Adaptively Combining Randomized and External Control Data Using a Mixture Prior in the Presence of Heterogeneity

Abstract

When resources are limited in a randomized trial, a common strategy is to augment data from the control arm with external controls. We propose a hybrid group sequential Bayesian design that uses this approach to compare time-to-event distributions. The design allows between-treatment effects to differ between patient subgroups, and adaptively combines subgroups that have similar hazard functions. The aim is to reduce the control arm sample size without compromising the validity of comparative inferences due to systematic trial-versus-external data differences. The model borrows external control data dynamically by using a self-adapting mixture prior, which is a weighted average of a non-informative prior and an informative prior constructed from the external controls. The amount of borrowing is proportional to the agreement between the randomized and external controls. At each interim analysis, the design compares the treatments in each subgroup, and if inferiority or superiority is concluded stops accrual for that subgroup. Simulations show that the proposed design outperforms other methods for incorporating external control data in a randomized trial, and improves efficiency.