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Department of Biostatistics HIV Working Group 2022 - 2023 |
ABSTRACT: Identifying characteristics associated with SARS-CoV-2 RNA shedding may be useful to understand viral compartmentalization, disease pathogenesis, and risks for viral transmission. Participants (n=537) enrolled August 2020 to February 2021 in ACTIV-2/A5401, a placebo-controlled platform trial evaluating investigational therapies for mild-to-moderate COVID-19, and underwent quantitative SARS-CoV-2 RNA testing on nasopharyngeal and anterior nasal swabs, oral wash/saliva, and plasma at entry and post-entry. Concordance of RNA levels (copies/mL) across compartments was assessed and predictors of nasopharyngeal RNA levels and changes were evaluated with censored linear regression models. We observed SARS-CoV-2 RNA shedding was concordant across compartments. Age was strongly associated with viral shedding and males had slower viral clearance than females, which could explain sex differences in COVID-related outcomes.
ABSTRACT: Over the years, the process of designing, monitoring, and analyzing clinical studies for evaluating new treatments has gradually fallen into a fixed pattern. Clinical trialists have sometimes been slow to utilize new methodologies–perhaps to avoid potential delays in the review process for drug approval or manuscript submission. The underlying attitude toward innovation in drug development is in sharp contrast to that in other technologically driven fields. Scientific investigation is an evolving process. What we have learned from previous studies about methodological shortcomings should help us better plan and analyze future trials. Unfortunately, use of inefficient or inappropriate procedures persists even when better alternatives are available. In this talk, we will explore various methodological issues and potential solutions to them. A goal of the clinical study is to obtain robust, clinically interpretable treatment effect estimate with respect to risk-benefit perspectives at the patient’s level via efficient and reliable quantitative procedures. We will discuss how to achieve this goal via various real trial examples.
ABSTRACT: Dr. Sudfeld will present the results of a randomized controlled trial of vitamin D supplementation for pregnant women living with HIV in Tanzania. He will also discuss secondary findings and analyses utilizing the trial data, ongoing follow-up studies of the cohort that focus on child development outcomes and introduce potential opportunities for collaborative student research.
ABSTRACT: Antiretrovirals used as pre-exposure prophylaxis (PrEP) have had proven success for preventing acquisition of HIV. Oral pills (TDF, FTC/TDF, F/TAF), either taken daily or at after exposure, and bi-monthly injections (CAB-LA) are highly effective for HIV prevention. A challenge to real-world effectiveness for any prevention activity, however, is providing sufficient selection of products to achieve high uptake and sustained adherence. There remains a need for new drugs and/or vaccines that lower the barriers to adherence through less frequent or more convenient modalities. Such drugs are currently in development for HIV prevention, but it is not clear what future trial designs are feasible or will be sufficient to prove efficacy.
In this seminar, Dr. Deborah Donnell will present the sequence of clinical trial designs and findings that have brought us the current successes in PrEP for HIV prevention, and then discuss current work on future design approaches, including estimates based on counterfactual placebo, cross-sectional HIV incidence assays and PrEP non-compliance designs.
ABSTRACT: Of the eight HIV preventive vaccine candidates studied in efficacy trials, only the regimen in the RV144 Thai trial has shown significant HIV acquisition reduction. Multiple correlates of risk of HIV acquisition have also been identified. To target the extremely diverse subtype C epidemic in sub-Saharan Africa, the vaccine regimen was redesigned and evaluated first in a Phase 1/2a trial and next in a Phase 2b/3 trial in South Africa. Despite demonstrated safety and immunogenicity, vaccine efficacy was essentially zero, meeting prespecified vaccine efficacy futility criteria. Although the vaccine regimen was not efficacious, the HVTN 702 trial provides a unique opportunity to assess the critical question of whether the immune correlates of HIV-1 acquisition risk identified in the RV144 Thai trial generalize to other at-risk populations. In this talk, Dr. Moodie will describe the study design, statistical methods, and main findings of the HVTN 702 correlates analysis and comment on lessons learned.
ABSTRACT: Estimating causal effects to inform policy and clinical practice using observational data is challenging. In this talk, I will present some recent work from the Tsepamo Birth Outcomes Surveillance Study that highlights some of these challenges and proposed solutions. Tsepamo is an ongoing birth outcomes surveillance study in Botswana, which captures pregnancy and delivery information for more than 200,000 deliveries nationwide from 2014 to present. In the first study I will present, we describe a prolific bias in observational research to evaluate medication safety and effectiveness in pregnancy – immortal time bias – and how to overcome it using a (sequential) target trial approach. We apply this approach to answer the question of the safety of antibiotic initiation in pregnancy on preterm delivery. In the second study I will present, we discuss how a ‘quasi-experimental approach’ – difference in difference – may overcome some challenges with using observational data to estimate causal effects, but those challenges remain. In this study, we apply the difference-in-difference approach to examine the potential effect of the COVID-19 lockdown on adverse birth outcomes in Botswana.
ABSTRACT: Cluster randomized trials (CRTs) refer to a popular set of experiments in which randomization is carried out at the group level. While methods have been developed for planning CRTs to study the overall treatment effect, and more recently, to study the heterogeneous treatment effect, the development for the latter objective has currently been limited to a continuous outcome. Because the sample size and power requirements for detecting differential treatment effect in CRTs with a binary outcome remain unclear but are highly relevant given the prevalence of binary outcomes, we develop formal sample size procedures for testing treatment effect heterogeneity in two-level CRTs under a generalized linear mixed model. Closed-form sample size expressions are derived with a binary effect modifier, whereas a Monte Carlo approach is developed with a continuous effect modifier. We present several numerical studies to elucidate features of the proposed formulas and to compare our method to the approximation calculation under a linear mixed model. Extensions to multiple effect modifiers are also discussed. We conduct simulations to examine the accuracy of the proposed sample size methods and use data from the STOP CRC cluster randomized trial to illustrate the proposed sample size procedure for testing treatment effect heterogeneity.
ABSTRACT: In clinical trials randomizing participants to active vs. control conditions and following study units until the occurrence of a primary clinical endpoint, evaluating the efficacy of quantitative treatments (e.g., drug dosage) or mediators (e.g., drug- or vaccine-induced immune activity) is challenging. This is due, in part, to the fact that statistical innovations in causal inference have historically focused on defining estimands compatible only with binary (or categorical) treatments. We will review a key issue with such approaches, which severely compromises their stability and, consequently, their applicability to real-world data analysis. We will then turn to an alternative class of causal effect estimands based on modified treatment policies (defined by stochastic interventions) and tailored to quantitative treatments and/or mediators. We will introduce stochastic-interventional causal effects, which provide a measure of the effect attributable to perturbing a treatment variable's “natural” (i.e., observed or induced) value, focusing primarily on how these effect definitions provide a scientifically informative solution when working with quantitative treatment variables. Unfortunately, the estimation of these estimands in vaccine efficacy trials often requires significant additional care, for such trials measure immunologic biomarkers – critical to understanding the mechanisms by which vaccines confer protection or as surrogate endpoints in future clinical trials – via outcome-dependent two-phase sampling (e.g., case-cohort) designs. These biased sampling designs have earned their popularity: they circumvent the administrative burden of collecting potentially expensive biomarker measurements on all study units without limiting opportunities to detect biomarkers mechanistically informative of the disease or infection process. To address this challenge, we outline a semi-parametric correction procedure that recovers population-level estimates (in spite of outcome-dependent, two-phase sampling), with guarantees of asymptotically efficient inference (i.e., minimal variance within a suitable regularity class), of a causally informed vaccine efficacy measure defined by contrasting assignments of study units to active vs. control conditions while simultaneously hypothetically shifting biomarker expression in the active condition. This results in a descriptive causal dose-response analysis informative of next-generation vaccine efficacy and useful for bridging vaccine efficacy from a source pathogen strain (e.g., index SARS-CoV-2 strain at outbreak) to reasonably similar variants of concern (e.g., Delta). We present the results of applying this approach in distinct analyses of two vaccine efficacy trials: (1) the HIV Vaccine Trials Network’s 505 trial of an HIV antibody boost vaccine and (2) the COVID-19 Prevention Network’s COVE trial of Moderna's two-dose COVID-19 mRNA-1273 vaccine.
ABSTRACT: What was the role of trust and misinformation in shaping individual preventive behaviors during an outbreak of Ebola virus disease (EVD) in the Democratic Republic of Congo (DRC)? Additionally, how did trust and misinformation impact the response to COVID-19 in the DRC and Uganda? By examining survey data collected in both countries, Dr. Phuong Pham will explore the research findings and practical implications of addressing misinformation in outbreak control.
ABSTRACT: Achieving equitable outcomes for oppressed and socially stigmatized communities—including people living with HIV—requires conscientious and rigorous science. This discussion will center on how to enact race-conscious data science, emphasizing the role of racism, rather than race, in determining health disparities. This talk will address questions regarding data collection, terminology, analytical strategies, and implications for clinical practice and policy advocacy. By developing more critical ways to interrogate the impact of racism on infectious disease, participants will disrupt the perpetuation of racial essentialism in biomedical research and call for increasingly reparative and liberative reforms.
ABSTRACT: Historically, the main goal for randomized clinical trials is to evaluate the overall treatment effect in efficacy and toxicity, but there has been a growing interest to understand the heterogeneity in treatment effect across participants characterized by their pre treatment characteristics. Much literature exists on the limitations of traditional subgroup analysis and new methods have emerged to address some of the issues, but their implementation to the clinical or public health research is limited, especially when the outcome is time to event type of endpoint. We implemented an individualized treatment selection approach involving kernel estimating procedure to conduct subgroup analyses for ACTG A5257, a phase III randomized trial on first line ART, to evaluate whether the overall treatment effect can be generalized to a more individualized level.
ABSTRACT: To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmissions among individuals in a population; we refer to the set of these interactions as the community contact network. The structure of the network can have profound effects on both the spread of infectious disease and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with transmission of infectious disease to more precisely and accurately estimate important properties of the contact network. In this presentation, we will discuss how integration of data associated with infectious diseases that are routinely collected can lead to large increases in precision and accuracy of our contact network estimates.
ABSTRACT: For this talk, we will go through the development of an agent-based model of HIV transmission. This ongoing work is foundational to estimating the success of treatment and prevention programs in Miami, both historically and potentially in the future. We will review the structure of the agent-based model, its calibration/validation and preliminary results.
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