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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...

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Autores principales: Lin, Jielu, Myers, Melanie F., Koehly, Laura M., Marcum, Christopher Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
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.
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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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