A causal roadmap for generating high-quality real-world evidence
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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2023-01-01
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| Series: | Journal of Clinical and Translational Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2059866123006350/type/journal_article |
| _version_ | 1797659424711507968 |
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| author | Lauren E. Dang Susan Gruber Hana Lee Issa J. Dahabreh Elizabeth A. Stuart Brian D. Williamson Richard Wyss Iván Díaz Debashis Ghosh Emre Kıcıman Demissie Alemayehu Katherine L. Hoffman Carla Y. Vossen Raymond A. Huml Henrik Ravn Kajsa Kvist Richard Pratley Mei-Chiung Shih Gene Pennello David Martin Salina P. Waddy Charles E. Barr Mouna Akacha John B. Buse Mark van der Laan Maya Petersen |
| author_facet | Lauren E. Dang Susan Gruber Hana Lee Issa J. Dahabreh Elizabeth A. Stuart Brian D. Williamson Richard Wyss Iván Díaz Debashis Ghosh Emre Kıcıman Demissie Alemayehu Katherine L. Hoffman Carla Y. Vossen Raymond A. Huml Henrik Ravn Kajsa Kvist Richard Pratley Mei-Chiung Shih Gene Pennello David Martin Salina P. Waddy Charles E. Barr Mouna Akacha John B. Buse Mark van der Laan Maya Petersen |
| author_sort | Lauren E. Dang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:14:51Z |
| format | Article |
| id | doaj.art-1b066ab2c2c342e3b214a06e4b384ce1 |
| institution | Directory Open Access Journal |
| issn | 2059-8661 |
| language | English |
| last_indexed | 2024-03-11T18:14:51Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Clinical and Translational Science |
| spelling | doaj.art-1b066ab2c2c342e3b214a06e4b384ce12023-10-16T09:07:18ZengCambridge University PressJournal of Clinical and Translational Science2059-86612023-01-01710.1017/cts.2023.635A causal roadmap for generating high-quality real-world evidenceLauren E. Dang0https://orcid.org/0000-0002-2967-0855Susan Gruber1Hana Lee2Issa J. Dahabreh3Elizabeth A. Stuart4Brian D. Williamson5https://orcid.org/0000-0002-7024-548XRichard Wyss6Iván Díaz7https://orcid.org/0000-0001-9056-2047Debashis Ghosh8Emre Kıcıman9Demissie Alemayehu10Katherine L. Hoffman11Carla Y. Vossen12Raymond A. Huml13https://orcid.org/0000-0003-3899-0217Henrik Ravn14Kajsa Kvist15Richard Pratley16Mei-Chiung Shih17Gene Pennello18https://orcid.org/0000-0002-9779-1165David Martin19Salina P. Waddy20Charles E. Barr21Mouna Akacha22John B. Buse23https://orcid.org/0000-0002-9723-3876Mark van der Laan24Maya Petersen25Department of Biostatistics, University of California, Berkeley, CA, USATL Revolution, Cambridge, MA, USAOffice of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USACAUSALab, Department of Epidemiology and Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USADepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USABiostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USADivision of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USADivision of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USADepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USAMicrosoft Research, Redmond, WA, USAGlobal Biometrics and Data Management, Pfizer Inc., New York, NY, USADepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USASyneos Health Clinical Solutions, Amsterdam, The NetherlandsSyneos Health Clinical Solutions, Morrisville, NC, USANovo Nordisk, Søborg, DenmarkNovo Nordisk, Søborg, DenmarkAdventHealth Translational Research Institute, Orlando, FL, USACooperative Studies Program Coordinating Center, VA Palo Alto Health Care System, Palo Alto, CA, USA Department of Biomedical Data Science, Stanford University, Stanford, CA, USADivision of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USAGlobal Real World Evidence Group, Moderna, Cambridge, MA, USANational Center for Advancing Translational Sciences, Bethesda, MD, USAGraticule Inc., Newton, MA, USA Adaptic Health Inc., Palo Alto, CA, USANovartis Pharma AG, Basel, SwitzerlandDivision of Endocrinology, Department of Medicine, University of North Carolina, Chapel Hill, NC, USADepartment of Biostatistics, University of California, Berkeley, CA, USADepartment of Biostatistics, University of California, Berkeley, CA, USAIncreasing 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.https://www.cambridge.org/core/product/identifier/S2059866123006350/type/journal_articleCausal inferencereal-world evidencesensitivity analysissimulationsestimandsmachine learning |
| spellingShingle | Lauren E. Dang Susan Gruber Hana Lee Issa J. Dahabreh Elizabeth A. Stuart Brian D. Williamson Richard Wyss Iván Díaz Debashis Ghosh Emre Kıcıman Demissie Alemayehu Katherine L. Hoffman Carla Y. Vossen Raymond A. Huml Henrik Ravn Kajsa Kvist Richard Pratley Mei-Chiung Shih Gene Pennello David Martin Salina P. Waddy Charles E. Barr Mouna Akacha John B. Buse Mark van der Laan Maya Petersen A causal roadmap for generating high-quality real-world evidence Journal of Clinical and Translational Science Causal inference real-world evidence sensitivity analysis simulations estimands machine learning |
| title | A causal roadmap for generating high-quality real-world evidence |
| title_full | A causal roadmap for generating high-quality real-world evidence |
| title_fullStr | A causal roadmap for generating high-quality real-world evidence |
| title_full_unstemmed | A causal roadmap for generating high-quality real-world evidence |
| title_short | A causal roadmap for generating high-quality real-world evidence |
| title_sort | causal roadmap for generating high quality real world evidence |
| topic | Causal inference real-world evidence sensitivity analysis simulations estimands machine learning |
| url | https://www.cambridge.org/core/product/identifier/S2059866123006350/type/journal_article |
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