Jemar R Bather, Larry Han, Alex S Bennett, Luther Elliott, Melody S Goodman
{"title":"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。","authors":"Jemar R Bather, Larry Han, Alex S Bennett, Luther Elliott, Melody S Goodman","doi":"10.1080/00952990.2024.2380463","DOIUrl":null,"url":null,"abstract":"<p><p><i>Background:</i> 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.<i>Objective:</i> To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes.<i>Methods:</i> 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.<i>Results:</i> 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.<i>Conclusions:</i> 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.</p>","PeriodicalId":48957,"journal":{"name":"American Journal of Drug and Alcohol Abuse","volume":" ","pages":"1-8"},"PeriodicalIF":2.7000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression.\",\"authors\":\"Jemar R Bather, Larry Han, Alex S Bennett, Luther Elliott, Melody S Goodman\",\"doi\":\"10.1080/00952990.2024.2380463\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Background:</i> 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.<i>Objective:</i> To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes.<i>Methods:</i> 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.<i>Results:</i> 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.<i>Conclusions:</i> 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.</p>\",\"PeriodicalId\":48957,\"journal\":{\"name\":\"American Journal of Drug and Alcohol Abuse\",\"volume\":\" \",\"pages\":\"1-8\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Drug and Alcohol Abuse\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/00952990.2024.2380463\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHOLOGY, CLINICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Drug and Alcohol Abuse","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/00952990.2024.2380463","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
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.
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.