GEE or MLM

Generalized estimating equations (GEE) or multilevel models (MLM)
Source: Andrew Gelman

[YCheng: This is a informative Q & A from the Andrew Gelman's website.]

Diana S. Grigsby-Toussaint writes,

I recently came across your “Statistical Modeling, Causal Inference, and Social Science” website in my attempt to determine the best analysis for my research. As there were some inquiries about whether GEE is a better approach than multilevel modeling, I was hoping you could help with my dilemma.

I am interested in neighborhood (defined as census tract) influences on childhood diabetes risk in the city of Chicago. Although I have a little over 1200 cases, ~40% of my tracts have only 1 case, and the average number of cases per tract is 5. GEE has been suggested as the better approach to HLM, but I am not getting much support for this option….any suggestions for the best approach or articles that might provide some insight?

My quick response: see here.

My longer response:

I think of GEE and multilevel (hierarchical) models as basically the same thing, with the main difference being that GEEs focus on estimating a nonvarying (or average) coefficient in the presence of clustering, whereas MLMs (HLMs) focus on estimating the aspects of the model that vary by group.

Looking further, there are differences in taste: GEEs appeal to people who don’t like distributional assumptions, whereas MLMs appeal to people who like generative models. I prefer MLMs because I like to set up an explicit model for the data; others prefer GEEs because they like to have a procedure that estimates parameters in the absence of assumptions for how the coefficients vary. To give myself the last word on this: I like MLMs because they are expandable to more complex models, and also because often I actually am intersested in the varying coefficients (particuarly varying slopes, as here).

Technical issues

To get to your technical question: there is no problem whatsoever in fitting MLMs when most of your groups have only 1 case, and the average number of groups per case is 5. No problem at all, and I have no idea why anyone would say otherwise. (See Section 12.9 of our new book for more on this, in particular the second full paragraph on page 276.)

You also might be interested in this article, which compares GEE and hierarchical logistic regression.

YCheng: Other articles:

• A brief conceptual tutorial of multilevel analysis in social epidemiology
• Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation
• When to use generalized estimating equations vs. mixed effects models? - CrossValidated
• Multilevel Modeling
• Multilevel and Longitudinal Modeling Using Stata, Second Edition - Google Book
• Slides: Multilevel Analysis and Complex Surveys -  Alan Hubbard
• Multilevel modelling of complex survey data - Habbard
• Multilevel modeling of complex survey data - Rabe-Hesketh 07' Stata Group Meeting
• Fitting multilevel models in complex survey data with design weights: Recommendations