A causal roadmap for generating high-quality real-world evidence

• Published in: Journal of clinical and translational science Vol. 7; no. 1; p. e212
• Main Authors: Dang, Lauren E., Gruber, Susan, Lee, Hana, Dahabreh, Issa J., Stuart, Elizabeth A., Williamson, Brian D., Wyss, Richard, Díaz, Iván, Ghosh, Debashis, Kıcıman, Emre, Alemayehu, Demissie, Hoffman, Katherine L., Vossen, Carla Y., Huml, Raymond A., Ravn, Henrik, Kvist, Kajsa, Pratley, Richard, Shih, Mei-Chiung, Pennello, Gene, Martin, David, Waddy, Salina P., Barr, Charles E., Akacha, Mouna, Buse, John B., van der Laan, Mark, Petersen, Maya
• Format: Journal Article
• Language: English
• Published: England Cambridge University Press 2023
• Subjects: 21st century; Advancing Translational Science through Real-World Data and Real-World Evidence; Causal inference; Clinical trials; Decision making; estimands; Intervention; machine learning; Observational studies; Product development; real-world evidence; Regulatory agencies; Research Methods and Technology; Review; sensitivity analysis; simulations; Vaccines; United States > US; Causal inference; estimands; real-world evidence; machine learning; sensitivity analysis; simulations

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world da...