Senior Research Scientist, Eli Lilly
Xiang Zhang received his BS in Statistics from the University of Science and Technology of China in 2008 and his MS/PhD in Statistics from the University of Kentucky in 2013. He joined Eli Lilly and Company in 2013 and has primarily supported medical affairs and real world evidence research across multiple disease areas. He also leads the development and implementation of advanced analytical methods to address rising challenges in real world data analysis. His research interests include causal inference in observational studies, unmeasured confounding assessment, and the use of real world evidence for clinical development and regulatory decisions. Currently, he is a Sr. Research Scientist at Eli Lilly and has been using SAS since 2008.
By This Author
Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS®
Real world health care data from observational studies, pragmatic trials, patient registries, and databases is common and growing in use. Real World Health Care Data Analysis: Causal Methods and Implementation in SAS® brings together best practices for causal-based comparative effectiveness analyses based on real world data in a single location. Example SAS code is provided to make the analyses relatively easy and efficient.
The book also presents several emerging topics of interest, including algorithms for personalized medicine, methods that address the complexities of time varying confounding, extensions of propensity scoring to comparisons between more than two interventions, sensitivity analyses for unmeasured confounding, and implementation of model averaging.