  Generalized Estimation Equations (GEE) are a well-known method for the
analysis of non-Gaussian longitudinal data. This method has computational
simplicity and marginal parameter interpretation. However, in the presence of
missing data, it is only valid under the strong assumption of missing
completely at random (MCAR). Some corrections can be done when the missing data
mechanism is missing at random (MAR): inverse probability weighting (WGEE) and
multiple imputation (MIGEE). In order to obtain consistent estimates, it is
necessary the correct specification of the weight model for WGEE or the
imputation model for the MIGEE. A recent method combining ideas of these two
approaches has doubly robust property. For consistency, it requires only the
weight or the imputation model to be correct. In this work it is assumed a
proportional odds model and it is proposed a doubly robust estimator for the
analysis of ordinal longitudinal data with intermittently missing response and
covariate under the MAR mechanism. Simulation results revealed better
performance of the proposed method compared to WGEE and MIGEE. The method is
applied to a data set related to Analgesia Pain in Childbirth study.
