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

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2014-07-01

Author

Asar, Oezguer
İlk Dağ, Özlem

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Clustered data, Multiple outcomes, Parsimonious model building, Statistical software, Quasilikelihood inference

URI

https://hdl.handle.net/11511/32827

Journal

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

DOI

https://doi.org/10.1016/j.cmpb.2014.04.005

Collections

Department of Statistics, Article

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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.