Collider bias undermines our understanding of COVID-19 disease risk and severity.
Nat Commun
; 11(1): 5749, 2020 11 12.
Article
in English
| MEDLINE | ID: covidwho-922259
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
Type of study:
Cohort study
/
Observational study
/
Prognostic study
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
Nat Commun
Journal subject:
Biology
/
Science
Year:
2020
Document Type:
Article
Affiliation country:
S41467-020-19478-2
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