|
Activity Number:
|
341
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biometrics Section
|
|
Abstract #319000
|
View Presentation
|
|
Title:
|
Dynamic Prediction of Alzheimer's Disease Risk Based on Longitudinal Biomarkers and Functional Data
|
|
Author(s):
|
Sheng Luo* and Kan Li
|
|
Companies:
|
The University of Texas at Houston and The University of Texas at Houston
|
|
Keywords:
|
Area under the ROC curve ;
Bayesian method ;
failure time ;
MRI ;
Precision medicine
|
|
Abstract:
|
In the study of Alzheimer's disease (AD), an important survival outcome is the progression from mild cognitive impairment (MCI) to AD. An accurate prediction of the time from MCI to AD is helpful for physicians to monitor patients' disease progression and make informative medical decisions. We propose a dynamic prediction framework based on a joint model that consists of a longitudinal regression model with functional exposure (high dimensional magnetic resonance imaging) and a survival model for event time. This framework provides accurate prediction of target patients' future outcome trajectories and risk of AD conversion. Our proposed model is motivated and applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI-1).
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2016 program
|
