-2546.
4. Kattan MW. Judging new markers by their ability to improve predective accuracy. Journal of the National Cancer Institute 2003; 95(9):634-635.
5. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. American Journal of Epidemiology 2004; 159(9):882-890.
6. Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clinical Chemistry 2008; 54(1):17-23.
7. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MSV, Go AS, Harrell FE, Howard BV, Howard VJ, Hsue PY, Kramer CM, McConnell JP, Normand SLT, O'Donnell CJ, Smith SC, Wilson PWF. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 2009; 119(17):2408-2416.
8. Vickers AJ, Cronin AM, Begg CB. One statistical test is sufficient for assessing new predictive markers. BMC Medical Research Methodology 2011; 11:13.
9. Cox DR, Hinkley DV. Theoretical Statistics. Chapman and Hall: London, 1974.
10. Ware JH, Cai T. Comments on evaluating the added predictive ability of a new marker by Pencina et al. Statistics in Medicine 2008; 27:185-187.
11. Kwon S, Kim Y, Shim J, Sung J, Han M, Kang D, Kim JY, Choi B, Chang HJ. Coronary artery calcium scoring does not add prognostic value to standard 64-section ct angiography protocol in low-risk patients suspected of having coronary artery disease. Radiology 2011; 259(1):92-99.
12. Berg K, Stenseth R, Pleym H, Wahba A, Videm V. Mortality risk prediction in cardiac surgery: comparing a novel model with the Euroscore. Acta Anaesthesiologica Scandinavica 2011; 55(3):313-321.
13. Resnic F, Normand SL, Piemonte T, Shubrooks S, Zelevinsky K, Lovett A, Ho K. Improvement in mortality risk prediction after percutaneous coronary intervention through the addition of a compassionate use variable to the National Cardiovascular Data Registry CathPCI Dataset: a study from the Massachusetts Angioplasty Registry. Journal of the American College of Cardiology 2011; 57(8):904-911.
14. Roe CM, Fagan AM, Williams MM, Ghoshal N, Aeschleman M, Grant EA, Marcus DS, Mintun MA, Holtzman DM, Morris JC. Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease. Neurology 2011; 76(6):501-510.
15. Thuret R, Sun M, Abdollah F, Budaus L, Lughezzani G, Liberman D, Morgan M, Johal R, Jeldres C, Latour M, Shariat SF, Iborra F, Guiter J, Patard JJ, Perrotte P, Karakiewicz PI. Tumor grade improves the prognostic ability of American Joint Committee on Cancer Stage in patients with penile carcinoma. Journal of Urology 2011; 185(2):501-507.
16. Hammill BG, Curtis LH, Fonarow GC, Heidenreich PA, Yancy CW, Peterson ED, Hernandez AF. Incremental value of clinical data beyond claims data in predicting 30-day outcomes after heart failure hospitalization. Circulation: Cardiovascular Quality and Outcomes 2011; 4(1):60-67.
17. Liang Y, Ankerst DP, Ketchum NS, Ercole B, Shah G, Shaughnessy Jr JD, Leach RJ, Thompson IM. Prospective evaluation of operating characteristics of prostate cancer detection biomarkers. Journal of Urology 2011; 185(1):104-110.
18. Cleaves MA. From the help desk: comparing areas under receiver operating characteristic curves from two or more probit or logit models. The Stata Journal 2002; 2:301-313.
19. Demler OV, Pencina MJ, D'Agostino RB. Equivalence of improvement in area under ROC curve and linear discriminant analysis coefficient under assumption of normality. Statistics in Medicine 2011; 30:1410-1408. DOI: 10.1002/sim.4196.
20. Pencina MJ, D'Agostino RB, Sr, D'Agostino RB, Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from the area under the ROC curve and beyond. Statistics in Medicine 2008; 27(2):157-172.
21. Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Annals of Internal Medicine 2009; 150(11):795-802.