Identifying the causal relationships of cognitive symptoms for
neurodegenerative diseases 
   Lead Investigator:    Yang Ni
   Institution      :    Texas AM University
   E-Mail           :    yni@stat.tamu.edu
   Proposal ID      :    1547



   Proposal Description:
   The goal of this project to improve our mechanistic understanding of the causal   relationships among cognitive symptoms, depression, and neuropsychiatric          symptoms while adjusting for race, age, and education level.                      Upon the completion of the project, we expect to have inferred a Bayesian         network that describes the causal relationships among cognitive symptoms,         depression, and neuropsychiatric symptoms using the UDS longitudinal dataset.     With a better understanding of causal relationships of cognitive symptoms         (namely, which set of symptoms tend to emerge before others) can potentially      assist in early detection of the Alzheimer???s disease.                           A major challenge in identifying the causal relationships of cognitive symptoms lies in the longitudinal nature of the dataset. Existing Bayesian networks are    only applicable to cross-sectional data or time series data for which the         measurements are obtained on the same regular time points. We will propose a      novel longitudinal Bayesian network approach to model variables that are          observed on irregular time points.