A Bayesian hybrid Dynamic Borrowing Framework Incorporating Covariates

Music City Center 

In early-phase drug development, the goal is to assess whether a novel agent adds activity to a monotherapy. A Bayesian hybrid design with dynamic borrowing from historical monotherapy data offers a robust approach to enhance study efficiency and decision-making. Lu et al. (2024) proposed a dynamic borrowing framework for binary outcomes based on the dynamic power prior (DPP) and the similarity of outcomes between the study control and historical control. This approach is more robust than traditional single-arm designs and can significantly improve statistical power at the design stage. At the analysis stage, adjusting for important covariates improves modeling by controlling for variability and confounding effects. In this work, we develop an analysis framework that incorporates covariates through propensity scores into the DPP (PS-DPP), enabling more informed borrowing by accounting for the similarity between historical and current control data based on both covariates and outcomes. Simulation studies show that PS-DPP improves analytical performance, especially when there are substantial differences in both response rates and covariates between historical and current controls.

Bayesian hybrid design

dynamic borrowing

nonconcurrent control

power prior

propensity score 

Biopharmaceutical Section