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

ABSTRACT: 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.

SUBMITTER: Li Y 

PROVIDER: S-EPMC9236217 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature