An application of Bayesian joint modeling to develop a new personalized VA Women CVD risk score

Thomas M. Menino Convention & Exhibition Center 

Precision medicine applies advanced statistical modeling integrating diverse data such as polygenic risk score (PRS), electronic health records data, and longitudinal biomarkers to improve accuracy of model prediction of disease. An appropriate framework for linking longitudinal biomarkers and female-specific conditions from electronic health records with time-to-event outcomes is joint modeling. However, its application in prediction modeling has been limited to small data sets. The current study is one of the first to apply Bayesian joint models (Rizopoulos et al, 2016) to the large-, representative and comprehensive Veterans Affairs (VA) Health Care System records, biomarkers, female sex-specific conditions and cardiovascular disease (CVD) PRS and develop a new personalized VA Women CVD risk score. The new joint models include multiple sub models-time-varying Cox model and general linear models for longitudinal biomarkers and female sex-specific conditions-linked via time of visit. The new personalized VA women CVD risk score improved model accuracy in predicting CVD events (Δ C statistics +0.05) compared to the original VA women CVD risk score (Jeon-Slaughter et al., 2021).

Bayesian Joint Model application to a large-scale data set

Precision Medicine

Prediction model

Women's Health

Clinical decision making

VA Women CVD risk score 

Health Policy Statistics Section