666 – Recent Advances of Statistical Application to the Health Policy Studies
Linear Combinations of Biomarkers to Improve Overall Diagnostic Accuracy with Three Ordinal Diagnostic Categories
Paul K. Crane
University of Washington
Le Kang
FDA/CDRH
Lili Tian
SUNY at Buffalo
Chengjie Xiong
Washington University School of Medicine
Many researchers have addressed the problem of finding the optimal linear combination of biomarkers to maximize the area under ROC curves (AUC) for scenarios with binary disease status. In practice, many disease processes such as Alzheimer can be naturally classified into three diagnostic categories such as normal, mild cognitive impairment and Alzheimer's disease, and for such diseases the volume under the ROC surface (VUS) is the most commonly used index of diagnostic accuracy. In this article, we propose a few parametric and nonparametric approaches to address the problem of finding the optimal linear combination to maximize the VUS. Simulation studies were carried out to investigate the performance of the proposed methods. All of the investigated approaches are applied to a real data set from a cohort study in early stage Alzheimer's disease (AD).
