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

Journal: Statistics in biopharmaceutical research
Published Date: Aug 18, 2020

Abstract

The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if "herd immunity" will eventually reduce the risk or if a successful vaccine can be developed - and this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.

Authors

  • William R Zame
    Department of Economics and Mathematics, UCLA, Los Angeles, CA, USA;
  • Ioana Bica
    University of Oxford, Oxford, UK;
  • Cong Shen
    Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA;
  • Alicia Curth
    University of Oxford, Oxford, UK;
  • Hyun-Suk Lee
    Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK;
  • Stuart Bailey
    Novartis Pharmaceuticals, Cambridge, MA, USA.
  • James Weatherall
    AstraZeneca, Cambridge, UK;
  • David Wright
    AstraZeneca, Cambridge, UK;
  • Frank Bretz
    Novartis Pharma AG, Basel, Switzerland;
  • Mihaela van der Schaar
    University of California, Los Angeles, CA, USA.

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