Using Healthcare Databases to Learn What Works When No Randomized Trials Exist

Event sponsored by:

Population Health Sciences
AI Health
Biostatistics and Bioinformatics
Duke Clinical Research Institute (DCRI)
School of Medicine (SOM)

Contact:

Wendy Goldstein

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Miguel Hernan, MD

Speaker:

Miguel Hernán, MD, Director of the CAUSALab, Harvard T.H. Chan School of Public Health

Topic: When randomized trials are not available, causal effects are often estimated from observational data. Therefore, causal inference from observational data can be viewed as an attempt to emulate a hypothetical randomized trial-the target trial-that would quantify the causal effect of interest. Contrary to what is generally believed, many well-known failures of observational studies were the result of not adequately emulating a target trial rather than limitations of the observational data. This talk explains those methodological failures in non-technical language and describes several examples of how observational data can be used when randomized trials do not exist.

About our Speaker: Miguel Hernán uses health data and causal inference methods to learn what works. As Director of the CAUSALab at Harvard, he and his collaborators repurpose real-world data into scientific evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. As the Kolokotrones Professor of Biostatistics and Epidemiology, he teaches at the Harvard T.H. Chan School of Public Health and at the Harvard-MIT Division of Health Sciences and Technology, where he has mentored dozens of trainees and students. His free online course "Causal Diagrams" and book "Causal Inference: What If", co-authored with James Robins, are widely used for the training of researchers. Miguel has received many awards for his work, including the Rousseeuw Prize for Statistics, the Rothman Epidemiology Prize, and a MERIT award from the National Institutes of Health. He is a Fellow of the American Association for the Advancement of Science and the American Statistical Association, Associate Editor of Annals of Internal Medicine, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and Journal of the American Statistical Association.

Zoom Info: https://duke.zoom.us/j/91371698063?pwd=RXNmNTkrYVk4bE1lS0JEQmRjOU0xQT09
Meeting ID: 913 7169 8063
Password: 156386


Population Health Sciences Research Seminar