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Article: Cure rate model with mismeasured covariates under transformation

TitleCure rate model with mismeasured covariates under transformation
Authors
KeywordsCure model
Errors-in-variables problem
Proportional hazards model
Semiparametric method
Survival fraction
Issue Date2008
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, 2008, v. 103 n. 482, p. 743-756 How to Cite?
AbstractCure rate models explicitly account for the survival fraction in failure time data. When the covariates are measured with errors, naively treating mismeasured covariates as error-free would cause estimation bias and thus lead to incorrect inference. Under the proportional hazards cure model, we propose a corrected score approach as well as its generalization, and implement a transformation on the mismeasured covariates toward error additivity and/or normality. The corrected score equations can be easily solved through the backfitting procedure, and the biases in the parameter estimates are successfully eliminated. We show that the proposed estimators for the regression coefficients are consistent and asymptotically normal. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a real data set for illustration. © 2008 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/146587
ISSN
2015 Impact Factor: 1.725
2015 SCImago Journal Rankings: 3.447
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMa, Yen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2012-05-02T08:37:13Z-
dc.date.available2012-05-02T08:37:13Z-
dc.date.issued2008en_HK
dc.identifier.citationJournal Of The American Statistical Association, 2008, v. 103 n. 482, p. 743-756en_HK
dc.identifier.issn0162-1459en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146587-
dc.description.abstractCure rate models explicitly account for the survival fraction in failure time data. When the covariates are measured with errors, naively treating mismeasured covariates as error-free would cause estimation bias and thus lead to incorrect inference. Under the proportional hazards cure model, we propose a corrected score approach as well as its generalization, and implement a transformation on the mismeasured covariates toward error additivity and/or normality. The corrected score equations can be easily solved through the backfitting procedure, and the biases in the parameter estimates are successfully eliminated. We show that the proposed estimators for the regression coefficients are consistent and asymptotically normal. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a real data set for illustration. © 2008 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.subjectErrors-in-variables problemen_HK
dc.subjectProportional hazards modelen_HK
dc.subjectSemiparametric methoden_HK
dc.subjectSurvival fractionen_HK
dc.titleCure rate model with mismeasured covariates under transformationen_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.doi10.1198/016214508000000319en_HK
dc.identifier.scopuseid_2-s2.0-49549125190en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-49549125190&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume103en_HK
dc.identifier.issue482en_HK
dc.identifier.spage743en_HK
dc.identifier.epage756en_HK
dc.identifier.eissn1537-274X-
dc.identifier.isiWOS:000257897500032-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridMa, Y=8908626500en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.citeulike2997693-

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