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Article: Phase II trial design with Bayesian adaptive randomization and predictive probability

TitlePhase II trial design with Bayesian adaptive randomization and predictive probability
Authors
KeywordsAdaptive randomization
Bayesian inference
Clinical trial ethics
Group sequential method
Posterior predictive distribution
Randomized trial
Type I error
Type II error
Issue Date2012
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, 2012, v. 61 n. 2, p. 219-235 How to Cite?
AbstractWe propose a randomized phase II clinical trial design based on Bayesian adaptive randomization and predictive probability monitoring. Adaptive randomization assigns more patients to a more efficacious treatment arm by comparing the posterior probabilities of efficacy between different arms. We continuously monitor the trial using the predictive probability. The trial is terminated early when it is shown that one treatment is overwhelmingly superior to others or that all the treatments are equivalent. We develop two methods to compute the predictive probability by considering the uncertainty of the sample size of the future data. We illustrate the proposed Bayesian adaptive randomization and predictive probability design using a phase II lung cancer clinical trial, and we conduct extensive simulation studies to examine the operating characteristics of the design. By coupling adaptive randomization and predictive probability approaches, the trial can treat more patients with a more efficacious treatment and allow for early stopping whenever sufficient information is obtained to conclude treatment superiority or equivalence. The design proposed also controls both the type I and the type II errors and offers an alternative Bayesian approach to the frequentist group sequential design. © 2011 Royal Statistical Society.
Persistent Identifierhttp://hdl.handle.net/10722/146601
ISSN
2015 Impact Factor: 1.354
2015 SCImago Journal Rankings: 1.092
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of Hong Kong
US National Institutes of HealthCA16672
CA97007
M. D. Anderson University Cancer Foundation80094548
Funding Information:

We thank the Associate Editor, two referees and the Joint Editor for many insightful suggestions which strengthened the work immensely. We also thank Valen Johnson, Gary Rosner and Diane Liu for helpful discussions. This research was supported in part by a grant from the Research Grants Council of Hong Kong, the US National Institutes of Health, grants CA16672 and CA97007, and M. D. Anderson University Cancer Foundation grant 80094548.

References

 

DC FieldValueLanguage
dc.contributor.authorYin, Gen_HK
dc.contributor.authorChen, Nen_HK
dc.contributor.authorJack Lee, Jen_HK
dc.date.accessioned2012-05-02T08:37:20Z-
dc.date.available2012-05-02T08:37:20Z-
dc.date.issued2012en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series C: Applied Statistics, 2012, v. 61 n. 2, p. 219-235en_HK
dc.identifier.issn0035-9254en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146601-
dc.description.abstractWe propose a randomized phase II clinical trial design based on Bayesian adaptive randomization and predictive probability monitoring. Adaptive randomization assigns more patients to a more efficacious treatment arm by comparing the posterior probabilities of efficacy between different arms. We continuously monitor the trial using the predictive probability. The trial is terminated early when it is shown that one treatment is overwhelmingly superior to others or that all the treatments are equivalent. We develop two methods to compute the predictive probability by considering the uncertainty of the sample size of the future data. We illustrate the proposed Bayesian adaptive randomization and predictive probability design using a phase II lung cancer clinical trial, and we conduct extensive simulation studies to examine the operating characteristics of the design. By coupling adaptive randomization and predictive probability approaches, the trial can treat more patients with a more efficacious treatment and allow for early stopping whenever sufficient information is obtained to conclude treatment superiority or equivalence. The design proposed also controls both the type I and the type II errors and offers an alternative Bayesian approach to the frequentist group sequential design. © 2011 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.subjectAdaptive randomizationen_HK
dc.subjectBayesian inferenceen_HK
dc.subjectClinical trial ethicsen_HK
dc.subjectGroup sequential methoden_HK
dc.subjectPosterior predictive distributionen_HK
dc.subjectRandomized trialen_HK
dc.subjectType I erroren_HK
dc.subjectType II erroren_HK
dc.titlePhase II trial design with Bayesian adaptive randomization and predictive probabilityen_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.doi10.1111/j.1467-9876.2011.01006.xen_HK
dc.identifier.scopuseid_2-s2.0-84858160948en_HK
dc.identifier.hkuros223928-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84858160948&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume61en_HK
dc.identifier.issue2en_HK
dc.identifier.spage219en_HK
dc.identifier.epage235en_HK
dc.identifier.isiWOS:000301224800003-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridChen, N=54882570100en_HK
dc.identifier.scopusauthoridJack Lee, J=54882854000en_HK

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