A Joint Model of Longitudinal CVD Risk Factors, Time-varying Medication Use, and Time-to-Terminal Events

Music City Center 

We introduce a novel Bayesian approach for jointly modeling longitudinal cardiovascular disease (CVD) risk factor trajectories, medication use, and time-to-events. Through this integrated framework, we connect models of longitudinal CVD risk factors, medication history, and CVD events. A novel component of our joint model is that the model for medication history accommodates uncertainty due to missing medication status as well as the age at which subjects switch off (or on) medication between visits. This history forms a key feature in the time to event model. Our research aims to provide a comprehensive understanding of CVD progression and the role of medications, thus enhancing predictive accuracy and informing personalized intervention strategies.

Bayesian methods

Longitudinal outcomes

Missing data

Multiple Imputation

Medication patterns