Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19

Abstract

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

Statistical Opportunities to Accelerate Development for COVID-19 Therapeutics

“…These can be incorporated as preplanned opportunities to use accumulating trial data to modify aspects of an ongoing trial while preserving the validity and integrity of the trial (Dimairo, 2018). The use of adaptive designs during epidemic outbreaks such as the 2014 Ebola virus outbreak and now COVID-19 have been acknowledged (Stallard, 2020) and are increasingly being accepted by regulators .…”

“…For example, the AGILE-ACCORD is an ongoing randomized, multicenter, phase 1/2 seamless, dose finding, platform study (Griffith, 2020;Stallard, 2020). The phase 1 portion seeks to determine the optimal and safe dose of each investigative molecule using an adaptive Bayesian model of dose-toxicity design to enter the efficacy evaluation phase.…”

Machine learning for clinical trials in the era of COVID-19

“…ML methods achieve significant reduction in error and require many fewer patients to achieve a prescribed level of confidence (Atan, Zame, and van der Schaar 2019;. Furthermore, these new methods can provide significant benefits that outweigh potential inflation of confidence under the circumstances of an active outbreak, see also Dodd et al (2016), Proschan, Dodd, and Price (2016), and Mulangu et al (2019), see Stallard et al (2020) for recent overview of such methods in the context of COVID-19 therapies.…”

Will COVID-19 Transform Translational Medicine?

“…Two other innovative adaptive trial designs deserve further mention. The AGILE (Accelerating COVID-19 Drug Development) trial is an example of a randomized, seamless, phase 1/2 trial in which multiple different candidate treatments undergo dose escalation to establish the safety profile of candidate drugs in different patient populations ( 4 ). Safe compounds can then be advanced, and the efficacy of the compound can be assessed via a randomized Bayesian group sequential trial.…”

“…Another innovative trial design is REMAP-CAP (Randomized, Embedded, Multifactorial Adaptive Platform Trial for Community-Acquired Pneumonia), which was designed to evaluate multiple interventions to improve outcomes of patients in the intensive care unit with severe community-acquired pneumonia. REMAP-CAP also had a pre-specified data analysis plan to evaluate respiratory pandemics ( 4 ). In REMAP-CAP, patients are stratified by disease state (e.g., hospitalized or ICU) and then receive 1 intervention from several mutually exclusive domains (e.g., antiviral, immunomodulatory).…”