Estimating real-world treatment effects in the presence of measurement error and sparse outcome data using propensity score methods
This study showed that PS methods proposed in the literature may not all perform well for individual datasets. The findings produced recommendations for using PS methods in the estimation of real-world treatment effect when the covariate measurement error and sparse outcome data are present.
Authors
- Gordon Prescott (ORCID: https://orcid.org/0000-0002-9156-2361)
- Chris Sutton (ORCID: https://orcid.org/0000-0002-6406-1318)
- Svetlana Tishkovskaya (ORCID: https://orcid.org/0000-0003-3087-6380)
- Jane Burnell (ORCID: https://orcid.org/0000-0002-4601-1632)
- Amitava Banerjee (ORCID: https://orcid.org/0000-0001-8741-3411)
Institutions
- University of Lancashire (GB)
- University College London (GB)
Publication Details
- Journal
- Frontiers in Pharmacology
- Published
- 2026-03-09
- DOI
- https://doi.org/10.3389/fphar.2026.1380586
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute for Health and Care Research
- European Commission