Articles

Statisticians Biostatistician Statistical Analysis Statistical Consulting Statistical Reports Statistics Consultant Statistical Quality Control Research Design Market Research Design Consulting Survey Research Design Experimental Research Design Research Design Strategies Time Series Research Design Quantitative Research Design Research Design Analysis Clinical Trial Design Clinical Research Trials Mathematical Modeling Statistical Modeling Financial Statistical Modeling Model Building Time Series Analysis Interrupted Time Series Design Time Series Forecasting Forecasting Methods Quantitative Forecasting Regression Analysis Survival Analysis Prediction Models Sample Size Calculation Power Calculation Logistic Regression Generalized Estimating Equations Linear Regression Models

Statisticians

Generalized Estimating Equations

by Scott Martin

If you are working with panel data and longitudinal analysis and need to manage the correlation structure, you should consider using generalized estimating equations (GEE). In fact, generalized estimating equations are one of the most important ways to analyze correlated data. This data is often encountered when the same units of data are collected across consecutive points over time.

Beginnings of Generalized Estimating Equations

Generalized estimating equations were first described in the 1986 publication by Liang and Zeger as a way to analyze correlated data. They were an outgrowth of generalized linear models and are one of the models used to model panel data. One of the ways to work with this data is through a random effects model.

The generalized estimating equations methodology is more frequently used when the dependent variable is dichotomous rather than continuous. Furthermore, one of the main focal points of GEE is on the specification of the model and the hypothesis testing.

There are certain parameters that need to be defined when working with a regression model using generalized estimation equations. The first item that needs to be defined is the dependent variable distribution. Additionally, the link function, the independent variables, and the covariance of the repeated measurements need to be specified.

Get all Market Research articles via