Collider bias undermines our understanding of COVID-19 disease risk and severity

Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic,…

• W
  Wellcome

  Award: 208806/Z/17/Z
• WT
  Wellcome Trust

  Awards: Z/17/Z, 208806/Z/17/Z
• UO
  University of Bristol

  Awards: MC_UU_00011/1, ES/T009101/1, MC_UU_00011/3
• NF
  Norges Forskningsråd

  Award: 295989
• MR
  Medical Research Council

  Awards: MR/S002634/1, MC_UU_00011/3, MC_PC_17228, MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/1, MC_UU_00011, MR/S002634/1, MC_UU_00011/1, MC_UU_00011/3
• EA
  Economic and Social Research Council

  Awards: ES/T009101/1, ES/T009101/1