Qi Long, PhD
Professor of Biostatistics
Dr. Long’s research purposefully includes novel statistical research and impactful biomedical research, each of which reinforces the other. Its thrust is to advance statistical methodology and data analytics in medicine and public health; Dr. Long is keenly interested in precision medicine and implementation science, and in big data.
Specifically, he has developed methods for the analysis of big biomedical data, predictive modeling, missing data, causal inference, Bayesian methods and clinical trials. He also has made significant contributions to biomedical research areas such as cancer, cardiovascular diseases, diabetes, mental health and stroke.
Dr. Long’s research has been supported by the National Institutes of Health (NIH), the Patient-Centered Outcomes Research Institute, the National Science Foundation, the U.S. Department of Veterans Affairs and the American Heart Association (AHA).
He was a member of the Emory Winship Cancer Institute, a senior statistician at the Emory Clinical Cardiovascular Research Institute, and a statistician at the Atlanta Veterans Administration Medical Center. He has directed the statistical and data coordinating centers of multiple NIH/AHA-supported studies—including multi-center clinical studies—supervising a team of database administrators and programmers, application developers and statistical analysts.
Dr. Long currently directs the Biostatistics Core in the Abramson Cancer Center at the University of Pennsylvania. He is an elected member of the International Statistical Institute and an elected fellow of the American Statistical Association.
Read more about the work of Dr. Long's research group.
Content Area Specialties
Cancer, cardiovascular diseases, diabetes, kidney diseases, mental health, stroke.
Big data (with application to omics, electronic health records, and mobile health data), predictive modeling, missing data, causal inference, Bayesian modeling and methods, functional data analysis, clinical trials, nonparametric and semi-parametric methods.