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Article: A semiparametric regression cure model with current status data

TitleA semiparametric regression cure model with current status data
Authors
KeywordsAsymptotically efficient estimator
Cured proportion
Mixture model
Partly linear model
Sieve maximum likelihood estimator
Issue Date2005
PublisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/
Citation
Biometrika, 2005, v. 92 n. 3, p. 573-586 How to Cite?
AbstractThis paper considers the analysis of current status data with a cured proportion in the population using a mixture model that combines a logistic regression formulation for the probability of cure with a semiparametric regression model for the time to occurrence of the event. The semiparametric regression model belongs to the flexible class of partly linear models that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies were carried out to investigate the performance of the proposed method and the model is fitted to a dataset from a study of calcification of the hydrogel intraocular lenses, a complication of cataract treatment. © 2005 Biometrika Trust.
Persistent Identifierhttp://hdl.handle.net/10722/82781
ISSN
2015 Impact Factor: 1.13
2015 SCImago Journal Rankings: 2.801
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_HK
dc.contributor.authorXue, Hen_HK
dc.date.accessioned2010-09-06T08:33:21Z-
dc.date.available2010-09-06T08:33:21Z-
dc.date.issued2005en_HK
dc.identifier.citationBiometrika, 2005, v. 92 n. 3, p. 573-586en_HK
dc.identifier.issn0006-3444en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82781-
dc.description.abstractThis paper considers the analysis of current status data with a cured proportion in the population using a mixture model that combines a logistic regression formulation for the probability of cure with a semiparametric regression model for the time to occurrence of the event. The semiparametric regression model belongs to the flexible class of partly linear models that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies were carried out to investigate the performance of the proposed method and the model is fitted to a dataset from a study of calcification of the hydrogel intraocular lenses, a complication of cataract treatment. © 2005 Biometrika Trust.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://biomet.oxfordjournals.org/en_HK
dc.relation.ispartofBiometrikaen_HK
dc.rightsBiometrika. Copyright © Oxford University Press.en_HK
dc.subjectAsymptotically efficient estimatoren_HK
dc.subjectCured proportionen_HK
dc.subjectMixture modelen_HK
dc.subjectPartly linear modelen_HK
dc.subjectSieve maximum likelihood estimatoren_HK
dc.titleA semiparametric regression cure model with current status dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-3444&volume=92&issue=3&spage=573&epage=586&date=2005&atitle=A+semiparametric+regression+cure+model+with+current+status+dataen_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/biomet/92.3.573en_HK
dc.identifier.scopuseid_2-s2.0-24144438218en_HK
dc.identifier.hkuros115883en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-24144438218&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume92en_HK
dc.identifier.issue3en_HK
dc.identifier.spage573en_HK
dc.identifier.epage586en_HK
dc.identifier.isiWOS:000231524600006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridXue, H=7202517221en_HK
dc.identifier.citeulike303909-

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