File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Hierarchical models for tumor xenograft experiments in drug development

TitleHierarchical models for tumor xenograft experiments in drug development
Authors
Issue Date2004
PublisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10543406.asp
Citation
Journal Of Biopharmaceutical Statistics, 2004, v. 14 n. 4, p. 931-945 How to Cite?
AbstractIn cancer drug development, demonstrated anticancer activity in animal models is an important step to bring a promising compound to clinic. Proper design and analysis of experiments using laboratory animals have received increasing attention recently. These experiments involve informatively censored longitudinal data with small samples. The problem is further complicated because of order constraints due to the intrinsic growth of control tumors without treatment. This article proposes a Bayesian hierarchical model to analyze informatively censored longitudinal data while accounting for the parameter constraints and providing valid small sample inference. We adopt a noniterative sampling approach, the inverse Bayes formulae (IBF) sampler, to generate independent posterior samples, which avoids convergence problems associated with Markov chain Monte-Carlo methods. To effectively deal with the restricted parameter problem, we use a linear transformation to simplify the constraints and exploit the IBF method to generate random samples from truncated multivariate normal distributions. Because diffuse priors are used, the posterior modes approximate the maximum likelihood estimates well, and the hierarchical model can be considered as an extended mixed-effects model. A real xenograft experiment on a new treatment is analyzed by using the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/172510
ISSN
2015 Impact Factor: 0.882
2015 SCImago Journal Rankings: 0.850
References

 

DC FieldValueLanguage
dc.contributor.authorFang, HBen_US
dc.contributor.authorTian, GLen_US
dc.contributor.authorTan, Men_US
dc.date.accessioned2012-10-30T06:22:51Z-
dc.date.available2012-10-30T06:22:51Z-
dc.date.issued2004en_US
dc.identifier.citationJournal Of Biopharmaceutical Statistics, 2004, v. 14 n. 4, p. 931-945en_US
dc.identifier.issn1054-3406en_US
dc.identifier.urihttp://hdl.handle.net/10722/172510-
dc.description.abstractIn cancer drug development, demonstrated anticancer activity in animal models is an important step to bring a promising compound to clinic. Proper design and analysis of experiments using laboratory animals have received increasing attention recently. These experiments involve informatively censored longitudinal data with small samples. The problem is further complicated because of order constraints due to the intrinsic growth of control tumors without treatment. This article proposes a Bayesian hierarchical model to analyze informatively censored longitudinal data while accounting for the parameter constraints and providing valid small sample inference. We adopt a noniterative sampling approach, the inverse Bayes formulae (IBF) sampler, to generate independent posterior samples, which avoids convergence problems associated with Markov chain Monte-Carlo methods. To effectively deal with the restricted parameter problem, we use a linear transformation to simplify the constraints and exploit the IBF method to generate random samples from truncated multivariate normal distributions. Because diffuse priors are used, the posterior modes approximate the maximum likelihood estimates well, and the hierarchical model can be considered as an extended mixed-effects model. A real xenograft experiment on a new treatment is analyzed by using the proposed method.en_US
dc.languageengen_US
dc.publisherTaylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/10543406.aspen_US
dc.relation.ispartofJournal of Biopharmaceutical Statisticsen_US
dc.subject.meshAlgorithmsen_US
dc.subject.meshAnimalsen_US
dc.subject.meshAntineoplastic Agents - Therapeutic Useen_US
dc.subject.meshAntineoplastic Agents, Phytogenic - Therapeutic Useen_US
dc.subject.meshAntineoplastic Combined Chemotherapy Protocols - Therapeutic Useen_US
dc.subject.meshBayes Theoremen_US
dc.subject.meshBrain Neoplasms - Drug Therapyen_US
dc.subject.meshCamptothecin - Therapeutic Useen_US
dc.subject.meshDacarbazine - Analogs & Derivatives - Therapeutic Useen_US
dc.subject.meshHumansen_US
dc.subject.meshLikelihood Functionsen_US
dc.subject.meshLongitudinal Studiesen_US
dc.subject.meshMarkov Chainsen_US
dc.subject.meshMiceen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshMonte Carlo Methoden_US
dc.subject.meshNeoplasm Transplantation - Physiologyen_US
dc.subject.meshNeuroblastoma - Drug Therapyen_US
dc.subject.meshPredictive Value Of Testsen_US
dc.subject.meshTransplantation, Heterologousen_US
dc.titleHierarchical models for tumor xenograft experiments in drug developmenten_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.doi10.1081/BIP-200035462en_US
dc.identifier.pmid15587973-
dc.identifier.scopuseid_2-s2.0-9244242568en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-9244242568&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume14en_US
dc.identifier.issue4en_US
dc.identifier.spage931en_US
dc.identifier.epage945en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridFang, HB=7402543028en_US
dc.identifier.scopusauthoridTian, GL=25621549400en_US
dc.identifier.scopusauthoridTan, M=7401464681en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats