A Seamless Phase I/II Platform Design with a Time-To-Event Efficacy Endpoint for Potential COVID-19 Therapies

TL;DR

This paper presents the AGILE platform, an adaptive Bayesian Phase I/II trial design for COVID-19 therapies that efficiently identifies safe and effective treatments with small to moderate sample sizes.

Contribution

It introduces a novel seamless Phase I/II platform design using Bayesian methods tailored for COVID-19 treatments, including single and combination therapies.

Findings

Design reliably identifies safe and efficacious treatments.

Efficient with small to moderate sample sizes.

Applicable to both single agent and combination treatments.

Abstract

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 using a Bayesian sequential trial design. 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.