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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2018
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5776781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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