In the search for effective treatments for COVID-19, initial emphasis has
been on re-purposed treatments. To maximise the chances of finding
successful treatments, novel treatments that have been developed for
this disease in particular, are needed. In this manuscript we describe and
evaluate the statistical design of the AGILE platform, an adaptive
randomized seamless Phase I/II trial platform that seeks to quickly
establish a safe range of doses and investigates treatments for potential
efficacy. The bespoke Bayesian design (i) utilizes randomization during
dose-finding, (ii) shares control arm information across the platform, and
(iii) uses a time-to-event endpoint with a formal testing structure and
error control for evaluation of potential efficacy. Both single agent and
combination treatments are considered. We find that the design can
identify potential treatments that are safe and efficacious reliably with
small to moderate sample sizes.