{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T16:20:50Z","timestamp":1789143650170,"version":"build-2803163510"},"reference-count":53,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["514653984"],"award-info":[{"award-number":["514653984"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrical J"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In multi\u2010state models based on high\u2010dimensional 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\u2010driven variable selection by extended regularization methods within multi\u2010state model building. We propose the fused sparse\u2010group lasso (FSGL) penalized Cox\u2010type regression in the framework of multi\u2010state models combining the penalization concepts of pairwise differences of covariate effects along with transition\u2010wise grouping. For optimization, we adapt the alternating direction method of multipliers (ADMM) algorithm to transition\u2010specific hazards regression in the multi\u2010state 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\u2010specific effects and similar cross\u2010transition effects. We investigate settings in which the combined penalty is beneficial compared to global lasso regularization.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Clinical Trial Registration:<\/jats:bold>\n                    The AMLSG 09\u201009 trial is registered with ClinicalTrials.gov (NCT00893399) and has been\u00a0completed.\n                  <\/jats:p>","DOI":"10.1002\/bimj.70087","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T07:31:15Z","timestamp":1761636675000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Variable Selection via Fused Sparse\u2010Group Lasso Penalized Multi\u2010state Models Incorporating Molecular Data"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0265-3529","authenticated-orcid":false,"given":"Kaya","family":"Miah","sequence":"first","affiliation":[{"name":"Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany"},{"name":"Medical Faculty Heidelberg University Heidelberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4283-0259","authenticated-orcid":false,"given":"Jelle J.","family":"Goeman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Sciences Leiden University Medical Center (LUMC) Leiden The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5395-1422","authenticated-orcid":false,"given":"Hein","family":"Putter","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Sciences Leiden University Medical Center (LUMC) Leiden The Netherlands"},{"name":"Mathematical Institute Leiden University Leiden The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1810-0267","authenticated-orcid":false,"given":"Annette","family":"Kopp\u2010Schneider","sequence":"additional","affiliation":[{"name":"Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7238-6956","authenticated-orcid":false,"given":"Axel","family":"Benner","sequence":"additional","affiliation":[{"name":"Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4348-9"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1191\/0962280202SM276ra"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2021.08.012"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13075"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200900064"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3516"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"e_1_2_13_9_1","volume-title":"Algorithms for Minimization Without Derivatives","author":"Brent R. 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