# st: RE: Mixed effects or GEE?

 From "FEIVESON, ALAN H. (AL) (JSC-SD) (NASA)" <[email protected]> To "'[email protected]'" <[email protected]> Subject st: RE: Mixed effects or GEE? Date Tue, 12 Nov 2002 08:57:37 -0600

```Mike - You should also consider the effect of time - i.e. as time goes on,
the effect of the intervention might wane. Suppose you have a logistic
regression model something like

logit p(prescribe antibiotics)=f(time,intervention) + e

In GEE, (with an equicorrelated assumption), you are only assuming equal
correlation of e within dentists - no other distributional assumptions on e.
With xtlogit, e is split into two random effects,  between and within
dentists, with each effect normally distributed and independent. So the
equicorrelation arises because the between-dentist random effect is the same
for all observations pertaining to that dentist.

As in most statistical modelling problems, the more sophisticated model
(xtlogit), if correct,  gives greater power. However there is a risk of bias
if the model is not correct. GEE makes fewer assumptions, so there is less
risk of bias, but it would in general be less powerful.

Also, in GEE you have the option of specifying other types of correlation
within dentists - see manual.

Hope this helps-

Al Feiveson

-----Original Message-----
Sent: Tuesday, November 12, 2002 8:17 AM
To: [email protected]
Subject: st: Mixed effects or GEE?

Dear listers,

I am confused about the differences between random
effects mixed effects and GEE models.

I am looking at prescribing patterens of dentists.  My
outcome variable will be % of prescriptions written
for antibiotics at baseline (t=0), 3 months (t=1), 6
months (t=2) and 12 months (t=3) post initiation of
intervention mailings (educational material).
Dentists were randomly assigned to intervention
groups: group 1- "no intervention"; group 2- "low
level of intervention"; group 3 -"high level of
intervention".

I will be analyzing this data with panel techniques
but I am not sure whether I should use GEE, random
effects, or mixed effects models.  I will be using
dentist ID as my clustering variable and indicator
variables for level of intervention.  Can anyone point
me in the right direction as to the main differences
in these techniques as I have not used these methods
before?

Mike

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