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Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time‐to‐event data
Author(s) -
Li Kan,
Luo Sheng
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8334
Subject(s) - neurocognitive , computer science , robustness (evolution) , longitudinal data , proportional hazards model , disease , alzheimer's disease neuroimaging initiative , neuroimaging , artificial intelligence , machine learning , longitudinal study , data mining , alzheimer's disease , medicine , cognition , psychiatry , biochemistry , chemistry , pathology , gene
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.