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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
2013 Impact Factor: 1.236
 
DOIhttp://dx.doi.org/10.1002/bimj.200390036
 
ISI Accession Number IDWOS:000184508400008
 
ReferencesReferences in Scopus
 
DC FieldValue
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
2013 Impact Factor: 1.236
 
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
 
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Author Affiliations
  1. Suwon University
  2. The University of North Carolina at Chapel Hill