Bayesian hierarchical change point model with variable selections in hidden
Markov model setting 


   Note: An Official Proposal has not been Submitted to NACC.


   Lead Investigator:    Hong Li
   Institution      :    Medical University of South Carolina
   E-Mail           :    liho@musc.edu
   Proposal ID      :    1703



   Proposal Description:
   Develop Bayesian hierarchical change point model with variable selection in hidden Markov model setting. The developed model will model patients' cognitive measure scores and allow each patient to have a unique random intercept, random slope before the change point, random change point time, and random slope after the change point. The difference in slope before and after a change point is constrained. Risk factors in the change point model and disease transition model could be time-dependent or time independent. The developed model will also be able to predict personalized risk of disease development in the next few years, and identify contributing risk factors to patients in different hidden disease stage. The model also studies the association between cognitive decline and all potential risk factors using state-of-the-art Bayesian variable selection approach.  Apply the proposed approach on NACC data. The proposed model may provide a useful tool for clinicians to identify high risk patients which may provide an opportunity to treat those patients earlier and increase their chance of survival.