A0173
Title: Adjusted predictions in generalized estimation equations
Authors: Francis Hui - The Australian National University (Australia) [presenting]
Muller Samuel - Macquarie University (Australia)
Alan Welsh - the Australian National University (Australia)
Abstract: Generalized estimating equations (GEEs) is a popular regression approach that requires specification of the first two marginal moments of the data, along with a working correlation matrix capturing the covariation between responses e.g., temporal correlations within clusters in longitudinal data. The majority of research and application of GEEs has focused on the estimation and inference of regression coefficients in the marginal mean. When it comes to prediction using GEEs, practitioners often simply and quite understandably also base it on the regression model characterizing the marginal mean. We propose a simple adjustment to predictions in GEEs based on utilizing information in the assumed working correlation matrix. By viewing the GEE from the perspective of solving a working linear model, we borrow ideas from universal kriging to construct a predictor that leverages temporal correlations between the new and current observations within the same cluster. We establish some theoretical conditions for the proposed adjusted GEE predictor to outperform the standard unadjusted predictor. Simulations show even when we misspecify the working correlation, adjusted GEE predictors (combined with an information criterion for choosing the working correlation matrix) can improve the predictive performance of standard GEE predictors as well as the so-called oracle GEE predictor using all observations.
