A seamless Phase I/II platform design with a time-to-event efficacy endpoint for potential COVID-19 therapies

Abstract

In the search for effective treatments for COVID-19, the initial emphasis has been on re-purposed treatments. To maximize the chances of finding successful treatments, novel treatments that have been developed for this disease in particular, are needed. In this article, 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.

Keywords: Adaptive platform trial; COVID-19; dose-escalation; randomized; seamless; time-to-improvement.

Figure 1.

Illustration of the AGILE platform design.

Figure 2.

Percentage of simulations that recommend all desirable doses (left) and the percentage of simulations that recommend any desirable dose (right) for different cohort sizes and compositions and with and without sharing control group data. Note that only 13 out of 25 efficacy/safety scenarios contain a desirable dose.

Figure 3.

Average total sample size across simulations for all scenarios.

Figure 4.

Percentage of 10,000 simulations that recommend all desirable dose combinations (left), the percentage of simulations that recommend any desirable dose combination (centre) and average total sample size. Note that only 7 out of 16 efficacy/safety scenarios contain a desirable dose combination.