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An introduction to instrumental variable assumptions, validation and estimation

The instrumental variable method has been employed within economics to infer causality in the presence of unmeasured confounding. Emphasising the parallels to randomisation may increase understanding of the underlying assumptions within epidemiology. An instrument is a variable that predicts exposur...

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Autor principal: Lousdal, Mette Lise
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5776781/
https://www.ncbi.nlm.nih.gov/pubmed/29387137
http://dx.doi.org/10.1186/s12982-018-0069-7
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author Lousdal, Mette Lise
author_facet Lousdal, Mette Lise
author_sort Lousdal, Mette Lise
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description The instrumental variable method has been employed within economics to infer causality in the presence of unmeasured confounding. Emphasising the parallels to randomisation may increase understanding of the underlying assumptions within epidemiology. An instrument is a variable that predicts exposure, but conditional on exposure shows no independent association with the outcome. The random assignment in trials is an example of what would be expected to be an ideal instrument, but instruments can also be found in observational settings with a naturally varying phenomenon e.g. geographical variation, physical distance to facility or physician’s preference. The fourth identifying assumption has received less attention, but is essential for the generalisability of estimated effects. The instrument identifies the group of compliers in which exposure is pseudo-randomly assigned leading to exchangeability with regard to unmeasured confounders. Underlying assumptions can only partially be tested empirically and require subject-matter knowledge. Future studies employing instruments should carefully seek to validate all four assumptions, possibly drawing on parallels to randomisation.
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spelling pubmed-57767812018-01-31 An introduction to instrumental variable assumptions, validation and estimation Lousdal, Mette Lise Emerg Themes Epidemiol Research Article The instrumental variable method has been employed within economics to infer causality in the presence of unmeasured confounding. Emphasising the parallels to randomisation may increase understanding of the underlying assumptions within epidemiology. An instrument is a variable that predicts exposure, but conditional on exposure shows no independent association with the outcome. The random assignment in trials is an example of what would be expected to be an ideal instrument, but instruments can also be found in observational settings with a naturally varying phenomenon e.g. geographical variation, physical distance to facility or physician’s preference. The fourth identifying assumption has received less attention, but is essential for the generalisability of estimated effects. The instrument identifies the group of compliers in which exposure is pseudo-randomly assigned leading to exchangeability with regard to unmeasured confounders. Underlying assumptions can only partially be tested empirically and require subject-matter knowledge. Future studies employing instruments should carefully seek to validate all four assumptions, possibly drawing on parallels to randomisation. BioMed Central 2018-01-22 /pmc/articles/PMC5776781/ /pubmed/29387137 http://dx.doi.org/10.1186/s12982-018-0069-7 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Lousdal, Mette Lise
An introduction to instrumental variable assumptions, validation and estimation
title An introduction to instrumental variable assumptions, validation and estimation
title_full An introduction to instrumental variable assumptions, validation and estimation
title_fullStr An introduction to instrumental variable assumptions, validation and estimation
title_full_unstemmed An introduction to instrumental variable assumptions, validation and estimation
title_short An introduction to instrumental variable assumptions, validation and estimation
title_sort introduction to instrumental variable assumptions, validation and estimation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5776781/
https://www.ncbi.nlm.nih.gov/pubmed/29387137
http://dx.doi.org/10.1186/s12982-018-0069-7
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