Description Details Author(s) References See Also Examples

Weighted Generalized estimating equations (WGEE) is an extension of generalized linear models to longitudinal or clustered data by incorporating the correlation within-cluster when data is missing at random (MAR). The parameters in mean, scale, correlation structures are estimate based on quasi-likelihood. The package wgeesel also contains model selection criteria for variable selection in the mean model and for the selection of a working correlation structure in longitudinal data with dropout or monotone missingness using WGEE.

The collection of functions includes:

`wgee`

estimates parameters based on WGEE in mean, scale, and correlation structures, through mean link, scale link, and correlation link.

`QIC.gee`

,`MQIC.gee`

,`RJ.gee`

calculate the QIC (QIC

*u*), MQIC (MQIC*u*), Rotnitzky-Jewell criteria for variable selection in the mean model and/or selection of a working correlation structure in GEE (unbalanced data is allowed).`MLIC.gee`

,`QICW.gee`

calculate the MLIC (MLICC) and QICW

*r*(QICW*p*) for variable selection in the mean model and the selection of a working correlation structure in WGEE, which can accommodate dropout missing at random (MAR).`data_sim`

can simulate longitudinal response data in different distribution (gaussian, binomial, poisson) with drop missingness.

For a complete list of functions, use `library(help = "wgeesel")`

.

Cong Xu congxu17@gmail.com, Zheng Li zheng.li@outlook.com, Ming Wang mwang@phs.psu.edu

Maintainer: Zheng Li zheng.li@outlook.com

Liang, K.Y. and Zeger, S.L., 1986. Longitudinal data analysis using generalized linear models. *Biometrika*, pp.13-22.

Preisser, J.S., Lohman, K.K. and Rathouz, P.J., 2002. Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random. *Statistics in medicine*, 21(20), pp.3035-3054.

Robins, J.M., Rotnitzky, A. and Zhao, L.P., 1995. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. *Journal of the American Statistical Association*, 90(429), pp.106-121.

Shen, C. W., & Chen, Y. H. (2012). Model selection for generalized estimating equations accommodating dropout missingness. *Biometrics*, 68(4), 1046-1054.

Wang, M., 2014. Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments. *Advances in Statistics*, 2014.

GEE methods exist for `geeglm`

(geepack)

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