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dc.contributor.authorMcMenamin, Martina E
dc.contributor.authorBarrett, Jessica K
dc.contributor.authorBerglind, Anna
dc.contributor.authorWason, James MS
dc.date.accessioned2022-02-24T09:01:07Z
dc.date.available2022-02-24T09:01:07Z
dc.date.issued2022-06-15
dc.date.submitted2020-04-24
dc.identifier.issn0277-6715
dc.identifier.othersim9356
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334406
dc.description.abstractMixed outcome endpoints that combine multiple continuous and discrete components are often employed as primary outcome measures in clinical trials. These may be in the form of co-primary endpoints, which conclude effectiveness overall if an effect occurs in all of the components, or multiple primary endpoints, which require an effect in at least one of the components. Alternatively, they may be combined to form composite endpoints, which reduce the outcomes to a one-dimensional endpoint. There are many advantages to joint modeling the individual outcomes, however in order to do this in practice we require techniques for sample size estimation. In this article we show how the latent variable model can be used to estimate the joint endpoints and propose hypotheses, power calculations and sample size estimation methods for each. We illustrate the techniques using a numerical example based on a four-dimensional endpoint 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 in the multiple primary case is similar to that needed for the outcome with the largest effect size. We show that the empirical power is achieved for each endpoint and that the FWER can be sufficiently controlled using a Bonferroni correction if the correlations between endpoints are less than 0.5. Otherwise, less conservative adjustments may be needed. We further illustrate empirically the efficiency gains that may be achieved in the composite endpoint setting.
dc.languageen
dc.publisherWiley
dc.subjectRESEARCH ARTICLE
dc.subjectRESEARCH ARTICLES
dc.subjectlatent variable modeling
dc.subjectmixed outcome endpoints
dc.subjectsample size estimation
dc.titleSample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints.
dc.typeArticle
dc.date.updated2022-02-24T09:01:06Z
prism.publicationNameStat Med
dc.identifier.doi10.17863/CAM.81822
dcterms.dateAccepted2022-02-02
rioxxterms.versionofrecord10.1002/sim.9356
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidMcMenamin, Martina E [0000-0001-7784-2271]
dc.contributor.orcidWason, James MS [0000-0002-4691-126X]
dc.identifier.eissn1097-0258
pubs.funder-project-idMRC (unknown)
pubs.funder-project-idNational Institute for Health Research (IS-BRC-1215-20014)
cam.issuedOnline2022-02-23


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