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Specification of Variance-Covariance Structure in Bivariate Mixed Model for Unequally Time-Spaced Longitudinal Data
Pages 370-377
Melike Bahçecitapar, Özge Karadağ and Serpil Aktaş
Published: 03 November 2015

Abstract: In medical studies, the longitudinal data sets obtained from more than one response variables and covariates are mostly analyzed to investigate the change in repeated measurements of each subject at different time points. In this study, the usability of multivariate models in the analysis of these kind of data sets is investigated, because it provides the joint analysis of multiple response variables over time and enables researchers to examine both the correlations of response variables and autocorrelation between measurements from each response variable over time. It has been shown that different parameter estimation methods affect the results in the analysis of multivariate unbalanced longitudinal data. We investigated that autocorrelation structure over time between measurements from same response variable should be truly specified. We also illustrated and compared the simpler, more standard models for fixed effects with multivariate models provided by SAS on a real-life data set in the joint analysis of two response variables. Results show that misspecification of autocorrelation structures has a negative impact on the parameter estimates and parameter estimation method should become of interest.

Keywords: Multivariate longitudinal data, mixed models, covariance structures.
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