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dc.contributor.authorCortés Martínez, Jordi
dc.contributor.authorGeskus, Ronald
dc.contributor.authorKim, Kyungman
dc.contributor.authorGómez Melis, Guadalupe
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2022-01-18T09:37:04Z
dc.date.available2022-01-18T09:37:04Z
dc.date.issued2021-05-06
dc.identifier.citationCortes, J. [et al.]. Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints. "BMC medical research methodology", 6 Maig 2021, vol. 21, p. 99:1-99:14.
dc.identifier.issn1471-2288
dc.identifier.urihttp://hdl.handle.net/2117/359874
dc.description.abstractBackground: Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case the proportional hazards assumption can be assumed to hold for the components, but not for the CE. Methods: The required number of events, sample size and power formulae are based on the non-centrality parameter of the logrank test under the alternative hypothesis which is a function of the gAHR. We use the web platform, CompARE, for the sample size computations. A simulation study evaluates the empirical power of the logrank test for the CE based on the sample size in terms of the gAHR. We consider different values of the component hazard ratios, the probabilities of observing the events in the control group and the degrees of association between the components. We illustrate the sample size computations using two published randomized controlled trials. Their primary CEs are, respectively, progression-free survival (time to progression of disease or death) and the composite of bacteriologically confirmed treatment failure or Staphylococcus aureus related death by 12 weeks. Results: For a target power of 0.80, the simulation study provided mean (± SE) empirical powers equal to 0.799 (±0.004) and 0.798 (±0.004) in the exponential and non-exponential settings, respectively. The power was attained in more than 95% of the simulated scenarios and was always above 0.78, regardless of compliance with the proportional-hazard assumption. Conclusions: The geometric average hazard ratio as an effect measure for a composite endpoint has a meaningful interpretation in the case of non-proportional hazards. Furthermore it is the natural effect measure when using the logrank test to compare the hazard rates of two groups and should be used instead of the standard hazard ratio.
dc.description.sponsorshipG. G´omez and J. Cort´es were partially supported by the Ministerio de Econom´ıa y Competitividad (Spain) [MTM2015-64465-C2-1-R (MINECO/FEDER)], the Ministerio de Ciencia, innovaci´on y Universidades [PID2019-104830RB-I00] and the Departament d’Economia i Coneixement de la Generalitat de Catalunya (Spain)[2017 SGR 622 (GRBIO)]. Ronald B. Geskus was supported by the Wellcome Trust (grant number 106680/Z/14/Z).
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Geometria::Geometria algebraica
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.lcshArithmetical algebraic geometry
dc.subject.lcshGeometric probabilities
dc.subject.otherTreatment Effect
dc.subject.otherComposite endpoints
dc.subject.otherSample size
dc.subject.otherRandomized controlled trial
dc.titleUsing the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints
dc.typeArticle
dc.subject.lemacGeometria algèbrica--Aritmètica
dc.subject.lemacProbabilitats
dc.contributor.groupUniversitat Politècnica de Catalunya. GRBIO - Grup de Recerca en Bioestadística i Bioinformàtica
dc.identifier.doi10.1186/s12874-021-01286-x
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::11 Number theory::11G Arithmetic algebraic geometry (Diophantine geometry)
dc.subject.amsClassificació AMS::60 Probability theory and stochastic processes::60D05 Geometric probability, stochastic geometry, random sets
dc.relation.publisherversionhttps://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01286-x
dc.rights.accessOpen Access
local.identifier.drac31275074
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO//MTM2015-64465-C2-1-R/ES/METODOS ESTADISTICOS PARA ENSAYOS CLINICOS, PATRONES DE CENSURA COMPLEJOS Y ANALISIS INTEGRADO DE DATOS OMICOS/
local.citation.authorCortes, J.; Geskus, R.; Kim, K.; Gómez Melis, Guadalupe
local.citation.publicationNameBMC medical research methodology
local.citation.volume21
local.citation.startingPage99:1
local.citation.endingPage99:14


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