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Article: Bayesian dose finding in phase I clinical trials based on a new statistical framework

TitleBayesian dose finding in phase I clinical trials based on a new statistical framework
Authors
KeywordsMarkov chain Monte Carlo
Penalty
Stochastic moves
Stopping rule
Toxicity
Issue Date2007
PublisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/
Citation
Statistica Sinica, 2007, v. 17 n. 2, p. 531-547 How to Cite?
AbstractPhase I clinical trials aim to find the maximum tolerated dose of an experimental drug. We consider dose escalation, de-escalation, or staying at the current dose as three different stochastic moves over the lattice of a sequence of prespecified dose levels. Each move is chosen by minimizing an expected penalty that determines the dose level for treating the next cohort of patients. We develop a stopping rule under which the termination of the trial ensures that the posterior probability that the current dose is the maximum tolerated dose is larger than a prespecified value. Under a new class of priors, posterior estimates for the dose toxicity probabilities are obtained using the Markov chain Monte Carlo method, We demonstrate the new designs using a real phase I clinical trial.
Persistent Identifierhttp://hdl.handle.net/10722/146583
ISSN
2015 Impact Factor: 0.838
2015 SCImago Journal Rankings: 2.292
References

 

DC FieldValueLanguage
dc.contributor.authorJi, Yen_HK
dc.contributor.authorLi, Yen_HK
dc.contributor.authorYin, Gen_HK
dc.date.accessioned2012-05-02T08:37:11Z-
dc.date.available2012-05-02T08:37:11Z-
dc.date.issued2007en_HK
dc.identifier.citationStatistica Sinica, 2007, v. 17 n. 2, p. 531-547en_HK
dc.identifier.issn1017-0405en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146583-
dc.description.abstractPhase I clinical trials aim to find the maximum tolerated dose of an experimental drug. We consider dose escalation, de-escalation, or staying at the current dose as three different stochastic moves over the lattice of a sequence of prespecified dose levels. Each move is chosen by minimizing an expected penalty that determines the dose level for treating the next cohort of patients. We develop a stopping rule under which the termination of the trial ensures that the posterior probability that the current dose is the maximum tolerated dose is larger than a prespecified value. Under a new class of priors, posterior estimates for the dose toxicity probabilities are obtained using the Markov chain Monte Carlo method, We demonstrate the new designs using a real phase I clinical trial.en_HK
dc.languageengen_US
dc.publisherAcademia Sinica, Institute of Statistical Science. The Journal's web site is located at http://www.stat.sinica.edu.tw/statistica/en_HK
dc.relation.ispartofStatistica Sinicaen_HK
dc.subjectMarkov chain Monte Carloen_HK
dc.subjectPenaltyen_HK
dc.subjectStochastic movesen_HK
dc.subjectStopping ruleen_HK
dc.subjectToxicityen_HK
dc.titleBayesian dose finding in phase I clinical trials based on a new statistical frameworken_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-34547529131en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547529131&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume17en_HK
dc.identifier.issue2en_HK
dc.identifier.spage531en_HK
dc.identifier.epage547en_HK
dc.publisher.placeTaiwan, Republic of Chinaen_HK
dc.identifier.scopusauthoridJi, Y=36570526400en_HK
dc.identifier.scopusauthoridLi, Y=15765879600en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK

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