The pseudo-GEE approach to the analysis of longitudinal surveys

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. Longitudinal data are often analyzed through the generalized estimating equations (GEE) approach. The vast majority of existing literature on the GEE method, however, is developed under non-survey settings and are inappropriate for data collected through complex sampling designs. In this paper the authors develop a pseudo-GEE approach for the analysis of survey data. They show that survey weights must and can be appropriately accounted in the GEE method under a joint randomization framework. The consistency of the resulting pseudo-GEE estimators is established under the proposed framework. Linearization variance estimators are developed for the pseudo-GEE estimators when the finite population sampling fractions are small or negligible, a scenario often held for large scale surveys. Finite sample performances of the proposed estimators are investigated through an extensive simulation study using data from the National Longitudinal Survey of Children and Youth. The results show that the pseudoGEE estimators and the linearization variance estimators perform well under several sampling designs and for both continuous and binary responses.