Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis
Investigating the relationship between trajectory patterns and covariates is of interest in many situations. However, it is a challenging task with potential pitfalls. Our Robustness Assessment of Regression using Cluster Analysis Typologies (RARCAT) may assist in ensuring the robustness of such association studies. The method is applicable wherever clustering is combined with regression analysis, so its relevance goes beyond State Sequence Analysis.
Authors
- Emilie Zuercher (ORCID: https://orcid.org/0000-0002-4597-893X)
- Léonard Roth (ORCID: https://orcid.org/0000-0002-5607-7010)
- Isabelle Peytremann‐Bridevaux (ORCID: https://orcid.org/0000-0002-6514-8781)
- Matthias Studer (ORCID: https://orcid.org/0000-0002-6269-1412)
Institutions
- University of Geneva (CH)
- Centre universitaire de médecine générale et santé publique, Lausanne (CH)
- University of Lausanne (CH)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12874-024-02435-8
- Citations
- 2
- Primary Topic
- Statistical Methods and Bayesian Inference
- Type
- article
- Field-Weighted Citation Impact
- 1.42
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Université de Lausanne