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Article: Efficiency improvement in a class of survival models through model-free covariate incorporation

TitleEfficiency improvement in a class of survival models through model-free covariate incorporation
Authors
KeywordsAugmented equation
Covariate adjustment
Efficient estimator
Non proportional hazards
Pseudo maximum likelihood
Issue Date2011
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, 2011, v. 17 n. 4, p. 552-565 How to Cite?
AbstractIn randomized clinical trials, we are often concerned with comparing two-sample survival data. Although the log-rank test is usually suitable for this purpose, it may result in substantial power loss when the two groups have nonproportional hazards. In a more general class of survival models of Yang and Prentice (Biometrika 92:1-17, 2005), which includes the log-rank test as a special case, we improve model efficiency by incorporating auxiliary covariates that are correlated with the survival times. In a model-free form, we augment the estimating equation with auxiliary covariates, and establish the efficiency improvement using the semiparametric theories in Zhang et al. (Biometrics 64:707-715, 2008) and Lu and Tsiatis (Biometrics, 95:674-679, 2008). Under minimal assumptions, our approach produces an unbiased, asymptotically normal estimator with additional efficiency gain. Simulation studies and an application to a leukemia study show the satisfactory performance of the proposed method. © 2011 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/139712
ISSN
2015 Impact Factor: 0.81
2015 SCImago Journal Rankings: 0.717
ISI Accession Number ID
Funding AgencyGrant Number
US NSFDMS-0906341
National Science Foundation
Research Grants Council of Hong Kong
Funding Information:

The authors thank two referees for their very insightful comments and suggestions which led to considerable improvement of this paper. This project was supported in part by a US NSF grant DMS-0906341, the National Science Foundation Bridge to Doctorate Fellowship and a grant from the Research Grants Council of Hong Kong. This research is a component of Tanya Garcia's doctoral dissertation.

References

 

DC FieldValueLanguage
dc.contributor.authorGarcia, TPen_HK
dc.contributor.authorMa, Yen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2011-09-23T05:54:44Z-
dc.date.available2011-09-23T05:54:44Z-
dc.date.issued2011en_HK
dc.identifier.citationLifetime Data Analysis, 2011, v. 17 n. 4, p. 552-565en_HK
dc.identifier.issn1380-7870en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139712-
dc.description.abstractIn randomized clinical trials, we are often concerned with comparing two-sample survival data. Although the log-rank test is usually suitable for this purpose, it may result in substantial power loss when the two groups have nonproportional hazards. In a more general class of survival models of Yang and Prentice (Biometrika 92:1-17, 2005), which includes the log-rank test as a special case, we improve model efficiency by incorporating auxiliary covariates that are correlated with the survival times. In a model-free form, we augment the estimating equation with auxiliary covariates, and establish the efficiency improvement using the semiparametric theories in Zhang et al. (Biometrics 64:707-715, 2008) and Lu and Tsiatis (Biometrics, 95:674-679, 2008). Under minimal assumptions, our approach produces an unbiased, asymptotically normal estimator with additional efficiency gain. Simulation studies and an application to a leukemia study show the satisfactory performance of the proposed method. © 2011 Springer Science+Business Media, LLC.en_HK
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_HK
dc.relation.ispartofLifetime Data Analysisen_HK
dc.rightsThe original publication is available at www.springerlink.comen_US
dc.subjectAugmented equationen_HK
dc.subjectCovariate adjustmenten_HK
dc.subjectEfficient estimatoren_HK
dc.subjectNon proportional hazardsen_HK
dc.subjectPseudo maximum likelihooden_HK
dc.subject.meshAntibodies, Monoclonal, Murine-Derived - therapeutic use-
dc.subject.meshAntineoplastic Agents - therapeutic use-
dc.subject.meshClinical Trials as Topic - methods-
dc.subject.meshKaplan-Meier Estimate-
dc.subject.meshModels, Statistical-
dc.titleEfficiency improvement in a class of survival models through model-free covariate incorporationen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10985-011-9195-zen_HK
dc.identifier.pmid21455700-
dc.identifier.scopuseid_2-s2.0-80052839255en_HK
dc.identifier.hkuros195629en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80052839255&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume17en_HK
dc.identifier.issue4en_HK
dc.identifier.spage552en_HK
dc.identifier.epage565en_HK
dc.identifier.isiWOS:000294964000005-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridGarcia, TP=37055617000en_HK
dc.identifier.scopusauthoridMa, Y=8908626500en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.citeulike9119620-

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