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Re: st: RE: Mixed effects or GEE?


From   Mike Schmader <mikeschmader@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Mixed effects or GEE?
Date   Tue, 12 Nov 2002 07:26:40 -0800 (PST)

Thanks very much for the advice.  I have thought about
the effect of time.  The mailings were sent out
periodically to provide continual education, so I
don't think the effect waned as much due to time. 
There is expected to be a large change from time 0 to
1 (3 months out) and probably not so large of a change
after the 3 month average of antibiotic prescriptions.
 I will use xtgee with probably an unstructured
(possibly auto-regressive) correlation assumption.

Greg

--- "FEIVESON, ALAN H. (AL) (JSC-SD) (NASA)"
<alan.h.feiveson@nasa.gov> wrote:
> 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-----
> From: Mike Schmader [mailto:mikeschmader@yahoo.com]
> Sent: Tuesday, November 12, 2002 8:17 AM
> To: statalist@hsphsun2.harvard.edu
> 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? 
> 
> Thank you in advance.
> 
> Mike
> 
> 
> 
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