Causal Inference from Large, Real-world and Complex Healthcare Data in the Era of Data Science

SCHEDULE

Presentation: May 6 2020, 1:00pm – 2:00pm
Where: Zoom Meeting
Presentation Survey: Link Here
Presentation Video Link: Link Here

ABSTRACT

Real-world data (RWD) gathered during the routine clinical practice in the form of electronic health records, health claims databases, clinical records, and disease-specific registries can help fill the knowledge gap between clinical trials and clinical practice. In the presence of known design limitations of RWD sources, comparative effectiveness researchers increasingly recognize the usefulness of causal inference approaches to infer causality from RWD analysis results. In this seminar, I will talk about how appropriate applications of data science methods within the causal inference framework can help health researchers achieve data-driven discovery and reliable real-world evidence (RWE).

PRESENTED BY

Dr. Ehsan Karim

Dr. M. Ehsan Karim is an Assistant Professor (partner) in the School of Population and Public Health (SPPH), UBC, a Michael Smith Foundation for Health Research (MSFHR) Scholar, a Scientist and Biostatistician at the Centre for Health Evaluation and Outcome Sciences (CHÉOS), St. Paul’s Hospital, and an Associate Member, UBC Statistics. He obtained his Ph.D. in Statistics from UBC, supported by a studentship from the Multiple Sclerosis Society of Canada. He completed his postgraduate training in the Department of Epidemiology / Biostatistics at McGill. The American Journal of Epidemiology has selected one of his first-authored articles as ‘Top 10 Articles of the Year’, and Pharmacoepidemiology and Drug Safety has twice recognized him as the ‘best reviewer for the year’. His current program of research focuses on developing causal inference and pharmacoepidemiological methodologies, and applications of data science approaches in the large healthcare data analysis context to answer real-world comparative effectiveness research questions. His program of research is supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the BC SUPPORT Unit. He supervises graduate students from both SPPH and Statistics Department. Within SPPH, he developed and teaches a core PhD capstone course that integrates state-of-the-art epidemiological, data science, and data analytic methods. He regularly offers pre-conference workshops about emerging methods nationally and internationally.