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Objective:While modern antiretroviral therapy (ART) is highly effective and safe, depressive symptoms have been associated with certain ART drugs. We examined the association between common ART regimens and depressive symptoms in women with HIV (WWH) with a focus on somatic vs. nonsomatic symptoms. Design:Analysis of longitudinal data from the Women's Interagency HIV Study. Methods:Participants were classified into three groups based on the frequency of positive depression screening (CES-D ≥16): chronic depression (≥50% of visits since study enrollment), infrequent depression (<50% of visits), and never depressed (no visits). Novel Bayesian machine learning methods building upon a subset-tree kernel approach were developed to estimate the combined effects of ART regimens on depressive symptoms in each group after covariate adjustment. Results:The analysis included 1538 WWH who participated in 12 924 (mean = 8.4) visits. The mean age was 49.9 years, 72% were Black, and 14% Hispanic. In the chronic depression group, combinations including tenofovir alafenamide and cobicistat-boosted elvitegravir and/or darunavir were associated with greater somatic symptoms of depression, whereas those combinations containing tenofovir disoproxil fumarate and efavirenz or rilpivirine were associated with less somatic depressive symptoms. ART was not associated with somatic symptoms in the infrequent depression or never depressed groups. ART regimens were not associated with nonsomatic symptoms in any group. Conclusions:Specific ART combinations are associated with somatic depressive symptoms in WWH with chronic depression. Future studies should consider specific depressive symptoms domains as well as complete drug combinations when assessing the relationship between ART and depression. 

Abstract It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer’s disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study. 

Abstract Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian processes, which is the key to unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional BN model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples demonstrate the practical utility of the proposed model. 

Abstract Although combination antiretroviral therapy (ART) with three or more drugs is highly effective in suppressing viral load for people with HIV (human immunodeficiency virus), many ART agents may exacerbate mental health‐related adverse effects including depression. Therefore, understanding the effects of combination ART on mental health can help clinicians personalize medicine with less adverse effects to avoid undesirable health outcomes. The emergence of electronic health records offers researchers' unprecedented access to HIV data including individuals' mental health records, drug prescriptions, and clinical information over time. However, modeling such data is challenging due to high dimensionality of the drug combination space, the individual heterogeneity, and sparseness of the observed drug combinations. To address these challenges, we develop a Bayesian nonparametric approach to learn drug combination effect on mental health in people with HIV adjusting for sociodemographic, behavioral, and clinical factors. The proposed method is built upon the subset‐tree kernel that represents drug combinations in a way that synthesizes known regimen structure into a single mathematical representation. It also utilizes a distance‐dependent Chinese restaurant process to cluster heterogeneous populations while considering individuals' treatment histories. We evaluate the proposed approach through simulation studies, and apply the method to a dataset from the Women's Interagency HIV Study, showing the clinical utility of our model in guiding clinicians to prescribe informed and effective personalized treatment based on individuals' treatment histories and clinical characteristics.