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

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  • Rebecca A Everett
  • Allison L Lewis
  • Alexis Kuerbis
  • Angela Peace
  • Jing Li
  • Jon Morgenstern

Abstract

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.

Suggested Citation

  • Rebecca A Everett & Allison L Lewis & Alexis Kuerbis & Angela Peace & Jing Li & Jon Morgenstern, 2023. "Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-24, August.
  • Handle: RePEc:plo:pone00:0265168
    DOI: 10.1371/journal.pone.0265168
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    References listed on IDEAS

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    1. J. C. Wakefield & A. F. M. Smith & A. Racine‐Poon & A. E. Gelfand, 1994. "Bayesian Analysis of Linear and Non‐Linear Population Models by Using the Gibbs Sampler," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 201-221, March.
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    4. Johan Grasman & Raoul P P P Grasman & Han L J van der Maas, 2016. "The Dynamics of Addiction: Craving versus Self-Control," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-11, June.
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