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

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mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models. / Asar, Özgür; Ilk, Ozlem.
In: Computer Methods and Programs in Biomedicine, Vol. 112, No. 3, 12.2013, p. 649-654.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Asar, Ö & Ilk, O 2013, 'mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.', Computer Methods and Programs in Biomedicine, vol. 112, no. 3, pp. 649-654. https://doi.org/10.1016/j.cmpb.2013.07.022

APA

Asar, Ö., & Ilk, O. (2013). mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models. Computer Methods and Programs in Biomedicine, 112(3), 649-654. https://doi.org/10.1016/j.cmpb.2013.07.022

Vancouver

Asar Ö, Ilk O. mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models. Computer Methods and Programs in Biomedicine. 2013 Dec;112(3):649-654. Epub 2013 Aug 5. doi: 10.1016/j.cmpb.2013.07.022

Author

Asar, Özgür ; Ilk, Ozlem. / mmm : an R package for analyzing multivariate longitudinal data with multivariate marginal models. In: Computer Methods and Programs in Biomedicine. 2013 ; Vol. 112, No. 3. pp. 649-654.

Bibtex

@article{f830669e45c24648871cd83dbbd13838,
title = "mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.",
abstract = "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.",
keywords = "Correlated data, Multiple outcomes, Medical studies, Package presentation, Population-averaged inference, Statistical software",
author = "{\"O}zg{\"u}r Asar and Ozlem Ilk",
year = "2013",
month = dec,
doi = "10.1016/j.cmpb.2013.07.022",
language = "English",
volume = "112",
pages = "649--654",
journal = "Computer Methods and Programs in Biomedicine",
issn = "1872-7565",
publisher = "Elsevier Ireland Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - mmm

T2 - an R package for analyzing multivariate longitudinal data with multivariate marginal models.

AU - Asar, Özgür

AU - Ilk, Ozlem

PY - 2013/12

Y1 - 2013/12

N2 - 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.

AB - 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.

KW - Correlated data

KW - Multiple outcomes

KW - Medical studies

KW - Package presentation

KW - Population-averaged inference

KW - Statistical software

U2 - 10.1016/j.cmpb.2013.07.022

DO - 10.1016/j.cmpb.2013.07.022

M3 - Journal article

VL - 112

SP - 649

EP - 654

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

SN - 1872-7565

IS - 3

ER -