Yun Li, PhD
Yun Li, PhD is Associate Professor of Biostatistics in the Perelman School of Medicine at University of Pennsylvania’s Department of Biostatistics, Epidemiology and Informatics (DBEI), and a faculty member at PennMed's Center for Clinical Epidemiology and Biostatistics (CCEB). She is also a faculty member in the Department of Pediatrics at University of Pennsylvania and Children's Hospital of Philadelphia (CHOP).
Dr. Li conducts methodological research in causal inference, unmeasured confounding, missing data, mediation, Bayesian analyses and survey methods. She has led the effort in database management, data integrity, rigorous and robust analytical methods in many multicenter studies and NIH-funded program projects in biomedical science, in areas including infectious disease, breast cancer, prostate cancer, head-and-neck cancer, kidney disease, cardiovascular diseases and liver disease in the past two decades. In her role at CHOP, she works closely with the Center for Pediatric Clinical Effectiveness and the Pediatrics IDEAS team.
Needs that arose in the course of her collaborative work have inspired her to innovate in statistical methods research, and she has led the development of statistical methods to tackle the statistical issues in these clinical studies. Her methodological work has involved four key areas: 1) intermediate outcomes; 2) time-dependent treatments; 3) unmeasured confounders; and 4) missing data. Of note, her early methods work was among the first to propose a causal inference framework for evaluating surrogate endpoints, such as intermediate biomarkers, surrogate markers or mediators.
Prior to her work at CHOP and Penn, Dr. Li was a Research Associate Professor at the University of Michigan (UM). Dr. Li earned her PhD in Biostatistics from UM, following a period of collaborative work at Duke University Clinical Research Institute. She joined the UM Biostatistics faculty as a Research Assistant Professor after graduation.
Infectious disease and pediatric medicine, breast cancer, kidney disease, cardiovascular disease.
Causal inference, unmeasured confounding, missing data, mediation, Bayesian analyses and survey methods