Lee Kennedy-Shaffer received his BS in mathematics from Yale College and his PhD in biostatistics from the Harvard T.H. Chan School of Public Health. He joined the Vassar faculty in 2020 after a brief postdoc with the Center for Communicable Disease Dynamics at Harvard. His research interests include the role of clustering and correlation in clinical trials and the design of clinical trials and epidemiologic studies for infectious diseases. This has led to various work on study design in the COVID-19 pandemic, including estimating epidemic trajectories and accounting for overdispersed transmission. He is also interested in the history of statistics, specifically its role in regulation and the law and the role of eugenics in the development of statistics.
Departments and Programs
- Hay J, Kennedy-Shaffer L, Kanjilal S, Lipsitch M, Mina M. Estimating epidemiologic dynamics from single cross-sectional viral load distributions. medRxiv (Preprint). DOI:10.1101/2020.10.08.20204222.
- Kennedy-Shaffer L, Qiu X, Hanage WP. Snowball sampling study design for serosurveys in the early COVID-19 pandemic. American Journal of Epidemiology, In Press (2021).
- Kennedy-Shaffer L, Baym M, Hanage WP. Perfect as the enemy of the good: tracing transmissions with low-sensitivity tests to mitigate SARS-CoV-2 outbreaks. Lancet Microbe 2021. DOI:10.1016/S2666-5247(21)00004-5.
- Kennedy-Shaffer L, Lipsitch M. Statistical properties of stepped-wedge cluster-randomized trials in infectious disease outbreaks. American Journal of Epidemiology 2020; 189: 1324–1332. DOI:10.1093/aje/kwaa141.
- Kennedy-Shaffer L, Hughes MD. Sample size estimation for stratified individual and cluster randomized trials with binary outcomes. Statistics in Medicine 2020; 39: 1489–1513. DOI:10.1002/sim.8492.
- Kennedy-Shaffer L, De Gruttola V, Lipsitch M. Novel methods for the analysis of stepped wedge cluster randomized trials. Statistics in Medicine 2020; 39: 815–844. DOI:10.1002/sim.8451.
- Kennedy-Shaffer L. Before p<0.05 to beyond p<0.05: using history to contextualize p-values and significance testing. The American Statistician 2019; 73: 82–90. DOI:10.1080/00031305.2018.1537891.