Regression calibration for time-to-event outcomes: mitigating bias due to measurement error in real-world endpoints
Outcome measurement error is important to address when combining trials and RWD, as it may lead to biased results. Our SRC method helps mitigate such bias, improving comparability between real-world and trial endpoints and strengthening evidence of treatment efficacy.
Authors
- Craig S. Meyer (ORCID: https://orcid.org/0000-0002-6655-719X)
- Jocelyn R. Wang
- Jennifer L. Lund (ORCID: https://orcid.org/0000-0002-1108-0689)
- Janick Weberpals (ORCID: https://orcid.org/0000-0003-0404-7394)
- Juned Siddique (ORCID: https://orcid.org/0000-0002-1501-4152)
- Khaled Sarsour
- Benjamin Ackerman (ORCID: https://orcid.org/0000-0003-2522-6623)
- James Roose (ORCID: https://orcid.org/0000-0002-4442-1742)
- Ryan W. Gan (ORCID: https://orcid.org/0000-0002-6111-0380)
- Ashita S. Batavia (ORCID: https://orcid.org/0000-0002-4209-8654)
- Youyi Zhang (ORCID: https://orcid.org/0000-0001-5186-2792)
- Jennifer Hayden
Institutions
- Northwestern University (US)
- University of North Carolina at Chapel Hill (US)
- Brigham and Women's Hospital (US)
- Johnson & Johnson (United States) (US)
- Flatiron Health (United States) (US)
Publication Details
- Journal
- Epidemiologic Methods
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1515/em-2025-0009
- Citations
- 1
- Primary Topic
- Statistical Methods in Clinical Trials
- Type
- article
- Field-Weighted Citation Impact
- 2.63