Incorporating Diversity, Equity and Inclusion in Biostatistics Courses
Friday, April 23, 2021
10 a.m. - 12 p.m. EDT
As we embrace conversations about improving diversity, equity, and inclusion (DEI) in the field of biostatistics, ideally, these perspectives should appear in every aspect of the profession, including incorporating these principles into how we teach our trainees. By incorporating DEI into biostatistics pedagogy, instructors and trainees can cultivate a more holistic understanding of both historical background and current challenges in the field by enabling all students to see themselves in the content and how they might contribute to making important contributions to both statistical theory and application.

We will begin this WebENAR by putting this into historical context to establish the importance of incorporating DEI into biostatistics training and coursework. We will then introduce practical examples from our own experiences of how to introduce these concepts into courses without compromising course objectives and without requiring additional time for these modifications. The session will conclude with open discussion where we encourage all those attending the WebENAR to share their own experiences and ideas for making biostatistics courses more diverse, equitable, and inclusive.
Presenters
Dr. Scarlett Bellamy, Drexel University, Dornsife School of Public Health
Dr. Scarlett Bellamy is a Professor of Biostatistics and Director of the Graduate Programs in Biostatistics, Department of Epidemiology and Biostatistics at Drexel University. She also serves as the Associate Dean of Diversity and Inclusion at Drexel’s Dornsife School of Public Health. Prior to her current position at Drexel, she was a Professor of Biostatistics in the Perelman School of Medicine at the University of Pennsylvania. She is also Co-Director of the Biostatistics and Informatics Core (BIC) and serves as a senior biostatistician for the Center for Health Equity Research and Promotion (CHERP) at the Corporal Michael J. Crescenz VA Medical Center. Dr. Bellamy’s research interests are in the design, analysis and implementation of cohort and longitudinal studies, particularly group/cluster randomized trials. She has published in a number of clinical and public health disciplines including: statistical methods; behavioral economics; HIV risk reduction; clinical investigations of HIV, cancer, cardiovascular health, obesity and physical activity, adult lung injury and lung transplantation; and critical care.
Dr. Reneé Moore, Emory University
Reneé H. Moore, PhD (she/her) is Research Associate Professor and Director of the Biostatistics Collaboration Core at Emory University. She earned a Bachelor of Science in mathematics and completed the secondary mathematics education program at Bennett College and earned her PhD in Biostatistics from Emory University. In her first faculty position at the University of Pennsylvania, Dr. Moore was actively involved in designing and implementing clinical trials via Data Coordinating Centers and was the faculty statistician in the Center for Weight and Eating Disorders. Next Dr. Moore taught up to seven classes per year and continued her obesity research at North Carolina State University, Department of Statistics. In 2015, Dr. Moore returned to Emory University. She spends her time mentoring, teaching, and collaborating with clinical investigators from Penn, UNC, Emory, and beyond. Dr. Moore is a Fellow of the American Statistical Association (2017). She is the current Treasurer of ENAR. Dr. Moore is a past chair of the ASA Committee on Minorities in Statistics (past chair of StatFest), past co-chair of the ENAR Fostering Diversity in Biostatistics Workshop, and remains very active in these and other initiatives within ENAR and ASA.
Andrea Lane, Emory University
Andrea Lane (she/her) is a biostatistics PhD candidate at Emory University. Prior to entering the PhD program, Andrea graduated from UNC Chapel Hill with bachelor’s degrees in biostatistics and mathematics. Her dissertation work is in mediation modeling with primary application to DNA methylation data.