% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Miah:305573,
      author       = {K. Miah$^*$ and J. J. Goeman and H. Putter and A.
                      Kopp-Schneider$^*$ and A. Benner$^*$},
      title        = {{V}ariable {S}election via {F}used {S}parse-{G}roup {L}asso
                      {P}enalized {M}ulti-state {M}odels {I}ncorporating
                      {M}olecular {D}ata.},
      journal      = {Biometrical journal},
      volume       = {67},
      number       = {6},
      issn         = {0323-3847},
      address      = {Berlin},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2025-02235},
      pages        = {e70087},
      year         = {2025},
      note         = {#EA:C060#LA:C060#},
      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
                      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.},
      keywords     = {Humans / Algorithms / Leukemia, Myeloid, Acute: drug
                      therapy / Models, Statistical / Biometry: methods /
                      Proportional Hazards Models / Computer Simulation /
                      Cox‐type regression (Other) / Markov models (Other) /
                      high‐dimensional data (Other) / regularization (Other) /
                      transition‐specific hazards (Other)},
      cin          = {C060},
      ddc          = {570},
      cid          = {I:(DE-He78)C060-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41146443},
      pmc          = {pmc:PMC12559784},
      doi          = {10.1002/bimj.70087},
      url          = {https://inrepo02.dkfz.de/record/305573},
}