Thursday, 20 June 2013

Modeling Longitudinal and Panel Data: GEE

IID:  "Independent, identically-distributed" is the assumption behind basic modeling methods.  The real world is rarely so conveniently arranged.  Data within a school may be correlated (when compared to observations from other schools in the study).  Likewise for patient treatment centers in multi-center clinical trials.  Learn how to deal with data like this in "Modeling Longitudinal and Panel Data: GEE" with Dr. Joseph Hilbe and Dr. James Hardin, in their online course at For more details please visit at

This course covers the extension of Generalized Linear Models (GLM) to model varieties of longitudinal and clustered data, called panel data. Specifically, the course treats generalized estimating equations (GEE), a population averaging method that models panel data in which the response is a member of the exponential family of distributions; e.g., continuous, binary, grouped, and count. GEE is one of several methods used to model panel data - the most noted alternative being random effect models. 

The course will discuss GEE theory, relevant correlation structures, and differences in both theory and application between population averaging GEE (PA-GEE) and random effects or subject specific panel models (SS-GEE).

Who can take this course:
Social scientists, medical and psychological researchers who need to analyze and model longitudinal or panel data.

Course Program:

Course outline: The course is structured as follows
  • Theory and history of GLM
  • Development of methods to analyze panel data
  • Software used for GEE and related models
  • Model Construction and Estimating Equations for Panel data in general and PA-GEE specifically
  • Parameterization of the working correlation matrix
  • Scale variance estimation
  • Alternating logistic regression models
  • SS-GEE models (random effect)
  • GEE2 models
  • Generalized and cumulative logistic regression
  • Problems with missing data
  • Residual analysis
  • Goodness-of-fit
  • Comparative testing of models
  • MCAR assumption for PA-GEE models

Dr. Hilbe and Dr. Hardin are the authors of "Generalized Estimating Equations" (Chapman & Hall/CRC). Dr. Joseph Hilbe is Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology.  He is an elected Fellow of both the American Statistical Association and Royal Statistical Society.  Dr. James Hardin is on the faculty at the University of S. Carolina, and also on the board of the "Stata Journal."

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at

Call: 020 66009116


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