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Collaborative Research: New Bayesian Methods for Modeling the Effect of Antiretroviral Drugs on Depressive Symptomatology in HIV patients

合作研究：用于模拟抗逆转录病毒药物对艾滋病毒患者抑郁症状影响的新贝叶斯方法

基本信息

批准号：
1918851
负责人：
Yang Ni
金额：
$ 5.18万
依托单位：
Texas A&M University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918851&HistoricalAwards=false
关键词：
Collaborative Research New Bayesian Methods

项目摘要

Antiretroviral therapy (ART) has transformed HIV infection into a manageable chronic disease, thereby shifting the focus of the care for people living with HIV more toward controlling the adverse effects of ART. Depression is the leading mental health comorbidity of HIV infection and may trigger negative consequences such as poor adherence to ART, more rapid HIV disease progression, and engagement in risky behaviors. Since ART is recommended for all HIV patients and must be continued indefinitely, minimizing the adverse effects of ART has garnered increasing attention. Due to the rapid generation of drug-resistant mutations, modern ART typically combines three or four ART drugs of different mechanisms or against different targets. Understanding the effects of a single ART drug or combinations of ART drugs can help physicians better manage patients' depression, guide treatment changes if needed, and facilitate individualized treatment. This project aims to fill a critical gap in the availability of appropriate statistical models to systematically investigate the effects of ART on depression. Recent technological advances in the biomedical field have led to rapid accumulation of health- and disease-related data, which provide researchers with an unprecedented opportunity to make reliable and efficient inference from these complex and heterogeneous datasets using novel statistical models. This project will use data from the Women's Interagency HIV Study (WIHS), a prospective, observational, multi-center study which includes more than 4,000 women living with HIV or at risk for HIV infection in the United States.This project aims to develop novel Bayesian parametric and nonparametric models to estimate the effects of ART based on patients' longitudinal medication data and depression outcomes, adjusting for socio-demographic, behavioral, and clinical factors. Specifically, a new Bayesian longitudinal graphical model will be developed with nodes representing drugs and depression items, and weighted edges representing the strength of the drug-depression relationships, which may vary across different clinical visits and different patients. In addition, a novel Bayesian framework that incorporates the similarity between different drug combinations as well as accounts for patients' treatment histories will be developed to learn arbitrary drug combination effects. The proposed work will bridge the gap between the experience/knowledge acquired during basic research and day-to-day practice by facilitating the understanding of the adverse effects of individual drugs, guiding more informed and effective treatment regimen selection, and eventually helping to reduce the healthcare resource burden. The proposed models can be easily generalized to learn other ART-related complications such as cognitive impairment, and may also be used in a wide range of applications across multiple biomedical fields and beyond, such as electronic health record data analysis for chronic conditions, study of combination therapy for cancer treatment, and injury prevention in sports medicine.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

抗逆转录病毒疗法（ART）已将艾滋病毒感染转化为一种可管理的慢性疾病，从而将艾滋病毒感染者的护理重点更多地转向控制ART的不良影响。抑郁症是艾滋病毒感染的主要心理健康并发症，并可能引发不良后果，如对ART的依从性差，艾滋病毒疾病进展更快，并参与危险行为。由于ART被推荐给所有HIV患者，并且必须无限期地持续下去，因此尽量减少ART的不良影响已经引起了越来越多的关注。由于耐药突变的快速产生，现代ART通常结合三种或四种不同机制或针对不同靶点的ART药物。了解单一ART药物或ART药物组合的效果可以帮助医生更好地管理患者的抑郁症，在需要时指导治疗变化，并促进个性化治疗。该项目旨在填补适当统计模型可用性方面的关键空白，以系统地研究ART对抑郁症的影响。生物医学领域的最新技术进步导致了健康和疾病相关数据的快速积累，这为研究人员提供了前所未有的机会，可以使用新的统计模型从这些复杂和异构的数据集进行可靠和有效的推断。该项目将使用来自妇女跨部门艾滋病研究（WIHS）的数据，这是一项前瞻性、观察性、多中心研究，包括美国4,000多名艾滋病病毒感染者或有感染艾滋病风险的妇女。该项目旨在开发新的贝叶斯参数和非参数模型，以根据患者的纵向药物数据和抑郁症结局估计ART的效果，根据社会人口统计学、行为和临床因素进行调整。具体而言，将开发一个新的贝叶斯纵向图形模型，其中节点代表药物和抑郁项目，加权边代表药物-抑郁关系的强度，这可能会因不同的临床访视和不同的患者而异。此外，将开发一种新的贝叶斯框架，该框架结合了不同药物组合之间的相似性，并考虑了患者的治疗史，以了解任意药物组合的效果。拟议的工作将弥合基础研究和日常实践中获得的经验/知识之间的差距，促进对个别药物不良反应的理解，指导更明智和有效的治疗方案选择，并最终帮助减少医疗资源负担。所提出的模型可以很容易地推广到学习其他ART相关并发症，如认知障碍，并且还可以用于跨多个生物医学领域和其他领域的广泛应用，如慢性病的电子健康记录数据分析，癌症治疗的联合疗法研究，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。