TY - JOUR
AU - Miah, Kaya
AU - Goeman, Jelle J
AU - Putter, Hein
AU - Kopp-Schneider, Annette
AU - Benner, Axel
TI - Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data.
JO - Biometrical journal
VL - 67
IS - 6
SN - 0323-3847
CY - Berlin
PB - Wiley-VCH
M1 - DKFZ-2025-02235
SP - e70087
PY - 2025
N1 - #EA:C060#LA:C060#
AB - 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.
KW - Humans
KW - Algorithms
KW - Leukemia, Myeloid, Acute: drug therapy
KW - Models, Statistical
KW - Biometry: methods
KW - Proportional Hazards Models
KW - Computer Simulation
KW - Cox‐type regression (Other)
KW - Markov models (Other)
KW - high‐dimensional data (Other)
KW - regularization (Other)
KW - transition‐specific hazards (Other)
LB - PUB:(DE-HGF)16
C6 - pmid:41146443
C2 - pmc:PMC12559784
DO - DOI:10.1002/bimj.70087
UR - https://inrepo02.dkfz.de/record/305573
ER -