Examining the Effect of Missing Data and Unmeasured Confounding on External Comparator Studies: Case Studies and Simulations
This exploratory study confirmed external comparator strengths and limitations by quantifying the impact of missing data and unmeasured confounding using case studies and simulations. In particular, missing data in key eligibility criteria were seen to limit the ability to derive the external comparator target analysis population accurately, while simulations demonstrated the magnitude of bias to expect for various settings.
Authors
- Joan Largent
- Theodor Framke (ORCID: https://orcid.org/0000-0002-5222-9072)
- Francesco Pignatti (ORCID: https://orcid.org/0000-0001-9296-2474)
- Wilhelmina E. Hoogendoorn
- Nicolás Ballarini (ORCID: https://orcid.org/0000-0002-3432-8931)
- Gerd Rippin (ORCID: https://orcid.org/0000-0001-6293-8465)
- Chantal Quinten (ORCID: https://orcid.org/0000-0003-2691-2326)
- Douwe Postmus (ORCID: https://orcid.org/0000-0002-9458-7038)
- Héctor Sanz (ORCID: https://orcid.org/0000-0001-6540-8427)
- Eleni Demas
- Lukas M. Aguirre Dávila
Institutions
- European Medicines Agency (NL)
- IQVIA (United Kingdom) (GB)
- Medizinische Hochschule Hannover (DE)
- Paul Ehrlich Institut (DE)
Publication Details
- Journal
- Drug Safety
- Published
- 2024-08-05
- DOI
- https://doi.org/10.1007/s40264-024-01467-9
- Citations
- 5
- Primary Topic
- Statistical Methods in Clinical Trials
- Type
- article
- Field-Weighted Citation Impact
- 3.55
Funders
- European Medicines Agency