  Generalized estimating equations (GEE) is one of the most commonly used
methods for marginal regression analysis of longitudinal data, especially with
discrete outcomes. The GEE method models the association among the responses of
a subject through a working correlation matrix and correct specification of the
working correlation structure ensures efficient estimation of the regression
parameters. This study proposes a predicted residual sum of squares (PRESS)
statistic as a working correlation selection criterion in GEE. An extensive
simulation study is designed to assess the performance of the proposed GEE
PRESS criterion and to compare its performance with well-known existing
criteria in the literature. The results show that the GEE PRESS criterion has
better performance than the weighted error sum of squares SC criterion in all
cases and is comparable with that of other existing criteria when the true
working correlation structure is AR(1) or exchangeable. Lastly, the working
correlation selection criteria are illustrated with the Coronary Artery Risk
Development in Young Adults study.
