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Abstract Details

Activity Number: 666
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract - #306028
Title: Linear Combinations of Biomarkers to Improve Overall Diagnostic Accuracy with Three Ordinal Diagnostic Categories
Author(s): Le Kang*+ and Lili Tian and Chengjie Xiong and Paul K Crane
Companies: FDA/CDRH and SUNY at Buffalo and Washington University School of Medicine and University of Washington
Address: 10903 New Hampshire Avenue WO62, Silver Spring, MD, 20993,
Keywords: diagnostic accuracy ; linear combinations ; ordinal categories ; volume under the ROC surface

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).

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