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The London School of Hygiene & Tropical Medicine logo 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.

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Workspaces

  • Workspace:
    bias
    Status:
    Active
    GitHub repository:
    bias
    Git branch:
    main
    Created at:
  • Workspace:
    cis-pop-validation
    Status:
    Active
    GitHub repository:
    CIS-pop-validation
    Git branch:
    main
    Created at:
  • Workspace:
    cis-pop-validation-ehrql-dev
    Status:
    Active
    GitHub repository:
    cis-pop-validation-ehrql
    Git branch:
    dev
    Created at:

Project timeline

  • Project created:

  • Code first run:

Public repos

Reports

Recently published reports

  • No reports

    This project does not currently have any published reports

Researchers

  • Emily Herrett
  • Linda Nab
  • Milan Wiedemann
  • Seb Bacon
  • Thomas Hartney
  • Will Hulme