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Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges
‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal ef...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484949/ https://www.ncbi.nlm.nih.gov/pubmed/29451093 http://dx.doi.org/10.1177/0962280218756158 |
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author | Arnold, KF Ellison, GTH Gadd, SC Textor, J Tennant, PWG Heppenstall, A Gilthorpe, MS |
author_facet | Arnold, KF Ellison, GTH Gadd, SC Textor, J Tennant, PWG Heppenstall, A Gilthorpe, MS |
author_sort | Arnold, KF |
collection | PubMed |
description | ‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care. |
format | Online Article Text |
id | pubmed-6484949 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-64849492019-06-03 Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges Arnold, KF Ellison, GTH Gadd, SC Textor, J Tennant, PWG Heppenstall, A Gilthorpe, MS Stat Methods Med Res Articles ‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care. SAGE Publications 2018-02-16 2019-05 /pmc/articles/PMC6484949/ /pubmed/29451093 http://dx.doi.org/10.1177/0962280218756158 Text en © The Author(s) 2018 http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Articles Arnold, KF Ellison, GTH Gadd, SC Textor, J Tennant, PWG Heppenstall, A Gilthorpe, MS Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges |
title | Adjustment for time-invariant and time-varying confounders in
‘unexplained residuals’ models for longitudinal data within a causal framework
and associated challenges |
title_full | Adjustment for time-invariant and time-varying confounders in
‘unexplained residuals’ models for longitudinal data within a causal framework
and associated challenges |
title_fullStr | Adjustment for time-invariant and time-varying confounders in
‘unexplained residuals’ models for longitudinal data within a causal framework
and associated challenges |
title_full_unstemmed | Adjustment for time-invariant and time-varying confounders in
‘unexplained residuals’ models for longitudinal data within a causal framework
and associated challenges |
title_short | Adjustment for time-invariant and time-varying confounders in
‘unexplained residuals’ models for longitudinal data within a causal framework
and associated challenges |
title_sort | adjustment for time-invariant and time-varying confounders in
‘unexplained residuals’ models for longitudinal data within a causal framework
and associated challenges |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484949/ https://www.ncbi.nlm.nih.gov/pubmed/29451093 http://dx.doi.org/10.1177/0962280218756158 |
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