Dear Marteen,
Thanks for the precious precisions about multilevel modelling of
survival data.
Let me seize this opportunity to ask questions this time about
multilevel modelling of binary data. I have smoking as one of my outcome
in a cluster randomised trial. The unit of randomisation are clinics. I
want to explore the difference between groups in term of smoking at
follow-up
1- I first used logistic regression using the following commands to
derive the odds ratio
xi:gllamm smoking i.randomgp, i(id clinic) family (binom) link (logit)
adapt eform , where id is the subject identifier
xi:xtmelogit smoking i.randomgp || clinic:,or
Is there any advantage of using one or the other command?
2- Later on, I was advised to analyse smoking as a continuous variable
in a linear fashion expressing my result as the adjusted difference in
proportion of smokers between the groups. I then used following command
xtmixed smoking i.randomgp || practice:, var
Is there reason why one should preferably use xtmixed isnetad of
xtmelogit for a binary variable?
Do I need absolutely need to correct the confidence intervals given by
the linear model? If yes, is the following command the right one to use?
parmest, li(, noobs sepby(parm)) dof(46) format(estimate stderr %9.3f
dof %9.0f t p min* max* %9.3f)
I could not use the gllamm command as I wasn't sure about the family and
link to use as smoking is binary.
Many thanks
Justin B.
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Maarten buis
Sent: 16 March 2009 10:58
To: stata list
Subject: RE: st: Multilevel modelling of survival data
--- On Mon, 16/3/09, Justin B Echouffo Tcheugui wrote:
> > in this case the option - cluster() in this case does
> > not fit the clinic into the model as a random
> > intercept
--- On Mon, 16/3/09, Maarten buis wrote:
> That is correct.
A point on terminology again: When discussing the
distrinction between these models, the models estimated
with the -cluster()- option are sometimes known as
population averaged models, while the random intercept
models are sometimes known as individual specific models.
-- Maarten
-----------------------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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