Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform

Elizabeth J. Williamson, John Tazare, Krishnan Bhaskaran, Alex J. Walker, Helen I. McDonald, Laurie Tomlinson, Sebastian Bacon, Chris Bates, Helen J. Curtis, Harriet Forbes, Caroline Minassian, Caroline E. Morton, Emily Nightingale, Amir Mehrkar, Dave Evans, Brian D. Nicholson, David Leon, Peter Inglesby, Brian MacKenna, Jonathan CockburnNicholas G. Davies, William J Hulme, Jessica Morley, Ian J. Douglas, Christopher T. Rentsch, Rohini Mathur, Angel Y S Wong, Anna Schultze, Richard Croker, John Parry, Frank Hester, Sam Harper, Rafael Perera, Richard Grieve, David Harrison, Ewout Steyerberg, Rosalind M. Eggo, Karla Diaz-Ordaz, Ruth Keogh, Stephen J.W. Evans, Liam Smeeth, Ben Goldacre

Research output: Contribution to journalArticlepeer-review


On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required. For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance. This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the Open SAFELY secure analytics platform.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalWellcome Open Research
Publication statusPublished - 15 Oct 2020
Externally publishedYes


  • COVID-19
  • infectious disease
  • mortality
  • risk prediction
  • statistical methodology

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)


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