Bayesian competing risks model with spatially varying coefficients

Music City Center 

Time-to-event models are commonly used to study associations between risk factors and disease outcomes in the setting of electronic health records (EHR). In recent years, focus has intensified on social determinants of health, highlighting the need for methods that account for patients' locations. We propose a Bayesian approach for introducing spatially varying coefficients into a competing risks proportional hazards model. Our method leverages a Gaussian process (GP) prior with a separable covariance structure for spatially varying intercept and slope. To improve computational efficiency under a large number of spatial locations, we implemented a Hilbert space low-rank approximation of the GP. We also introduced a novel multiplicative gamma process shrinkage prior for the baseline hazard which induces smoother hazard rate curves. We demonstrate the utility of this method through simulation and a real-world analysis of EHR from Duke Hospital on elderly patients with upper extremity fractures. Our results show that the proposed method is capable of identifying spatially varying associations with time-to-event outcomes, including emergency department visits and hospital readmissions.

Survival analysis

Geospatial analysis

Competing risks

Bayesian modeling

Electronic health records data

Scalable Gaussian process 

Section on Bayesian Statistical Science