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Article: Estimating the proportion of cured patients in a censored sample

TitleEstimating the proportion of cured patients in a censored sample
Authors
KeywordsCured proportion
Mixture models
Multiple imputations
Issue Date2005
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2005, v. 24 n. 12, p. 1865-1879 How to Cite?
AbstractThere has been a recurring interest in modelling survival data which hypothesize subpopulations of individuals highly susceptible to some types of adverse events while other individuals are assumed to be at much less risk, like recurrence of breast cancer. A binary random effect is assumed in this article to model the susceptibility of each individual. We propose a simple multiple imputation algorithm for the analysis of censored data which combines a binary regression formulation for the probability of occurrence of an event, say recurrence of the breast cancer tumour, and a Cox's proportional hazards regression model for the time to occurrence of the event if it does. The model distinguishes the effects of the covariates on the probability of cure and on the time to recurrence of the disease. A SAS macro has been written to implement the proposed multiple imputation algorithm so that sophisticated programming effort can be rendered into a user-friendly application. Simulation results show that the estimates are reasonably efficient. The method is applied to analyse the breast cancer recurrence data. The proposed method can be modified easily to accommodate more general random effects other than the binary random effects so that the random effects not only affect the probability of occurrence of the event, but also the heterogeneity of the time to recurrence of the event among the uncured patients. Copyright © 2005 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/87610
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_HK
dc.contributor.authorFong, DYTen_HK
dc.contributor.authorTang, OYen_HK
dc.date.accessioned2010-09-06T09:32:04Z-
dc.date.available2010-09-06T09:32:04Z-
dc.date.issued2005en_HK
dc.identifier.citationStatistics In Medicine, 2005, v. 24 n. 12, p. 1865-1879en_HK
dc.identifier.issn0277-6715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/87610-
dc.description.abstractThere has been a recurring interest in modelling survival data which hypothesize subpopulations of individuals highly susceptible to some types of adverse events while other individuals are assumed to be at much less risk, like recurrence of breast cancer. A binary random effect is assumed in this article to model the susceptibility of each individual. We propose a simple multiple imputation algorithm for the analysis of censored data which combines a binary regression formulation for the probability of occurrence of an event, say recurrence of the breast cancer tumour, and a Cox's proportional hazards regression model for the time to occurrence of the event if it does. The model distinguishes the effects of the covariates on the probability of cure and on the time to recurrence of the disease. A SAS macro has been written to implement the proposed multiple imputation algorithm so that sophisticated programming effort can be rendered into a user-friendly application. Simulation results show that the estimates are reasonably efficient. The method is applied to analyse the breast cancer recurrence data. The proposed method can be modified easily to accommodate more general random effects other than the binary random effects so that the random effects not only affect the probability of occurrence of the event, but also the heterogeneity of the time to recurrence of the event among the uncured patients. Copyright © 2005 John Wiley & Sons, Ltd.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/en_HK
dc.relation.ispartofStatistics in Medicineen_HK
dc.rightsStatistics in Medicine. Copyright © John Wiley & Sons Ltd.en_HK
dc.subjectCured proportionen_HK
dc.subjectMixture modelsen_HK
dc.subjectMultiple imputationsen_HK
dc.titleEstimating the proportion of cured patients in a censored sampleen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0277-6715&volume=24&spage=1865&epage=1879&date=2005&atitle=Estimating+the+proportion+of+cured+patients+in+a+censored+sampleen_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.emailFong, DYT: dytfong@hku.hken_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.identifier.authorityFong, DYT=rp00253en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/sim.2137en_HK
dc.identifier.pmid15900587-
dc.identifier.scopuseid_2-s2.0-21044436007en_HK
dc.identifier.hkuros98011en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-21044436007&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue12en_HK
dc.identifier.spage1865en_HK
dc.identifier.epage1879en_HK
dc.identifier.isiWOS:000229688600007-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridFong, DYT=35261710300en_HK
dc.identifier.scopusauthoridTang, OY=8615462600en_HK

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