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

Gareth J. Griffith, Tim T. Morris, Matthew J. Tudball, Annie Herbert, Giulia Mancano, Lindsey Pike, Gemma C. Sharp, Jonathan Sterne, Tom M. Palmer, George Davey Smith, Kate Tilling, Luisa Zuccolo, Neil M. Davies and Gibran Hemani ()
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Gareth J. Griffith: University of Bristol
Tim T. Morris: University of Bristol
Matthew J. Tudball: University of Bristol
Annie Herbert: University of Bristol
Giulia Mancano: University of Bristol
Lindsey Pike: University of Bristol
Gemma C. Sharp: University of Bristol
Jonathan Sterne: University of Bristol, Oakfield House, Oakfield Grove
Tom M. Palmer: University of Bristol
George Davey Smith: University of Bristol
Kate Tilling: University of Bristol
Luisa Zuccolo: University of Bristol
Neil M. Davies: University of Bristol
Gibran Hemani: University of Bristol

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract 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, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

Date: 2020
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DOI: 10.1038/s41467-020-19478-2

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