Innovative statistical method for longitudinal and hierarchical data modeling: the GMEXGBoost method
The GMEXGBoost algorithm, by combining the estimates of the GLMM and XGBoost models, leverages the capabilities of both and delivers improved performance in complex problems. Although it is not universally superior, but demonstrates clear advantages in the analysis of hierarchical and longitudinal datasets with strong correlations. These properties make it a valuable tool for decision-making in healthcare and other domains that involve complex, structured data.
Authors
- Chiara Masci (ORCID: https://orcid.org/0000-0002-9208-3194)
- Farid Zayeri (ORCID: https://orcid.org/0000-0002-7791-8122)
- Reza Homayounfar (ORCID: https://orcid.org/0000-0001-5398-9519)
- Yaser Mehrali (ORCID: https://orcid.org/0000-0002-7234-9039)
- Fariba Asadi
Institutions
- University of Milan (IT)
- National Nutrition and Food Technology Research Institute (IR)
- Fasa University of Medical Sciences (IR)
- Shahid Beheshti University of Medical Sciences (IR)
- Birjand University of Medical Sciences (IR)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2026-01-08
- DOI
- https://doi.org/10.1186/s12874-025-02751-7
- Primary Topic
- Statistical Methods and Bayesian Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00