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Article: Comparing k Cumulative Incidence Functions Through Resampling Methods

TitleComparing k Cumulative Incidence Functions Through Resampling Methods
Authors
Issue Date2002
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, 2002, v. 8 n. 4, p. 401-412 How to Cite?
AbstractTests for the equality of k cumulative incidence functions in a competing risks model are proposed. Test statistics are based on a vector of processes related to the cumulative incidence functions. Since their asymptotic distributions appear very complicated and depend on the underlying distribution of the data, two resampling techniques, namely the well-known bootstrap method and the so-called random symmetrization method, are used to approximate the critical values of the tests. Without making any assumptions on the nature of dependence between the risks, the tests allow one to compare k risks simultaneously for k ≥ 2 under the random censorship model. Tests against ordered alternatives are also considered. Simulation studies indicate that the proposed tests perform very well with moderate sample size. A real application to cancer mortality data is given.
Persistent Identifierhttp://hdl.handle.net/10722/172393
ISSN
2015 Impact Factor: 0.81
2015 SCImago Journal Rankings: 0.717
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYuen, KCen_US
dc.contributor.authorZhu, Len_US
dc.contributor.authorZhang, Den_US
dc.date.accessioned2012-10-30T06:22:19Z-
dc.date.available2012-10-30T06:22:19Z-
dc.date.issued2002en_US
dc.identifier.citationLifetime Data Analysis, 2002, v. 8 n. 4, p. 401-412en_US
dc.identifier.issn1380-7870en_US
dc.identifier.urihttp://hdl.handle.net/10722/172393-
dc.description.abstractTests for the equality of k cumulative incidence functions in a competing risks model are proposed. Test statistics are based on a vector of processes related to the cumulative incidence functions. Since their asymptotic distributions appear very complicated and depend on the underlying distribution of the data, two resampling techniques, namely the well-known bootstrap method and the so-called random symmetrization method, are used to approximate the critical values of the tests. Without making any assumptions on the nature of dependence between the risks, the tests allow one to compare k risks simultaneously for k ≥ 2 under the random censorship model. Tests against ordered alternatives are also considered. Simulation studies indicate that the proposed tests perform very well with moderate sample size. A real application to cancer mortality data is given.en_US
dc.languageengen_US
dc.publisherSpringer Verlag Dordrecht. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1380-7870en_US
dc.relation.ispartofLifetime Data Analysisen_US
dc.subject.meshAnimalsen_US
dc.subject.meshCause Of Deathen_US
dc.subject.meshHong Kong - Epidemiologyen_US
dc.subject.meshMiceen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshNeoplasms, Experimental - Mortalityen_US
dc.subject.meshProbabilityen_US
dc.subject.meshRisk Assessment - Statistics & Numerical Dataen_US
dc.titleComparing k Cumulative Incidence Functions Through Resampling Methodsen_US
dc.typeArticleen_US
dc.identifier.emailYuen, KC: kcyuen@hku.hken_US
dc.identifier.authorityYuen, KC=rp00836en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1023/A:1020575022980en_US
dc.identifier.pmid12471948-
dc.identifier.scopuseid_2-s2.0-0036884316en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036884316&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume8en_US
dc.identifier.issue4en_US
dc.identifier.spage401en_US
dc.identifier.epage412en_US
dc.identifier.isiWOS:000178435000007-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridYuen, KC=7202333703en_US
dc.identifier.scopusauthoridZhu, L=7404201068en_US
dc.identifier.scopusauthoridZhang, D=8672422800en_US

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