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

Li, Kan

Luo, Sheng

2020-01-01T15:46:16Z

2020-01-01T15:46:16Z

2019-10

2020-01-01T15:46:09Z

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.

0277-6715

1097-0258

https://hdl.handle.net/10161/19678

eng

Wiley

Statistics in medicine

10.1002/sim.8334

AUC

functional data analysis

multivariate longitudinal data

neuroimaging

two stage

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

Journal article

Luo, Sheng|0000-0003-4214-5809

4804

4818

24

School of Medicine

Duke

Duke Clinical Research Institute

Institutes and Centers

Biostatistics & Bioinformatics

Basic Science Departments

Published

38