Accounting for missing data in public health research using a synthesis of statistical and mathematical models
Positivity violations pose a threat to quantitative medical research, and standard approaches to addressing non-positivity rely on restrictive untestable assumptions. Using a synthesis model, like the one detailed here, offers a viable alternative.
Authors
- Edwards Jg
- Erin Clancey (ORCID: https://orcid.org/0000-0003-4728-4023)
- Paul N Zivich
- Bonnie E Shook-Sa
Institutions
- University of North Carolina at Chapel Hill (US)
- University of Oxford (GB)
- Washington State University (US)
Publication Details
- Journal
- Journal of Epidemiology & Community Health
- Published
- 2026-01-09
- DOI
- https://doi.org/10.1136/jech-2025-224750
- Primary Topic
- Statistical Methods and Bayesian Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Cancer Research UK
- National Institutes of Health