Marginal methods have been widely used for analyzing longitudinal ordinal data due to their simplicity in model assumptions, robustness in inference results, and easiness in the implementation. However, they are often inapplicable in the presence of measurement errors in the variables. Under the setup of longitudinal studies with ordinal responses and covariates subject to misclassification, Chen et al. (2014) developed marginal methods for misclassification adjustments using the second-order estimating equations and proposed a two-stage estimation approach when the validation subsample is available. Parameter estimation is conducted through the Newton-Raphson algorithm, and the asymptotic distribution of the estimators is established. While the methods of Chen et al. (2014) can successfully correct the misclassification effects, its implementation is not accessible to general users due to the lack of a software package. In this paper, we develop an R package, mgee2, to implement the marginal methods proposed by Chen et al. (2014). To evaluate the performance and illustrate the features of the package, we conduct numerical studies.
mgee2, SAMBA, misclassGLM, augSIMEX, kml, kml3d, gee, wgeesel, swgee, MASS, Matrix, ggplot2, mgee2k, mgee2v, ordGEE2
Cluster, Distributions, Econometrics, Environmetrics, MissingData, MixedModels, NumericalMathematics, Phylogenetics, Psychometrics, Robust, Spatial, TeachingStatistics
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For attribution, please cite this work as
Xu, et al., "mgee2: An R package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates", The R Journal, 2021
BibTeX citation
@article{RJ-2021-093,
author = {Xu, Yuliang and Liu, Shuo Shuo and Yi, Grace Y.},
title = {mgee2: An R package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-093},
doi = {10.32614/RJ-2021-093},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {471-484}
}