A Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV.

Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals' treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women's Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.

A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.

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.

A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks.

We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions, indexed by a sensitivity parameter, and show how to draw inference using our BNP approach. We conduct simulation studies and illustrate our methodology using data from a brain cancer trial. The R code implementing our model and algorithm is available for download at https://github.com/YanxunXu/BaySemiCompeting.

Trauma Across the Life Span and Multisystem Morbidity in Women With HIV.

OBJECTIVE: Sexual and physical abuse are highly prevalent among women living with HIV (WLWH) and are risk factors for the development of mental health and substance use disorders (MHDs, SUDs), and cognitive and medical comorbidities. We examined empirically derived patterns of trauma, MHD, and SUD, and associations with later cognitive and health outcomes. METHODS: A total of 1027 WLWH (average age = 48.6 years) in the Women's Interagency HIV Study completed the World Mental Health Composite International Diagnostic Interview from 2010 to 2013 to identify MHDs, SUDs, and age at onset of sexual and physical abuse. Then, cognitive impairment, cardiovascular/metabolic conditions, and HIV disease outcomes were assessed for up to 8.8 years. Latent class analysis identified patterns of co-occurring trauma, MHDs, and/or SUDs. Generalized estimating equations determined associations between these patterns and midlife cognitive and medical outcomes. RESULTS: Six distinct profiles emerged: no/negligible sexual/physical trauma, MHD, or SUD (39%); preadolescent/adolescent sexual trauma with anxiety and SUD (22%); SUD only (16%); MHD + SUD only (12%); early childhood sexual/physical trauma only (6%); and early childhood sexual/physical trauma with later MHD + SUD (4%). Profiles including early childhood trauma had the largest number of midlife conditions (i.e., cognitive, cardiovascular, HIV-related). Preadolescent/adolescent sexual trauma with anxiety and SUD predicted both global and domain-specific cognitive declines. Only SUD without trauma predicted lower CD4, whereas childhood trauma with MHD + SUD predicted increased CD8. CONCLUSIONS: WLWH have complex multisystem profiles of abuse, MHD, and/or SUD that predict midlife cognitive, metabolic/cardiovascular, and HIV outcomes. Understanding the interplay between these factors over time can identify risks and personalize preventative and treatment interventions.

Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis.

INTRODUCTION: High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders. METHODS: Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R. RESULTS: Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen's d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition. CONCLUSION: These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain's ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.

Functional Bayesian networks for discovering causality from multivariate functional data.

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.

Real-World Effectiveness of Remdesivir in Adults Hospitalized With Coronavirus Disease 2019 (COVID-19): A Retrospective, Multicenter Comparative Effectiveness Study.

BACKGROUND: There is an urgent need to understand the real-world effectiveness of remdesivir in the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: This was a retrospective comparative effectiveness study. Individuals hospitalized in a large private healthcare network in the United States from 23 February 2020 through 11 February 2021 with a positive test for SARS-CoV-2 and ICD-10 diagnosis codes consistent with symptomatic coronavirus disease 2019 (COVID-19) were included. Remdesivir recipients were matched to controls using time-dependent propensity scores. The primary outcome was time to improvement with a secondary outcome of time to death. RESULTS: Of 96 859 COVID-19 patients, 42 473 (43.9%) received at least 1 remdesivir dose. The median age of remdesivir recipients was 65 years, 23 701 (55.8%) were male, and 22 819 (53.7%) were non-White. Matches were found for 18 328 patients (43.2%). Remdesivir recipients were significantly more likely to achieve clinical improvement by 28 days (adjusted hazard ratio [aHR] 1.19, 95% confidence interval [CI], 1.16-1.22). Remdesivir patients on no oxygen (aHR 1.30, 95% CI, 1.22-1.38) or low-flow oxygen (aHR 1.23, 95% CI, 1.19-1.27) were significantly more likely to achieve clinical improvement by 28 days. There was no significant impact on the likelihood of mortality overall (aHR 1.02, 95% CI, .97-1.08). Remdesivir recipients on low-flow oxygen were significantly less likely to die than controls (aHR 0.85, 95% CI, .77-.92; 28-day mortality 8.4% [865 deaths] for remdesivir patients, 12.5% [1334 deaths] for controls). CONCLUSIONS: These results support the use of remdesivir for hospitalized COVID-19 patients on no or low-flow oxygen. Routine initiation of remdesivir in more severely ill patients is unlikely to be beneficial.

The Effectiveness of High-Flow Nasal Cannula in Coronavirus Disease 2019 Pneumonia: A Retrospective Cohort Study.

OBJECTIVES: High-flow nasal cannula is widely used in acute hypoxemic respiratory failure due to coronavirus disease 2019, yet data regarding its effectiveness is lacking. More evidence is needed to guide patient selection, timing of high-flow nasal cannula initiation, and resource allocation. We aimed to assess time to discharge and time to death in severe coronavirus disease 2019 in patients treated with high-flow nasal cannula compared with matched controls. We also evaluated the ability of the respiratory rate-oxygenation ratio to predict progression to invasive mechanical ventilation. DESIGN: Time-dependent propensity score matching was used to create pairs of individuals who were then analyzed in a Cox proportional-hazards regression model to estimate high-flow nasal cannula's effect on time to discharge and time to death. A secondary analysis excluded high-flow nasal cannula patients intubated within 6 hours of admission. A Cox proportional-hazards regression model was used to assess risk of invasive mechanical ventilation among high-flow nasal cannula patients stratified by respiratory rate-oxygenation. SETTING: The five hospitals of the Johns Hopkins Health System. PATIENTS: All patients who were admitted with a laboratory-confirmed diagnosis of coronavirus disease 2019 were eligible for inclusion. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: High-flow nasal cannula was associated with longer median time to discharge: 10.6 days (interquartile range, 7.1-15.8 d) versus 7.8 days (interquartile range, 4.9-12.1 d). Respiratory rate-oxygenation index performed poorly in predicting ventilation or death. In the primary analysis, there was no significant association between high-flow nasal cannula and hazard of death (adjusted hazard ratio, 0.79; 95% CI, 0.57-1.09). Excluding patients intubated within 6 hours of admission, high-flow nasal cannula was associated with reduced hazard of death (adjusted hazard ratio, 0.67; 95% CI, 0.45-0.99). CONCLUSIONS: Among unselected patients with severe coronavirus disease 2019 pneumonia, high-flow nasal cannula was not associated with a statistically significant reduction in hazard of death. However, in patients not mechanically ventilated within 6 hours of admission, high-flow nasal cannula was associated with a significantly reduced hazard of death.

Inflammatory Subtypes in Antipsychotic-Naïve First-Episode Schizophrenia are Associated with Altered Brain Morphology and Topological Organization.

BACKGROUND: Peripheral inflammation is implicated in schizophrenia, however, not all individuals demonstrate inflammatory alterations. Recent studies identified inflammatory subtypes in chronic psychosis with high inflammation having worse cognitive performance and displaying neuroanatomical enlargement compared to low inflammation subtypes. It is unclear if inflammatory subtypes exist earlier in the disease course, thus, we aim to identify inflammatory subtypes in antipsychotic naïve First-Episode Schizophrenia (FES). METHODS: 12 peripheral inflammatory markers, clinical, cognitive, and neuroanatomical measures were collected from a naturalistic study of antipsychotic-naïve FES patients. A combination of unsupervised principal component analysis and hierarchical clustering was used to categorize inflammatory subtypes from their cytokine data (17 FES High, 30 FES Low, and 33 healthy controls (HCs)). Linear regression analysis was used to assess subtype differences. Neuroanatomical correlations with clinical and cognitive measures were performed using partial Spearman correlations. Graph theoretical analyses were performed to assess global and local network properties across inflammatory subtypes. RESULTS: The FES High group made up 36% of the FES group and demonstrated significantly greater levels of IL1ß, IL6, IL8, and TNFα compared to FES Low, and higher levels of IL1ß and IL8 compared to HCs. FES High had greater right parahippocampal, caudal anterior cingulate, and bank superior sulcus thicknesses compared to FES Low. Compared to HCs, FES Low showed smaller bilateral amygdala volumes and widespread cortical thickness. FES High and FES Low groups demonstrated less efficient topological organization compared to HCs. Individual cytokines and/or inflammatory signatures were positively associated with cognition and symptom measures. CONCLUSIONS: Inflammatory subtypes are present in antipsychotic-naïve FES and are associated with inflammation-mediated cortical expansion. These findings support our previous findings in chronic psychosis and point towards a connection between inflammation and blood-brain barrier disruption. Thus, identifying inflammatory subtypes may provide a novel therapeutic avenue for biomarker-guided treatment involving anti-inflammatory medications.

Multivariate relationships between peripheral inflammatory marker subtypes and cognitive and brain structural measures in psychosis.

Elevations in peripheral inflammatory markers have been reported in patients with psychosis. Whether this represents an inflammatory process defined by individual or subgroups of markers is unclear. Further, relationships between peripheral inflammatory marker elevations and brain structure, cognition, and clinical features of psychosis remain unclear. We hypothesized that a pattern of plasma inflammatory markers, and an inflammatory subtype established from this pattern, would be elevated across the psychosis spectrum and associated with cognition and brain structural alterations. Clinically stable psychosis probands (Schizophrenia spectrum, n = 79; Psychotic Bipolar disorder, n = 61) and matched healthy controls (HC, n = 60) were assessed for 15 peripheral inflammatory markers, cortical thickness, subcortical volume, cognition, and symptoms. A combination of unsupervised exploratory factor analysis and hierarchical clustering was used to identify inflammation subtypes. Levels of IL6, TNFα, VEGF, and CRP were significantly higher in psychosis probands compared to HCs, and there were marker-specific differences when comparing diagnostic groups. Individual and/or inflammatory marker patterns were associated with neuroimaging, cognition, and symptom measures. A higher inflammation subgroup was defined by elevations in a group of 7 markers in 36% of Probands and 20% of HCs. Probands in the elevated inflammatory marker group performed significantly worse on cognitive measures of visuo-spatial working memory and response inhibition, displayed elevated hippocampal, amygdala, putamen and thalamus volumes, and evidence of gray matter thickening compared to the proband group with low inflammatory marker levels. These findings specify the nature of peripheral inflammatory marker alterations in psychotic disorders and establish clinical, neurocognitive and neuroanatomic associations with increased inflammatory activation in psychosis. The identification of a specific subgroup of patients with inflammatory alteration provides a potential means for targeting treatment with anti-inflammatory medications.

A Bayesian nonparametric approach for inferring drug combination effects on mental health in people with HIV.

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.

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.

BACKGROUND: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. OBJECTIVE: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. DESIGN: Retrospective observational cohort study. SETTINGS: Five hospitals in Maryland and Washington, D.C. PATIENTS: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. MEASUREMENTS: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. RESULTS: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. LIMITATION: The SCARP tool was developed by using data from a single health system. CONCLUSION: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Enter the Matrix: Factorization Uncovers Knowledge from Omics.

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

Sex-specific associations between cerebrospinal fluid inflammatory marker levels and cognitive function in antiretroviral treated people living with HIV in rural Uganda.

People with HIV (PWH) taking antiretroviral therapy (ART) have persistent cognitive impairment. The prevalence of cognitive impairment is higher in women with HIV (WWH) compared to men with HIV (MWH), possibly due to sex differences in immune function. Here we report sex differences in cerebrospinal fluid (CSF) immune markers in relation to cognitive performance. A subset of 83 PWH on ART (52% WWH; mean age = 37.6 years, SD = 7.9) from the Rakai community cohort study Cohort and Rakai Health Sciences Program supported clinics in rural Uganda completed a neuropsychological (NP) assessment and a lumbar puncture. CSF was used to measure 16 cytokines/chemokines. Individual NP test z-scores were generated based on local normative data. A series of least absolute shrinkage and selection operator (lasso) regressions examined associations between CSF inflammatory markers and NP outcomes. Overall, there were no sex differences in CSF inflammatory marker levels. However, MWH displayed more associations between inflammatory markers and cognitive performance than WWH. Among MWH, inflammatory markers were associated with a number of cognitive domains, including attention, processing speed, fluency, executive function, learning and memory. MIP-1ß, INF-γ, GM-CSF, IL-7 and IL-12p70 were associated with multiple domains. Among WWH, few inflammatory markers were associated cognition. Degree of associations between CSF inflammatory biomarkers and cognitive performance varied by sex in this young, ART-treated, Ugandan cohort. Further investigation into sex-specific inflammatory mechanisms of cognitive impairment among PWH is warranted to inform sex-specific management strategies.

Starting or Switching to an Integrase Inhibitor-Based Regimen Affects PTSD Symptoms in Women with HIV.

As the use of Integrase inhibitor (INSTI)-class antiretroviral medications becomes more common to maintain long-term viral suppression, early reports suggest the potential for CNS side-effects when starting or switching to an INSTI-based regimen. In a population already at higher risk for developing mood and anxiety disorders, these drugs may have significant effects on PTSD scale symptom scores, particularly in women with HIV (WWH). A total of 551 participants were included after completing ≥ 1 WIHS study visits before and after starting/switching to an INSTI-based ART regimen. Of these, 14% were ART naïve, the remainder switched from primarily a protease inhibitor (PI) or non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimen. Using multivariable linear mixed effects models, we compared PTSD Civilian Checklist subscale scores before and after a "start/switch" to dolutegravir (DTG), raltegravir (RAL), or elvitegravir (EVG). Start/switch to EVG improved re-experiencing subscale symptoms (P's < 0.05). Switching to EVG improved symptoms of avoidance (P = 0.01). Starting RAL improved arousal subscale symptoms (P = 0.03); however, switching to RAL worsened re-experiencing subscale symptoms (P < 0.005). Starting DTG worsened avoidance subscale symptoms (P = 0.03), whereas switching to DTG did not change subscale or overall PTSD symptoms (P's > 0.08). In WWH, an EVG-based ART regimen is associated with improved PTSD symptoms, in both treatment naïve patients and those switching from other ART. While a RAL-based regimen was associated with better PTSD symptoms than in treatment naïve patients, switching onto a RAL-based regimen was associated with worse PTSD symptoms. DTG-based regimens either did not affect, or worsened symptoms, in both naïve and switch patients. Further studies are needed to determine mechanisms underlying differential effects of EVG, RAL and DTG on stress symptoms in WWH.

BAREB: A Bayesian repulsive biclustering model for periodontal data.

Preventing periodontal diseases (PD) and maintaining the structure and function of teeth are important goals for personal oral care. To understand the heterogeneity in patients with diverse PD patterns, we develop a Bayesian repulsive biclustering method that can simultaneously cluster the PD patients and their tooth sites after taking the patient- and site-level covariates into consideration. BAREB uses the determinantal point process prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Since PD progression is hypothesized to be spatially referenced, BAREB factors in the spatial dependence among tooth sites. In addition, since PD is the leading cause for tooth loss, the missing data mechanism is nonignorable. Such nonrandom missingness is incorporated into BAREB. For the posterior inference, we design an efficient reversible jump Markov chain Monte Carlo sampler. Simulation studies show that BAREB is able to accurately estimate the biclusters, and compares favorably to alternatives. For real world application, we apply BAREB to a dataset from a clinical PD study, and obtain desirable and interpretable results. A major contribution of this article is the Rcpp implementation of our methodology, available in the R package BAREB.

ASIED: a Bayesian adaptive subgroup-identification enrichment design.

Developing targeted therapies based on patients' baseline characteristics and genomic profiles such as biomarkers has gained growing interests in recent years. Depending on patients' clinical characteristics, the expression of specific biomarkers or their combinations, different patient subgroups could respond differently to the same treatment. An ideal design, especially at the proof of concept stage, should search for such subgroups and make dynamic adaptation as the trial goes on. When no prior knowledge is available on whether the treatment works on the all-comer population or only works on the subgroup defined by one biomarker or several biomarkers, it is necessary to incorporate the adaptive estimation of the heterogeneous treatment effect to the decision-making at interim analyses. To address this problem, we propose an Adaptive Subgroup-Identification Enrichment Design, ASIED, to simultaneously search for predictive biomarkers, identify the subgroups with differential treatment effects, and modify study entry criteria at interim analyses when justified. More importantly, we construct robust quantitative decision-making rules for population enrichment when the interim outcomes are heterogeneous in the context of a multilevel target product profile, which defines the minimal and targeted levels of treatment effect. Through extensive simulations, the ASIED is demonstrated to achieve desirable operating characteristics and compare favorably against alternatives.

TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data.

Motivation: The Cancer Genome Atlas (TCGA) program has produced huge amounts of cancer genomics data providing unprecedented opportunities for research. In 2014, we developed TCGA-Assembler, a software pipeline for retrieval and processing of public TCGA data. In 2016, TCGA data were transferred from the TCGA data portal to the Genomic Data Commons (GDCs), which is supported by a different set of data storage and retrieval mechanisms. In addition, new proteomics data of TCGA samples have been generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program, which were not available for downloading through TCGA-Assembler. It is desirable to acquire and integrate data from both GDC and CPTAC. Results: We develop TCGA-assembler 2 (TA2) to automatically download and integrate data from GDC and CPTAC. We make substantial improvement on the functionality of TA2 to enhance user experience and software performance. TA2 together with its previous version have helped more than 2000 researchers from 64 countries to access and utilize TCGA and CPTAC data in their research. Availability of TA2 will continue to allow existing and new users to conduct reproducible research based on TCGA and CPTAC data. Availability and implementation: http://www.compgenome.org/TCGA-Assembler/ or https://github.com/compgenome365/TCGA-Assembler-2. Contact: zhuyitan@gmail.com or koaeraser@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Sex Differences in Neurocognitive Function in Adults with HIV: Patterns, Predictors, and Mechanisms.

PURPOSE OF REVIEW: Sex differences in cognitive function are well documented yet few studies had adequate numbers of women and men living with HIV (WLWH; MLWH) to identify sex differences in neurocognitive impairment (NCI) and the factors contributing to NCI. Here, we review evidence that WLWH may be at greater risk for NCI. RECENT FINDINGS: We conducted a systematic review of recent studies of NCI in WLWH versus MLWH. A power analysis showed that few HIV studies have sufficient power to address male/female differences in NCI but studies with adequate power find evidence of greater NCI in WLWH, particularly in the domains of memory, speed of information processing, and motor function. Sex is an important determinant of NCI in HIV, and may relate to male/female differences in cognitive reserve, comorbidities (mental health and substance use disorders), and biological factors (e.g., inflammation, hormonal, genetic).

A nonparametric Bayesian basket trial design.

Targeted therapies on the basis of genomic aberrations analysis of the tumor have shown promising results in cancer prognosis and treatment. Regardless of tumor type, trials that match patients to targeted therapies for their particular genomic aberrations have become a mainstream direction of therapeutic management of patients with cancer. Therefore, finding the subpopulation of patients who can most benefit from an aberration-specific targeted therapy across multiple cancer types is important. We propose an adaptive Bayesian clinical trial design for patient allocation and subpopulation identification. We start with a decision theoretic approach, including a utility function and a probability model across all possible subpopulation models. The main features of the proposed design and population finding methods are the use of a flexible nonparametric Bayesian survival regression based on a random covariate-dependent partition of patients, and decisions based on a flexible utility function that reflects the requirement of the clinicians appropriately and realistically, and the adaptive allocation of patients to their superior treatments. Through extensive simulation studies, the new method is demonstrated to achieve desirable operating characteristics and compares favorably against the alternatives.