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A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases

BACKGROUND: The natural history of many chronic diseases is characterized by periods of increased disease activity, commonly referred to as flare-ups or exacerbations. Accurate characterization of the burden of these exacerbations is an important research objective. METHODS: The purpose of this work...

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Autores principales: Safari, Abdollah, Petkau, John, FitzGerald, Mark J., Sadatsafavi, Mohsen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837953/
https://www.ncbi.nlm.nih.gov/pubmed/36635713
http://dx.doi.org/10.1186/s12911-022-02080-5
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author Safari, Abdollah
Petkau, John
FitzGerald, Mark J.
Sadatsafavi, Mohsen
author_facet Safari, Abdollah
Petkau, John
FitzGerald, Mark J.
Sadatsafavi, Mohsen
author_sort Safari, Abdollah
collection PubMed
description BACKGROUND: The natural history of many chronic diseases is characterized by periods of increased disease activity, commonly referred to as flare-ups or exacerbations. Accurate characterization of the burden of these exacerbations is an important research objective. METHODS: The purpose of this work was to develop a statistical framework for nuanced characterization of the three main features of exacerbations: their rate, duration, and severity, with interrelationships among these features being a particular focus. We jointly specified a zero-inflated accelerated failure time regression model for the rate, an accelerated failure time regression model for the duration, and a logistic regression model for the severity of exacerbations. Random effects were incorporated into each component to capture heterogeneity beyond the variability attributable to observed characteristics, and to describe the interrelationships among these components. RESULTS: We used pooled data from two clinical trials in asthma as an exemplary application to illustrate the utility of the joint modeling approach. The model fit clearly indicated the presence of heterogeneity in all three components. A novel finding was that the new therapy reduced not just the rate but also the duration of exacerbations, but did not have a significant impact on their severity. After controlling for covariates, exacerbations among more frequent exacerbators tended to be shorter and less likely to be severe. CONCLUSIONS: We conclude that a joint modeling framework, programmable in available software, can provide novel insights about how the rate, duration, and severity of episodic events interrelate, and enables consistent inference on the effect of treatments on different disease outcomes. Trial registration Ethics approval was obtained from the University of British Columbia Human Ethics Board (H17-00938). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-022-02080-5.
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spelling pubmed-98379532023-01-14 A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases Safari, Abdollah Petkau, John FitzGerald, Mark J. Sadatsafavi, Mohsen BMC Med Inform Decis Mak Research BACKGROUND: The natural history of many chronic diseases is characterized by periods of increased disease activity, commonly referred to as flare-ups or exacerbations. Accurate characterization of the burden of these exacerbations is an important research objective. METHODS: The purpose of this work was to develop a statistical framework for nuanced characterization of the three main features of exacerbations: their rate, duration, and severity, with interrelationships among these features being a particular focus. We jointly specified a zero-inflated accelerated failure time regression model for the rate, an accelerated failure time regression model for the duration, and a logistic regression model for the severity of exacerbations. Random effects were incorporated into each component to capture heterogeneity beyond the variability attributable to observed characteristics, and to describe the interrelationships among these components. RESULTS: We used pooled data from two clinical trials in asthma as an exemplary application to illustrate the utility of the joint modeling approach. The model fit clearly indicated the presence of heterogeneity in all three components. A novel finding was that the new therapy reduced not just the rate but also the duration of exacerbations, but did not have a significant impact on their severity. After controlling for covariates, exacerbations among more frequent exacerbators tended to be shorter and less likely to be severe. CONCLUSIONS: We conclude that a joint modeling framework, programmable in available software, can provide novel insights about how the rate, duration, and severity of episodic events interrelate, and enables consistent inference on the effect of treatments on different disease outcomes. Trial registration Ethics approval was obtained from the University of British Columbia Human Ethics Board (H17-00938). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-022-02080-5. BioMed Central 2023-01-12 /pmc/articles/PMC9837953/ /pubmed/36635713 http://dx.doi.org/10.1186/s12911-022-02080-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Safari, Abdollah
Petkau, John
FitzGerald, Mark J.
Sadatsafavi, Mohsen
A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases
title A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases
title_full A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases
title_fullStr A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases
title_full_unstemmed A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases
title_short A parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases
title_sort parametric model to jointly characterize rate, duration, and severity of exacerbations in episodic diseases
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837953/
https://www.ncbi.nlm.nih.gov/pubmed/36635713
http://dx.doi.org/10.1186/s12911-022-02080-5
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