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

TitleTime-dependent classification accuracy curve under marker-dependent sampling.
Publication TypeJournal Article
Year of Publication2016
AuthorsZhu, Zhaoyin, Xiaofei Wang, Paramita Saha-Chaudhuri, Andrzej S. Kosinski, and Stephen L. George
JournalBiom J
Date Published2016 Jul
KeywordsArea Under Curve, Biomarkers, Diagnostic Techniques and Procedures, Humans, Predictive Value of Tests, ROC Curve, Validation Studies as Topic

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.

Alternate JournalBiom J
Original PublicationTime-dependent classification accuracy curve under marker-dependent sampling.
PubMed ID27119599
PubMed Central IDPMC4930889
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States