Causal Estimands for Clinical and Regulatory Decision Making
Join us on Wednesday, December 2nd for the Department of Epidemiology seminar series featuring Dr. Miguel Hernan.
Abstract: The estimand is what we want to estimate. When conducting research for clinical and regulatory purposes, the causal estimand typically involves the causal effect of an intervention. Three frameworks have been proposed to describe the causal estimand in randomized trials and observational studies: PICO, the target trial framework, and the E9 (R1) Addendum of the International Council for Harmonisation (ICH). This talks provides a side-by-side comparison of these three estimand frameworks for causal inference, translate across their terminologies, and highlight their commonalities and peculiarities. For decision makers who ask causal questions, the target trial framework subsumes the other two frameworks by providing the most flexibility and level of detail.
Bio: Miguel Hernán is the Director of CAUSALab, the Kolokotrones Professor of Biostatistics and Epidemiology at the Harvard T.H. Chan School of Public Health, and faculty at the Harvard-MIT Division of Health Sciences and Technology. His team repurposes real world data into actionable evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. Their work has contributed to shape health research methodology worldwide.
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