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
• ISSN: 2059-8661; 2059-8661
• DOI: 10.1017/cts.2023.635

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 data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.