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Activity Number:
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405
- Statistical Issues Specific to Therapeutic Areas
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Type:
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Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #301772
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Title:
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Dynamic Prediction of Alzheimer's Disease Progression Using Features of Multiple Longitudinal Outcomes
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Author(s):
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Kan Li* and Sheng Luo and Richard Entsuah
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Companies:
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Merck & Co. and Duke University Medical Center and Merck & Co.
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Keywords:
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Multivariate longitudinal data;
Functional data analysis;
Joint modeling
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Abstract:
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This study 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 markers to predict the progression of AD. The conventional joint modeling longitudinal and survival data approach is not capable to deal with a large number of outcomes efficiently. We introduce various approaches based on functional principal components 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.
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Authors who are presenting talks have a * after their name.
