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Activity Number: 30 - Missing Data and Measurement Error
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #304268
Title: Alzheimer's Disease Risk Prediction with Multidimensional Biomarkers
Author(s): Zheyu Wang*
Companies: Johns Hopkins University
Keywords: Latent variable; risk prediction; biomarkers; Alzheimer's diseaase

Accumulating evidence suggest that the initiation of the Alzheimer disease (AD) pathogenic process precede the first symptoms by a decade or more. The recognition of this decade-long asymptomatic stage has greatly impact AD research and therapeutic development to focus on preclinical stage of AD pathogenic process, at which time disease modifying therapy is more likely to be effective but also imposes 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. Since AD pathophysiology has been recognized as a multidimensional process that involves amyloid deposition, neurofibrillary tangles and neurodegeneration among other aspects, we proposed latent variable model to study the underlying AD pathophysiology process revealed by multidimensional markers. We will outline a model that considers continuous latent disease progression and intends to adopt biomarker information to help predict subjects’ AD progression trajectory. Applications to Alzheimer’s disease data will be discussed.

Authors who are presenting talks have a * after their name.

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