Increasing the flexibility of mixed models by using fractional polynomials

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dc.contributor.authorGarcia, Edijane Paredes
dc.contributor.authorTrinca, Luzia Aparecida [UNESP]
dc.contributor.institutionFederal University of Amazonas
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T16:04:43Z
dc.date.available2023-07-29T16:04:43Z
dc.date.issued2022-01-01
dc.description.abstractThe class of regression models incorporating Fractional Polynomials (FPs), proposed by Royston and colleagues in the 1990’s, has been extensively studied and shown to be fruitful in the presence of non-linearity between the response variable and continuous covariates. FP functions provide an alternative to higher-order polynomials and splines for dealing with lack-of-fit. Mixed models may also benefit from this class of curves in the presence of non-linearity. The inclusion of FP functions into the structure of linear mixed models has been previously explored, though for simple layouts, e.g. a single covariate in the random intercept model. This paper proposes a general strategy for model-building and variable selection that takes advantage of the FPs within the framework of linear mixed models. Application of the method to three data sets from the literature, known for violating the linearity assumption, illustrates that it is possible to solve the problem of lack-of-fit by using fewer terms in the model than the usual approach of fitting higher-order polynomials.en
dc.description.affiliationDepartment of Statistics Federal University of Amazonas
dc.description.affiliationBiosciences Institute São Paulo State University “Júlio de Mesquita Filho”
dc.description.affiliationUnespBiosciences Institute São Paulo State University “Júlio de Mesquita Filho”
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdCAPES: 001
dc.format.extent469-489
dc.identifierhttp://dx.doi.org/10.28951/bjb.v40i4.619
dc.identifier.citationRevista Brasileira de Biometria, v. 40, n. 4, p. 469-489, 2022.
dc.identifier.doi10.28951/bjb.v40i4.619
dc.identifier.issn1983-0823
dc.identifier.scopus2-s2.0-85147232362
dc.identifier.urihttp://hdl.handle.net/11449/249623
dc.language.isoeng
dc.relation.ispartofRevista Brasileira de Biometria
dc.sourceScopus
dc.subjectLack-of-fit
dc.subjectLongitudinal data
dc.subjectRandom effects
dc.subjectSelection of variables
dc.subjectTransformation
dc.subjectVariance-covariance structure
dc.titleIncreasing the flexibility of mixed models by using fractional polynomialsen
dc.typeArtigo

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