File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Survival analysis of microarray expression data by transformation models

TitleSurvival analysis of microarray expression data by transformation models
Authors
KeywordsMicroarray
Proportional hazards model
Transformation models
Issue Date2005
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cbac
Citation
Computational Biology and Chemistry, 2005, v. 29, p. 91-94 How to Cite?
AbstractMany microarray experiments involve examining the time elapsed prior to the occurrence of a specific event. One purpose of these studies is to relate the gene expressions to the survival times. The Cox proportional hazards model has been the major tool for analyzing such data. The transformation model provides a viable alternative to the classical Cox's model. We investigate the use of transformation models in microarray survival data in this paper. The transformation model, which can be viewed as a generalization of proportional hazards model and the proportional odds model, is more robust than the proportional hazards model, because it is not susceptible to erroneous results for cases when the assumption of proportional hazards is violated. We analyze a gene expression dataset from Beer et al. [Beer, D.G., Kardia, S.L., Huang, C.C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M., Iannettoni, M.D., Orringer, M.B., Hanash, S., 2002. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8 (8), 816–824] and show that the transformation model provides higher prediction precision than the proportional hazards model.
Persistent Identifierhttp://hdl.handle.net/10722/221691
ISSN
2015 Impact Factor: 1.014
2015 SCImago Journal Rankings: 0.491
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXu, J-
dc.contributor.authorYang, Y-
dc.contributor.authorOtt, J-
dc.date.accessioned2015-12-04T15:29:08Z-
dc.date.available2015-12-04T15:29:08Z-
dc.date.issued2005-
dc.identifier.citationComputational Biology and Chemistry, 2005, v. 29, p. 91-94-
dc.identifier.issn1476-9271-
dc.identifier.urihttp://hdl.handle.net/10722/221691-
dc.description.abstractMany microarray experiments involve examining the time elapsed prior to the occurrence of a specific event. One purpose of these studies is to relate the gene expressions to the survival times. The Cox proportional hazards model has been the major tool for analyzing such data. The transformation model provides a viable alternative to the classical Cox's model. We investigate the use of transformation models in microarray survival data in this paper. The transformation model, which can be viewed as a generalization of proportional hazards model and the proportional odds model, is more robust than the proportional hazards model, because it is not susceptible to erroneous results for cases when the assumption of proportional hazards is violated. We analyze a gene expression dataset from Beer et al. [Beer, D.G., Kardia, S.L., Huang, C.C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M., Iannettoni, M.D., Orringer, M.B., Hanash, S., 2002. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8 (8), 816–824] and show that the transformation model provides higher prediction precision than the proportional hazards model.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cbac-
dc.relation.ispartofComputational Biology and Chemistry-
dc.subjectMicroarray-
dc.subjectProportional hazards model-
dc.subjectTransformation models-
dc.titleSurvival analysis of microarray expression data by transformation models-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1016/j.compbiolchem.2005.02.001-
dc.identifier.pmid15833436-
dc.identifier.scopuseid_2-s2.0-16344373053-
dc.identifier.volume29-
dc.identifier.spage91-
dc.identifier.epage94-
dc.identifier.isiWOS:000228764000002-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats