Lee Kennedy-Shaffer, PhD
Assistant Professor, Department of Biostatistics
Lee Kennedy-Shaffer is an Assistant Professor (Educator-Scholar Track) in Biostatistics. He received his PhD in Biostatistics from the Harvard T.H. Chan School of Public Health in 2020 and was an Assistant Professor in the Vassar College Department of Mathematics and Statistics from 2020–2024. His research focuses on randomized and observational study designs and methods for the analysis of infectious disease interventions. This includes mathematical modeling, cluster-randomized trials, and quasi-experimental designs, all with an eye toward broader population health impacts than are usually addressed by individually randomized trials. His work on the study design and analysis of COVID-19 data focused on maximizing the value of test results to understand epidemic dynamics and intervention impacts. His articles have appeared in Science, Statistics in Medicine, Clinical Trials, the American Journal of Epidemiology, The American Statistician, the Journal of Statistics and Data Science Education, and the American Journal of Public Health, among others. In addition, he has written on the history of statistics, FDA policy, statistics education, and causal inference in baseball.
What do you do with Data Science?
My work in data science focuses on the principled collection of data through the design of studies and the efficient use of data in analyzing interventions and understanding epidemiologic processes. This includes using methods such as Bayesian inference, difference-in-differences, synthetic control, and mixed effects models for the analysis of data. It also encompasses describing what conclusions we can and, perhaps more importantly, cannot draw from data collected through various study designs or other means. Issues of causal inference, bias, and generalizability or transportability are key to this work, especially in determining policy impacts. I have studied applications of this in health policy, vaccine and drug approval, and sports analytics, and have written on these topics for a variety of audiences. Considering how to teach these topics in statistics and biostatistics curricula is crucial here as well.