Lifetime Data Analysis

, Volume 14, Issue 1, pp 86–113

Evaluating the ROC performance of markers for future events

  • Margaret S. Pepe
  • Yingye Zheng
  • Yuying Jin
  • Ying Huang
  • Chirag R. Parikh
  • Wayne C. Levy
Article

DOI: 10.1007/s10985-007-9073-x

Cite this article as:
Pepe, M., Zheng, Y., Jin, Y. et al. Lifetime Data Anal (2008) 14: 86. doi:10.1007/s10985-007-9073-x

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.

Keywords

Prediction Diagnostic test Prognosis Sensitivity Specificity 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Margaret S. Pepe
    • 1
  • Yingye Zheng
    • 1
  • Yuying Jin
    • 2
  • Ying Huang
    • 2
  • Chirag R. Parikh
    • 3
  • Wayne C. Levy
    • 4
  1. 1.Biostatistics and BiomathematicsFred Hutchinson Cancer Research CenterSeattleUSA
  2. 2.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  3. 3.Section of Nephrology, Department of Internal MedicineYale University School of MedicineNew HavenUSA
  4. 4.University of Washington Medical CenterSeattleUSA

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