Wellcome Open Research (Dec 2024)
Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform [version 2; peer review: 2 approved]
- Richard Croker,
- Anna Schultze,
- Frank Hester,
- John Parry,
- Rafael Perera,
- Sam Harper,
- Rosalind M. Eggo,
- Liam Smeeth,
- Ewout Steyerberg,
- Caroline Minassian,
- Ruth Keogh,
- Karla Diaz-Ordaz,
- Stephen J.W. Evans,
- Elizabeth J. Williamson,
- Krishnan Bhaskaran,
- John Tazare,
- Helen I McDonald,
- Alex J. Walker,
- Sebastian Bacon,
- Laurie A. Tomlinson,
- Helen J. Curtis,
- Chris Bates,
- Caroline E. Morton,
- Harriet Forbes,
- Amir Mehrkar,
- Emily Nightingale,
- Brian D Nicholson,
- Richard Grieve,
- Dave Evans,
- Peter Inglesby,
- David Harrison,
- Ben Goldacre,
- David Leon,
- Jonathan Cockburn,
- Brian MacKenna,
- Rohini Mathur,
- Will J. Hulme,
- Nicholas G. Davies,
- Ian J. Douglas,
- Jessica Morley,
- Angel Wong,
- Christopher T. Rentsch
Affiliations
- Richard Croker
- ORCiD
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Anna Schultze
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Frank Hester
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
- John Parry
- ORCiD
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
- Rafael Perera
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
- Sam Harper
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
- Rosalind M. Eggo
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Liam Smeeth
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Ewout Steyerberg
- Leiden University Medical Center and Erasmus MC, Leiden, The Netherlands
- Caroline Minassian
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Ruth Keogh
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Karla Diaz-Ordaz
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Stephen J.W. Evans
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Elizabeth J. Williamson
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Krishnan Bhaskaran
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- John Tazare
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Helen I McDonald
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Alex J. Walker
- ORCiD
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Sebastian Bacon
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Laurie A. Tomlinson
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Helen J. Curtis
- ORCiD
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Chris Bates
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
- Caroline E. Morton
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Harriet Forbes
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Amir Mehrkar
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Emily Nightingale
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Brian D Nicholson
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Richard Grieve
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Dave Evans
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Peter Inglesby
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- David Harrison
- ICNARC, 24 High Holborn, Holborn, London, WC1V 6AZ, UK
- Ben Goldacre
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- David Leon
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Jonathan Cockburn
- TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK
- Brian MacKenna
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Rohini Mathur
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Will J. Hulme
- ORCiD
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Nicholas G. Davies
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Ian J. Douglas
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Jessica Morley
- The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK
- Angel Wong
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Christopher T. Rentsch
- ORCiD
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Journal volume & issue
-
Vol. 5
Abstract
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 OpenSAFELY secure analytics platform.
