wgeesel (version 1.5)

wgeesel-package: Weighted Generalized Estimating Equations and Model Selection

Description

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.

Details

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").

References

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.

See Also

GEE methods exist for geeglm (geepack)

Examples

# NOT RUN {
data(imps)

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

# }