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Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder

Alcohol use disorder (AUD) comprises a continuum of symptoms and associated problems that has led AUD to be a leading cause of morbidity and mortality across the globe. Given the heterogeneity of AUD from mild to severe, consideration is being given to providing a spectrum of interventions that offe...

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Autores principales: Everett, Rebecca A., Lewis, Allison L., Kuerbis, Alexis, Peace, Angela, Li, Jing, Morgenstern, Jon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406226/
https://www.ncbi.nlm.nih.gov/pubmed/37549160
http://dx.doi.org/10.1371/journal.pone.0265168
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author Everett, Rebecca A.
Lewis, Allison L.
Kuerbis, Alexis
Peace, Angela
Li, Jing
Morgenstern, Jon
author_facet Everett, Rebecca A.
Lewis, Allison L.
Kuerbis, Alexis
Peace, Angela
Li, Jing
Morgenstern, Jon
author_sort Everett, Rebecca A.
collection PubMed
description Alcohol use disorder (AUD) comprises a continuum of symptoms and associated problems that has led AUD to be a leading cause of morbidity and mortality across the globe. Given the heterogeneity of AUD from mild to severe, consideration is being given to providing a spectrum of interventions that offer goal choice to match this heterogeneity, including helping individuals with AUD to moderate or control their drinking at low-risk levels. Because so much remains unknown about the factors that contribute to successful moderated drinking, we use dynamical systems modeling to identify mechanisms of behavior change. Daily alcohol consumption and daily desire (i.e., craving) are modeled using a system of delayed difference equations. Employing a mixed effects implementation of this system allows us to garner information about these mechanisms at both the population and individual levels. Use of this mixed effects framework first requires a parameter set reduction via identifiability analysis. The model calibration is then performed using Bayesian parameter estimation techniques. Finally, we demonstrate how conducting a parameter sensitivity analysis can assist in identifying optimal targets of intervention at the patient-specific level. This proof-of-concept analysis provides a foundation for future modeling to describe mechanisms of behavior change and determine potential treatment strategies in patients with AUD.
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spelling pubmed-104062262023-08-08 Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder Everett, Rebecca A. Lewis, Allison L. Kuerbis, Alexis Peace, Angela Li, Jing Morgenstern, Jon PLoS One Research Article Alcohol use disorder (AUD) comprises a continuum of symptoms and associated problems that has led AUD to be a leading cause of morbidity and mortality across the globe. Given the heterogeneity of AUD from mild to severe, consideration is being given to providing a spectrum of interventions that offer goal choice to match this heterogeneity, including helping individuals with AUD to moderate or control their drinking at low-risk levels. Because so much remains unknown about the factors that contribute to successful moderated drinking, we use dynamical systems modeling to identify mechanisms of behavior change. Daily alcohol consumption and daily desire (i.e., craving) are modeled using a system of delayed difference equations. Employing a mixed effects implementation of this system allows us to garner information about these mechanisms at both the population and individual levels. Use of this mixed effects framework first requires a parameter set reduction via identifiability analysis. The model calibration is then performed using Bayesian parameter estimation techniques. Finally, we demonstrate how conducting a parameter sensitivity analysis can assist in identifying optimal targets of intervention at the patient-specific level. This proof-of-concept analysis provides a foundation for future modeling to describe mechanisms of behavior change and determine potential treatment strategies in patients with AUD. Public Library of Science 2023-08-07 /pmc/articles/PMC10406226/ /pubmed/37549160 http://dx.doi.org/10.1371/journal.pone.0265168 Text en © 2023 Everett et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Everett, Rebecca A.
Lewis, Allison L.
Kuerbis, Alexis
Peace, Angela
Li, Jing
Morgenstern, Jon
Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder
title Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder
title_full Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder
title_fullStr Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder
title_full_unstemmed Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder
title_short Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder
title_sort data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406226/
https://www.ncbi.nlm.nih.gov/pubmed/37549160
http://dx.doi.org/10.1371/journal.pone.0265168
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