Dr. Changbao Wu

Department of Statistics and Actuarial Science

University of Waterloo

Title: " Analysis of Longitudinal Surveys with Missing Responses "

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

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

This is joint work with Ivan Carrillo Garcia of Statistics Canada.