Thanks for this, very clear
Justin B.
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Maarten buis
Sent: 16 March 2009 20:39
To: [email protected]
Subject: RE: st: Multilevel modelling of survival data
--- On Mon, 16/3/09, Justin B Echouffo Tcheugui wrote:
> 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 -gllamm- and -xtmelogit-
>
> Is there any advantage of using one or the other command?
-xtmelogit- is quicker, and since you are only estimating
a random intercept model you could also use -xtlogit-, which
is even quicker.
> 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.
>
> Is there reason why one should prefer -xtmixed- instead of
> xtmelogit for a binary variable?
I am no fan of using linear models for categorical data, and
that is an understatement. The arguments that I have heard
in their favor come roughly in two flovours:
1) "I (or my readers) don't know how to interpret odds ratios"
The answer is: it is not that hard, the odds is how many
successes for every failure, and is a measure of the
likelihood of success. The odds ratio is how many times
larger this odds of success is for group 2 relative to group 1.
2) "The results of non-linear models like -logit- are a biased
biased estimate of the causal effect, even when analyzing data
obtained in a randomized experiment"
The answer is: true, but the same is also true for the linear
model when applied to a categorical dependent variable.
So, I just would not bother with -xtmixed-, and stick with
-xtlogit-.
> Do I need absolutely need to correct the confidence
> intervals given by the linear model?
Probably, the residual error is not going to be homoskedastic.
> If yes, is the following command the right one to use?
I have no idea, primarily because I would not use the linear
model in this case anyhow.
-- 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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