Time-dependent classification accuracy curve under marker-dependent sampling.

Published

Journal Article

Evaluating the classification accuracy of a candidate biomarker signaling the onset of disease or disease status is essential for medical decision making. A good biomarker would accurately identify the patients who are likely to progress or die at a particular time in the future or who are in urgent need for active treatments. To assess the performance of a candidate biomarker, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are commonly used. In many cases, the standard simple random sampling (SRS) design used for biomarker validation studies is costly and inefficient. In order to improve the efficiency and reduce the cost of biomarker validation, marker-dependent sampling (MDS) may be used. In a MDS design, the selection of patients to assess true survival time is dependent on the result of a biomarker assay. In this article, we introduce a nonparametric estimator for time-dependent AUC under a MDS design. The consistency and the asymptotic normality of the proposed estimator is established. Simulation shows the unbiasedness of the proposed estimator and a significant efficiency gain of the MDS design over the SRS design.

Full Text

Duke Authors

Cited Authors

  • Zhu, Z; Wang, X; Saha-Chaudhuri, P; Kosinski, AS; George, SL

Published Date

  • July 2016

Published In

Volume / Issue

  • 58 / 4

Start / End Page

  • 974 - 992

PubMed ID

  • 27119599

Pubmed Central ID

  • 27119599

Electronic International Standard Serial Number (EISSN)

  • 1521-4036

International Standard Serial Number (ISSN)

  • 0323-3847

Digital Object Identifier (DOI)

  • 10.1002/bimj.201500171

Language

  • eng