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Article: Quantile inference with multivariate failure time data

TitleQuantile inference with multivariate failure time data
Authors
KeywordsBootstrap
Kernel smoothing
Multivariate failure times
Quantile estimation
Issue Date2003
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, 2003, v. 45 n. 5, p. 602-617 How to Cite?
AbstractQuantites, especially the medians, of survival times are often used as summary statistics to compare the survival experiences between different groups. Quantiles are robust against outliers and preferred over the mean. Multivariate failure time data often arise in biomedical research. For example, in clinical trials, each patient in the study may experience multiple events which may be of the same type or distinct types, while in family studies of genetic diseases or litter matched mice studies, failure times for subjects in the same cluster may be correlated. In this article, we propose nonparametric procedures for the estimation of quantiles with multivariate failure time data. We show that the proposed estimators asymptotically follow a multivariate normal distribution. The asymptotic variance-covariance matrix of the estimated quantiles is estimated based on the kernel smoothing and bootstrap techniques. Simulation results show that the proposed estimators perform well in finite samples. The methods are illustrated with the burn-wound infection data and the Diabetic Retinopathy Study (DRS) data.
Persistent Identifierhttp://hdl.handle.net/10722/146556
ISSN
2015 Impact Factor: 0.683
2015 SCImago Journal Rankings: 0.784
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorCai, Jen_HK
dc.contributor.authorKim, Jen_HK
dc.date.accessioned2012-05-02T08:36:58Z-
dc.date.available2012-05-02T08:36:58Z-
dc.date.issued2003en_HK
dc.identifier.citationBiometrical Journal, 2003, v. 45 n. 5, p. 602-617en_HK
dc.identifier.issn0323-3847en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146556-
dc.description.abstractQuantites, especially the medians, of survival times are often used as summary statistics to compare the survival experiences between different groups. Quantiles are robust against outliers and preferred over the mean. Multivariate failure time data often arise in biomedical research. For example, in clinical trials, each patient in the study may experience multiple events which may be of the same type or distinct types, while in family studies of genetic diseases or litter matched mice studies, failure times for subjects in the same cluster may be correlated. In this article, we propose nonparametric procedures for the estimation of quantiles with multivariate failure time data. We show that the proposed estimators asymptotically follow a multivariate normal distribution. The asymptotic variance-covariance matrix of the estimated quantiles is estimated based on the kernel smoothing and bootstrap techniques. Simulation results show that the proposed estimators perform well in finite samples. The methods are illustrated with the burn-wound infection data and the Diabetic Retinopathy Study (DRS) data.en_HK
dc.languageengen_US
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.subjectBootstrapen_HK
dc.subjectKernel smoothingen_HK
dc.subjectMultivariate failure timesen_HK
dc.subjectQuantile estimationen_HK
dc.titleQuantile inference with multivariate failure time dataen_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.doi10.1002/bimj.200390036en_HK
dc.identifier.scopuseid_2-s2.0-0043071132en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0043071132&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume45en_HK
dc.identifier.issue5en_HK
dc.identifier.spage602en_HK
dc.identifier.epage617en_HK
dc.identifier.isiWOS:000184508400008-
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridCai, J=7403153136en_HK
dc.identifier.scopusauthoridKim, J=13411104100en_HK

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