Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder.
Data driven mixed effects modeling of the dual process framework of addiction among individuals with alcohol use disorder.
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DOI：
10.1371/journal.pone.0265168
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发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
酒精使用障碍（AUD）包括一系列症状和相关问题，已导致AUD成为全球发病率和死亡率的主要原因。鉴于AUD从轻度到重度的异质性，考虑提供一系列干预措施，提供目标选择，以匹配这种异质性，包括帮助AUD患者将饮酒调节或控制在低风险水平。由于对成功适度饮酒的影响因素仍有很多未知因素，我们使用动态系统建模来确定行为改变的机制。每日酒精消费量和每日欲望（即渴望）使用延迟差分方程系统建模。采用该系统的混合效果实现使我们能够在群体和个人层面上收集有关这些机制的信息。使用这种混合效果框架首先需要通过可识别性分析减少参数集。然后使用贝叶斯参数估计技术进行模型校准。最后，我们展示了如何进行参数敏感性分析可以帮助确定患者特定水平的最佳干预目标。这种概念验证分析为未来的建模提供了基础，以描述AUD患者的行为改变机制并确定潜在的治疗策略。
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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影响因子：
5.9
作者：
Fisher, Aaron J.;Newman, Michelle G.;Molenaar, Peter C. M.
通讯作者：
Molenaar, Peter C. M.
DOI：
10.15288/jsa.2003.64.120
发表时间：
2003-01-01
期刊：
JOURNAL OF STUDIES ON ALCOHOL
影响因子：
--
作者：
Flannery, BA;Poole, SA;Volpicelli, JR
通讯作者：
Volpicelli, JR
影响因子：
2.2
作者：
Dal Forno, Arianna;Merlone, Ugo
通讯作者：
Merlone, Ugo
影响因子：
3.2
作者：
Fazzino, Tera L.;Harder, Valerie S.;Helzer, John E.
通讯作者：
Helzer, John E.
影响因子：
3.9
作者：
Browne, Kendall C.;Wray, Tyler B.;Simpson, Tracy L.
通讯作者：
Simpson, Tracy L.