Estimation of AUC or Partial AUC under Test-Result-Dependent Sampling.

Published

Journal Article

The area under the ROC curve (AUC) and partial area under the ROC curve (pAUC) are summary measures used to assess the accuracy of a biomarker in discriminating true disease status. The standard sampling approach used in biomarker validation studies is often inefficient and costly, especially when ascertaining the true disease status is costly and invasive. To improve efficiency and reduce the cost of biomarker validation studies, we consider a test-result-dependent sampling (TDS) scheme, in which subject selection for determining the disease state is dependent on the result of a biomarker assay. We first estimate the test-result distribution using data arising from the TDS design. With the estimated empirical test-result distribution, we propose consistent nonparametric estimators for AUC and pAUC and establish the asymptotic properties of the proposed estimators. Simulation studies show that the proposed estimators have good finite sample properties and that the TDS design yields more efficient AUC and pAUC estimates than a simple random sampling (SRS) design. A data example based on an ongoing cancer clinical trial is provided to illustrate the TDS design and the proposed estimators. This work can find broad applications in design and analysis of biomarker validation studies.

Full Text

Duke Authors

Cited Authors

  • Wang, X; Ma, J; George, S; Zhou, H

Published Date

  • January 1, 2012

Published In

Volume / Issue

  • 4 / 4

Start / End Page

  • 313 - 323

PubMed ID

  • 23393612

Pubmed Central ID

  • 23393612

International Standard Serial Number (ISSN)

  • 1946-6315

Digital Object Identifier (DOI)

  • 10.1080/19466315.2012.692514

Language

  • eng

Conference Location

  • United States