Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data.

Journal Article (Journal Article)

This paper is motivated by combining serial neurocognitive assessments and other clinical variables for monitoring the progression of Alzheimer's disease (AD). We propose a novel framework for the use of multiple longitudinal neurocognitive markers to predict the progression of AD. The conventional joint modeling longitudinal and survival data approach is not applicable when there is a large number of longitudinal outcomes. We introduce various approaches based on functional principal component for dimension reduction and feature extraction from multiple longitudinal outcomes. We use these features to extrapolate the health outcome trajectories and use scores on these features as predictors in a Cox proportional hazards model to conduct predictions over time. We propose a personalized dynamic prediction framework that can be updated as new observations collected to reflect the patient's latest prognosis, and thus intervention could be initiated in a timely manner. Simulation studies and application to the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate the robustness of the method for the prediction of future health outcomes and risks of target events under various scenarios.

Full Text

Duke Authors

Cited Authors

  • Li, K; Luo, S

Published Date

  • October 30, 2019

Published In

Volume / Issue

  • 38 / 24

Start / End Page

  • 4804 - 4818

PubMed ID

  • 31386218

Pubmed Central ID

  • PMC6800781

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

Digital Object Identifier (DOI)

  • 10.1002/sim.8334


  • eng

Conference Location

  • England