Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide

Abstract Exhaled breath biomarkers are an important emerging field. The fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation with clinical and epidemiological applications (e.g., air pollution health effects studies). Systems of differential equations describe F...

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Autores principales:	Jingying Weng, Noa Molshatzki, Paul Marjoram, W. James Gauderman, Frank D. Gilliland, Sandrah P. Eckel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/61777ce793d649de8e315f5856cce3ca
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spelling	oai:doaj.org-article:61777ce793d649de8e315f5856cce3ca2021-12-02T15:09:16ZHierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide10.1038/s41598-021-96176-z2045-2322https://doaj.org/article/61777ce793d649de8e315f5856cce3ca2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96176-zhttps://doaj.org/toc/2045-2322Abstract Exhaled breath biomarkers are an important emerging field. The fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation with clinical and epidemiological applications (e.g., air pollution health effects studies). Systems of differential equations describe FeNO—measured non-invasively at the mouth—as a function of exhalation flow rate and parameters representing airway and alveolar sources of NO in the airway. Traditionally, NO parameters have been estimated separately for each study participant (Stage I) and then related to covariates (Stage II). Statistical properties of these two-step approaches have not been investigated. In simulation studies, we evaluated finite sample properties of existing two-step methods as well as a novel Unified Hierarchical Bayesian (U-HB) model. The U-HB is a one-step estimation method developed with the goal of properly propagating uncertainty as well as increasing power and reducing type I error for estimating associations of covariates with NO parameters. We demonstrated the U-HB method in an analysis of data from the southern California Children’s Health Study relating traffic-related air pollution exposure to airway and alveolar airway inflammation.Jingying WengNoa MolshatzkiPaul MarjoramW. James GaudermanFrank D. GillilandSandrah P. EckelNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jingying Weng Noa Molshatzki Paul Marjoram W. James Gauderman Frank D. Gilliland Sandrah P. Eckel Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide
description	Abstract Exhaled breath biomarkers are an important emerging field. The fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation with clinical and epidemiological applications (e.g., air pollution health effects studies). Systems of differential equations describe FeNO—measured non-invasively at the mouth—as a function of exhalation flow rate and parameters representing airway and alveolar sources of NO in the airway. Traditionally, NO parameters have been estimated separately for each study participant (Stage I) and then related to covariates (Stage II). Statistical properties of these two-step approaches have not been investigated. In simulation studies, we evaluated finite sample properties of existing two-step methods as well as a novel Unified Hierarchical Bayesian (U-HB) model. The U-HB is a one-step estimation method developed with the goal of properly propagating uncertainty as well as increasing power and reducing type I error for estimating associations of covariates with NO parameters. We demonstrated the U-HB method in an analysis of data from the southern California Children’s Health Study relating traffic-related air pollution exposure to airway and alveolar airway inflammation.
format	article
author	Jingying Weng Noa Molshatzki Paul Marjoram W. James Gauderman Frank D. Gilliland Sandrah P. Eckel
author_facet	Jingying Weng Noa Molshatzki Paul Marjoram W. James Gauderman Frank D. Gilliland Sandrah P. Eckel
author_sort	Jingying Weng
title	Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide
title_short	Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide
title_full	Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide
title_fullStr	Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide
title_full_unstemmed	Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide
title_sort	hierarchical bayesian estimation of covariate effects on airway and alveolar nitric oxide
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/61777ce793d649de8e315f5856cce3ca
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Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide

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