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RE: st: Unobserved heterogeneity in logistic regression


From   "daniel waxman" <dan@amplecat.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Unobserved heterogeneity in logistic regression
Date   Mon, 30 Jan 2006 18:55:13 -0500

Thanks for the response.  As it turns out, things are quite consistent
between hospitals, and between everything else... perhaps I am just being
paranoid.

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Karen Norberg
Sent: Monday, January 30, 2006 2:48 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Unobserved heterogeneity in logistic regression

Depending on the kind of heterogeneity you think might exist, one option 
is to use a 'fixed effects' or conditional logistic model. If you think
there is unobserved heterogeneity between hospitals, you stratify the
sample by hospital, thus 'conditioning out' of your model any
characteristics that may vary between hospitals, but remain constant among
observations within hospital.

Hospital isn't the only thing you could condition on - you could use
month or year fixed effects, other attributes - 

Karen Norberg, MD



On Mon, 30 Jan 2006, Maarten buis wrote:

> Dear Daniel:
> 
> The problem with unobserved heterogeneity is that it is well...
unobserved. Apparently you have
> many predictors of mortality available, so an obvious solution is to add
some of these predictors.
> In an earlier post you suggested that your variables are collinear, so you
probably don't want to
> add them all. That is no problem since the fact that they are collinear
with the variables left
> out means that most of the variance is captured by the variables in the
model (it does make the
> causal interpretation of these control variables more difficult, but the
roll of control variables
> is to control, and that is what they do).
> 
> I see the results of my models more as a rough indication than anything
else. So I tend to worry
> less about technicalities like these.  In my own research I deal with
survey data, and in my
> department they tape trained and experienced interviewers from reputable
agencies while they are
> interviewing and code the interactions between interviewer and
interviewed. The results make me
> very skeptical about the precision of my data. (See aside below) The paper
was written more to
> satisfy my nerdish tendencies than that I thought that the impact of this
phenomenon would be
> large enough to be noticeable above the random noise coming from data
collection. (I may be wrong
> though; the simulations by Glenn Hoetker seem to point in that direction,
though I have not yet
> read it as carefully as I should). I pointed you to this phenomenon
because in such a sensitivity
> analysis this phenomenon might be worthy of a footnote, and my working
paper might be helpful in
> understanding the literature to which it refers (and also the literature
to which Richard Williams
> referred).
> 
> So, my not entirely satisfactory answer is: dealing with "observed
heterogeneity" is much easier
> than unobserved heterogeneity. If you use additional modeling on top of
that and you get different
> results make sure you understand why that is the case and convince
yourself that that is
> plausible. 
> 
> HTH,
> Maarten
> 
> Aside
> Taping interviews does result in some funny interactions though:
Interviewer: How many times do
> you eat grain products for breakfast? Respondent: Well.... never ....
eh.... well no, that's not
> right, beer is a grain product too, isn't it? 
> 
> More often the interactions aren't that funny. For instance, the
"experienced" interviewer looks
> around the room and decides for the respondent in which income and
educational category he/she
> falls, or asks very suggestive questions, makes mistakes while entering
the data, etc. etc. etc.
> 
> --- daniel waxman <dan@amplecat.com> wrote:
> 
> > Maartin Buis directed me to a short paper of his:  "Unobserved
heterogeneity
> > in logistic regression":
> > 
> > http://home.fsw.vu.nl/m.buis/
> > 
> > The concept makes sense--the question is what to do about it.
>  
> <snip>
> 
> > There are of course many unobserved causes for in-hospital mortality,
but
> > insofar as this particular model seems to work, do I need to deal with
this?
> > If one does try to deal with it in a situation such as mine, is it a
matter
> > of using a method other than simple logistic regression to fit the
model, or
> > is it more a matter of assessment of goodness if fit? 
> > 
> 
> 
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
> 
> visiting adress:
> Buitenveldertselaan 3 (Metropolitan), room Z214
> 
> +31 20 5986715
> 
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
> 
> 
> 		
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