论文标题
百吉饼：一种用于纵向推断艾滋病毒抑郁症的药物作用的贝叶斯图形模型
BAGEL: A Bayesian Graphical Model for Inferring Drug Effect Longitudinally on Depression in People with HIV
论文作者
论文摘要
对抗逆转录病毒疗法（ART）的访问和依从性已将艾滋病毒感染的表面从致命的疾病转变为慢性疾病。但是，艺术也以其副作用而闻名。研究报告说，艺术与抑郁症状有关。带有个人纵向抑郁症记录，艺术药物和临床特征的大规模艾滋病毒临床数据库为研究人员提供了前所未有的机会，可以随着时间的推移研究艺术药物对抑郁症的影响。我们开发了百吉饼，这是一种贝叶斯图形模型，可以研究艺术药物对一系列抑郁症状的纵向影响，同时调整参与者的人口统计学，行为和临床特征，并通过贝叶斯非参数先验考虑异质种群。我们通过模拟研究评估百吉饼。从妇女跨性别HIV研究中应用于数据集可产生可解释和临床上有用的结果。百吉饼不仅可以提高我们对艺术药物对不同抑郁症状的影响，而且还具有指导知情有效的治疗选择以促进艾滋病毒精确医学的临床实用性。
Access and adherence to antiretroviral therapy (ART) has transformed the face of HIV infection from a fatal to a chronic disease. However, ART is also known for its side effects. Studies have reported that ART is associated with depressive symptomatology. Large-scale HIV clinical databases with individuals' longitudinal depression records, ART medications, and clinical characteristics offer researchers unprecedented opportunities to study the effects of ART drugs on depression over time. We develop BAGEL, a Bayesian graphical model to investigate longitudinal effects of ART drugs on a range of depressive symptoms while adjusting for participants' demographic, behavior, and clinical characteristics, and taking into account the heterogeneous population through a Bayesian nonparametric prior. We evaluate BAGEL through simulation studies. Application to a dataset from the Women's Interagency HIV Study yields interpretable and clinically useful results. BAGEL not only can improve our understanding of ART drugs effects on disparate depression symptoms, but also has clinical utility in guiding informed and effective treatment selection to facilitate precision medicine in HIV.
