Science Café: Understanding Rare Diseases Using Statistics and Data Science

Science Café: Understanding Rare Diseases Using Statistics and Data Science

Categories: General | Intended for

Wednesday, March 01, 2023

2:00 PM - 3:00 PM | Add to calendar

Location Details

Virtual Event

Contact Information

Jessie Cartwright, 613-520-4388,



About this Event

Host Organization: Office of the Dean of Science

Rare diseases are rare individually, but not collectively, resulting in substantial morbidity and mortality. Duchenne muscular dystrophy (DMD) is an X-linked, progressive, severe, neuromuscular disorder without a cure that affects approximately 1 in 3600 male births. Due to the rare nature of the disease, running large research studies and conducting well-powered analyses remain ongoing issues. Furthermore, many challenges still remain for patients, clinicians, and the pharmaceutical industry. In this talk, I will provide an overview of some types of data gathered for research in DMD, e.g., motor outcomes, biomarkers (inflammatory, adrenal, etc.), patient reported outcomes, actigraphy, pharmacokinetic, genetic modifiers, etc. I will highlight their uses, the problems they have helped us solve, some continuing issues in DMD care, the importance of good statistical and data science models, and touch on which learned lessons can also apply to an allelic rare disease, Becker muscular dystrophy.

About the speaker:

Dr. Utkarsh Dang is an Assistant Professor in the Department of Health Sciences at Carleton University. He completed his doctoral studies in Statistics at the University of Guelph with a postdoctoral fellowship in bioinformatics at McMaster University. He was previously at the School of Pharmacy and Pharmaceutical Sciences at Binghamton University. He is a statistician and data scientist who does research in health outcomes and biomarkers, clinical trials in rare diseases, and classification analysis. Dr. Dang’s primary areas of research are in biomarkers and outcomes in Duchenne and Becker muscular dystrophies, specifically in understanding the natural history of these diseases through modeling phenotypic, genotypic, and treatment response variability.