Fitting drug data using generalized estimating equations as regression model. – C . E. Onwukwe, E. Eteng, J. A. Ugboh and T. A. Ugbe
Researchers are always interested in analyzing data that arise from a longitudinal or clustered design. Although there are a variety of standard likelihood-based approaches to analysis when the outcome variables are approximately multivariate normal, models for discrete-type outcomes generally require a different approach. Liang and Zeger (1986) formalized an approach to this problem using generalized estimating equations(GEEs) to extend generalized linear models(GLMs) to a regression setting with correlated observations within subjects. SAS Proc enmod was used to fit a model in a drug data . The model fitted is =0.3291-1.1553period-1.4994older+1.2542A+0.3404B.