Longitudinal surveys have emerged in recent years as an important data collection tool for population studies where the primary interest is to examine population changes over time at the individual level. The generalized estimating equation (GEE) approach is the most popular statistical inference tool for longitudinal studies. The vast majority of existing literature on the GEE method, however, uses the method for non-survey settings, and issues related to complex sampling designs are ignored.
We propose methods for the analysis of longitudinal surveys when the response variable contains missing values. Our methods are built within the GEE framework, with a major focus on using the GEE method when missing responses are handled through imputation.
We first argue why and further show how the survey weights can be incorporated into the so-called Pseudo GEE method under a joint randomization framework, and the missing responses are handled either by a re-weighting method or by imputation. Consistency of the resulting GEE estimators of the regression coefficients are established under certain regularity conditions. Linearization variancce estimators are developed under the assumption that the finite population sampling fraction is small or negligible, a scenerio often held for large scale population surveys. Finite sample performances of the proposed estimators are investigated through a simulation study. The results show that the proposed GEE estimators and the linearization variance estimators perform well under several sampling designs for both continuous and binary responses. This is joint work with Ivan Carrillo Garcia of Statistics Canada. The talk is sponsored by MITACS project Statistical Methods for Complex Survey Data.