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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.
27:25
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Quick takeaways
- Counterfactuals are critical for causal inference in decision-making processes.
- Target trial emulation minimizes biases in observational data but has limitations regarding unmeasured confounders.
Deep dives
Understanding Causal Inference and Counterfactuals
Causal inference involves determining what works or what harms in making decisions between different courses of action. Counterfactuals, answering 'what if' questions, are foundational to causal inference and are essential in decision-making processes.
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