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| Home > Publications database > Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data. |
| Home > Publications database > Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data. |
| Journal Article | DKFZ-2025-02235 |
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2025
Wiley-VCH
Berlin
Abstract: 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.
Keyword(s): Humans (MeSH) ; Algorithms (MeSH) ; Leukemia, Myeloid, Acute: drug therapy (MeSH) ; Models, Statistical (MeSH) ; Biometry: methods (MeSH) ; Proportional Hazards Models (MeSH) ; Computer Simulation (MeSH) ; Cox‐type regression ; Markov models ; high‐dimensional data ; regularization ; transition‐specific hazards
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; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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