JAMAevidence JAMA Guide to Statistics and Methods Target Trial Emulation for Causal Inference From Observational Data With Dr Hernán
May 2, 2024
Dr. Miguel A. Hernán, professor of epidemiology at Harvard T.H. Chan School of Public Health, discusses target trial emulation for causal inference from observational data with JAMA Statistical Editor. They explore the concept of target trial emulation, the importance of randomized clinical trials, estimating the effectiveness of Tocilizumab in ICU patients with COVID-19, and the complementary role of randomized trials and observational studies in generating evidence.
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Causal Inference
- Causal inference helps determine the best course of action.
- It guides decisions in medicine and public health, comparing different interventions.
Counterfactuals
- Counterfactuals explore potential outcomes under different actions.
- They are fundamental to human decision-making and causal inference.
Randomized Trials
- Randomized trials excel at determining causality due to random treatment assignment.
- They also define specific causal questions with protocols, enhancing reliability.