“Precision Medicine for Alzheimer’s Disease Monitoring and Progression: the Dynamic Prediction Approach”
Abstract: This research work 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 components (FPC) 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 survival 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 (ADNI) dataset demonstrate the robustness of the method for the prediction of future health outcomes and risks of target events under various scenarios.
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