  In this article, we propose a new method for analyzing longitudinal data
which contain responses that are missing at random. This method consists in
solving the generalized estimating equation (GEE) of Liang and Zeger (1986) in
which the incomplete responses are replaced by values adjusted using the
inverse probability weights proposed in Yi, Ma and Carroll (2012). We show that
the root estimator is consistent and asymptotically normal, essentially under
the some conditions on the marginal distribution and the surrogate correlation
matrix as those presented in Xie and Yang (2003) in the case of complete data,
and under minimal assumptions on the missingness probabilities. This method is
applied to a real-life dataset taken from Sommer, Katz and Tarwotjo (1984),
which examines the incidence of respiratory disease in a sample of 250
pre-school age Indonesian children which were examined every 3 months for 18
months, using as covariates the age, gender, and vitamin A deficiency.
