{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:26:15Z","timestamp":1759335975492},"reference-count":49,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2015,9,29]],"date-time":"2015-09-29T00:00:00Z","timestamp":1443484800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biometrical J"],"published-print":{"date-parts":[[2015,11]]},"abstract":"<jats:p>One important task in translational cancer research is the search for new prognostic biomarkers to improve survival prognosis for patients. The use of high\u2010throughput technologies allows simultaneous measurement of genome\u2010wide gene expression or other genomic data for all patients in a clinical trial. Penalized likelihood methods such as lasso regression can be applied to such high\u2010dimensional data, where the number of (genomic) covariables is usually much larger than the sample size. There is a connection between the lasso and the Bayesian regression model with independent Laplace priors on the regression parameters, and understanding this connection has been useful for understanding the properties of lasso estimates in linear models (e.g. Park and Casella,<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"#bimj1635-bib-0039\"\/>). In this paper, we study the lasso in the frequentist and Bayesian frameworks in the context of Cox models. For the Bayesian lasso we extend the approach by Lee et\u00a0al. (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"#bimj1635-bib-0032\"\/>). In particular, we impose the lasso penalty only on the genome features, but not on relevant clinical covariates, to allow the mandatory inclusion of important established factors. We investigate the models in high\u2010 and low\u2010dimensional simulation settings and in an application to chronic lymphocytic leukemia.<\/jats:p>","DOI":"10.1002\/bimj.201400160","type":"journal-article","created":{"date-parts":[[2015,9,29]],"date-time":"2015-09-29T19:04:41Z","timestamp":1443553481000},"page":"959-981","source":"Crossref","is-referenced-by-count":5,"title":["Nonidentical twins: Comparison of frequentist and Bayesian lasso for Cox models"],"prefix":"10.1002","volume":"57","author":[{"given":"Manuela","family":"Zucknick","sequence":"first","affiliation":[{"name":"Division of Biostatistics German Cancer Research Center Heidelberg 69120 Germany"},{"name":"Oslo Center for Biostatistics and Epidemiology Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo PO Box 1122 Blindern 0317 Oslo Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maral","family":"Saadati","sequence":"additional","affiliation":[{"name":"Division of Biostatistics German Cancer Research Center Heidelberg 69120 Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Axel","family":"Benner","sequence":"additional","affiliation":[{"name":"Division of Biostatistics German Cancer Research Center Heidelberg 69120 Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2015,9,29]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1111\/j.2517-6161.1974.tb00989.x","article-title":"Scale mixtures of normal distributions","volume":"36","author":"Andrews D.","year":"1974","journal-title":"Journal of the Royal Statistical Society, Series B"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2059"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.2202\/1544-6115.1346"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","unstructured":"Bogdan M. Berg E. v. d. Sabatti C. Su W.andCandes E. J. (2015).SLOPE \u2013 adaptive variable selection via convex optimization.Annals of Applied Statistics ArXiv preprint arXiv:1407.3824.","DOI":"10.1214\/15-AOAS842"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm305"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-413"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2"},{"key":"e_1_2_10_10_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1111\/j.2517-6161.1981.tb01150.x","article-title":"Empirical Bayes analysis for survival time data","volume":"43","author":"Burridge J.","year":"1981","journal-title":"Journal of the Royal Statistical Society, Series B"},{"key":"e_1_2_10_11_1","first-page":"73","volume-title":"AISTATS","author":"Carvalho C.","year":"2009"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/2.4.485"},{"key":"e_1_2_10_13_1","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life tables (with discussion)","volume":"74","author":"Cox D.","year":"1972","journal-title":"Journal of the Royal Statistical Society B"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/62.2.269"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2007-06-094029"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2010-09-304881"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10476353"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5681"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200900028"},{"key":"e_1_2_10_21_1","unstructured":"Goeman J. Meijer R.andChaturvedi N. (2012).penalized:L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model.http:\/\/CRAN.R\u2010project.org\/package=penalized.Rpackage version 0.9\u201342."},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19990915\/30)18:17\/18<2529::AID-SIM274>3.0.CO;2-5"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-BA507"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.993077"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(10)61381-5"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asp047"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1982.03320430047030"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/0470011815.b2a11006"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/4.2.249"},{"key":"e_1_2_10_30_1","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/j.2517-6161.1978.tb01666.x","article-title":"Nonparametric Bayesian analysis of survival time data","volume":"40","author":"Kalbfleisch J.","year":"1978","journal-title":"Journal of the Royal Statistical Society, Series B"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.2307\/2685466"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200800228"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.2202\/1557-4679.1301"},{"key":"e_1_2_10_34_1","unstructured":"Lee K. Chakraborty S.andSun J. (2013).psbcGroup:Penalized semi\u2010parametric Bayesian Cox (PSBC) models with shrinkage and grouping priors.http:\/\/CRAN.R\u2010project.org\/package=psbcGroup.Rpackage version 1.0."},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.leu.2403099"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1214\/009053606000000281"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1988.10478694"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v050.i11"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-BA403"},{"key":"e_1_2_10_40_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000337"},{"key":"e_1_2_10_41_1","first-page":"7","article-title":"CODA: convergence diagnosis and output analysis for MCMC","volume":"6","author":"Plummer M.","year":"2006","journal-title":"R News"},{"key":"e_1_2_10_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-00032-9_35"},{"key":"e_1_2_10_43_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v039.i05"},{"key":"e_1_2_10_44_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani R.","year":"1996","journal-title":"Journal of the Royal Statistical Society B"},{"key":"e_1_2_10_45_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_46_1","unstructured":"Tierney L. Rossini A. Li N.andSevcikova H. (2013).snow: Simple Network of Workstations.http:\/\/CRAN.R\u2010project.org\/package=snow. R package version 0.3\u201313."},{"key":"e_1_2_10_47_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2353"},{"key":"e_1_2_10_48_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780122407"},{"key":"e_1_2_10_49_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780132307"},{"key":"e_1_2_10_50_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"}],"container-title":["Biometrical Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fbimj.201400160","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/bimj.201400160","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T13:05:24Z","timestamp":1718111124000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/bimj.201400160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,9,29]]},"references-count":49,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2015,11]]}},"alternative-id":["10.1002\/bimj.201400160"],"URL":"https:\/\/doi.org\/10.1002\/bimj.201400160","archive":["Portico"],"relation":{},"ISSN":["0323-3847","1521-4036"],"issn-type":[{"value":"0323-3847","type":"print"},{"value":"1521-4036","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,9,29]]}}}