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Article: Bayesian dose finding in oncology for drug combinations by copula regression

TitleBayesian dose finding in oncology for drug combinations by copula regression
Authors
KeywordsAdaptive design
Bayesian inference
Combining drugs
Continual reassessment method
Copula model
Maximum tolerated dose
Phase I trial
Toxicity probability
Issue Date2009
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSC
Citation
Journal Of The Royal Statistical Society. Series C: Applied Statistics, 2009, v. 58 n. 2, p. 211-224 How to Cite?
AbstractTreating patients with a combination of agents is becoming commonplace in cancer clinical trials, with biochemical synergism often the primary focus. In a typical drug combination trial, the toxicity profile of each individual drug has already been thoroughly studied in single-agent trials, which naturally offers rich prior information. We propose a Bayesian adaptive design for dose finding that is based on a copula-type model to account for the synergistic effect of two or more drugs in combination. To search for the maximum tolerated dose combination, we continuously update the posterior estimates for the toxicity probabilities of the combined doses. By reordering the dose toxicities in the two-dimensional probability space, we adaptively assign each new cohort of patients to the most appropriate dose. Dose escalation, de-escalation or staying at the same doses is determined by comparing the posterior estimates of the probabilities of toxicity of combined doses and the prespecified toxicity target. We conduct extensive simulation studies to examine the operating characteristics of the design and illustrate the proposed method under various practical scenarios. © 2009 Royal Statistical Society.
DescriptionComment: Journal of the Royal Statistical Society. Series C: Applied Statistics 59 (3), pp. 543-544 ; and replied in Journal of the Royal Statistical Society. Series C: Applied Statistics 59 (3), pp. 544-546
Persistent Identifierhttp://hdl.handle.net/10722/139732
ISSN
2015 Impact Factor: 1.354
2015 SCImago Journal Rankings: 1.092
ISI Accession Number ID
Funding AgencyGrant Number
Physician Referral Service at the M. D. Anderson Cancer Center
breast cancer 'Specialized program of research excellence'5 P50 CA116199-03
US Department of DefenseW81XWH-05-2-0027
Funding Information:

We thank the referees, Associate Editor and Joint Editor for helpful comments that substantially improved the paper. The research was partially supported by funds from the Physician Referral Service at the M. D. Anderson Cancer Center, 5 P50 CA116199-03 breast cancer 'Specialized program of research excellence' and US Department of Defense grant W81XWH-05-2-0027.

References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorYuan, Yen_HK
dc.date.accessioned2011-09-23T05:54:49Z-
dc.date.available2011-09-23T05:54:49Z-
dc.date.issued2009en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series C: Applied Statistics, 2009, v. 58 n. 2, p. 211-224en_HK
dc.identifier.issn0035-9254en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139732-
dc.descriptionComment: Journal of the Royal Statistical Society. Series C: Applied Statistics 59 (3), pp. 543-544 ; and replied in Journal of the Royal Statistical Society. Series C: Applied Statistics 59 (3), pp. 544-546-
dc.description.abstractTreating patients with a combination of agents is becoming commonplace in cancer clinical trials, with biochemical synergism often the primary focus. In a typical drug combination trial, the toxicity profile of each individual drug has already been thoroughly studied in single-agent trials, which naturally offers rich prior information. We propose a Bayesian adaptive design for dose finding that is based on a copula-type model to account for the synergistic effect of two or more drugs in combination. To search for the maximum tolerated dose combination, we continuously update the posterior estimates for the toxicity probabilities of the combined doses. By reordering the dose toxicities in the two-dimensional probability space, we adaptively assign each new cohort of patients to the most appropriate dose. Dose escalation, de-escalation or staying at the same doses is determined by comparing the posterior estimates of the probabilities of toxicity of combined doses and the prespecified toxicity target. We conduct extensive simulation studies to examine the operating characteristics of the design and illustrate the proposed method under various practical scenarios. © 2009 Royal Statistical Society.en_HK
dc.languageengen_US
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSCen_HK
dc.relation.ispartofJournal of the Royal Statistical Society. Series C: Applied Statisticsen_HK
dc.rightsThe definitive version is available at www3.interscience.wiley.com-
dc.subjectAdaptive designen_HK
dc.subjectBayesian inferenceen_HK
dc.subjectCombining drugsen_HK
dc.subjectContinual reassessment methoden_HK
dc.subjectCopula modelen_HK
dc.subjectMaximum tolerated doseen_HK
dc.subjectPhase I trialen_HK
dc.subjectToxicity probabilityen_HK
dc.titleBayesian dose finding in oncology for drug combinations by copula regressionen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0035-9254&volume=58&issue=2&spage=211&epage=224&date=2009&atitle=Bayesian+dose+finding+in+oncology+for+drug+combinations+by+copula+regression-
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1467-9876.2009.00649.xen_HK
dc.identifier.scopuseid_2-s2.0-63849316345en_HK
dc.identifier.hkuros195718en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-63849316345&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue2en_HK
dc.identifier.spage211en_HK
dc.identifier.epage224en_HK
dc.identifier.isiWOS:000264892500004-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridYuan, Y=7402709174en_HK

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