%0 Journal Article
%A Miah, Kaya
%A Goeman, Jelle J
%A Putter, Hein
%A Kopp-Schneider, Annette
%A Benner, Axel
%T Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data.
%J Biometrical journal
%V 67
%N 6
%@ 0323-3847
%C Berlin
%I Wiley-VCH
%M DKFZ-2025-02235
%P e70087
%D 2025
%Z #EA:C060#LA:C060#
%X 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.
%K Humans
%K Algorithms
%K Leukemia, Myeloid, Acute: drug therapy
%K Models, Statistical
%K Biometry: methods
%K Proportional Hazards Models
%K Computer Simulation
%K Cox‐type regression (Other)
%K Markov models (Other)
%K high‐dimensional data (Other)
%K regularization (Other)
%K transition‐specific hazards (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:41146443
%2 pmc:PMC12559784
%R 10.1002/bimj.70087
%U https://inrepo02.dkfz.de/record/305573