Variable selection via fused sparse-group lasso penalized multi-state models incorporating molecular data
In multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model. In particular, linking covariate effects across transitions is needed to conduct joint variable selection. A useful technique to reduce model complexi...
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| Autori principali: | , , , , |
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| Natura: | Article (Journal) |
| Lingua: | inglese |
| Pubblicazione: |
December 2025
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| In: |
Biometrical journal
Year: 2025, Volume: 67, Fascicolo: 6, Pages: 1-20 |
| ISSN: | 1521-4036 |
| DOI: | 10.1002/bimj.70087 |
| Accesso online: | Verlag, kostenfrei, Volltext: https://doi.org/10.1002/bimj.70087 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.70087 
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| Note sull'autore: | Kaya Miah, Jelle J. Goeman, Hein Putter, Annette Kopp-Schneider, Axel Benner |
| Riassunto: | In multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model. In particular, linking covariate effects across transitions is needed to conduct joint variable selection. A useful technique to reduce model complexity is to address homogeneous covariate effects for distinct transitions. We integrate this approach to data-driven variable selection by extended regularization methods within multi-state model building. We propose the fused sparse-group lasso (FSGL) penalized Cox-type regression in the framework of multi-state models combining the penalization concepts of pairwise differences of covariate effects along with transition-wise grouping. For optimization, we adapt the alternating direction method of multipliers (ADMM) algorithm to transition-specific hazards regression in the multi-state setting. In a simulation study and application to acute myeloid leukemia (AML) data, we evaluate the algorithm's ability to select a sparse model incorporating relevant transition-specific effects and similar cross-transition effects. We investigate settings in which the combined penalty is beneficial compared to global lasso regularization. Clinical Trial Registration: The AMLSG 09-09 trial is registered with ClinicalTrials.gov (NCT00893399) and has been completed. |
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| Descrizione del documento: | Zuerst veröffentlicht: 27. Oktober 2025 Gesehen am 26.03.2026 |
| Descrizione fisica: | Online Resource |
| ISSN: | 1521-4036 |
| DOI: | 10.1002/bimj.70087 |