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

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Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R. / Asar, Özgür; Ilk, Ozlem.
In: Computer Methods and Programs in Biomedicine, Vol. 115, No. 3, 07.2014, p. 135-146.

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

Harvard

Asar, Ö & Ilk, O 2014, 'Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R', Computer Methods and Programs in Biomedicine, vol. 115, no. 3, pp. 135-146. https://doi.org/10.1016/j.cmpb.2014.04.005

APA

Asar, Ö., & Ilk, O. (2014). Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R. Computer Methods and Programs in Biomedicine, 115(3), 135-146. https://doi.org/10.1016/j.cmpb.2014.04.005

Vancouver

Asar Ö, Ilk O. Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R. Computer Methods and Programs in Biomedicine. 2014 Jul;115(3):135-146. Epub 2014 Apr 18. doi: 10.1016/j.cmpb.2014.04.005

Author

Asar, Özgür ; Ilk, Ozlem. / Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R. In: Computer Methods and Programs in Biomedicine. 2014 ; Vol. 115, No. 3. pp. 135-146.

Bibtex

@article{47bae43ffa634f15a101bde225a6fe75,

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

abstract = "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.",

keywords = "Clustered data, Multiple outcomes , Parsimonious model building , Statistical software, Quasi-likelihood inference",

author = "{\"O}zg{\"u}r Asar and Ozlem Ilk",

year = "2014",

month = jul,

doi = "10.1016/j.cmpb.2014.04.005",

language = "English",

volume = "115",

pages = "135--146",

journal = "Computer Methods and Programs in Biomedicine",

issn = "1872-7565",

publisher = "Elsevier Ireland Ltd",

number = "3",

}

RIS

TY - JOUR

T1 - Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R

AU - Asar, Özgür

AU - Ilk, Ozlem

PY - 2014/7

Y1 - 2014/7

N2 - 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.

AB - 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.

KW - Clustered data

KW - Multiple outcomes

KW - Parsimonious model building

KW - Statistical software

KW - Quasi-likelihood inference

U2 - 10.1016/j.cmpb.2014.04.005

DO - 10.1016/j.cmpb.2014.04.005

M3 - Journal article

VL - 115

SP - 135

EP - 146

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

SN - 1872-7565

IS - 3

ER -

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