Pseudo-GEE Approach to Analyzing Longitudinal Surveys under Imputation for Missing Responses

This paper presents a pseudo-GEE approach to the analysis of longitudinal surveys when the response variable contains missing values. A cycle-specific marginal hotdeck imputation method is proposed to fill in the missing responses and the pseudo-GEE method described in Carrillo et al. (2009) is applied to the imputed data set. Consistency of the resulting pseudo-GEE estimators is established under a joint randomization framework. Linearization variance estimators are also developed for the pseudo-GEE estimators under the assumption that the finite population sampling fraction is small or negligible. 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.