New Faculty Friday: Fan Li, biostatistician, urban explorer, museum goer
The Yale School of Public Health proudly welcomes 13 new tenure track faculty this academic year. These individuals bring a broad range of research, scholarship, and teaching expertise to the school and will be instrumental in helping us address many of the public health challenges of the 21st century.
Today we spotlight, Fan Li, assistant professor in the Department of Biostatistics. He holds a Ph.D. (2019) and an M.S. (2014) in biostatistics from Duke University. Besides his independent research, Fan will be working with Susan Dwight Bliss Professor of Biostatistics Donna Spiegelman in the Center for Methods in Implementation and Prevention Science (CMIPS).
Q: Describe your primary academic focus or research specialty?
FL: As a biostatistician, my research interests include developing statistical methods for the design and analysis of pragmatic cluster randomized trials. Cluster randomized trials differ from traditional randomized controlled trials in that intact clusters of participants, such as groups of patients in hospitals or clinics, are randomized to an intervention. Analytical strategies must therefore account for this participant clustering. These pragmatic studies are increasingly conducted within healthcare delivery systems so that the we can learn the comparative effectiveness of certain interventions for a real-world population.
My current research focuses on a popular variant of cluster randomized trials, namely “stepped wedge cluster randomized trials”, where the interventions are sequentially assigned to randomly selected groups of clusters until there is complete roll-out among all clusters. The design and analysis of stepped wedge cluster trials requires special considerations because the intervention is confounded with time and because repeated assessments from each cluster represent an additional level of clustering. I have been developing tools to address these challenges. Besides pragmatic trials, I am also developing causal inference tools to better address bias in observational studies in real-world, full-scale settings.
Q: What are your long-term goals in public health?
FL: Through my research on causal inference methods, either in the context of randomized studies or observational studies, I hope to develop novel statistical and computational tools that help investigators generate valid, reliable and efficient results that can be used to address outcomes of interest to patients and their caregivers. By leveraging multiple sources of data, I hope to help rigorously identify and assess sustainable and scalable intervention strategies for disease prevention.
Q: How will the resources available at the Yale School of Public Health help you achieve your goals?
FL: Both the Department of Biostatistics and the Center for Methods in Implementation and Prevention Science (CMIPS) at the Yale School of Public Health provide great resources to support my work. It is a privilege to work with faculty members in the Department of Biostatistics, all of whom are world-class researchers that have greatly contributed to the advancement of statistical methodology in public health and biomedical research.
Yale also has a great environment that facilitates interdisciplinary collaboration across departments, schools and campuses, which I am excited about. It is a truly wonderful opportunity for junior faculty to learn from experts in all disease areas and receive advice based on their years of institutional knowledge.
Q: Tell us something about yourself away from public health (E.g., hobbies, interests, pursuits, etc.).
FL: I love travelling and exploring new cities. I also love to discover good food places and visit museums, ranging from fine arts to natural history. I am excited to find out more about New Haven and the Greater New York Area.