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Article: Bayesian dose finding by jointly modelling toxicity and efficacy as time-to-event outcomes

TitleBayesian dose finding by jointly modelling toxicity and efficacy as time-to-event outcomes
Authors
KeywordsAdaptive design
Bivariate survival function
Cure rate model
Phase I trial
Phase II trial
Survival curve
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. 5, p. 719-736 How to Cite?
AbstractIn traditional phase I and II clinical trial designs, toxicity and efficacy are often modelled as binary outcomes. Such methods ignore information on when the outcome event occurs, such as experiencing toxicity or achieving cure or remission. They also have difficulty accommodating a high accrual rate under which toxicity and efficacy outcomes cannot be observed in a timely manner, and thus delay treatment assignment. To address these issues, we propose a Bayesian adaptive phase I-II design that jointly models toxicity and efficacy as time-to-event outcomes. At each decision-making time, patients who have not experienced toxicity or efficacy are naturally censored. We apply the marginal cure rate model to account explicitly for those patients who are insusceptible to efficacy owing to drug resistance. The correlation between the bivariate time-to-toxicity and time-to-efficacy outcomes is properly adjusted through the Clayton model. After screening out the excessively toxic or futile doses, we adaptively assign each new patient to the most appropriate dose on the basis of the ratio of the areas under the predicted survival curves corresponding to toxicity and efficacy. We conducted extensive simulation studies to examine the operating characteristics of the method proposed, and we illustrate the application of the method in a clinical trial in prostate cancer. Our design selects the target dose with a high probability, treats most patients at the desirable dose and potentially shortens the duration of a trial. © 2009 Royal Statistical Society.
Persistent Identifierhttp://hdl.handle.net/10722/139731
ISSN
2015 Impact Factor: 1.354
2015 SCImago Journal Rankings: 1.092
References

 

DC FieldValueLanguage
dc.contributor.authorYuan, Yen_HK
dc.contributor.authorYin, Gen_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. 5, p. 719-736en_HK
dc.identifier.issn0035-9254en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139731-
dc.description.abstractIn traditional phase I and II clinical trial designs, toxicity and efficacy are often modelled as binary outcomes. Such methods ignore information on when the outcome event occurs, such as experiencing toxicity or achieving cure or remission. They also have difficulty accommodating a high accrual rate under which toxicity and efficacy outcomes cannot be observed in a timely manner, and thus delay treatment assignment. To address these issues, we propose a Bayesian adaptive phase I-II design that jointly models toxicity and efficacy as time-to-event outcomes. At each decision-making time, patients who have not experienced toxicity or efficacy are naturally censored. We apply the marginal cure rate model to account explicitly for those patients who are insusceptible to efficacy owing to drug resistance. The correlation between the bivariate time-to-toxicity and time-to-efficacy outcomes is properly adjusted through the Clayton model. After screening out the excessively toxic or futile doses, we adaptively assign each new patient to the most appropriate dose on the basis of the ratio of the areas under the predicted survival curves corresponding to toxicity and efficacy. We conducted extensive simulation studies to examine the operating characteristics of the method proposed, and we illustrate the application of the method in a clinical trial in prostate cancer. Our design selects the target dose with a high probability, treats most patients at the desirable dose and potentially shortens the duration of a trial. © 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.subjectBivariate survival functionen_HK
dc.subjectCure rate modelen_HK
dc.subjectPhase I trialen_HK
dc.subjectPhase II trialen_HK
dc.subjectSurvival curveen_HK
dc.titleBayesian dose finding by jointly modelling toxicity and efficacy as time-to-event outcomesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0035-9254&volume=58&issue=5&spage=719&epage=736&date=2009&atitle=Bayesian+dose+finding+by+jointly+modelling+toxicity+and+efficacy+as+time-to-event+outcomes-
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.1467-9876.2009.00674.xen_HK
dc.identifier.scopuseid_2-s2.0-70350136475en_HK
dc.identifier.hkuros195712en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70350136475&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue5en_HK
dc.identifier.spage719en_HK
dc.identifier.epage736en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYuan, Y=55176534400en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.citeulike6032965-

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