SUP Program: CFAR

Presentation Time: 12:20-12:40

Home University: UNC-Chapel Hill

Research Mentor: Ann Marie Weideman, Biostatistics

Program: CFAR

Research Title: Improving statistical analysis in immunology research: An exploration of partial correlation

Partial correlation coefficients are used to determine the strength of an association between two variables while controlling for the effects of confounding variables. Partial correlations range between -1 and 1 and can be computed in the presence of continuous or categorical confounding variables. These correlations are useful in laboratory studies where the sample size is often too small to compute separate correlations within each category of the confounding variable. As a conventional example, we might hypothesize that two biomarkers are positively correlated, but that one (or both) of the biomarkers varies with advancing age and sex. Thus, partial correlation could be used to determine if there exists a direct association between the two biomarkers by controlling for the confounding effects of age and sex. Failing to control for age and sex may result in false evidence of an association between the biomarkers due to contaminating relationships between the confounders and the variables of interest. As part of this short analytics review, we have chosen to review several manuscripts, along with their associated datasets, that could have benefited from reporting partial correlations. Additionally, we have developed a user-friendly web-application that allows users to securely upload their data to compute partial correlations in a point-and-click manner. Together, this manuscript and web-application will help to improve understanding of partial correlation coefficients and allow readers to easily compute them.

Presentation Time: 3:10-3:30

Home University: UNC-Chapel Hill

Research Mentor: Bonnie Shook-Sa, Biostatistics

Program: CFAR

Research Title: Assessing Risk of HIV Acquisition in Sub-Saharan Africa

Approximately 37.7 million people globally were living with HIV in 2020. Although sub-Saharan Africa has about 14% of the global population, it is home to two thirds (67%) of people living with HIV. Due to this disproportionate burden of HIV in sub-Saharan Africa, HIV prevention in sub-Saharan Africa has become a top priority to fight against the epidemic. Since 2014, the Population-based HIV Impact Assessment (PHIA) Project has been conducting nationally representative surveys in 15 of the most-affected countries to capture the state of the HIV epidemic. Here, we explain the PHIA’s complex multi-stage sample design and methods for computing estimates of HIV prevalence and incidence. With available datasets from ten countries, we present comparisons of estimates of prevalence and annual incidence of HIV among men and women in these countries.

In sub-Saharan Africa, around 4200 adolescent girls and young women (AGYW) aged 15–24 years became infected with HIV every week in 2020. Because of the high incidence of HIV among this population, our next step is to explore risk factors associated with recent HIV infection among AGYW in sub-Saharan Africa. We plan to use machine learning methods to build prediction models for HIV acquisition that will be used to develop a risk assessment tool for AGYW. This risk assessment tool can be used in HIV prevention efforts.