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000305573 1001_ $$0P:(DE-He78)b97fc5666ea8f9db9ef499de6b2397cf$$aMiah, Kaya$$b0$$eFirst author$$udkfz
000305573 245__ $$aVariable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data.
000305573 260__ $$aBerlin$$bWiley-VCH$$c2025
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000305573 520__ $$aIn 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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000305573 650_7 $$2Other$$aCox‐type regression
000305573 650_7 $$2Other$$aMarkov models
000305573 650_7 $$2Other$$ahigh‐dimensional data
000305573 650_7 $$2Other$$aregularization
000305573 650_7 $$2Other$$atransition‐specific hazards
000305573 650_2 $$2MeSH$$aHumans
000305573 650_2 $$2MeSH$$aAlgorithms
000305573 650_2 $$2MeSH$$aLeukemia, Myeloid, Acute: drug therapy
000305573 650_2 $$2MeSH$$aModels, Statistical
000305573 650_2 $$2MeSH$$aBiometry: methods
000305573 650_2 $$2MeSH$$aProportional Hazards Models
000305573 650_2 $$2MeSH$$aComputer Simulation
000305573 7001_ $$00000-0003-4283-0259$$aGoeman, Jelle J$$b1
000305573 7001_ $$00000-0001-5395-1422$$aPutter, Hein$$b2
000305573 7001_ $$0P:(DE-He78)bb6a7a70f976eb8df1769944bf913596$$aKopp-Schneider, Annette$$b3$$udkfz
000305573 7001_ $$0P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aBenner, Axel$$b4$$eLast author$$udkfz
000305573 773__ $$0PERI:(DE-600)1479920-0$$a10.1002/bimj.70087$$gVol. 67, no. 6, p. e70087$$n6$$pe70087$$tBiometrical journal$$v67$$x0323-3847$$y2025
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