Online Program Home
  My Program

Abstract Details

Activity Number: 595 - Dynamic Methods for Functional Data with Application to Clinical Data Analysis
Type: Invited
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #322032
Title: Dynamic Prediction of Alzheimer's Disease Progression with Longitudinal Functional Joint Model
Author(s): Sheng Luo* and Kan Li
Companies: The University of Texas Health Science Center at Houston and The University of Texas Health Science Center at Houston
Keywords: Area under the ROC curve ; Bayesian methods ; failure time ; MRI ; precision medicine

Functional exposures are commonly measured longitudinally. But the current joint models involve only scalar variables. We propose a functional joint model (FJM) that consists of a longitudinal regression model with longitudinal functional exposure (high dimensional MRI) and a survival model for event time. We also develop methods for model-based personalized dynamic predictions of future outcome trajectories and risks of target events. Our proposed model is motivated and applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI), a motivating clinical study to determine the relationships among the clinical, cognitive, imaging, genetic and biochemical biomarkers characteristics of the entire spectrum of Alzheimer's disease (AD).

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

Back to the full JSM 2017 program

Copyright © American Statistical Association