Title
Weighted Generalized Estimating Functions for Incomplete (MAR) Longitudinal Response and Covariates Data
Speaker
Baojiang Chen, University of Washington
Abstract
Longitudinal studies often feature incomplete response and covariate data. It is well known that biases can arise from naive analyses of available data, but the precise impact of incomplete data depends on the frequency of missing data and the strength of the association between the response variables and covariates and the missing data indicators. Different factors may influence the availability of response and covariate data at scheduled assessment times, and at any given assessment time the response may be missing, covariate data may be missing, or both response and covariate data may be missing. Here we show that it is important to take the association between the missing data indicators for these two processes into account through joint models. Inverse probability weighted generalized estimating equations offer an appealing approach for doing this and we develop these here for a particular model generating intermittently missing at random data. Empirical studies demonstrate that the consistent estimators arising from the proposed methods have very small empirical biases in moderate samples.
