Status:
Postponed
Project number:
#117
Understanding and adjusting for bias in OpenSAFELY COVID testing data
Understanding and adjusting for bias in OpenSAFELY COVID testing data is an OpenSAFELY project from The London School of Hygiene & Tropical Medicine. Every time a researcher runs their analytic code against patient data, it is audited in public here.
Project Postponed
Due to uncertainty about whether individuals had consented to long-term data linkage, and the fact that the pandemic emergency had passed, this data was not made available for research.
Workspaces
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- GitHub repository:
- bias
- Git branch:
- main
- Created at:
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- GitHub repository:
- CIS-pop-validation
- Git branch:
- main
- Created at:
-
- GitHub repository:
- cis-pop-validation-ehrql
- Git branch:
- dev
- Created at:
Project timeline
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Project created:
-
Code first run:
Public repos
Reports
Recently published reports
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No reports
This project does not currently have any published reports
Researchers
- Emily Herrett
- Linda Nab
- Milan Wiedemann
- Seb Bacon
- Thomas Hartney
- Will Hulme