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Activity Number:
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216
- Statistical Opportunities in Disease Interception: Screening, Intervening, and Evaluating Benefit-Risk Trade-Offs
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 31, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Medical Devices and Diagnostics
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Abstract #322841
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Title:
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Personalizing Early Detection for Alzheimer's Disease: Biomarker Assessment in the Absence of a Gold Standard
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Author(s):
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Zheyu Wang* and Yanxun Xu and Marilyn Albert
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Companies:
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Johns Hopkins University and Johns Hopkins University and Johns Hopkins University
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Keywords:
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diagnostics ;
biomarker ;
latent variables ;
Alzheimer's disease
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Abstract:
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The initiation of the Alzheimer disease (AD) pathogenic process is typically unobserved and has been thought to precede the first symptoms by 10 years or more. This impose a major challenge in investigating biomarkers for early AD detection, because 1) using clinical diagnosis as the reference point can be in error, especially in the early course of the disease; and 2) most AD studies do not have autopsy data to confirm diagnoses. Until technology advance allows for brain examination with "autopsy level" clarity, an appropriate statistical method that directly address the unobservable nature of preclinical AD progression is necessary for any rigorous AD biomarker evaluation and for efficient analyzing AD study data where only clinical data are available and neuropathology data are not yet available. In this talk, we will discuss two latent variable models for this purpose. Most of the attention will focus on a model that considers categorical latent disease status and intends to assess biomarker's utility in help making medical decision.
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Authors who are presenting talks have a * after their name.
