The effects of missing data due to study dropout on longitudinal analysis inference using outcome-dependent sampling
Dropout is common in longitudinal cohort studies. Investigators utilizing ODS methods must consider the effect of dropout in both the retrospective sampling design and analysis.
Authors
- Kristine Mace Erlandson (ORCID: https://orcid.org/0000-0003-0808-6729)
- Melissa P. Wilson (ORCID: https://orcid.org/0000-0001-9176-3131)
- Camille M. Moore (ORCID: https://orcid.org/0000-0002-7363-8684)
- Samantha MaWhinney (ORCID: https://orcid.org/0000-0002-9818-0059)
Institutions
- Colorado School of Public Health (US)
- National Jewish Health (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- International Journal of Epidemiology
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1093/ije/dyaf150
- Primary Topic
- Statistical Methods and Bayesian Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00