| Home > Publications database > Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data. > print |
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| 005 | 20251030115607.0 | ||
| 024 | 7 | _ | |a 10.1002/bimj.70087 |2 doi |
| 024 | 7 | _ | |a pmid:41146443 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC12559784 |2 pmc |
| 024 | 7 | _ | |a 0323-3847 |2 ISSN |
| 024 | 7 | _ | |a 0006-3452 |2 ISSN |
| 024 | 7 | _ | |a 1521-4036 |2 ISSN |
| 037 | _ | _ | |a DKFZ-2025-02235 |
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| 100 | 1 | _ | |a Miah, Kaya |0 P:(DE-He78)b97fc5666ea8f9db9ef499de6b2397cf |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data. |
| 260 | _ | _ | |a Berlin |c 2025 |b Wiley-VCH |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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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| 650 | _ | 7 | |a Cox‐type regression |2 Other |
| 650 | _ | 7 | |a Markov models |2 Other |
| 650 | _ | 7 | |a high‐dimensional data |2 Other |
| 650 | _ | 7 | |a regularization |2 Other |
| 650 | _ | 7 | |a transition‐specific hazards |2 Other |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Algorithms |2 MeSH |
| 650 | _ | 2 | |a Leukemia, Myeloid, Acute: drug therapy |2 MeSH |
| 650 | _ | 2 | |a Models, Statistical |2 MeSH |
| 650 | _ | 2 | |a Biometry: methods |2 MeSH |
| 650 | _ | 2 | |a Proportional Hazards Models |2 MeSH |
| 650 | _ | 2 | |a Computer Simulation |2 MeSH |
| 700 | 1 | _ | |a Goeman, Jelle J |0 0000-0003-4283-0259 |b 1 |
| 700 | 1 | _ | |a Putter, Hein |0 0000-0001-5395-1422 |b 2 |
| 700 | 1 | _ | |a Kopp-Schneider, Annette |0 P:(DE-He78)bb6a7a70f976eb8df1769944bf913596 |b 3 |u dkfz |
| 700 | 1 | _ | |a Benner, Axel |0 P:(DE-He78)e15dfa1260625c69d6690a197392a994 |b 4 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1002/bimj.70087 |g Vol. 67, no. 6, p. e70087 |0 PERI:(DE-600)1479920-0 |n 6 |p e70087 |t Biometrical journal |v 67 |y 2025 |x 0323-3847 |
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