mmm: An R package for analyzing multivariate longitudinal data with multivariate marginal models

Date

2013-12-01

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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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 generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and mmm are valid for binary, continuous and count multivariate longitudinal responses. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

Subject Keywords

Correlated data, Multiple outcomes, Medical studies, Package presentation, Population-averaged inference, Statistical software

URI

https://hdl.handle.net/11511/48490

Journal

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

DOI

https://doi.org/10.1016/j.cmpb.2013.07.022

Collections

Department of Statistics, Article

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Citation Formats

O. Asar and Ö. İlk Dağ, “mmm: An R package for analyzing multivariate longitudinal data with multivariate marginal models,” COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, pp. 649–654, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48490.