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A Bayesian hierarchical logistic regression model of multiple informant family health histories
BACKGROUND: Family health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. Traditionally, such information has been collected from a single informant. More recently, research has suggested that a multiple informant approach to collecting FHH re...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419428/ https://www.ncbi.nlm.nih.gov/pubmed/30871571 http://dx.doi.org/10.1186/s12874-019-0700-5 |
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author | Lin, Jielu Myers, Melanie F. Koehly, Laura M. Marcum, Christopher Steven |
author_facet | Lin, Jielu Myers, Melanie F. Koehly, Laura M. Marcum, Christopher Steven |
author_sort | Lin, Jielu |
collection | PubMed |
description | BACKGROUND: Family health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. Traditionally, such information has been collected from a single informant. More recently, research has suggested that a multiple informant approach to collecting FHH results in improved individual risk assessments. Likewise, recent work has emphasized the importance of incorporating health-related behaviors into FHH-based risk calculations. Integrating both multiple accounts of FHH with behavioral information on family members represents a significant methodological challenge as such FHH data is hierarchical in nature and arises from potentially error-prone processes. METHODS: In this paper, we introduce a statistical model that addresses these challenges using informative priors for background variation in disease prevalence and the effect of other, potentially correlated, variables while accounting for the nested structure of these data. Our empirical example is drawn from previously published data on families with a history of diabetes. RESULTS: The results of the comparative model assessment suggest that simply accounting for the structured nature of multiple informant FHH data improves classification accuracy over the baseline and that incorporating family member health-related behavioral information into the model is preferred over alternative specifications. CONCLUSIONS: The proposed modelling framework is a flexible solution to integrate multiple informant FHH for risk prediction purposes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0700-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6419428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64194282019-03-27 A Bayesian hierarchical logistic regression model of multiple informant family health histories Lin, Jielu Myers, Melanie F. Koehly, Laura M. Marcum, Christopher Steven BMC Med Res Methodol Technical Advance BACKGROUND: Family health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. Traditionally, such information has been collected from a single informant. More recently, research has suggested that a multiple informant approach to collecting FHH results in improved individual risk assessments. Likewise, recent work has emphasized the importance of incorporating health-related behaviors into FHH-based risk calculations. Integrating both multiple accounts of FHH with behavioral information on family members represents a significant methodological challenge as such FHH data is hierarchical in nature and arises from potentially error-prone processes. METHODS: In this paper, we introduce a statistical model that addresses these challenges using informative priors for background variation in disease prevalence and the effect of other, potentially correlated, variables while accounting for the nested structure of these data. Our empirical example is drawn from previously published data on families with a history of diabetes. RESULTS: The results of the comparative model assessment suggest that simply accounting for the structured nature of multiple informant FHH data improves classification accuracy over the baseline and that incorporating family member health-related behavioral information into the model is preferred over alternative specifications. CONCLUSIONS: The proposed modelling framework is a flexible solution to integrate multiple informant FHH for risk prediction purposes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0700-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-12 /pmc/articles/PMC6419428/ /pubmed/30871571 http://dx.doi.org/10.1186/s12874-019-0700-5 Text en © The Author(s). 2019 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 | Technical Advance Lin, Jielu Myers, Melanie F. Koehly, Laura M. Marcum, Christopher Steven A Bayesian hierarchical logistic regression model of multiple informant family health histories |
title | A Bayesian hierarchical logistic regression model of multiple informant family health histories |
title_full | A Bayesian hierarchical logistic regression model of multiple informant family health histories |
title_fullStr | A Bayesian hierarchical logistic regression model of multiple informant family health histories |
title_full_unstemmed | A Bayesian hierarchical logistic regression model of multiple informant family health histories |
title_short | A Bayesian hierarchical logistic regression model of multiple informant family health histories |
title_sort | bayesian hierarchical logistic regression model of multiple informant family health histories |
topic | Technical Advance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419428/ https://www.ncbi.nlm.nih.gov/pubmed/30871571 http://dx.doi.org/10.1186/s12874-019-0700-5 |
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