ROC curve estimation under test-result-dependent sampling

• Published in: Biostatistics (Oxford, England) Vol. 14; no. 1; pp. 160 - 172
• Main Authors: Wang, X., Ma, J., George, S. L.
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.01.2013; Oxford University Press
• Subjects: Biomarkers; Biomarkers - analysis; Biostatistics; Carcinoma, Non-Small-Cell Lung - drug therapy; Carcinoma, Non-Small-Cell Lung - enzymology; Celecoxib; Clinical trials; Clinical Trials, Phase III as Topic; Computer Simulation; Cyclooxygenase 2 - blood; Cyclooxygenase 2 Inhibitors - therapeutic use; Estimation bias; Female; Humans; Likelihood Functions; Lung cancer; Lung Neoplasms - drug therapy; Lung Neoplasms - enzymology; Male; Pyrazoles - therapeutic use; ROC Curve; Sampling; Simulation; Sulfonamides - therapeutic use
• ISSN: 1465-4644; 1468-4357; 1468-4357
• DOI: 10.1093/biostatistics/kxs020

The receiver operating characteristic (ROC) curve is often used to evaluate the performance of a biomarker measured on continuous scale to predict the disease status or a clinical condition. Motivated by the need for novel study designs with better estimation efficiency and reduced study cost, we consider a biased sampling scheme that consists of a SRC and a supplemental TDC. Using this approach, investigators can oversample or undersample subjects falling into certain regions of the biomarker measure, yielding improved precision for the estimation of the ROC curve with a fixed sample size. Test-result-dependent sampling will introduce bias in estimating the predictive accuracy of the biomarker if standard ROC estimation methods are used. In this article, we discuss three approaches for analyzing data of a test-result-dependent structure with a special focus on the empirical likelihood method. We establish asymptotic properties of the empirical likelihood estimators for covariate-specific ROC curves and covariate-independent ROC curves and give their corresponding variance estimators. Simulation studies show that the empirical likelihood method yields good properties and is more efficient than alternative methods. Recommendations on number of regions, cutoff points, and subject allocation is made based on the simulation results. The proposed methods are illustrated with a data example based on an ongoing lung cancer clinical trial.