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Article: Merits of modelling multivariate survival data using random effects proportional odds model

TitleMerits of modelling multivariate survival data using random effects proportional odds model
Authors
KeywordsConditional heterogeneity
Hazard ratio
Intra-cluster correlation
Mixing distribution
Proportional odds model
Random effects
Issue Date2004
PublisherWiley - V C H Verlag GmbH & Co KGaA. The Journal's web site is located at http://www.interscience.wiley.com/biometricaljournal
Citation
Biometrical Journal, 2004, v. 46 n. 3, p. 331-342 How to Cite?
AbstractRecently, there has been a great deal of interest in the analysis of multivariate survival data. In most epidemiological studies, survival times of the same cluster are related because of some unobserved risk factors such as the environmental or genetic factors. Therefore, modelling of dependence between events of correlated individuals is required to ensure a correct inference on the effects of treatments or covariates on the survival times. In the past decades, extension of proportional hazards model has been widely considered for modelling multivariate survival data by incorporating a random effect which acts multiplicatively on the hazard function. In this article, we consider the proportional odds model, which is an alternative to the proportional hazards model at which the hazard ratio between individuals converges to unity eventually. This is a reasonable property particularly when the treatment effect fades out gradually and the homogeneity of the population increases over time. The objective of this paper is to assess the influence of the random effect on the within-subject correlation and the population heterogeneity. We are particularly interested in the properties of the proportional odds model with univariate random effect and correlated random effect. The correlations between survival times are derived explicitly for both choices of mixing distributions and are shown to be independent of the covariates. The time path of the odds function among the survivors are also examined to study the effect of the choice of mixing distribution. Modelling multivariate survival data using a univariate mixing distribution may be inadequate as the random effect not only characterises the dependence of the survival times, but also the conditional heterogeneity among the survivors. A robust estimate for the correlation of the logarithm of the survival times within a cluster is obtained disregarding the choice of the mixing distributions. The sensitivity of the estimate of the regression parameter under a misspecification of the mixing distribution is studied through simulation. © 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Persistent Identifierhttp://hdl.handle.net/10722/82997
ISSN
2015 Impact Factor: 0.683
2015 SCImago Journal Rankings: 0.784
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KFen_HK
dc.contributor.authorLee, YWen_HK
dc.date.accessioned2010-09-06T08:35:46Z-
dc.date.available2010-09-06T08:35:46Z-
dc.date.issued2004en_HK
dc.identifier.citationBiometrical Journal, 2004, v. 46 n. 3, p. 331-342en_HK
dc.identifier.issn0323-3847en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82997-
dc.description.abstractRecently, there has been a great deal of interest in the analysis of multivariate survival data. In most epidemiological studies, survival times of the same cluster are related because of some unobserved risk factors such as the environmental or genetic factors. Therefore, modelling of dependence between events of correlated individuals is required to ensure a correct inference on the effects of treatments or covariates on the survival times. In the past decades, extension of proportional hazards model has been widely considered for modelling multivariate survival data by incorporating a random effect which acts multiplicatively on the hazard function. In this article, we consider the proportional odds model, which is an alternative to the proportional hazards model at which the hazard ratio between individuals converges to unity eventually. This is a reasonable property particularly when the treatment effect fades out gradually and the homogeneity of the population increases over time. The objective of this paper is to assess the influence of the random effect on the within-subject correlation and the population heterogeneity. We are particularly interested in the properties of the proportional odds model with univariate random effect and correlated random effect. The correlations between survival times are derived explicitly for both choices of mixing distributions and are shown to be independent of the covariates. The time path of the odds function among the survivors are also examined to study the effect of the choice of mixing distribution. Modelling multivariate survival data using a univariate mixing distribution may be inadequate as the random effect not only characterises the dependence of the survival times, but also the conditional heterogeneity among the survivors. A robust estimate for the correlation of the logarithm of the survival times within a cluster is obtained disregarding the choice of the mixing distributions. The sensitivity of the estimate of the regression parameter under a misspecification of the mixing distribution is studied through simulation. © 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.en_HK
dc.languageengen_HK
dc.publisherWiley - V C H Verlag GmbH & Co KGaA. The Journal's web site is located at http://www.interscience.wiley.com/biometricaljournalen_HK
dc.relation.ispartofBiometrical Journalen_HK
dc.subjectConditional heterogeneityen_HK
dc.subjectHazard ratioen_HK
dc.subjectIntra-cluster correlationen_HK
dc.subjectMixing distributionen_HK
dc.subjectProportional odds modelen_HK
dc.subjectRandom effectsen_HK
dc.titleMerits of modelling multivariate survival data using random effects proportional odds modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0323-3847&volume=46&spage=331&epage=342&date=2004&atitle=Merits+of+modelling+multivariate+survival+data+using+random+effects+proportional+odds+modelen_HK
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hken_HK
dc.identifier.authorityLam, KF=rp00718en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/bimj.200210034en_HK
dc.identifier.scopuseid_2-s2.0-26844492891en_HK
dc.identifier.hkuros96530en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-26844492891&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume46en_HK
dc.identifier.issue3en_HK
dc.identifier.spage331en_HK
dc.identifier.epage342en_HK
dc.identifier.isiWOS:000222731300004-
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridLam, KF=8948421200en_HK
dc.identifier.scopusauthoridLee, YW=8948421100en_HK

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