Bivariate Copula-based Linear Mixed-effects Models: An Application to Longitudinal Child Growth Data

Paulo Henrique Ferreira, Rosemeire L Fiaccone, Jairo S Lordelo, Samila O. L. Sena, Victor R. Duran

Abstract


Multiple longitudinal outcomes are common in public health research and adequate methods are required when there is interest in the joint evolution of response variables over time. However, the main drawback of joint modeling procedures is the requirement to specify the joint density of all outcomes and their correlation structure, as well as numerical difficulties in statistical inference, when the dimension of these outcomes increases. To overcome such difficulty, we present two procedures to deal with multivariate longitudinal data. We first present an univariate approach, for which linear mixed-effects models are considered for each response variable separately. Then, a novel copula-based modeling is presented, in order to characterize relationships among the response variables. Both methodologies are applied to a real Brazilian data set on child growth.


Keywords


bivariate copula; linear mixed-effects model; longitudinal growth data; time-varying dependence

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References


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DOI: https://doi.org/10.5540/tema.2019.020.01.37

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TEMA - Trends in Applied and Computational Mathematics

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