Efficient adaptive designs for clinical trials of interventions for COVID-19 Nigel Stallarda1, Lisa Hampsona*2, Norbert Benda3, Werner Brannath4, Tom Burnett5, Tim Friede6, Peter K. Kimani1, Franz Koenig7, Johannes Krisam8, Pavel Mozgunov5, Martin Posch7, James Wason9,10, Gernot Wassmer11, John Whitehead5, S. Faye Williamson5, Sarah Zohar12, Thomas Jakia5,10 1 Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, UK. 2 Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland 3 The Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany. 4 Institute for Statistics, University of Bremen, Bremen, Germany 5 Department of Mathematics and Statistics, Lancaster University, UK. 6 Department of Medical Statistics, University Medical Center Göttingen, Germany. 7 Section of Medical Statistics, CeMSIIS, Medical University of Vienna, Austria. 8 Institute of Medical Biometry and Informatics, University of Heidelberg, Germany. 9 Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK. 10 MRC Biostatistics Unit, University of Cambridge, Cambridge, UK. 11 RPACT GbR, Sereetz, Germany 12 INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, France * Corresponding author.
[email protected] a These authors contributed equally 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.