Design and Analysis of Cancer Biomarker Validation Study with Biased Sampling Schemes

• Activity Number: 146
• Type: Invited
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Xiaofei Wang*+
• Companies: Duke University Medical Center
• Keywords: AUC ; Biased sampling ; Biomarker evaluation ; partial AUC ; ROC

Abstract

The receiver operating characteristic (ROC) curve and its summary measure, the area under the ROC curve (AUC), are useful statistical tool to evaluate the performance of a biomarker predicting disease status or clinical condition. Motivated by the need for novel study designs with better estimation efficiency and reduced study cost in biomarker validation, we consider statistical inference on ROC and AUC under test-result-dependent sampling scheme (TDS). The TDS design is a biased sampling scheme that consists of a simple random component and a supplemental test-result-dependent component. Using such design, investigators can oversample or undersample subjects falling into certain ranges of the biomarker score, yielding an improved precision for the estimation of ROC and AUC with a fixed size of subjects. Test-result-dependent sampling will introduce bias in the predictive accuracy of the biomarker if standard ROC and AUC estimation methods are used. In this presentation, we will discuss semiparametric methods for analyzing data with a test-result-dependent structure. The asymptotic properties of the proposed estimators will be discussed as well as their finite sample properties.