Skip to main content

Advertisement

Log in

Evaluating the ROC performance of markers for future events

  • Published:
Lifetime Data Analysis Aims and scope Submit manuscript

Abstract

Receiver operating characteristic (ROC) curves play a central role in the evaluation of biomarkers and tests for disease diagnosis. Predictors for event time outcomes can also be evaluated with ROC curves, but the time lag between marker measurement and event time must be acknowledged. We discuss different definitions of time-dependent ROC curves in the context of real applications. Several approaches have been proposed for estimation. We contrast retrospective versus prospective methods in regards to assumptions and flexibility, including their capacities to incorporate censored data, competing risks and different sampling schemes. Applications to two datasets are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Antolini L, Boracchi P and Biganzoli E (2005). A time dependent discrimination index for survival data. Stat Med 24: 3927–3944

    Article  MathSciNet  Google Scholar 

  • Baker SG (2003). The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer. J Natl Cancer Inst 95: 511–515

    Article  Google Scholar 

  • Begg CB and Greenes RA (1983). Assessment of diagnostic tests when disease verification is subject to selection bias. Biometrics 39: 207–215

    Article  MathSciNet  Google Scholar 

  • Cai T, Pepe MS, Zheng Y, Lumley T and Jenny NS (2006). The sensitivity and specificity of markers for event times. Biostatistics 7: 182–197

    Article  MATH  Google Scholar 

  • Cai T and Pepe MS (2002). Semi-parametric ROC analysis to evaluate biomarkers for disease. J Am Stat Assoc 97: 1099–1107

    Article  MATH  MathSciNet  Google Scholar 

  • Chambless LE and Diao G (2006). Estimation of time-dependent area under the ROC curve for long-term risk prediction. Stat Med 25: 3474–3486

    Article  MathSciNet  Google Scholar 

  • Cohn JN and Tognoni G (2001). A randomized trial of the angiotensin-receptor blocker valsartan in chronic heart failure. N Engl J Med 345: 1667–1675

    Article  Google Scholar 

  • Cook NR (2007). Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115: 928–935

    Article  Google Scholar 

  • Delong ER, Vernon WB and Bollinger RR (1985). Sensitivity and specificity of a monitoring test. Biometrics 41: 947–958

    Article  Google Scholar 

  • Emir B, Wieand S, Su JQ and Cha S (1998). Analysis of repeated markers used to predict progression of cancer. Stat Med 17: 2563–2578

    Article  Google Scholar 

  • Etzioni R, Pepe M, Longton G, Hu C and Goodman G (1999). Incorporating the time dimension in receiver operating characteristic curves: a case study of prostate cancer. Med Decis Making 19: 242–251

    Article  Google Scholar 

  • Heagerty PJ and Zheng Y (2005). Survival model predictive accuracy and ROC curves. Biometrics 61: 92–105

    Article  MATH  MathSciNet  Google Scholar 

  • Heagerty PJ, Lumley T and Pepe MS (2000). Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56: 337–344

    Article  MATH  Google Scholar 

  • Kalbfleisch JD and Prentice RL (1980). The statistical analysis of failure time data. Wiley, New York

    MATH  Google Scholar 

  • Koenker R and Bassett G (1978). Regression quantiles. Econometrica 46: 33–50

    Article  MATH  MathSciNet  Google Scholar 

  • Leisenring W, Pepe MS and Longton G (1997). A marginal regression modelling framework for evaluating medical diagnostic tests. Stat Med 16: 1263–1281

    Article  Google Scholar 

  • Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, Anand I, Maggioni A, Burton P, Sullivan MD, Pitt B, Poole-Wilson PA, Mann DL and Packer M (2006). The Seattle Heart Failure model: prediction of survival in heart failure. Circulation 113: 1424–1433

    Article  Google Scholar 

  • McIntosh M and Pepe MS (2002). Combining several screening tests: optimality of the risk score. Biometrics 58: 657–664

    Article  MathSciNet  Google Scholar 

  • Packer M, O’Connor CM, Ghali JK, Pressler ML, Carson PE, Belkin RN, Miller AB, Neuberg GW, Frid D, Wertheimer JH, Cropp AB, DeMets DL and for the Prospective Randomized Amlodipine Survival Evaluation Study Group (1996). Effect of amlodipine on morbidity and mortality in severe chronic heart failure. New Eng J Med 335: 1107–1114

  • Parker CB and Delong ER (2003). ROC methodology within a monitoring framework. Stat Med 22: 3473–3488

    Article  Google Scholar 

  • Pepe MS (2003). The statistical evaluation of medical tests for classification and prediction. Oxford University Press, New York

    MATH  Google Scholar 

  • Song X, Zhou XH (in press) A semiparametric approach for the covariate specific ROC curve with survival outcome. Stat Sinca.

  • Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C, Jacques PF, Rifai N, Selhub J, Robins SJ, Benjamin EJ, D’Agostino RB and Vasan RS (2006). Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med 355: 2631–2639

    Article  Google Scholar 

  • Wieand S, Gail MH, James BR and James KL (1989). A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data. Biometrika 76: 585–592

    Article  MATH  MathSciNet  Google Scholar 

  • Xu R and O’Quigley J (2000). Proportional hazards estimate of the conditional survival function. J Roy Stat Soc Ser B 62: 667–680

    Article  MATH  MathSciNet  Google Scholar 

  • Zheng Y and Heagerty PJ (2004). Semiparametric estimation of time-dependent ROC curves for longitudinal marker data. Biostatistics 5: 615–632

    Article  MATH  Google Scholar 

  • Zheng Y and Heagerty PJ (2007). Prospective accuracy for longitudinal markers. Biometrics 63: 332–341

    Article  MATH  MathSciNet  Google Scholar 

  • Zheng Y, Cai T and Feng Z (2006). Application of the time-dependent ROC curves for prognostic accuracy with multiple biomarkers. Biometrics 62: 279–287

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Margaret S. Pepe.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pepe, M., Zheng, Y., Jin, Y. et al. Evaluating the ROC performance of markers for future events. Lifetime Data Anal 14, 86–113 (2008). https://doi.org/10.1007/s10985-007-9073-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10985-007-9073-x

Keywords

Navigation