Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random

Longitudinal studies of ten feature incomplete response and covariate data It is well known that biases can arise from naive analyses of available data. but the precise impact of Incomplete data depends on the frequency of missing data and the strength of the association between the response variables and emanates and the missing-data indicators Various factors may influence the availability of response and covariate data at scheduled assessment times, and at any given assessment time the response may be missing, covariate data may be missing. or both response and covariate data may he missing Here we show that ills important to take the association between the missing data indicators for these two processes into account through Joint models Inverse probability-weighted generalized estimating equations offer an appealing approach for doing this Mete we develop these equations for a particular model generating intermittently missing-at-random data Empirical studies demonstrate that the consistent estimators arising from the proposed methods have very small empirical biases in moderate samples Supplemental materials are available online