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Article: Marginal Likelihood Estimation for Proportional Odds Models with Right Censored Data

TitleMarginal Likelihood Estimation for Proportional Odds Models with Right Censored Data
Authors
KeywordsCensoring
Marginal likelihood
Monte Carlo method
Proportional odds model
Rank invariant transformation
Issue Date2001
PublisherSpringer Verlag Dordrecht. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1380-7870
Citation
Lifetime Data Analysis, 2001, v. 7 n. 1, p. 39-54 How to Cite?
AbstractOne major aspect in medical research is to relate the survival times of patients with the relevant covariates or explanatory variables. The proportional hazards model has been used extensively in the past decades with the assumption that the covariate effects act multiplicatively on the hazard function, independent of time. If the patients become more homogeneous over time, say the treatment effects decrease with time or fade out eventually, then a proportional odds model may be more appropriate. In the proportional odds model, the odds ratio between patients can be expressed as a function of their corresponding covariate vectors, in which, the hazard ratio between individuals converges to unity in the long run. In this paper, we consider the estimation of the regression parameter for a semiparametric proportional odds model at which the baseline odds function is an arbitrary, non-decreasing function but is left unspecified. Instead of using the exact survival times, only the rank order information among patients is used. A Monte Carlo method is used to approximate the marginal likelihood function of the rank invariant transformation of the survival times which preserves the information about the regression parameter. The method can be applied to other transformation models with censored data such as the proportional hazards model, the generalized probit model or others. The proposed method is applied to the Veteran's Administration lung cancer trial data.
Persistent Identifierhttp://hdl.handle.net/10722/83062
ISSN
2015 Impact Factor: 0.81
2015 SCImago Journal Rankings: 0.717
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_HK
dc.contributor.authorLeung, TLen_HK
dc.date.accessioned2010-09-06T08:36:29Z-
dc.date.available2010-09-06T08:36:29Z-
dc.date.issued2001en_HK
dc.identifier.citationLifetime Data Analysis, 2001, v. 7 n. 1, p. 39-54en_HK
dc.identifier.issn1380-7870en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83062-
dc.description.abstractOne major aspect in medical research is to relate the survival times of patients with the relevant covariates or explanatory variables. The proportional hazards model has been used extensively in the past decades with the assumption that the covariate effects act multiplicatively on the hazard function, independent of time. If the patients become more homogeneous over time, say the treatment effects decrease with time or fade out eventually, then a proportional odds model may be more appropriate. In the proportional odds model, the odds ratio between patients can be expressed as a function of their corresponding covariate vectors, in which, the hazard ratio between individuals converges to unity in the long run. In this paper, we consider the estimation of the regression parameter for a semiparametric proportional odds model at which the baseline odds function is an arbitrary, non-decreasing function but is left unspecified. Instead of using the exact survival times, only the rank order information among patients is used. A Monte Carlo method is used to approximate the marginal likelihood function of the rank invariant transformation of the survival times which preserves the information about the regression parameter. The method can be applied to other transformation models with censored data such as the proportional hazards model, the generalized probit model or others. The proposed method is applied to the Veteran's Administration lung cancer trial data.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag Dordrecht. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1380-7870en_HK
dc.relation.ispartofLifetime Data Analysisen_HK
dc.subjectCensoringen_HK
dc.subjectMarginal likelihooden_HK
dc.subjectMonte Carlo methoden_HK
dc.subjectProportional odds modelen_HK
dc.subjectRank invariant transformationen_HK
dc.titleMarginal Likelihood Estimation for Proportional Odds Models with Right Censored Dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1380-7870&volume=7&spage=39&epage=54&date=2001&atitle=Marginal+likelihood+estimation+for+proportional+odds+models+with+right+censored+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.doi10.1023/A:1009673026121en_HK
dc.identifier.pmid11280846-
dc.identifier.scopuseid_2-s2.0-0035293623en_HK
dc.identifier.hkuros56562en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035293623&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume7en_HK
dc.identifier.issue1en_HK
dc.identifier.spage39en_HK
dc.identifier.epage54en_HK
dc.identifier.isiWOS:000166940100003-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridLeung, TL=7202110906en_HK

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