Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.
Nigel StallardLisa HampsonNorbert BendaWerner BrannathThomas BurnettTim FriedePeter K KimaniFranz KoenigJohannes KrisamPavel MozgunovMartin PoschJames M S WasonGernot WassmerJohn WhiteheadS Faye WilliamsonSarah ZoharThomas F JakiPublished in: Statistics in biopharmaceutical research (2020)
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.
Keyphrases
- clinical trial
- coronavirus disease
- phase ii
- phase iii
- sars cov
- open label
- double blind
- study protocol
- end stage renal disease
- placebo controlled
- ejection fraction
- chronic kidney disease
- respiratory syndrome coronavirus
- newly diagnosed
- finite element analysis
- prognostic factors
- physical activity
- climate change
- data analysis
- machine learning
- randomized controlled trial