Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R

Asar, Oezguer
İlk Dağ, Özlem
Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model. (C) 2014 Elsevier Ireland Ltd. All rights reserved.


mmm: An R package for analyzing multivariate longitudinal data with multivariate marginal models
Asar, Oezguer; İlk Dağ, Özlem (2013-12-01)
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by genera...
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Citation Formats
O. Asar and Ö. İlk Dağ, “Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R,” COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, pp. 135–146, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32827.