{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:10:10Z","timestamp":1790151010318,"version":"4.0.1"},"reference-count":36,"publisher":"Wiley","issue":"20-22","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01DK109581"],"award-info":[{"award-number":["R01DK109581"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R21AI172077"],"award-info":[{"award-number":["R21AI172077"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Mixed\u2010type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed\u2010type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed\u2010type outcome, any method used would require a variable selection strategy for high\u2010dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed\u2010type outcome when the covariate space is high dimensional. This study develops a high\u2010dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed\u2010type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure\u2014the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model\u2010X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24\u2009months post\u2010transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed\u2010type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.<\/jats:p>","DOI":"10.1002\/sim.70723","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T10:02:16Z","timestamp":1787911336000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8810-3690","authenticated-orcid":false,"given":"Shuai","family":"Sun","sequence":"first","affiliation":[{"name":"Division of Biostatistics College of Public Health, The Ohio State University  Columbus Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeria R.","family":"Mas","sequence":"additional","affiliation":[{"name":"Surgical Sciences Division, Department of Surgery University of Maryland School of Medicine  College Park Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1555-5781","authenticated-orcid":false,"given":"Kellie J.","family":"Archer","sequence":"additional","affiliation":[{"name":"Division of Biostatistics College of Public Health, The Ohio State University  Columbus Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"key":"e_1_2_12_2_1","volume-title":"Statistics for Censored Environmental Data","author":"Helsel D. 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