|Title||Estimation of AUC or Partial AUC under Test-Result-Dependent Sampling.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Wang, Xiaofei, Junling Ma, Stephen George, and Haibo Zhou|
|Journal||Stat Biopharm Res|
|Date Published||2012 Jan 01|
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.
|Alternate Journal||Stat Biopharm Res|
|Original Publication||Estimation of AUC or Partial AUC under test-result-dependent sampling.|
|PubMed Central ID||PMC3564679|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R03 CA131596 / CA / NCI NIH HHS / United States
UL1 RR024128 / RR / NCRR NIH HHS / United States
Estimation of AUC or Partial AUC under Test-Result-Dependent Sampling.