Analysis of Longitudinal Surveys with Missing Responses
Changbao Wu, University of Waterloo
Abstract:
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.