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Joint modeling of longitudinal data and survival data in hidden markov model setting

Principal Investigator
Name
hong li
Degrees
PhD
Institution
medical university of south carolina
Position Title
associate professor
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-782
Initial CDAS Request Approval
May 7, 2021
Title
Joint modeling of longitudinal data and survival data in hidden markov model setting
Summary
We will develop a joint modeling of longitudinal biomarker data and survival data using Bayesian inference in hidden Markov model setting to separate participants into different latent disease progression groups (low risk, intermedium risk, and high risk), which is the hidden state. We plan to apply the developed approach on colorectal cancer screening data. Modeling longitudinal biomarker trajectories uses a change point model. Patients in different hidden states may have different change points, different slopes of the trajectories, or different risk factors to predict the trajectory development. Risk factors in the change point model and disease transition model could be time-dependent or time-independent. The model will also jointly model survival data using polyps data. 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.
Aims

1. Develop joint Bayesian model of longitudinal data and survival data in hidden Markov model setting. The
developed model allows each patient's biomarker trajectory 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 second model in this joint model is the survival model, which will use polyps data and the approach of shared parameters with the biomarker trajectory model. 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 to colorectal cancer 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.

Collaborators

Brian Neelon, Medical university of south carolina