19: Bayesian Borrowing to Support Stable Prediction of Romosozumab Completion in Small-Sample Population

Thomas M. Menino Convention & Exhibition Center 

Romosozumab (Romo) is a monthly injectable osteoporosis therapy intended for 12 doses. Reliably predicting completion can inform targeted adherence support but is challenging in populations with lower fracture risk due to small samples and sparse covariates. Using U.S. Medicare claims (2019–2022), we identified women ≥65 initiating Romo with ≥15 months of follow-up. Completion was defined as receiving 12 doses of Romo within 15 months. We fit Bayesian hierarchical logistic models (BHM) to predict completion in Asian and Black users, borrowing information from White users. A heterogeneity parameter (τ²) quantified the degree of borrowing. Performance was evaluated via AUC and calibration plots. We identified 13,373 romo users (White: 12,094, Asian: 457, Black: 142). Predictors were selected by clinical experts and LASSO in White group, then incorporated as commensurate priors in BHM. AUCs were similar for Frequentist vs. Bayesian models. However, partial borrowing (τ²≈0.17–0.21) stabilized rare-predictor coefficients and improved calibration across risk deciles. Bayesian borrowing can produce more reliable subgroup predictions than frequentist models in small sample settings.

Bayesian Hierarchical Modeling

Commensurate Prior

Treatment Adherence 

Section on Bayesian Statistical Science