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

• 批准号: 1918854
• 负责人: Yanxun Xu
• 金额: $ 59.82万
• 依托单位: Johns Hopkins 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 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918854&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Bayesian; Methods

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变为一种可控制的慢性病，从而将对艾滋病毒感染者的护理重点更多地转向控制抗逆转录病毒疗法的不良影响。抑郁症是艾滋病毒感染的主要精神健康合并症，可能引发不良后果，如抗逆转录病毒治疗依从性差、艾滋病毒疾病进展更快以及从事危险行为。由于抗逆转录病毒治疗被推荐给所有艾滋病毒患者，并且必须无限期地持续下去，因此尽量减少抗逆转录病毒治疗的不良影响已引起越来越多的关注。由于耐药突变的快速产生，现代ART通常将三到四种不同机制或针对不同靶点的ART药物组合在一起。了解单一抗逆转录病毒药物或抗逆转录病毒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，并促进个体化治疗。该项目旨在填补适当统计模型可用性的关键空白，以系统地调查抗逆转录病毒治疗对抑郁症的影响。生物医学领域的最新技术进步导致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中做出可靠和有效的推断。该项目将使用来自妇女跨机构艾滋病毒研究（WIHS）的数据，这是一项前瞻性、观察性、多中心研究，包括美国4000多名感染艾滋病毒或有感染艾滋病毒风险的妇女。本项目旨在开发新的贝叶斯参数和非参数模型，根据患者的纵向用药数据和抑郁结果，调整社会人口统计学、行为和临床因素，估计抗逆转录病毒治疗的效果。具体来说，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，这可能在不同的临床就诊和不同的患者中有所不同。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以及患者治疗史的解释，以了解任意药物组合效应。建议的工作将弥合在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了解，指导更明智和有效的治疗方案选择，最终帮助减轻医疗资源负担。所提出的模型可以很容易地推广到其他与art相关的并发症，如认知障碍，也可以在多个生物医学领域和其他领域广泛应用，如慢性病的电子健康记录数据分析，癌症治疗的联合治疗研究，以及运动医学中的伤害预防。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Plasma microRNAs are associated with domain-specific cognitive function in people with HIV.

• DOI: 10.1097/qad.0000000000002966
• 发表时间: 2021-09-01
• 期刊: AIDS (London, England)
• 作者: Massanett Aparicio J;Xu Y;Li Y;Colantuoni C;Dastgheyb R;Williams DW;Asahchop EL;McMillian JM;Power C;Fujiwara E;Gill MJ;Rubin LH
• 通讯作者: Rubin LH

Efficient Estimation for Random Dot Product Graphs via a One-Step Procedure

通过一步程序有效估计随机点积图

• DOI: 10.1080/01621459.2021.1948419
• 发表时间: 2021
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Xie, Fangzheng;Xu, Yanxun
• 通讯作者: Xu, Yanxun

Bayesian tensor-on-tensor regression with efficient computation

具有高效计算的贝叶斯张量对张量回归

• DOI: 10.4310/23-sii786
• 发表时间: 2024
• 期刊: Statistics and Its Interface
• 影响因子: 0.8
• 作者: Wang, Kunbo;Xu, Yanxun
• 通讯作者: Xu, Yanxun

Weight and Body Mass Index Change After Switching to Integrase Inhibitors or Tenofovir Alafenamide Among Women Living with HIV

• DOI: 10.1089/aid.2020.0197
• 发表时间: 2021-01-12
• 期刊: AIDS RESEARCH AND HUMAN RETROVIRUSES
• 影响因子: 1.5
• 作者: Lahiri, Cecile D.;Xu, Yanxun;Rubin, Leah H.
• 通讯作者: Rubin, Leah H.

Bayesian Projected Calibration of Computer Models

• DOI: 10.1080/01621459.2020.1753519
• 发表时间: 2018-03
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Fangzheng Xie;Yanxun Xu