This paper describes the core features of the R package geepack, which implements the generalized estimating equations (GEE) approach for fitting marginal generalized linear models to clustered data. Clustered data arise in many applications such as longitudinal data and repeated measures. The GEE approach focuses on models for the mean of the correlated observations within clusters without fully specifying the joint distribution of the observations. It has been widely used in statistical practice. This paper illustrates the application of the GEE approach with geepack through an example of clustered binary data
The most common analysis used for binary data is generalised linear model (GLM) with either a binom...
negative binomial and ZI tobit models. Recently, extensions of these models to the clustered data ca...
The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other...
This paper describes the core features of the R package geepack, which implements the generalized es...
This paper describes the core features of the R package geepack, which implements the generalized es...
Clustered binary data frequently occur in epidemiology and other applied fields such as clinical tri...
Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/cluster...
International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are ...
This project discusses the Generalized Estimating Equation (GEE) model and its application for longi...
International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are ...
International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are ...
In the analysis of clustered or hierarchical data, a variety of statistical techniques can be applie...
Studies in epidemiology and social sciences are often longitudinal and outcome measures are frequent...
Studies in epidemiology and social sciences are often longitudinal and outcome measures are frequent...
Studies in epidemiology and social sciences are often longitudinal and outcome measures are frequent...
The most common analysis used for binary data is generalised linear model (GLM) with either a binom...
negative binomial and ZI tobit models. Recently, extensions of these models to the clustered data ca...
The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other...
This paper describes the core features of the R package geepack, which implements the generalized es...
This paper describes the core features of the R package geepack, which implements the generalized es...
Clustered binary data frequently occur in epidemiology and other applied fields such as clinical tri...
Generalized Estimating Equation (GEE) is a marginal model popularly applied for longitudinal/cluster...
International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are ...
This project discusses the Generalized Estimating Equation (GEE) model and its application for longi...
International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are ...
International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are ...
In the analysis of clustered or hierarchical data, a variety of statistical techniques can be applie...
Studies in epidemiology and social sciences are often longitudinal and outcome measures are frequent...
Studies in epidemiology and social sciences are often longitudinal and outcome measures are frequent...
Studies in epidemiology and social sciences are often longitudinal and outcome measures are frequent...
The most common analysis used for binary data is generalised linear model (GLM) with either a binom...
negative binomial and ZI tobit models. Recently, extensions of these models to the clustered data ca...
The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other...