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Article: High-dimensional cox regression analysis in genetic studies with censored survival outcomes

TitleHigh-dimensional cox regression analysis in genetic studies with censored survival outcomes
Authors
Issue Date2012
PublisherHindawi Publishing Corporation. The Journal's web site is located at http://www.hindawi.com/journals/jps
Citation
Journal of Probability and Statistics, 2012, p. Article no. 478680 How to Cite?
AbstractWith the advancement of high-throughput technologies, nowadays high-dimensional genomic and proteomic data are easy to obtain and have become ever increasingly important in unveiling the complex etiology of many diseases. While relating a large number of factors to a survival outcome through the Cox relative risk model, various techniques have been proposed in the literature. We review some recently developed methods for such analysis. For high-dimensional variable selection in the Cox model with parametric relative risk, we consider the univariate shrinkage method (US) using the lasso penalty and the penalized partial likelihood method using the folded penalties (PPL). The penalization methods are not restricted to the finite-dimensional case. For the high-dimensional (p → ∞, p ≪ n) or ultrahigh-dimensional case (n → ∞, n ≪ p), both the sure independence screening (SIS) method and the extended Bayesian information criterion (EBIC) can be further incorporated into the penalization methods for variable selection. We also consider the penalization method for the Cox model with semiparametric relative risk, and the modified partial least squares method for the Cox model. The comparison of different methods is discussed and numerical examples are provided for the illustration. Finally, areas of further research are presented. Copyright © 2012 Jinfeng Xu.
Persistent Identifierhttp://hdl.handle.net/10722/221680
ISSN
2015 SCImago Journal Rankings: 0.232

 

DC FieldValueLanguage
dc.contributor.authorXu, J-
dc.date.accessioned2015-12-04T15:29:02Z-
dc.date.available2015-12-04T15:29:02Z-
dc.date.issued2012-
dc.identifier.citationJournal of Probability and Statistics, 2012, p. Article no. 478680-
dc.identifier.issn1687-952X-
dc.identifier.urihttp://hdl.handle.net/10722/221680-
dc.description.abstractWith the advancement of high-throughput technologies, nowadays high-dimensional genomic and proteomic data are easy to obtain and have become ever increasingly important in unveiling the complex etiology of many diseases. While relating a large number of factors to a survival outcome through the Cox relative risk model, various techniques have been proposed in the literature. We review some recently developed methods for such analysis. For high-dimensional variable selection in the Cox model with parametric relative risk, we consider the univariate shrinkage method (US) using the lasso penalty and the penalized partial likelihood method using the folded penalties (PPL). The penalization methods are not restricted to the finite-dimensional case. For the high-dimensional (p → ∞, p ≪ n) or ultrahigh-dimensional case (n → ∞, n ≪ p), both the sure independence screening (SIS) method and the extended Bayesian information criterion (EBIC) can be further incorporated into the penalization methods for variable selection. We also consider the penalization method for the Cox model with semiparametric relative risk, and the modified partial least squares method for the Cox model. The comparison of different methods is discussed and numerical examples are provided for the illustration. Finally, areas of further research are presented. Copyright © 2012 Jinfeng Xu.-
dc.languageeng-
dc.publisherHindawi Publishing Corporation. The Journal's web site is located at http://www.hindawi.com/journals/jps-
dc.relation.ispartofJournal of Probability and Statistics-
dc.titleHigh-dimensional cox regression analysis in genetic studies with censored survival outcomes-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1155/2012/478680-
dc.identifier.scopuseid_2-s2.0-84864953107-
dc.identifier.spageArticle no. 478680-

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