Paper of the Month: November 2018

Once a month during the academic year, the statistics faculty select a paper for our students to read and discuss. Papers are selected based on their impact or historical value, or because they contain useful techniques or results.

Liang, K. Y., & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.

Notes preparer: Dipak Dey

Liang and Zeger (1986) proposed the generalized estimating equation (GEE), a multivariate extension of the generalized linear model to handle clustered data such as longitudinal data. The GEE method focuses on the regression parameters of the marginal means without specifying the multivariate dependence. A working correlation structure is used to improve efficiency. The resulting estimators of the mean parameters are consistent even if the working correlation is not correctly specified. The closer the working correlation is to the truth, the higher the efficiency. When the outcomes are multivariate normal, the GEEs reduces to the score equation if the working correlation is correctly specified. The estimators are asymptotically normal with a variance that can be estimated by a sandwich estimator. This paper has been cited widely (approximately 16,000 citations to date). The method has been a standard tool in applied statisticians’ toolbox and is widely used in many fields. It is implemented in standard software packages such as SAS and R (Our faculty Jun Yan developed the R package geepack during his graduate study).