Tools for Selecting Working Correlation Structures When Using Weighted GEE to Model Longitudinal Survey Data

Weighted generalized estimating equations (GEEs) are popular for the marginal analysis of longitudinal survey data. This popularity is due to the ability of these estimating equations to provide consistent regression parameter estimates and corresponding standard error estimates as long as the population mean and survey weights are correctly specified. Although the data analyst must incorporate a working correlation structure within the weighted GEEs, this structure need not be correctly specified. However, accurate modeling of this structure has the potential to improve regression parameter estimation (i.e., reduce standard errors) and therefore, the selection of a working correlation structure for use within GEEs has received considerable attention in standard longitudinal data analysis settings. In this article, we describe how correlation selection criteria can be extended for use with weighted GEE in the context of analyzing longitudinal survey data. Importantly, we provide and demonstrate an R function that we have created for such analyses. Furthermore, we discuss correlation selection in the context of using existing software that does not have this explicit capability. The methods are demonstrated via the use of data from a real survey in which we are interested in the mean number of falls that elderly individuals in a specific subpopulation experience over time.