
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:
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- GitHub repository:
- cis-pop-validation-ehrql
- Git branch:
- dev
- Created at:
Project timeline
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Project created:
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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