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

ABSTRACT: 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 dyna

SUBMITTER: Li K 

PROVIDER: S-EPMC6800781 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature