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Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk
We address a problem in inference from retrospective studies where the value of a variable is measured at the date of the survey but is used as covariate to events that have occurred long before the survey. This causes problem because the value of the current-date (anticipatory) covariate does not f...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9041697/ https://www.ncbi.nlm.nih.gov/pubmed/35707119 http://dx.doi.org/10.1080/02664763.2020.1864812 |
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author | Munezero, Parfait Ghilagaber, Gebrenegus |
author_facet | Munezero, Parfait Ghilagaber, Gebrenegus |
author_sort | Munezero, Parfait |
collection | PubMed |
description | We address a problem in inference from retrospective studies where the value of a variable is measured at the date of the survey but is used as covariate to events that have occurred long before the survey. This causes problem because the value of the current-date (anticipatory) covariate does not follow the temporal order of events. We propose a dynamic Bayesian approach for modelling jointly the anticipatory covariate and the event of interest, and allowing the effects of the anticipatory covariate to vary over time. The issues are illustrated with data on the effects of education attained by the survey-time on divorce risks among Swedish men. The overall results show that failure to adjust for the anticipatory nature of education leads to elevated relative risks of divorce across educational levels. The results are partially in accordance with previous findings based on analyses of the same data set. More importantly, our findings provide new insights in that the bias due to anticipatory covariates varies over marriage duration. |
format | Online Article Text |
id | pubmed-9041697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-90416972022-06-14 Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk Munezero, Parfait Ghilagaber, Gebrenegus J Appl Stat Articles We address a problem in inference from retrospective studies where the value of a variable is measured at the date of the survey but is used as covariate to events that have occurred long before the survey. This causes problem because the value of the current-date (anticipatory) covariate does not follow the temporal order of events. We propose a dynamic Bayesian approach for modelling jointly the anticipatory covariate and the event of interest, and allowing the effects of the anticipatory covariate to vary over time. The issues are illustrated with data on the effects of education attained by the survey-time on divorce risks among Swedish men. The overall results show that failure to adjust for the anticipatory nature of education leads to elevated relative risks of divorce across educational levels. The results are partially in accordance with previous findings based on analyses of the same data set. More importantly, our findings provide new insights in that the bias due to anticipatory covariates varies over marriage duration. Taylor & Francis 2020-12-23 /pmc/articles/PMC9041697/ /pubmed/35707119 http://dx.doi.org/10.1080/02664763.2020.1864812 Text en © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. |
spellingShingle | Articles Munezero, Parfait Ghilagaber, Gebrenegus Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk |
title | Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk |
title_full | Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk |
title_fullStr | Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk |
title_full_unstemmed | Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk |
title_short | Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk |
title_sort | dynamic bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9041697/ https://www.ncbi.nlm.nih.gov/pubmed/35707119 http://dx.doi.org/10.1080/02664763.2020.1864812 |
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