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dc.contributor.authorMcMenamin, Martina
dc.contributor.authorBarrett, Jessica
dc.contributor.authorBerglind, Anna
dc.contributor.authorWason, James MS
dc.date.accessioned2022-02-26T00:30:09Z
dc.date.available2022-02-26T00:30:09Z
dc.identifier.issn0277-6715
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334457
dc.description.abstractMixed outcome endpoints that combine multiple continuous and discrete components to form co-primary, multiple primary or composite endpoints are often employed as primary outcome measures in clinical trials. There are many advantages to joint modelling the individual outcomes using a latent variable framework, however in order to make use of the model in practice we require techniques for sample size estimation. In this paper we show how the latent variable model can be applied to the three types of joint endpoints and propose appropriate hypotheses, power and sample size estimation methods for each. We illustrate the techniques using a numerical example based on the four dimensional endpoint in the MUSE trial and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required for the multiple primary endpoint is reduced from that required for the individual outcome with the largest effect size. We show that the analytical technique agrees with the empirical power from simulation studies. We further illustrate the reduction in required sample size that may be achieved in trials of mixed outcome composite endpoints through a simulation study and find that the sample size primarily depends on the components driving response and the correlation structure and much less so on the treatment effect structure in the individual endpoints.
dc.publisherWiley
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectstat.ME
dc.subjectstat.ME
dc.subjectstat.AP
dc.titleSample Size Estimation using a Latent Variable Model for Mixed Outcome Co-Primary, Multiple Primary and Composite Endpoints
dc.typeArticle
dc.publisher.departmentMrc Biostatistics Unit
dc.date.updated2022-02-24T14:52:07Z
prism.publicationNameStatistics in Medicine
dc.identifier.doi10.17863/CAM.81874
rioxxterms.versionofrecord10.1002/sim.9356
rioxxterms.versionVoR
dc.contributor.orcidBarrett, Jessica [0000-0003-1889-9803]
dc.identifier.eissn1097-0258
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMRC (unknown)
cam.issuedOnline2022-02-23
cam.depositDate2022-02-24
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International