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To Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure
Advice regarding the analysis of observational studies of exposure effects usually is against adjustment for factors that occur after the exposure, as they may be caused by the exposure (or mediate the effect of exposure on outcome), so potentially leading to collider stratification bias. However, s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7850592/ https://www.ncbi.nlm.nih.gov/pubmed/33470711 http://dx.doi.org/10.1097/EDE.0000000000001312 |
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author | Groenwold, Rolf H. H. Palmer, Tom M. Tilling, Kate |
author_facet | Groenwold, Rolf H. H. Palmer, Tom M. Tilling, Kate |
author_sort | Groenwold, Rolf H. H. |
collection | PubMed |
description | Advice regarding the analysis of observational studies of exposure effects usually is against adjustment for factors that occur after the exposure, as they may be caused by the exposure (or mediate the effect of exposure on outcome), so potentially leading to collider stratification bias. However, such factors could also be caused by unmeasured confounding factors, in which case adjusting for them will also remove some of the bias due to confounding. We derive expressions for collider stratification bias when conditioning and confounding bias when not conditioning on the mediator, in the presence of unmeasured confounding (assuming that all associations are linear and there are no interactions). Using simulations, we show that generally neither the conditioned nor the unconditioned estimate is unbiased, and the trade-off between them depends on the magnitude of the effect of the exposure that is mediated relative to the effect of the unmeasured confounders and their relations with the mediator. We illustrate the use of the bias expressions via three examples: neuroticism and mortality (adjusting for the mediator appears the least biased option), glycated hemoglobin levels and systolic blood pressure (adjusting gives smaller bias), and literacy in primary school pupils (not adjusting gives smaller bias). Our formulae and simulations can inform quantitative bias analysis as well as analysis strategies for observational studies in which there is a potential for unmeasured confounding. |
format | Online Article Text |
id | pubmed-7850592 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-78505922021-02-02 To Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure Groenwold, Rolf H. H. Palmer, Tom M. Tilling, Kate Epidemiology Original Article Advice regarding the analysis of observational studies of exposure effects usually is against adjustment for factors that occur after the exposure, as they may be caused by the exposure (or mediate the effect of exposure on outcome), so potentially leading to collider stratification bias. However, such factors could also be caused by unmeasured confounding factors, in which case adjusting for them will also remove some of the bias due to confounding. We derive expressions for collider stratification bias when conditioning and confounding bias when not conditioning on the mediator, in the presence of unmeasured confounding (assuming that all associations are linear and there are no interactions). Using simulations, we show that generally neither the conditioned nor the unconditioned estimate is unbiased, and the trade-off between them depends on the magnitude of the effect of the exposure that is mediated relative to the effect of the unmeasured confounders and their relations with the mediator. We illustrate the use of the bias expressions via three examples: neuroticism and mortality (adjusting for the mediator appears the least biased option), glycated hemoglobin levels and systolic blood pressure (adjusting gives smaller bias), and literacy in primary school pupils (not adjusting gives smaller bias). Our formulae and simulations can inform quantitative bias analysis as well as analysis strategies for observational studies in which there is a potential for unmeasured confounding. Lippincott Williams & Wilkins 2021-01-13 2021-03 /pmc/articles/PMC7850592/ /pubmed/33470711 http://dx.doi.org/10.1097/EDE.0000000000001312 Text en Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (http://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Original Article Groenwold, Rolf H. H. Palmer, Tom M. Tilling, Kate To Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure |
title | To Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure |
title_full | To Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure |
title_fullStr | To Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure |
title_full_unstemmed | To Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure |
title_short | To Adjust or Not to Adjust? When a “Confounder” Is Only Measured After Exposure |
title_sort | to adjust or not to adjust? when a “confounder” is only measured after exposure |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7850592/ https://www.ncbi.nlm.nih.gov/pubmed/33470711 http://dx.doi.org/10.1097/EDE.0000000000001312 |
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