Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data.

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

SUBMITTER: Miah K 

PROVIDER: S-EPMC12559784 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature