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Commonly employed penalized Cox regression considers each event type separately through cause\u2010specific models, neglecting possibly shared information between them. We adapt the feature\u2010weighted elastic net (fwelnet), an elastic net generalization, to survival outcomes and competing risks. For two causes, our proposed algorithm fits two alternating cause\u2010specific models, where each model receives the coefficient vector of the complementary model as prior information. We dub this \u201ccooperative penalized regression,\u201d as it enables the modeling of competing risk data with cause\u2010specific models while accounting for shared effects between causes. Coefficients that are shrunken toward zero in the model for the first cause will receive larger penalization weights in the model for the second cause and vice versa. Through multiple iterations, this process ensures stronger penalization of uninformative predictors in both models. We demonstrate our method's variable selection capabilities on simulated genomics data and apply it to bladder cancer microarray data. We evaluate selection performance using the positive predictive value for the correct selection of informative features and the false positive rate for the selection of uninformative variables. The benchmark compares results with cause\u2010specific penalized Cox regression, random survival forests, and likelihood\u2010boosted Cox regression. Results indicate that our approach is more effective at selecting informative features and removing uninformative features. In settings without shared effects, variable selection performance is similar to cause\u2010specific penalized Cox\u00a0regression.<\/jats:p>","DOI":"10.1002\/bimj.70036","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T06:33:47Z","timestamp":1739946827000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["High\u2010Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7528-3795","authenticated-orcid":false,"given":"Lukas","family":"Burk","sequence":"first","affiliation":[{"name":"Leibniz Institute for Prevention Research and Epidemiology \u2010 BIPS  Bremen Germany"},{"name":"Department of Statistics Ludwig\u2010Maximilians\u2010Universit\u00e4t M\u00fcnchen  M\u00fcnchen Germany"},{"name":"Faculty of Mathematics and Computer Science University of Bremen  Bremen Germany"},{"name":"Munich Center for Machine Learning (MCML)  Munchen Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[{"name":"Department of Statistics Ludwig\u2010Maximilians\u2010Universit\u00e4t M\u00fcnchen  M\u00fcnchen Germany"},{"name":"Munich Center for Machine Learning (MCML)  Munchen Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marvin N.","family":"Wright","sequence":"additional","affiliation":[{"name":"Leibniz Institute for Prevention Research and Epidemiology \u2010 BIPS  Bremen Germany"},{"name":"Department of Statistics Ludwig\u2010Maximilians\u2010Universit\u00e4t M\u00fcnchen  M\u00fcnchen Germany"},{"name":"Department of Public Health University of Copenhagen  Kobenhavn Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxw017"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"Bender A. andF.Scheipl.2018. \u201cPammtools: Piece\u2010Wise Exponential Additive Mixed Modeling Tools.\u201d Preprint arxiv: 1806.01042.https:\/\/arxiv.org\/abs\/1806.01042.","DOI":"10.32614\/CRAN.package.pammtools"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2059"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3516"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp088"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.2202\/1544\u20106115.1346"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102105\u20109\u201014"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx124"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1158\/1078\u20100432.CCR\u201004\u20102095"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781138384484"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxu010"},{"key":"e_1_2_8_15_1","unstructured":"Ishwaran H. andU.Kogalur.2023.Fast Unified Random Forests for Survival Regression and Classification (RF\u2010SRC). 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