wgeesel-package: Weighted Generalized Estimating Equations and Model Selection
In wgeesel: Weighted Generalized Estimating Equations and Model Selection

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 (QICu), MQIC (MQICu), 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 QICWr (QICWp) 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)

data(imps)

fit <- wgee(Y ~ Drug+Sex+Time,data=imps,id=imps$ID,family="binomial",
            corstr="exchangeable",scale=NULL,mismodel= R ~ Drug+Time)