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Article: Bayesian hybrid dose-finding design in phase I oncology clinical trials

TitleBayesian hybrid dose-finding design in phase I oncology clinical trials
Authors
KeywordsBayes factor
Hypothesis testing
Model-based
Model-free
Robust
Issue Date2011
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/0277-6715/
Citation
Statistics In Medicine, 2011, v. 30 n. 17, p. 2098-2108 How to Cite?
AbstractIn oncology, dose escalation is often carried out to search for the maximum tolerated dose (MTD) in phase I clinical trials. We propose a Bayesian hybrid dose-finding method that inherits the robustness of model-free methods and the efficiency of model-based methods. In the Bayesian hypothesis testing framework, we compute the Bayes factor and adaptively assign a dose to each cohort of patients based on the adequacy of the dose-toxicity information that has been collected thus far. If the data observed at the current treatment dose are adequately informative about the toxicity probability of this dose (e.g. whether this dose is below or above the MTD), we make the decision of dose assignment (e.g. either to escalate or to de-escalate the dose) directly without assuming a parametric dose-toxicity curve. If the observed data at the current dose are not sufficient to deliver such a definitive decision, we resort to a parametric dose-toxicity curve, such as that of the continual reassessment method (CRM), in order to borrow strength across all the doses under study to guide dose assignment. We examine the properties of the hybrid design through extensive simulation studies, and also compare the new method with the CRM and the '3 + 3' design. The simulation results show that our design is more robust than parametric model-based methods and more efficient than nonparametric model-free methods. © 2011 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/139719
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
National Cancer Institute (U.S.A.)R01CA154591-01A1
Research Grants Council of Hong Kong
Funding Information:

We thank the referees, the Associate Editor and the Editor for very helpful comments that substantially improved this paper. The research of Ying Yuan was partially supported by the National Cancer Institute (U.S.A.) grant R01CA154591-01A1, and the research of Guosheng Yin was partially supported by a grant from the Research Grants Council of Hong Kong.

References

 

DC FieldValueLanguage
dc.contributor.authorYuan, Yen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2011-09-23T05:54:46Z-
dc.date.available2011-09-23T05:54:46Z-
dc.date.issued2011en_HK
dc.identifier.citationStatistics In Medicine, 2011, v. 30 n. 17, p. 2098-2108en_HK
dc.identifier.issn0277-6715en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139719-
dc.description.abstractIn oncology, dose escalation is often carried out to search for the maximum tolerated dose (MTD) in phase I clinical trials. We propose a Bayesian hybrid dose-finding method that inherits the robustness of model-free methods and the efficiency of model-based methods. In the Bayesian hypothesis testing framework, we compute the Bayes factor and adaptively assign a dose to each cohort of patients based on the adequacy of the dose-toxicity information that has been collected thus far. If the data observed at the current treatment dose are adequately informative about the toxicity probability of this dose (e.g. whether this dose is below or above the MTD), we make the decision of dose assignment (e.g. either to escalate or to de-escalate the dose) directly without assuming a parametric dose-toxicity curve. If the observed data at the current dose are not sufficient to deliver such a definitive decision, we resort to a parametric dose-toxicity curve, such as that of the continual reassessment method (CRM), in order to borrow strength across all the doses under study to guide dose assignment. We examine the properties of the hybrid design through extensive simulation studies, and also compare the new method with the CRM and the '3 + 3' design. The simulation results show that our design is more robust than parametric model-based methods and more efficient than nonparametric model-free methods. © 2011 John Wiley & Sons, Ltd.en_HK
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_HK
dc.relation.ispartofStatistics in Medicineen_HK
dc.rightsStatistics in Medicine. Copyright © John Wiley & Sons Ltd.en_US
dc.subjectBayes factoren_HK
dc.subjectHypothesis testingen_HK
dc.subjectModel-baseden_HK
dc.subjectModel-freeen_HK
dc.subjectRobusten_HK
dc.subject.meshBayes Theoremen_HK
dc.subject.meshClinical Trials as Topic - methodsen_HK
dc.subject.meshMaximum Tolerated Doseen_HK
dc.subject.meshMedical Oncology - methodsen_HK
dc.subject.meshModels, Statisticalen_HK
dc.titleBayesian hybrid dose-finding design in phase I oncology clinical trialsen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1002/sim.4164en_HK
dc.identifier.pmid21365672-
dc.identifier.pmcidPMC3286188-
dc.identifier.scopuseid_2-s2.0-79960029322en_HK
dc.identifier.hkuros195641en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79960029322&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume30en_HK
dc.identifier.issue17en_HK
dc.identifier.spage2098en_HK
dc.identifier.epage2108en_HK
dc.identifier.isiWOS:000292739500006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYuan, Y=7402709174en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK

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