The Generalised Estimating Equations (GEE) proposed by Liang and Zeger (1986) and Zeger and Liang (1986) have found considerable attention in the last ten years and several extensions have been proposed. In this annotated bibliography we describe the development of the GEE and its extensions during the last decade. Additionally, we discuss advantages and disadvantages of the different parametri...

menorrhagia is one of the most common gynecological problem and leading causes of poor quality of life and iron deficiency anemia in women of reproductive age. research in gynecological field relies heavily on repeated measure designs. repeated measure studies are helpful in understanding how factors of interest change over time. our goal is to apply statistical methods which are appropriate fo...

The logistic Generalized Estimating Equations (logisticGEE) models have been extensively used for analyzing clustered binary data. However, assessing the goodness-of-fit and predictability of these models is problematic due to the fact that no likelihood is available and the observations can be correlated within a cluster. In this paper we propose a new measure for estimating the generalization...

Multi-wave self-report data on age at menopause in 2182 female twin pairs (1355 monozygotic and 827 dizygotic pairs), were analysed to estimate the genetic, common and unique environmental contribution to variation in age at menopause. Two complementary approaches for analysing correlated time-to-onset twin data are considered: the generalized estimating equations (GEE) method in which one can ...

The generalized estimating equation (GEE) has been a popular tool for marginal regression analysis with longitudinal data, and its extension, the weighted GEE approach, can further accommodate data that are missing at random (MAR). Model selection methodologies for GEE, however, have not been systematically developed to allow for missing data. We propose the missing longitudinal information cri...

BACKGROUND Investigators designing clinical trials often use composite outcomes to overcome many statistical issues. Trialists want to maximize power to show a statistically significant treatment effect and avoid inflation of Type I error rate due to evaluation of multiple individual clinical outcomes. However, if the treatment effect is not similar among the components of this composite outcom...

Correlated response data often arise in longitudinal and familial studies The marginal regression model and its associated generalized estimating equation GEE method are becoming more and more popular in handling such data Pepe and Anderson pointed out that there is an important yet implicit assumption behind the marginal model and GEE If the assumption is violated and a non diagonal working co...

We extend Wei and Tanner's (1991) multiple imputation approach in semi-parametric linear regression for univariate censored data to clustered censored data. The main idea is to iterate the following two steps: 1) using the data augmentation to impute for censored failure times; 2) fitting a linear model with imputed complete data, which takes into consideration of clustering among failure times...

This project discusses the Generalized Estimating Equation (GEE) model and its application for longitudinal data. For many longitudinal studies involving a discrete (count or categorical) response variable, and for some studies involving a continuous response variable, it is difficult to justify a fully parametric model for the data. In these circumstances, the method of GEE provides consistent...