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Article: Semiparametric transformation models for survival data with a cure fraction

TitleSemiparametric transformation models for survival data with a cure fraction
Authors
KeywordsCure model
Linear transformation models
Proportional hazards model
Proportional odds model
Semiparametric efficiency
Issue Date2006
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
Citation
Journal Of The American Statistical Association, 2006, v. 101 n. 474, p. 670-684 How to Cite?
AbstractWe propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations and includes both the proportional ha/ards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators (MLEs). Furthermore, the MLEs for the regression coefficients are shown to be consistent and asymptotically normal, and their asymptotic variances attain the semiparametric efficiency bound. Simulation studies arc conducted to examine the finite-sample properties of the proposed estimators. The method is illustrated on data from a clinical trial involving the treatment of melanoma. © 2006 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/146574
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZeng, Den_HK
dc.contributor.authorYin, Gen_HK
dc.contributor.authorIbrahim, JGen_HK
dc.date.accessioned2012-05-02T08:37:07Z-
dc.date.available2012-05-02T08:37:07Z-
dc.date.issued2006en_HK
dc.identifier.citationJournal Of The American Statistical Association, 2006, v. 101 n. 474, p. 670-684en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146574-
dc.description.abstractWe propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations and includes both the proportional ha/ards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators (MLEs). Furthermore, the MLEs for the regression coefficients are shown to be consistent and asymptotically normal, and their asymptotic variances attain the semiparametric efficiency bound. Simulation studies arc conducted to examine the finite-sample properties of the proposed estimators. The method is illustrated on data from a clinical trial involving the treatment of melanoma. © 2006 American Statistical Association.en_HK
dc.languageengen_US
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=mainen_HK
dc.relation.ispartofJournal of the American Statistical Associationen_HK
dc.subjectCure modelen_HK
dc.subjectLinear transformation modelsen_HK
dc.subjectProportional hazards modelen_HK
dc.subjectProportional odds modelen_HK
dc.subjectSemiparametric efficiencyen_HK
dc.titleSemiparametric transformation models for survival data with a cure fractionen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1198/016214505000001122en_HK
dc.identifier.scopuseid_2-s2.0-33645578210en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33645578210&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume101en_HK
dc.identifier.issue474en_HK
dc.identifier.spage670en_HK
dc.identifier.epage684en_HK
dc.identifier.isiWOS:000238033200022-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZeng, D=8725807700en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridIbrahim, JG=7005341361en_HK
dc.identifier.citeulike644122-

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