Covariate adjustment in the analysis of microarray data from clinical studies

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dc.contributor.author Chinnaiyan, Arul M. en_US
dc.contributor.author Ghosh, Debashis en_US
dc.date.accessioned 2006-09-11T19:34:16Z
dc.date.available 2006-09-11T19:34:16Z
dc.date.issued 2005-01 en_US
dc.identifier.citation Ghosh, Debashis; Chinnaiyan, Arul M.; (2005). "Covariate adjustment in the analysis of microarray data from clinical studies." Functional & Integrative Genomics 5(1): 18-27. <http://hdl.handle.net/2027.42/47936> en_US
dc.identifier.issn 1438-7948 en_US
dc.identifier.issn 1438-793X en_US
dc.identifier.uri http://hdl.handle.net/2027.42/47936
dc.identifier.uri http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15378393&dopt=citation en_US
dc.description.abstract There is tremendous scientific interest in the analysis of gene expression data in clinical settings, such as oncology. In this paper, we describe the importance of adjusting for confounders and other prognostic factors in order to select for differentially expressed genes for follow-up validation studies. We develop two approaches to the analysis of microarray data in non-randomized clinical settings. The first is an extension of the current significance analysis of microarray procedures, where other covariates are taken into account. The second is a novel covariate-adjusted regression modelling based on the receiver operating characteristic (ROC) curve for the analysis of gene expression data. The ideas are illustrated using data from a prostate cancer molecular profiling study. en_US
dc.format.extent 168585 bytes
dc.format.extent 3115 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso en_US
dc.publisher Springer-Verlag en_US
dc.subject.other Gene Expression en_US
dc.subject.other Simultaneous Inference en_US
dc.subject.other Differential Expression en_US
dc.subject.other LifeSciences en_US
dc.subject.other Multiple Comparisons en_US
dc.title Covariate adjustment in the analysis of microarray data from clinical studies en_US
dc.type Original Paper en_US
dc.subject.hlbsecondlevel Genetics en_US
dc.subject.hlbtoplevel Health Sciences en_US
dc.description.peerreviewed Peer Reviewed en_US
dc.contributor.affiliationum Departments of Pathology and Urology, University of Michigan, 1301 Catherine Road, Ann Arbor, MI, 48109-1063, USA en_US
dc.contributor.affiliationum Department of Biostatistics, School of Public Health, University of Michigan, Room M4057, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA en_US
dc.contributor.affiliationumcampus Ann Arbor en_US
dc.identifier.pmid 15378393 en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/47936/1/10142_2004_Article_120.pdf en_US
dc.identifier.doi http://dx.doi.org/10.1007/s10142-004-0120-3 en_US
dc.identifier.source Functional & Integrative Genomics en_US
dc.owningcollname Interdisciplinary and Peer-Reviewed
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