利用贝叶斯核机器回归检测药物混合物的单变量、双变量和总体效应。

IF 2.7 3区 医学 Q2 PSYCHOLOGY, CLINICAL American Journal of Drug and Alcohol Abuse Pub Date : 2024-07-23 DOI:10.1080/00952990.2024.2380463
Jemar R Bather, Larry Han, Alex S Bennett, Luther Elliott, Melody S Goodman
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引用次数: 0

摘要

背景:为了更好地了解非法药物使用者同时使用阿片类药物和非阿片类药物的情况,需要对药物研究采用创新的分析方法。其中一种方法是贝叶斯核机器回归(BKMR),它被广泛应用于环境流行病学研究暴露混合物,但在药物使用研究中却很少受到关注:描述 BKMR 方法在研究毒品物质混合物对健康结果的影响时的实用性:方法:我们模拟了 200 人的数据。使用 Vale 和 Maurelli 方法,我们模拟了多元非正态分布的药物暴露数据:恶嗪(平均值 = 300 毫微克/毫升,标差 = 100 毫微克/毫升)、芬太尼(平均值 = 200 毫微克/毫升,标差 = 71 毫微克/毫升)、苯二氮卓(平均值 = 300 毫微克/毫升,标差 = 55 毫微克/毫升)和硝氮(平均值 = 200 毫微克/毫升,标差 = 141 毫微克/毫升)的浓度。我们使用三个马尔可夫链进行了 10,000 次 MCMC 采样迭代。模型诊断包括轨迹图、r-hat 值和有效样本量。我们还提供了单变量和双变量暴露-反应及总体混合物效应的直观关系:结果:在控制年龄的情况下,芬太尼和硝基苯浓度越高,模拟健康结果越高。示踪图、r-hat 值和有效样本量统计表明,BKMR 在多个马尔可夫链中具有稳定性:我们对药物混合物的了解往往局限于单药模型的研究。BKMR 提供了一种创新的方法,可用于鉴别哪些物质比其他物质对健康构成更大的风险,并可用于评估单变量、双变量和累积药物对健康结果的影响。
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Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression.

Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research.Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes.Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect.Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains.Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.

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来源期刊
CiteScore
4.70
自引率
3.70%
发文量
68
期刊介绍: The American Journal of Drug and Alcohol Abuse (AJDAA) is an international journal published six times per year and provides an important and stimulating venue for the exchange of ideas between the researchers working in diverse areas, including public policy, epidemiology, neurobiology, and the treatment of addictive disorders. AJDAA includes a wide range of translational research, covering preclinical and clinical aspects of the field. AJDAA covers these topics with focused data presentations and authoritative reviews of timely developments in our field. Manuscripts exploring addictions other than substance use disorders are encouraged. Reviews and Perspectives of emerging fields are given priority consideration. Areas of particular interest include: public health policy; novel research methodologies; human and animal pharmacology; human translational studies, including neuroimaging; pharmacological and behavioral treatments; new modalities of care; molecular and family genetic studies; medicinal use of substances traditionally considered substances of abuse.
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