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Educational Note: Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: a reproducible illustration and web application.

dc.contributor.authorLuque-Fernandez, Miguel Angel
dc.contributor.authorSchomaker, Michael
dc.contributor.authorRedondo-Sanchez, Daniel
dc.contributor.authorJose Sanchez Perez, Maria
dc.contributor.authorVaidya, Anand
dc.contributor.authorSchnitzer, Mireille E
dc.date.accessioned2023-01-25T10:26:28Z
dc.date.available2023-01-25T10:26:28Z
dc.date.issued2019
dc.description.abstractClassical epidemiology has focused on the control of confounding, but it is only recently that epidemiologists have started to focus on the bias produced by colliders. A collider for a certain pair of variables (e.g. an outcome Y and an exposure A) is a third variable (C) that is caused by both. In a directed acyclic graph (DAG), a collider is the variable in the middle of an inverted fork (i.e. the variable C in A → C ← Y). Controlling for, or conditioning an analysis on a collider (i.e. through stratification or regression) can introduce a spurious association between its causes. This potentially explains many paradoxical findings in the medical literature, where established risk factors for a particular outcome appear protective. We use an example from non-communicable disease epidemiology to contextualize and explain the effect of conditioning on a collider. We generate a dataset with 1000 observations, and run Monte-Carlo simulations to estimate the effect of 24-h dietary sodium intake on systolic blood pressure, controlling for age, which acts as a confounder, and 24-h urinary protein excretion, which acts as a collider. We illustrate how adding a collider to a regression model introduces bias. Thus, to prevent paradoxical associations, epidemiologists estimating causal effects should be wary of conditioning on colliders. We provide R code in easy-to-read boxes throughout the manuscript, and a GitHub repository [https://github.com/migariane/ColliderApp] for the reader to reproduce our example. We also provide an educational web application allowing real-time interaction to visualize the paradoxical effect of conditioning on a collider [http://watzilei.com/shiny/collider/].
dc.identifier.doi10.1093/ije/dyy275
dc.identifier.essn1464-3685
dc.identifier.pmcPMC6469301
dc.identifier.pmid30561628
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6469301/pdf
dc.identifier.unpaywallURLhttps://academic.oup.com/ije/article-pdf/48/2/640/31517845/dyy275.pdf
dc.identifier.urihttp://hdl.handle.net/10668/13323
dc.issue.number2
dc.journal.titleInternational journal of epidemiology
dc.journal.titleabbreviationInt J Epidemiol
dc.language.isoen
dc.organizationEscuela Andaluza de Salud Pública-EASP
dc.page.number640-653
dc.pubmedtypeJournal Article
dc.pubmedtypeResearch Support, N.I.H., Extramural
dc.pubmedtypeResearch Support, Non-U.S. Gov't
dc.rights.accessRightsopen access
dc.subjectEpidemiological methods
dc.subjectcausality
dc.subjectnon-communicable disease epidemiology
dc.subject.meshBias
dc.subject.meshCausality
dc.subject.meshConfounding Factors, Epidemiologic
dc.subject.meshEpidemiologic Methods
dc.subject.meshHumans
dc.subject.meshLogistic Models
dc.subject.meshNoncommunicable Diseases
dc.titleEducational Note: Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: a reproducible illustration and web application.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number48
dspace.entity.typePublication

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