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.
