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

Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising stud...

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Autores principales:	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, Gibran Hemani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/09f9c535f46548a4a4affd7e1fc10cce
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spelling	oai:doaj.org-article:09f9c535f46548a4a4affd7e1fc10cce2021-12-02T17:31:09ZCollider bias undermines our understanding of COVID-19 disease risk and severity10.1038/s41467-020-19478-22041-1723https://doaj.org/article/09f9c535f46548a4a4affd7e1fc10cce2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19478-2https://doaj.org/toc/2041-1723Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising study design to account for this.Gareth J. GriffithTim T. MorrisMatthew J. TudballAnnie HerbertGiulia MancanoLindsey PikeGemma C. SharpJonathan SterneTom M. PalmerGeorge Davey SmithKate TillingLuisa ZuccoloNeil M. DaviesGibran HemaniNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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 Gibran Hemani Collider bias undermines our understanding of COVID-19 disease risk and severity
description	Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising study design to account for this.
format	article
author	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 Gibran Hemani
author_facet	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 Gibran Hemani
author_sort	Gareth J. Griffith
title	Collider bias undermines our understanding of COVID-19 disease risk and severity
title_short	Collider bias undermines our understanding of COVID-19 disease risk and severity
title_full	Collider bias undermines our understanding of COVID-19 disease risk and severity
title_fullStr	Collider bias undermines our understanding of COVID-19 disease risk and severity
title_full_unstemmed	Collider bias undermines our understanding of COVID-19 disease risk and severity
title_sort	collider bias undermines our understanding of covid-19 disease risk and severity
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/09f9c535f46548a4a4affd7e1fc10cce
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Collider bias undermines our understanding of COVID-19 disease risk and severity

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