11: BNPDB: Bayesian Nonparametric Dynamic Borrowing with Application to Oncology Trials

Music City Center 

In oncology trials, attaining sufficient events to ensure adequate statistical power can prolong trial durations and delay access to potentially life-changing treatments. In the Bayesian paradigm, existing historical and/or external data can be leveraged to supplement current trial data, reducing this burden. However, borrowing information has traditionally required parametric models, whereas frequentist models of time-to-event data are typically non/semi-parametric. If violated, these parametric assumptions can introduce bias and type I error inflation, posing regulatory concerns. We introduce Bayesian nonparametric dynamic borrowing (BNPDB), which generalizes the latent exchangeability prior (LEAP) to enable individual-specific discounting without assuming a parametric model for the current data. Unlike LEAP, BNPDB is effectively model-free, allowing for flexible borrowing while mitigating bias and type I error risks. Through extensive simulations, we demonstrate BNPDB's performance against correctly specified and misspecified parametric alternatives. We showcase its practical applicability in a real oncology trial.