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Article: Repeated-measures models with constrained parameters for incomplete data in tumour xenograft experiments

TitleRepeated-measures models with constrained parameters for incomplete data in tumour xenograft experiments
Authors
Issue Date2005
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2005, v. 24 n. 1, p. 109-119 How to Cite?
AbstractIn cancer drug development, xenograft experiments (models) where mice are grafted with human cancer cells are used to elucidate the mechanism of action and/or to assess efficacy of a promising compound. Demonstrated activity in this model is an important step to bring a promising compound to humans. A key outcome variable in these experiments is tumour volumes measured over a period of time, while mice are treated with an anticancer agent following certain schedules. However, a mouse may die during the experiment or may be sacrificed when its tumour volume quadruples and then incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumour shrinkage (<0.01 cm3) or random truncation. In addition, if no treatment were given to the tumour-bearing mice, the tumours would keep growing until the mice die or are sacrificed. This intrinsic growth of tumour in the absence of treatment constrains the parameters in the regression and causes further difficulties in statistical analysis. We develop a maximum likelihood method based on the expectation/conditional maximization (ECM) algorithm to estimate the dose-response relationship while accounting for the informative censoring and the constraints of model parameters. A real xenograft study on a new antitumour agent temozolomide combined with irinotecan is analysed using the proposed method. Copyright © 2004 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/172409
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
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.accessioned2012-10-30T06:22:23Z-
dc.date.available2012-10-30T06:22:23Z-
dc.date.issued2005en_US
dc.identifier.citationStatistics In Medicine, 2005, v. 24 n. 1, p. 109-119en_US
dc.identifier.issn0277-6715en_US
dc.identifier.urihttp://hdl.handle.net/10722/172409-
dc.description.abstractIn cancer drug development, xenograft experiments (models) where mice are grafted with human cancer cells are used to elucidate the mechanism of action and/or to assess efficacy of a promising compound. Demonstrated activity in this model is an important step to bring a promising compound to humans. A key outcome variable in these experiments is tumour volumes measured over a period of time, while mice are treated with an anticancer agent following certain schedules. However, a mouse may die during the experiment or may be sacrificed when its tumour volume quadruples and then incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumour shrinkage (<0.01 cm3) or random truncation. In addition, if no treatment were given to the tumour-bearing mice, the tumours would keep growing until the mice die or are sacrificed. This intrinsic growth of tumour in the absence of treatment constrains the parameters in the regression and causes further difficulties in statistical analysis. We develop a maximum likelihood method based on the expectation/conditional maximization (ECM) algorithm to estimate the dose-response relationship while accounting for the informative censoring and the constraints of model parameters. A real xenograft study on a new antitumour agent temozolomide combined with irinotecan is analysed using the proposed method. Copyright © 2004 John Wiley & Sons, Ltd.en_US
dc.languageengen_US
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/en_US
dc.relation.ispartofStatistics in Medicineen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshAnimalsen_US
dc.subject.meshAntineoplastic Combined Chemotherapy Protocols - Pharmacologyen_US
dc.subject.meshCamptothecin - Administration & Dosage - Analogs & Derivativesen_US
dc.subject.meshDacarbazine - Administration & Dosage - Analogs & Derivativesen_US
dc.subject.meshFemaleen_US
dc.subject.meshHumansen_US
dc.subject.meshLikelihood Functionsen_US
dc.subject.meshLongitudinal Studiesen_US
dc.subject.meshMiceen_US
dc.subject.meshMice, Sciden_US
dc.subject.meshModels, Biologicalen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshNeoplasms, Experimental - Drug Therapyen_US
dc.subject.meshRhabdomyosarcoma - Drug Therapyen_US
dc.subject.meshXenograft Model Antitumor Assays - Methodsen_US
dc.titleRepeated-measures models with constrained parameters for incomplete data in tumour xenograft experimentsen_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.doi10.1002/sim.1775en_US
dc.identifier.pmid15523707-
dc.identifier.scopuseid_2-s2.0-11344295069en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-11344295069&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume24en_US
dc.identifier.issue1en_US
dc.identifier.spage109en_US
dc.identifier.epage119en_US
dc.identifier.isiWOS:000226103000009-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridTan, M=7401464681en_US
dc.identifier.scopusauthoridFang, HB=7402543028en_US
dc.identifier.scopusauthoridTian, GL=25621549400en_US
dc.identifier.scopusauthoridHoughton, PJ=36044344200en_US

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