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Bayesian hierarchical EMAX model for doseâ response in early phase efficacy clinical trials
dc.contributor.authorGajewski, Byron J.
dc.contributor.authorMeinzer, Caitlyn
dc.contributor.authorBerry, Scott M.
dc.contributor.authorRockswold, Gaylan L.
dc.contributor.authorBarsan, William G.
dc.contributor.authorKorley, Frederick K.
dc.contributor.authorMartin, Renee’ H.
dc.date.accessioned2019-07-03T19:55:43Z
dc.date.availableWITHHELD_13_MONTHS
dc.date.available2019-07-03T19:55:43Z
dc.date.issued2019-07-30
dc.identifier.citationGajewski, Byron J.; Meinzer, Caitlyn; Berry, Scott M.; Rockswold, Gaylan L.; Barsan, William G.; Korley, Frederick K.; Martin, Renee’ H. (2019). "Bayesian hierarchical EMAX model for doseâ response in early phase efficacy clinical trials." Statistics in Medicine 38(17): 3123-3138.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/149669
dc.publisherCRC Press
dc.publisherWiley Periodicals, Inc.
dc.subject.otherhierarchical models
dc.subject.otherdosing design, Bayesian models
dc.subject.otherEMAX
dc.subject.otherlogistic
dc.titleBayesian hierarchical EMAX model for doseâ response in early phase efficacy clinical trials
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/149669/1/sim8167_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/149669/2/sim8167.pdf
dc.identifier.doi10.1002/sim.8167
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceZaslavsky BG. Bayesian hypothesis testing in twoâ arm trials with dichotomous outcomes. Biometrics. 2013; 69 ( 1 ): 157 â 163.
dc.identifier.citedreferenceGelman A, Carlin J, Stern H, Dunson D, Vehtari A, Rubin D. Bayesian Data Analysis. New York, NY: CRC Press; 2014.
dc.identifier.citedreferenceBornkamp B, Pinheiro J, Bretz F. MCPMod: an R package for the design and analysis of doseâ finding studies. J Stat Softw. 2009; 29 ( 7 ): 1 â 23.
dc.identifier.citedreferenceThomas N, Sweeney K, Somayaji V. Metaâ analysis of clinical doseâ response in a large drug development portfolio. Stat Biopharm Res. 2014; 6 ( 4 ): 302 â 317.
dc.identifier.citedreferenceBretz F, Pinheiro JC, Branson M. Combining multiple comparisons and modeling techniques in doseâ response studies. Biometrics. 2005; 61 ( 3 ): 738 â 748.
dc.identifier.citedreferenceHjort NL, Claeskens G. Frequentist model average estimators. J am Stat Assoc. 2003; 98 ( 464 ): 879 â 899.
dc.identifier.citedreferenceLunn DJ, Thomas A, Best N, Spiegelhalter D. WinBUGS â a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput. 2000; 10: 325 â 337.
dc.identifier.citedreferenceBerry SM, Carlin BP, Lee JJ, Muller P. Bayesian Adaptive Methods for Clinical Trials. New York, NY: CRC Press; 2011.
dc.identifier.citedreferenceSteyerberg EW, Mushkudiani N, Perel P, et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Medicine. 2008; 5 ( 8 ):e165
dc.identifier.citedreferenceGajewski B, Berry S, Barsan W, et al. Hyperbaric oxygen brain injury treatment (HOBIT) trial: a novel multiâ factor design with response adaptive randomization and longitudinal modeling. Pharmaceutical Statistics. 2016; 15 ( 5 ): 396 â 404.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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