Biostatistics, Bioinformatics and Epidemiology Scientific Publications

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Model-Based Predictions of HIV Incidence Among African Women Using HIV Risk Behaviors and Community-Level Data on Male HIV Prevalence and Viral Suppression

J Acquir Immune Defic Syndr

2020 Deborah Donnell; Dobromir Dimitrov; James (Mia) Moore

BACKGROUND: Pre-exposure prophylaxis (PrEP) with tenofovir disoproxil fumarate and emtricitabine has proven highly effective in preventing HIV acquisition and is therefore offered to all participants in the control group as part of the standard of care package in many new HIV prevention studies. We propose a methodology for predicting HIV incidence in a hypothetical "placebo arm" for open-label studies or clinical trials with active control among African women. We apply the method to an open-label PrEP study, HIV Prevention Trials Network 082, which tested strategies to improve PrEP adherence in young African women all of whom were offered PrEP. METHODS: Our model predicted HIV infection risk for female study cohorts in sub-Saharan Africa using baseline behavioral risk factors and contemporary HIV prevalence and viral suppression in the local male population. The model was calibrated to HIV incidence in the Vaginal and Oral Interventions to Control the Epidemic study. RESULTS: Our model reproduced the annual HIV incidence of 3.2%-4.8% observed over 1 year of follow-up in the placebo groups of 4 completed clinical studies. We predicted an annual HIV incidence of 3.7% (95% confidence interval: 3.2 to 4.2) among HIV Prevention Trials Network 082 participants in the absence of PrEP and other risk reduction interventions. CONCLUSIONS: We demonstrated the potential of the proposed methodology to provide HIV incidence predictions based on assessment of individual risk behaviors and community and time-specific HIV exposure risk using HIV treatment and viral suppression data. These estimates may serve as comparators in HIV prevention trials without a placebo group.

Identifying Regions of Greatest Need for Ending the HIV Epidemic: A Plan for America

J Acquir Immune Defic Syndr

2020 Dobromir Dimitrov; James (Mia) Moore; Deborah Donnell

BACKGROUND: In the 2019 State of the Union Address, President Trump announced a plan for "Ending the HIV Epidemic" in the United States, with a goal to reduce new HIV infections by 90% by 2030. Phase I of the plan set an intermediate goal of a 75% reduction within 5 years, focusing on select states and counties. METHODS: We assessed the feasibility of the first phase of the plan by estimating the fraction of HIV diagnoses that occur within the targeted region, using a statistical model to predict new HIV cases in each county. We suggested new areas that should be added to the current plan, prioritizing by both a "Density Metric" of new HIV cases and a "Gap Metric" quantifying shortcomings in antiretroviral therapy and pre-exposure prophylaxis uptake. RESULTS: We found the current plan targets less than 60% of new diagnoses. The plan should be expanded to Puerto Rico, Florida, Georgia, Louisiana, and Maryland as well as parts of New York, North Carolina, Texas, and Virginia, areas which were prioritized by both metrics. CONCLUSION: Many of the highest priority areas, both by density of HIV cases and by lack of viral suppression and pre-exposure prophylaxis use, were not covered by the original plan, particularly in the South. The current plan to end the HIV epidemic must be expanded to these areas to feasibly allow for a 75% reduction in new HIV cases within 5 years.

Risk Factors for Cytomegalovirus Reactivation and Association with Outcomes in Critically Ill Adults with Sepsis: A Pooled Analysis of Prospective Studies

J Infect Dis

2020 Ying Chen; Michael Boeckh; Hannah Imlay; Sayan Dasgupta

We performed a multivariable analysis of potential risk factors (including CMV reactivation) for clinical outcomes by day 28 (death or continued hospitalization, ventilator-free days [VFD], ICU-free days [ICUFD], hospital free days [HFD]) from pooled cohorts of two previous prospective studies of CMV seropositive adults with sepsis. CMV reactivation at any level, >100, >1,000 IU/mL, peak viral load, and area under the curve were independently associated with the clinical outcomes. We identified the potential effect size of CMV on outcomes that could be used as endpoints for future interventional trials of CMV prevention using antiviral prophylaxis in ICU patients with sepsis.

The association of a4ß7 expression with HIV acquisition and disease progression in people who inject drugs and men who have sex with men: Case control studies


2020 Holly Janes; Kyle Marshall; Ashley Clayton; Larry Corey

BACKGROUND: 47 is a gut-homing integrin heterodimer that can act as a non-essential binding molecule for HIV. A previous study in heterosexual African women found that individuals with higher proportions of 47 expressing CD4+ T cells were more likely to become infected with HIV, as well as present with faster disease progression. It is unknown if this phenomenon is also observed in men who have sex with men (MSM) or people who inject drugs (PWID). METHODS: MSM and transgender women who seroconverted as part of the HVTN 505 HIV vaccine trial and PWID who seroconverted during the ALIVE cohort study were selected as cases and matched to HIV-uninfected controls from the same studies (1:1 and 1:3, respectively). Pre-seroconversion PBMC samples from cases and controls in both studies were examined by flow cytometry to measure levels of 47 expression on CD4+ T cells. Multivariable conditional logistic regression was used to compare 47 expression levels between cases and controls. A Kaplan-Meier curve was used to examine the association of 47 expression pre-seroconversion with HIV disease progression. FINDINGS: In MSM and transgender women (n=103 cases, 103 controls), there was no statistically significant difference in the levels of 47 expression on CD4+ T cells between cases and controls (adjusted odds ratio [adjOR] =1.10, 95% confidence interval [CI]=0.94,1.29; p=0.246). Interestingly, in PWID (n=49 cases, 143 controls), cases had significantly lower levels of 47 expression compared to their matched controls (adjOR=0.80, 95% CI=0.68, 0.93; p=0.004). Among HIV-positive PWID (n=47), there was no significant association in HIV disease progression in individuals above or below the median level of 47 expression (log-rank p=0.84). INTERPRETATION: In contrast to findings in heterosexual women, higher 47 expression does not predict HIV acquisition or disease progression in PWID or MSM. FUNDING: This study was supported in part by the Division of Intramural Research, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health. The study was also supported by extramural grants from NIAID T32AI102623 (E.U.P.), and UM1AI069470.

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19


2020 Raphael Gottardo; Jason Goldman; Valentin Voillet; Philip Greenberg; Michael Zager

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Detection of Chemotherapy-Resistant Pancreatic Cancer Using a Glycan Biomarker, sTRA

Clin Cancer Res

2020 Ying Huang

PURPOSE: A subset of pancreatic ductal adenocarcinomas (PDACs) is highly resistant to systemic chemotherapy, but no markers are available in clinical settings to identify this subset. We hypothesized that a glycan biomarker for PDAC called sTRA could be used for this purpose. EXPERIMENTAL DESIGN: We tested for differences between PDACs classified by glycan expression in multiple systems: sets of cell lines, organoids, and isogenic cell lines; primary tumors; and blood plasma from human subjects. RESULTS: The sTRA-expressing models tended to have stem-like gene expression and the capacity for mesenchymal differentiation, in contrast to the non-expressing models. The sTRA cell lines also had significantly increased resistance to seven different chemotherapeutics commonly used against pancreatic cancer. Patients with primary tumors that were positive for a gene-expression classifier for sTRA received no statistically significant benefit from adjuvant chemotherapy, in contrast to those negative for the signature. In another cohort, based on direct measurements of sTRA in tissue microarrays, the patients who were high in sTRA again had no statistically significant benefit from adjuvant chemotherapy. Furthermore, a blood-plasma test for the sTRA glycan identified the PDACs that showed rapid relapse following neoadjuvant chemotherapy. CONCLUSIONS: This research demonstrates that a glycan biomarker could have value to detect chemotherapy-resistant PDAC in clinical settings. This capability could aid in the development of stratified treatment plans and facilitate biomarker-guided trials targeting resistant PDAC.

Clinical Endpoints for Evaluating Efficacy in COVID-19 Vaccine Trials

Ann Intern Med

2020 Deborah Donnell; Holly Janes; Yunda Huang; Michal Juraska; Elizabeth Brown; Larry Corey; Peter Gilbert; Thomas Fleming; Ying Huang; Marco Carone; Youyi Fong; Lindsay Carpp; Ollivier Hyrien

Several vaccine candidates to protect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (COVID-19) have entered or will soon enter large-scale, phase 3, placebo-controlled randomized clinical trials. To facilitate harmonized evaluation and comparison of the efficacy of these vaccines, a general set of clinical endpoints is proposed, along with considerations to guide the selection of the primary endpoints on the basis of clinical and statistical reasoning. The plausibility that vaccine protection against symptomatic COVID-19 could be accompanied by a shift toward more SARS-CoV-2 infections that are asymptomatic is highlighted, as well as the potential implications of such a shift.

Introduction to Special Issue on 'Statistical Methods for HIV/AIDS Research'

Stat Biosci

2020 Ying Chen


Prospects for a safe COVID-19 vaccine

Sci Transl Med

2020 Larry Corey; Peter Gilbert

Rapid development of an efficacious vaccine against the viral pathogen SARS-CoV-2, the cause of the coronavirus disease-2019 (COVID-19) pandemic, is essential, but rigorous studies are required to determine the safety of candidate vaccines. Here, on behalf of the Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Working Group, we evaluate research on the potential risk of immune enhancement of disease by vaccines and viral infections, including coronavirus infections, together with emerging data about COVID-19 disease. Vaccine-associated enhanced disease has been rarely encountered with existing vaccines or viral infections. Although animal models of SARS-CoV-2 infection may elucidate mechanisms of immune protection, we need observations of enhanced disease in people receiving candidate COVID-19 vaccines to understand the risk of immune enhancement of disease. Neither principles of immunity nor preclinical studies provide a basis for prioritizing among the COVID-19 vaccine candidates with respect to safety at this time. Rigorous clinical trial design and post-licensure surveillance should provide a reliable strategy to identify adverse events, including the potential for enhanced severity of COVID-19 disease, following vaccination.

Nonparametric variable importance assessment using machine learning techniques


2020 Marco Carone; Noah Simon; Peter Gilbert

In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often sub-optimal for predicting the response. Additionally, because the variable importance measures native to different regression techniques generally have a different interpretation, comparisons across techniques can be difficult. In this work, we study a variable importance measure that can be used with any regression technique, and whose interpretation is agnostic to the technique used. This measure is a property of the true data-generating mechanism. Specifically, we discuss a generalization of the ANOVA variable importance measure, and discuss how it facilitates the use of machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. The importance of each feature or group of features in the data can then be described individually, using this measure. We describe how to construct an efficient estimator of this measure as well as a valid confidence interval. Through simulations, we show that our proposal has good practical operating characteristics, and we illustrate its use with data from a study of risk factors for cardiovascular disease in South Africa. This article is protected by copyright. All rights reserved.