Control arm augmentation and hierarchical modeling in time-to-event trials: advantages and pitfalls.

Abstract

In clinical trials, it is often valuable to borrow information from external data sources. Unfortunately, when the external data are fully or partially incompatible with the current trial data, type I error rates can be highly inflated under traditional blanket discounting schemes such as power priors, commensurate priors, and meta-analytic predictive priors. However, such inflation of the probability of a false positive can be necessary, as the alternative is to have an underpowered study. For clinical trials with time-to-event (TTE) outcomes, this problem is exacerbated since many observations are censored. In this paper, we develop the latent exchangeability prior for TTE data. We also present a novel framework to borrow information about the treatment effect between groups as well as incorporate information from external controls. Simulation results suggest that, although efficiency gains can be achieved by borrowing information among external controls, operating characteristics in general can be quite poor under a lack of exchangeability. We apply our approach to a real clinical trial in second-line metastatic colorectal cancer.