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Article: Small-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft models

TitleSmall-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft models
Authors
Issue Date2002
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM
Citation
Biometrics, 2002, v. 58 n. 3, p. 612-620 How to Cite?
AbstractIn cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods.
Persistent Identifierhttp://hdl.handle.net/10722/172391
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorTan, Men_US
dc.contributor.authorFang, HBen_US
dc.contributor.authorTian, GLen_US
dc.contributor.authorHoughton, PJen_US
dc.date.accessioned2012-10-30T06:22:18Z-
dc.date.available2012-10-30T06:22:18Z-
dc.date.issued2002en_US
dc.identifier.citationBiometrics, 2002, v. 58 n. 3, p. 612-620en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/172391-
dc.description.abstractIn cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods.en_US
dc.languageengen_US
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOMen_US
dc.relation.ispartofBiometricsen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshAnimalsen_US
dc.subject.meshAntineoplastic Combined Chemotherapy Protocols - Therapeutic Useen_US
dc.subject.meshBayes Theoremen_US
dc.subject.meshBiometryen_US
dc.subject.meshCamptothecin - Analogs & Derivatives - Therapeutic Useen_US
dc.subject.meshDacarbazine - Analogs & Derivatives - Therapeutic Useen_US
dc.subject.meshHumansen_US
dc.subject.meshLongitudinal Studiesen_US
dc.subject.meshMiceen_US
dc.subject.meshNeoplasm Transplantationen_US
dc.subject.meshNeoplasms, Experimental - Drug Therapy - Pathologyen_US
dc.subject.meshXenograft Model Antitumor Assays - Statistics & Numerical Dataen_US
dc.titleSmall-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft modelsen_US
dc.typeArticleen_US
dc.identifier.emailTian, GL: gltian@hku.hken_US
dc.identifier.authorityTian, GL=rp00789en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1111/j.0006-341X.2002.00612.x-
dc.identifier.pmid12229996-
dc.identifier.scopuseid_2-s2.0-0036712379en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036712379&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume58en_US
dc.identifier.issue3en_US
dc.identifier.spage612en_US
dc.identifier.epage620en_US
dc.identifier.isiWOS:000182975800015-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridTan, M=7401464906en_US
dc.identifier.scopusauthoridFang, HB=7402543028en_US
dc.identifier.scopusauthoridTian, GL=25621549400en_US
dc.identifier.scopusauthoridHoughton, PJ=36044344200en_US

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