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Collider bias undermines our understanding of COVID-19 disease risk and severity.
Griffith, Gareth J; Morris, Tim T; Tudball, Matthew J; Herbert, Annie; Mancano, Giulia; Pike, Lindsey; Sharp, Gemma C; Sterne, Jonathan; Palmer, Tom M; Davey Smith, George; Tilling, Kate; Zuccolo, Luisa; Davies, Neil M; Hemani, Gibran.
  • Griffith GJ; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
  • Morris TT; Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
  • Tudball MJ; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
  • Herbert A; Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
  • Mancano G; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
  • Pike L; Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
  • Sharp GC; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
  • Sterne J; Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
  • Palmer TM; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
  • Davey Smith G; Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
  • Tilling K; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
  • Zuccolo L; Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
  • Davies NM; Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
  • Hemani G; Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
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.
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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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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