Dr. Hong Zhu | University of Virginia

Abstract: Large observational healthcare data, such as registry, claims, and electronic health record data, are primary research tools for comparative effectiveness research (CER). Compared to randomized clinical trials, CER studies are more representative of real-world clinical practice for the afflicted population. Nevertheless, CER using large, complex observational healthcare data presents unique methodological challenges related to semi-competing risks, confounding, and missing data. To address these challenges, Dr. Zhu has developed and implemented novel and robust analytical methods and algorithms to large observational healthcare data of different sources and complex structures. The experience motivated a Patient-Centered Outcomes Research Institute (PCORI) funded project on improving CER methodology. In this talk, Dr. Zhu will present her research work on improving methods for design and analysis of CER using large, complex observational healthcare data. She will also briefly discuss her work on improving methods for design of pragmatic clinical trials.