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

抗逆转录病毒疗法(ART)将艾滋病毒感染转变为一种可控的慢性疾病，从而将对艾滋病毒携带者的护理重点更多地转移到控制ART的不良影响上。抑郁症是HIV感染的主要心理健康共病，可能会引发负面后果，如不遵守抗逆转录病毒疗法，艾滋病毒疾病进展更快，以及参与危险行为。由于抗逆转录病毒疗法被推荐给所有艾滋病毒患者，并且必须无限期地继续下去，因此将抗逆转录病毒疗法的不良影响降至最低已得到越来越多的关注。由于耐药突变的快速产生，现代ART通常结合三到四种不同机制或针对不同靶点的ART药物。了解单一抗逆转录病毒药物或联合抗逆转录病毒药物的效果可以帮助医生更好地管理患者的抑郁，在需要时指导治疗变化，并促进个性化治疗。该项目旨在填补在系统研究抗逆转录病毒疗法对抑郁症的影响的适当统计模型的可用性方面的一个关键空白。生物医学领域的最新技术进步导致了与健康和疾病相关的数据的快速积累，这为研究人员提供了一个前所未有的机会，可以使用新的统计模型从这些复杂和不同种类的数据集中做出可靠和有效的推断。该项目将使用女性机构间艾滋病毒研究(WIHS)的数据，这是一项前瞻性、观察性、多中心的研究，包括美国4000多名艾滋病毒携带者或艾滋病毒感染风险女性。该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、行为和临床因素，评估ART的效果。具体地说，将开发一个新的贝叶斯纵向图形模型，其中节点表示药物和抑郁项，加权边表示药物-抑郁关系的强度，这可能会因不同的临床就诊和不同的患者而异。此外，还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历史，以了解任意药物组合的效果。拟议的工作将通过促进对个别药物的不良影响的了解，指导更知情和有效的治疗方案选择，最终帮助减轻医疗资源负担，弥合在基础研究期间获得的经验/知识与日常实践之间的差距。建议的模型可以很容易地推广到学习其他与艺术相关的并发症，如认知障碍，也可以在多个生物医学领域的广泛应用中使用，例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，以及运动医学中的损伤预防。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.