BAGEL: A BAYESIAN GRAPHICAL MODEL FOR INFERRING DRUG EFFECT LONGITUDINALLY ON DEPRESSION IN PEOPLE WITH HIV

• 成果类型: Article
• 署名作者: Li, Yuliang; Ni, Yang; Rubin, Leah H.; Spence, Amanda B.; Xu, Yanxun
• 署名单位: Johns Hopkins University; Texas A&M University System; Texas A&M University College Station; Johns Hopkins University; Johns Hopkins University; Georgetown University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1492
• 发表日期: 2022
• 页码: 21-39
• 关键词: tenofovir disoproxil fumarate; womens interagency hiv; antiretroviral therapy; adverse events; increased risk; symptoms; infection; dolutegravir; association; raltegravir

摘要

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.