1. Develop Bayesian hierarchical change point model with variable selection in hidden Markov model setting. The developed model allows 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.
2. Apply the proposed approach on PLCO 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.

Brian Neelon, Medical university of south carolina