{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:21:10Z","timestamp":1784031670091,"version":"3.55.0"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11701021"],"award-info":[{"award-number":["11701021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11971001"],"award-info":[{"award-number":["11971001"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,1,15]]},"abstract":"<jats:p>Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates the patients' molecular profiles with the patients' survival. With complex relationships between survival and high\u2010dimensional molecular predictors, it is challenging to conduct nonparametric modeling and irrelevant predictors removing simultaneously. In this article, we build a kernel Cox proportional hazards semi\u2010parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and nonparametric predictors through a LASSO penalty. An efficient high\u2010dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict the patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes.<\/jats:p>","DOI":"10.1002\/sim.9938","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T02:37:56Z","timestamp":1698201476000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Kernel Cox partially linear regression: Building predictive models for cancer patients' survival"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5200-9723","authenticated-orcid":false,"given":"Yaohua","family":"Rong","sequence":"first","affiliation":[{"name":"Faculty of Science Beijing University of Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sihai Dave","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Statistics University of Illinois at Urbana\u2010Champaign  Champaign Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Zheng","sequence":"additional","affiliation":[{"name":"Faculty of Science Beijing University of Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of Michigan  Ann Arbor Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10552-005-0271-0"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2007.09.004"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1015362185"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.2202\/1544-6115.1423"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2011.08.002"},{"key":"e_1_2_10_8_1","volume-title":"Generalized Additive Models","author":"Hastie T","year":"1990"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-AOS780"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2013.02.003"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2007.00799.x"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-9-292"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2011.01598.x"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2012.681213"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/gepi.21993"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.4310\/SII.2018.v11.n4.a2"},{"issue":"4","key":"e_1_2_10_17_1","first-page":"573","article-title":"A more accurate estimation of semiparametric logistic regression","volume":"11","author":"Zheng X","year":"2021","journal-title":"Mathematics"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/10485250601027042"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1214\/009053606000000722"},{"issue":"12","key":"e_1_2_10_20_1","first-page":"65","article-title":"Kernel Cox regression models for linking gene expression profiles to censored survival data","volume":"8","author":"Li H","year":"2003","journal-title":"Pac Symp Biocomput"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7681"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1159\/000312641"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511543241"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1162\/153244302760185252"},{"key":"e_1_2_10_26_1","volume-title":"Learning with Kernels","author":"Sch\u00f6lkopf B","year":"2002"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2011.05.029"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/0022-247X(71)90184-3"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1214\/07-AOAS131"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"issue":"4","key":"e_1_2_10_31_1","first-page":"1","article-title":"BB: An R package for solving a large system of nonlinear equations and for optimizing a high\u2010dimensional nonlinear objective function","volume":"32","author":"Varadhan R","year":"2010","journal-title":"J Stat Softw"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2353"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v039.i05"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4154"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2005.030814.x"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2009-12-261032"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2021.107453"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1093\/hmg\/dds486"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1420159112"}],"container-title":["Statistics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sim.9938","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T09:39:30Z","timestamp":1704274770000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/sim.9938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,24]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,15]]}},"alternative-id":["10.1002\/sim.9938"],"URL":"https:\/\/doi.org\/10.1002\/sim.9938","archive":["Portico"],"relation":{},"ISSN":["0277-6715","1097-0258"],"issn-type":[{"value":"0277-6715","type":"print"},{"value":"1097-0258","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,24]]},"assertion":[{"value":"2023-02-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-03","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}