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dc.contributor.authorBofill Roig, Marta
dc.contributor.authorGómez Melis, Guadalupe
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2019-11-04T09:26:43Z
dc.date.issued2019-05-20
dc.identifier.citationBofill, M.; Gómez Melis, G. A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components. "Statistics in medicine", 20 Maig 2019, vol. 38, núm. 11, p. 1935-1956.
dc.identifier.issn0277-6715
dc.identifier.urihttp://hdl.handle.net/2117/171418
dc.descriptionThis is the peer reviewed version of the following article: Bofill, M.; Gómez Melis, G. A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components. "Statistics in medicine", 20 Maig 2019, vol. 38, núm. 11, p. 1935-1956, which has been published in final form at https://doi.org/10.1002/sim.8092. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
dc.description.abstractComposite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a composite binary endpoint to size a trial, one needs to specify the event rates and the effect sizes of the composite components as well as the correlation between them. In practice, the marginal parameters of the components can be obtained from previous studies or pilot trials; however, the correlation is often not previously reported and thus usually unknown. We first show that the sample size for composite binary endpoints is strongly dependent on the correlation and, second, that slight deviations in the prior information on the marginal parameters may result in underpowered trials for achieving the study objectives at a pre-specified significance level. We propose a general strategy for calculating the required sample size when the correlation is not specified and accounting for uncertainty in the marginal parameter values. We present the web platform CompARE to characterize composite endpoints and to calculate the sample size just as we propose in this paper. We evaluate the performance of the proposal with a simulation study and illustrate it by means of a real case study using CompARE.
dc.format.extent22 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Mètodes estadístics
dc.subject.lcshBiomathematics
dc.subject.lcshComputing Methodologies.
dc.subject.lcshSampling (Statistics)
dc.subject.othercomposite binary endpoints
dc.subject.othercorrelated endpoints
dc.subject.othersample size
dc.titleA new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components
dc.typeArticle
dc.subject.lemacBiomatemàtica
dc.subject.lemacInformàtica
dc.subject.lemacMostreig (Estadística)
dc.contributor.groupUniversitat Politècnica de Catalunya. GRBIO - Grup de Recerca en Bioestadística i Bioinformàtica
dc.identifier.doi10.1002/sim.8092
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::92 Biology and other natural sciences::92B Mathematical biology in general
dc.subject.amsClassificació AMS::68 Computer science::68U Computing methodologies and applications
dc.subject.amsClassificació AMS::62 Statistics::62D05 Sampling theory, sample surveys
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/full/10.1002/sim.8092
dc.rights.accessRestricted access - publisher's policy
drac.iddocument23637085
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/MDM-2014-0445
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/MTM2015-64465-C2-1-R
dc.relation.projectidinfo:eu-repo/grantAgreement/MICINN/1PE/BIA2017-90856-REDT
dc.date.lift2020-05-20
upcommons.citation.authorBofill, M.; Gómez Melis, Guadalupe
upcommons.citation.publishedtrue
upcommons.citation.publicationNameStatistics in medicine
upcommons.citation.volume38
upcommons.citation.number11
upcommons.citation.startingPage1935
upcommons.citation.endingPage1956


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