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Activity Number:
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137
- Joint Modeling for Longitudinal and Survival Outcomes in Health Studies
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 8, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #320805
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Title:
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Joint Model for Survival and Multivariate Sparse Functional Data with Application to a Study of Alzheimer's Disease
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Author(s):
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Cai Li* and Luo Xiao and Sheng Luo
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Companies:
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St. Jude Children's Research Hospital and North Carolina State University and Duke University
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Keywords:
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EM algorithm;
functional mixed model;
multivariate longitudinal data;
smoothing;
survival
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Abstract:
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Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes, which are correlated and predictive of AD progression. It is of great scientific interest to investigate the association between the outcomes and time to AD onset. We model the multiple longitudinal outcomes as multivariate sparse functional data and propose a functional joint model linking multivariate functional data to event time data. In particular, we propose a multivariate functional mixed model to identify the shared progression pattern and outcome-specific progression patterns of the outcomes, which enables more interpretable modeling of associations between outcomes and AD onset. The proposed method is applied to the Alzheimer's Disease Neuroimaging Initiative study (ADNI) and the functional joint model sheds new light on inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model.
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Authors who are presenting talks have a * after their name.
