Reducing selection bias while maintaining precision through an integrated analysis: 2-year longitudinal analysis of imaging outcomes in the SPondyloArthritis Caught Early cohort
Multilevel models retain level variability by integrating information across patients, time points, readers and rounds to reduce selection bias and improve precision of change, which would otherwise be lost in simpler models.
Authors
- Inger Jorid Berg (ORCID: https://orcid.org/0000-0002-8605-5488)
- R. B. M. Landewé
- Liese J. E. de Bruin (ORCID: https://orcid.org/0000-0001-5106-0175)
- F. van Gaalen (ORCID: https://orcid.org/0000-0001-8448-7407)
- M. van Lunteren (ORCID: https://orcid.org/0000-0002-0601-4863)
- Désirée M F M van der Heijde (ORCID: https://orcid.org/0000-0002-5781-158X)
- Carl Turesson
- Mary Lucy Marques
- Monique Reijnierse
- Manouk de Hooge
- Sofia Ramiro
- Roberta Ramonda
Institutions
- University of Padua (IT)
- Lund University (SE)
- Leiden University Medical Center (NL)
- Ghent University Hospital (BE)
- Diakonhjemmet Hospital (NO)
- Zuyderland Medisch Centrum (NL)
- Amsterdam University Medical Centers (NL)
- Escola Superior de Enfermagem de Coimbra (PT)
- University of Coimbra (PT)
Publication Details
- Journal
- RMD Open
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1136/rmdopen-2025-006634
- Primary Topic
- Spondyloarthritis Studies and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00