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Access and adherence to antiretroviral therapy (ART) has transformed the faceof HIV infection from a fatal to a chronic disease. However, ART is also knownfor its side effects. Studies have reported that ART is associated withdepressive symptomatology. Large-scale HIV clinical databases with individuals'longitudinal depression records, ART medications, and clinical characteristicsoffer researchers unprecedented opportunities to study the effects of ART drugson depression over time. We develop BAGEL, a Bayesian graphical model toinvestigate longitudinal effects of ART drugs on a range of depressive symptomswhile adjusting for participants' demographic, behavior, and clinicalcharacteristics, and taking into account the heterogeneous population through aBayesian nonparametric prior. We evaluate BAGEL through simulation studies.Application to a dataset from the Women's Interagency HIV Study yieldsinterpretable and clinically useful results. BAGEL not only can improve ourunderstanding of ART drugs effects on disparate depression symptoms, but alsohas clinical utility in guiding informed and effective treatment selection tofacilitate precision medicine in HIV.

BAGEL: A Bayesian Graphical Model for Inferring Drug Effect Longitudinally on Depression in People with HIV