Probabilistic Clustering Using Multivariate Growth Mixture Model in Clinical Settings—A Scleroderma Example
Our method calculates the probability of being a fast progressor at baseline when no FVC and DLCO are observed, then sequentially updates it as more information becomes available. This sequential integration of patient data and classification of her disease trajectory has the potential to improve clinical decisions and ultimately patient outcomes.
Authors
- Scott L. Zeger (ORCID: https://orcid.org/0000-0001-8907-1603)
- Robyn K. Wojeck
- Rachel Wallwork (ORCID: https://orcid.org/0000-0002-0073-7438)
- Ajay M. Shah (ORCID: https://orcid.org/0000-0002-6547-0631)
- Yizhen Xu
- Ji Soo Kim
Institutions
- Johns Hopkins University (US)
- University of Utah (US)
- Johns Hopkins Medicine (US)
- Utah Department of Health (US)
Publication Details
- Journal
- Statistics in Medicine
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1002/sim.70450
- Primary Topic
- Systemic Sclerosis and Related Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00