Estimation of ROC Curve with Multiple Types of Missing Gold Standard

• Activity Number: 484 - Recent Statistical Advances in Biomarker Studies in the Era of Precision Medicine
• Type: Invited
• Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Danping Liu* and Xiao-Hua Zhou
• Companies: National Institutes of Health and University of Washington
• Keywords: ROC curve ; Verification bias ; Missing gold standard ; Multiple types of missingness ; Alzheimer's disease

Abstract

In evaluating the diagnostic accuracy of a test, the gold standard might be missing because of high cost or harmfulness to the patient. The estimation of the diagnostic accuracy could be biased if the missingness is not handled appropriately. In this paper, we propose a likelihood-based approach to jointly estimate the selection model and disease model when the missing data mechanism is a mixture of ignorable and nonignorable missingness. The receiver operating characteristic (ROC) curve and its area are estimated empirically using imputation and reweighting techniques. The proposed method extends the results of Liu and Zhou (2010, Biometrics, 66, 1119-1128), as the latter assumes a single source of nonignorable missingness. We perform simulation studies to compare the performance of the proposed method and the existing approaches in the literature. This methodology is motivated from and applied to a study in Alzheimer's disease (AD), where two reasons of missingness are modeled sequentially.