A0274
Title: Joint model for survival and multivariate sparse functional data for Alzheimer's disease
Authors: Luo Xiao - North Carolina State University (United States) [presenting]
Abstract: 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. The multiple longitudinal outcomes are modeled as multivariate sparse functional data, and a functional joint model linking multivariate functional data to event time data is proposed. In particular, a multivariate functional mixed model is proposed to identify the shared progression pattern and outcome-specific progression patterns of the outcomes, which enables more interpretable modelling 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 the inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model. In addition, the model is extended and applied to multiple cohorts of AD studies.
