A Framework for Robust Information Borrowing via Ensembling Priors and Models

Music City Center 

Oncology clinical trials often involve time-to-event outcomes, where censoring can delay decision-making. Bayesian methods improve precision by borrowing historical data through informative priors, but improper borrowing can inflate type I error rates if historical and current data are incompatible. Nevertheless, regulatory guidelines require the borrowing approach to be pre-specified in the trial protocol, making prior selection difficult. While existing priors offer different advantages, no single approach is optimal in all cases. We illustrate this challenge using E1690 (current study) and E1684 (historical study), where the current data alone provide insufficient evidence of treatment efficacy, yet borrowing may increase the risk of a false discovery. To address this, we propose an ensemble framework that combines multiple informative priors using Bayesian model averaging or stacking techniques. Our method also accommodates different outcome models, incorporating propensity score-based approaches and distinct survival models. Rigorous simulations shows that our approach offers greater flexibility and robustness than selecting a single model-prior combination.

Information borrowing

Prior Elicitation

Bayesian analysis 

Section on Bayesian Statistical Science