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Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.
Stallard, Nigel; Hampson, Lisa; Benda, Norbert; Brannath, Werner; Burnett, Thomas; Friede, Tim; Kimani, Peter K; Koenig, Franz; Krisam, Johannes; Mozgunov, Pavel; Posch, Martin; Wason, James; Wassmer, Gernot; Whitehead, John; Williamson, S Faye; Zohar, Sarah; Jaki, Thomas.
  • Stallard N; Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
  • Hampson L; Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland.
  • Benda N; The Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany.
  • Brannath W; Institute for Statistics, University of Bremen, Bremen, Germany.
  • Burnett T; Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
  • Friede T; Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
  • Kimani PK; Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
  • Koenig F; Section for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria.
  • Krisam J; Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany.
  • Mozgunov P; Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
  • Posch M; Section for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria.
  • Wason J; Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
  • Wassmer G; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Whitehead J; RPACT GbR, Sereetz, Germany.
  • Williamson SF; Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
  • Zohar S; Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
  • Jaki T; INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France.
Stat Biopharm Res ; 12(4): 483-497, 2020 Jul 29.
Article in English | MEDLINE | ID: covidwho-630389
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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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Stat Biopharm Res Year: 2020 Document Type: Article Affiliation country: 19466315.2020.1790415

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Language: English Journal: Stat Biopharm Res Year: 2020 Document Type: Article Affiliation country: 19466315.2020.1790415