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Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R
被引:3
|作者:
Asar, Oezguer
[1
]
Ilk, Ozlem
[2
]
机构:
[1] Lancaster Med Sch, CHICAS, Fac Hlth & Med, Lancaster LA1 4YG, England
[2] Middle E Tech Univ, Fac Arts & Sci, Dept Stat, TR-06800 Ankara, Turkey
关键词:
Clustered data;
Multiple outcomes;
Parsimonious model building;
Statistical software;
Quasi-likelihood inference;
BINARY DATA;
OUTCOMES;
D O I:
10.1016/j.cmpb.2014.04.005
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
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.
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页码:135 / 146
页数:12
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