Use of Unbiased Estimating Equations to Estimate Correlation in Generalized Estimating Equation Analysis of Longitudinal Trials
In a recent publication, Wang and Carey (Journal of the American Statistical Association, 99, pp. 845-853, 2004) presented a new approach for estimation of the correlation parameters in the framework of generalized estimating equations (GEE). They considered correlated continuous, binary and count data with a generalized Markov correlation structure that includes the first-order autoregressive AR(1) and Markov structures as special cases. They made detailed comparisons with pseudo-likelihood (PL) and the first stage of quasi-least squares (QLS), a two-stage approach in the framework of generalized estimating equations (GEE). In this note we extend their comparisons for the second (bias corrected) stage of QLS. We comment on their earlier findings, which were overwhelmingly in favor of the Wang-Carey (WC) approach relative to stage one of QLS. We prove that WC and QLS are identical for equally spaced data with an AR(1) structure. Furthermore, we demonstrate via simulations that neither QLS, PL or WC is uniformly superior for unequally spaced data with a Markov structure. We give general recommendations regarding the relative merits of each approach for analysis of unbalanced and unequally spaced longitudinal data and demonstrate their application in an analysis of a longitudinal study of obesity following renal transplantation in children.
Longitudinal Data Analysis and Time Series
Sun, Wenguang; Shults, Justine; and Leonard, Mary, "Use of Unbiased Estimating Equations to Estimate Correlation in Generalized Estimating Equation Analysis of Longitudinal Trials" (January 2006). UPenn Biostatistics Working Papers. Working Paper 4.