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