Adjusting for clustered data in regression

[#1]

I am looking to predict the proportion of patients in a hospital with a specific disease, adjusting for hospital characteristics. However, the data are clustered (hospitals clustered within states) and I want to adjust for this, too. I am using JMP Pro 13.

Is the proper approach to use a Generalized Linear Model and select state with the "Nest" button? This seems correct, but when I do that JMP includes each state (n=51) as a variable. This is effectively what I want, but adds a lot of DF and variables to a data set with relatively few observations.

Am I doing this right, or is there a better way?

Thanks

[#2 · Accepted solution]

Normally, I would want to make state and hospital[state] random effects. That would dramatically reduce the impact of the total number of parameters to estimate. However, random effects are not supported for GLMs in JMP.
You could do a cluster analysis and use the clusters in place of states.
If you have access to SAS, you could use Proc GLIMMIX to do the mixed model GLM.

[#3]

Thank you for the quick response. Would the new cluster variable be entered as a "Nest" variable in GLM? I already know there are 4 clusters I can group the states into.

[#4]

You certainly can do that.