Article: Quantile inference with multivariate failure time data

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TitleQuantile inference with multivariate failure time data
AuthorsYin, G2
Cai, J2
Kim, J1
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
CitationBiometrical Journal, 2003, v. 45 n. 5, p. 602-617 [How to Cite?]
DOI: http://dx.doi.org/10.1002/bimj.200390036
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.
ISSN0323-3847
2011 Impact Factor: 1.252
2011 SCImago Journal Rankings: 0.137
DOIhttp://dx.doi.org/10.1002/bimj.200390036
ISI Accession Number IDWOS:000184508400008
ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorYin, G
dc.contributor.authorCai, J
dc.contributor.authorKim, J
dc.date.accessioned2012-05-02T08:36:58Z
dc.date.available2012-05-02T08:36:58Z
dc.date.issued2003
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.
dc.description.natureLink_to_subscribed_fulltext
dc.identifier.citationBiometrical Journal, 2003, v. 45 n. 5, p. 602-617 [How to Cite?]
DOI: http://dx.doi.org/10.1002/bimj.200390036
dc.identifier.doihttp://dx.doi.org/10.1002/bimj.200390036
dc.identifier.epage617
dc.identifier.isiWOS:000184508400008
dc.identifier.issn0323-3847
2011 Impact Factor: 1.252
2011 SCImago Journal Rankings: 0.137
dc.identifier.issue5
dc.identifier.scopuseid_2-s2.0-0043071132
dc.identifier.spage602
dc.identifier.urihttp://hdl.handle.net/10722/146556
dc.identifier.volume45
dc.languageeng
dc.publisherWiley - V C H Verlag GmbH & Co KGaA. The Journal's web site is located at http://www.interscience.wiley.com/biometricaljournal
dc.publisher.placeGermany
dc.relation.ispartofBiometrical Journal
dc.relation.referencesReferences in Scopus
dc.subjectBootstrap
dc.subjectKernel smoothing
dc.subjectMultivariate failure times
dc.subjectQuantile estimation
dc.titleQuantile inference with multivariate failure time data
dc.typeArticle
Author Affiliations
  1. Suwon University
  2. The University of North Carolina at Chapel Hill