Semiparametric Transformation Models for Multiple Biomarkers in ROC Analysis

• Activity Number: 114
• Type: Topic Contributed
• Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Eunhee Kim*+ and Donglin Zeng+
• Companies: Brown University and The University of North Carolina at Chapel Hill
• Keywords: Biomarkers ; ROC analysis ; Transformation models ; Nonparametric maximum likelihood estimation ; Semiparametric efficiency

Abstract

In medical diagnostic research, biomarkers are used as the basis for detecting or predicting disease. In certain situations, a single biomarker is not enough to achieve the desired level of accuracy, and newly discovered biomarkers can provide additional information for a specific disease. Even though numerous methods have been developed for a single biomarker, few statistical methods exist to accommodate multiple biomarkers simultaneously. In this paper, we propose a multivariate binormal ROC model to assess multiple biomarkers. Our model assumes that biomarkers follow multivariate normal distribution after unknown and marker-specific transformations. Random effects are introduced to account for within-subject correlation among biomarkers. Nonparametric maximum likelihood estimation is used for inference and the parameter estimators are shown to be asymptotically normal and efficient.