Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62284
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorXia, Xen_US
dc.creatorJiang, Ben_US
dc.creatorLi, Jen_US
dc.creatorZhang, Wen_US
dc.date.accessioned2016-12-19T09:00:01Z-
dc.date.available2016-12-19T09:00:01Z-
dc.identifier.issn1380-7870en_US
dc.identifier.urihttp://hdl.handle.net/10397/62284-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© Springer Science+Business Media New York 2015en_US
dc.rightsThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10985-015-9350-zen_US
dc.subjectAccelerated failure time modelen_US
dc.subjectConfounder adjustmenten_US
dc.subjectGene expressionen_US
dc.subjectIndependent screeningen_US
dc.subjectVariable selectionen_US
dc.titleLow-dimensional confounder adjustment and high-dimensional penalized estimation for survival analysisen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage547en_US
dc.identifier.epage569en_US
dc.identifier.volume22en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1007/s10985-015-9350-zen_US
dcterms.abstractHigh-throughput profiling is now common in biomedical research. In this paper we consider the layout of an etiology study composed of a failure time response, and gene expression measurements. In current practice, a widely adopted approach is to select genes according to a preliminary marginal screening and a follow-up penalized regression for model building. Confounders, including for example clinical risk factors and environmental exposures, usually exist and need to be properly accounted for. We propose covariate-adjusted screening and variable selection procedures under the accelerated failure time model. While penalizing the high-dimensional coefficients to achieve parsimonious model forms, our procedure also properly adjust the low-dimensional confounder effects to achieve more accurate estimation of regression coefficients. We establish the asymptotic properties of our proposed methods and carry out simulation studies to assess the finite sample performance. Our methods are illustrated with a real gene expression data analysis where proper adjustment of confounders produces more meaningful results.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationLifetime data analysis, Oct. 2016, v. 22, no. 4, p. 547-569en_US
dcterms.isPartOfLifetime data analysisen_US
dcterms.issued2016-10-
dc.identifier.isiWOS:000384534500004-
dc.identifier.pmid26463818-
dc.identifier.eissn1572-9249en_US
dc.identifier.rosgroupid2015000344-
dc.description.ros2015-2016 > Academic research: refereed > Publication in refereed journalen_US
dc.description.validate202206 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberAMA-0550-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS6586447-
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Jiang_Low-dimensional_Confounder_Adjustment.pdfPre-Published version1.04 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

117
Last Week
0
Last month
Citations as of Mar 24, 2024

Downloads

45
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

17
Last Week
0
Last month
Citations as of Mar 28, 2024

WEB OF SCIENCETM
Citations

16
Last Week
0
Last month
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.