| Title: |
Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform. |
| Authors: |
OpenSAFELY Collaborative; Williamson, Elizabeth J; Tazare, John; Bhaskaran, Krishnan; Walker, Alex J; McDonald, Helen I; Tomlinson, Laurie A; Bacon, Sebastian; Bates, Chris; Curtis, Helen J; Forbes, Harriet; Minassian, Caroline; Morton, Caroline E; Nightingale, Emily; Mehrkar, Amir; Evans, Dave; Nicholson, Brian D; Leon, David; Inglesby, Peter; MacKenna, Brian; Cockburn, Jonathan; Davies, Nicholas G; Hulme, Will J; Morley, Jessica; Douglas, Ian J; Rentsch, Christopher T; Mathur, Rohini; Wong, Angel; Schultze, Anna; Croker, Richard; Parry, John; Hester, Frank; Harper, Sam; Perera, Rafael; Grieve, Richard; Harrison, David; Steyerberg, Ewout; Eggo, Rosalind M; Diaz-Ordaz, Karla; Keogh, Ruth; Evans, Stephen JW; Smeeth, Liam; Goldacre, Ben |
| Publisher Information: |
F1000 Research Ltd |
| Publication Year: |
2024 |
| Collection: |
London School of Hygiene & Tropical Medicine: LSHTM Research Online |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| ISSN: |
2398-502X |
| Relation: |
https://researchonline.lshtm.ac.uk/id/eprint/4678782/1/TheOpenSafelyCollaborative-etal-2024-Study-protocol-comaprison.pdf; OpenSAFELY Collaborative; Williamson, Elizabeth J ORCID logo; Tazare, John ORCID logo; Bhaskaran, Krishnan ORCID logo; Walker, Alex JORCID logo; McDonald, Helen IORCID logo; Tomlinson, Laurie A ORCID logo; Bacon, Sebastian; Bates, Chris; Curtis, Helen JORCID logo; +33 more.Forbes, Harriet ORCID logo; Minassian, Caroline; Morton, Caroline E; Nightingale, Emily ORCID logo; Mehrkar, Amir; Evans, Dave; Nicholson, Brian D; Leon, David ORCID logo; Inglesby, Peter; MacKenna, Brian; Cockburn, Jonathan; Davies, Nicholas G ORCID logo; Hulme, Will JORCID logo; Morley, Jessica; Douglas, Ian J ORCID logo; Rentsch, Christopher T ORCID logo; Mathur, RohiniORCID logo; Wong, Angel ORCID logo; Schultze, Anna ORCID logo; Croker, RichardORCID logo; Parry, JohnORCID logo; Hester, Frank; Harper, Sam; Perera, Rafael; Grieve, Richard; Harrison, David; Steyerberg, Ewout; Eggo, Rosalind M ORCID logo; Diaz-Ordaz, Karla; Keogh, Ruth ORCID logo; Evans, Stephen JW ORCID logo; Smeeth, Liam ORCID logo; and Goldacre, Ben (2024) Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform. Wellcome Open Res, 5. 243-. ISSN 2398-502X DOI:10.12688/wellcomeopenres.16353.2 |
| DOI: |
10.12688/wellcomeopenres.16353.2 |
| Availability: |
https://researchonline.lshtm.ac.uk/id/eprint/4678782/; https://doi.org/10.12688/wellcomeopenres.16353.2 |
| Rights: |
cc_by_4 |
| Accession Number: |
edsbas.AFE166FA |
| Database: |
BASE |