10/17/2024: 2:30 PM - 4:00 PM EDT
Panel
Room: Spruce Oak
This session brings together three experts to discuss innovative statistical approaches designed to address the unique challenges posed by the complex nature of HIV data.
Sarah Lotspeich will present a talk titled, "Missing and misclassified wells: Challenges in quantifying the HIV reservoir from dilution assays." She plans to address the challenges of quantifying HIV reservoirs using dilution assays, such as the Quantitative Viral Outgrowth Assay (QVOA) and the Ultra Deep Sequencing Assay (UDSA). Her work highlights the development of statistical methods to account for imperfect assay sensitivity and specificity, as well as missing sequencing data, providing more accurate estimates of the infectious units per million of persistent HIV.
Bonnie Shook-Sa will present, "Developing HIV risk assessment tools with pooled complex survey data from 15 African countries," where she will discuss the development of HIV risk assessment tools using pooled complex survey data from 15 African countries. By employing Lasso regression models, her research overcomes challenges posed by the design features of complex surveys and missing data to construct robust predictive models.
Kathryn Lancaster will present, "Examining dynamic patterns of change: Using time-varying equation modeling for associations of depression and alcohol use throughout adulthood among a cohort of people with HIV," where she will explore the dynamic relationships between depression, alcohol use, and HIV outcomes among individuals receiving care in Cameroon. Utilizing time-varying effect modeling, her study reveals how the interactions between depressive symptoms and heavy episodic drinking vary across different ages and genders.
This session seeks to underscore the critical role of sophisticated statistical techniques in enhancing our understanding of HIV epidemiology and in developing targeted tools that address the epidemic's complexity.
Organizer(s)
Lucy D'Agostino McGowan, Wake Forest University
Sarah Lotspeich, Wake Forest University
Target Audience
Mid-Level
Tracks
Knowledge
Women in Statistics and Data Science 2024
Presentations
Southern, Eastern, and West African countries host 15% of the global population, yet account for more than half of new global HIV acquisition events. Tools for identifying adults at greatest risk of acquiring HIV can guide focused HIV prevention. Historically, risk assessment tools have had challenges with both internal and external validity due to limitations with the data sources from which they were developed and the models used to construct them. Using data from complex sample surveys to construct risk assessment tools may improve external validity, but these data sources pose analytic challenges due to design features and missing data. We develop risk assessment tools for women and men by fitting Lasso regression models to pooled, complex survey data from 15 countries in Africa. Models were trained on the full population and internally cross-validated. Performance was evaluated using area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity. Both the male and female models had favorable predictive performance and were parsimonious, with strong external validity.
Speaker
Bonnie Shook-Sa, UNC Chapel Hill
Understanding the potential for dynamic changes in the interrelationship between depression and alcohol throughout the lifespan will ultimately inform HIV prevention and treatment efforts. We investigated associations between depressive symptoms and heavy episodic drinking (HED) and the extent the relationship differed across ages among PWH receiving HIV care in Cameroon. We conducted a retrospective analysis of 18-60-year-old PWH on antiretroviral therapy in Cameroon from January 2016 to March 2020. Age-varying effect modeling was conducted to assess associations between depressive symptoms and HED across ages and by gender.
Speaker
Kathryn Lancaster, Wake Forest University
People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. Dilution assays, including the quantitative viral outgrowth assay (QVOA) and more detailed Ultra Deep Sequencing Assay of the outgrowth virus (UDSA), are commonly used to estimate the reservoir size, i.e., the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. This paper considers efficient statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Moreover, existing inference methods for the IUPM assumed that the assays are "perfect" (i.e., they have 100% sensitivity and specificity), which can be unrealistic in practice. The proposed methods accommodate assays with imperfect sensitivity and specificity, wells sequenced at multiple dilution levels, and include a novel bias-corrected estimator for small samples. The proposed methods are evaluated in a simulation study, applied to data from the University of North Carolina HIV Cure Center, and implemented in the open-source R package SLDeepAssay.
Speaker
Sarah Lotspeich, Wake Forest University