  The method of generalized estimating equations (GEE) is popular in the
biostatistics literature for analyzing longitudinal binary and count data. It
assumes a generalized linear model (GLM) for the outcome variable, and a
working correlation among repeated measurements. In this paper, we introduce a
viable competitor: the weighted scores method for GLM margins. We weight the
univariate score equations using a working discretized multivariate normal
model that is a proper multivariate model. Since the weighted scores method is
a parametric method based on likelihood, we propose composite likelihood
information criteria as an intermediate step for model selection. The same
criteria can be used for both correlation structure and variable selection.
Simulations studies and the application example show that our method
outperforms other existing model selection methods in GEE. From the example, it
can be seen that our methods allow for correct analysis, and may change the
inferential results.
