Thank you to everyone that responded to my post. I
appreciate it very much.
Best,
Ricardo.
--- Chris Rohlfs <[email protected]> wrote:
> yes. sorry; i wasn't thinking.
>
> On Mon, 8 Sep 2003, Roger Harbord wrote:
>
> > --On 08 September 2003 11:27 -0500 Chris Rohlfs
> <[email protected]> wrote:
> >
> > > ricardo,
> > >
> > > don't worry about controlling for practice.
> you've already controlled for
> > > it with the doctor id (i.e., they're perfectly
> multicollinear). so just
> > > run:
> > >
> > > clogit referred, group(doctorid)
> > >
> > > and any possible confounding effects of
> "practice" will be captured &
> > > controlled for by doctorid.
> > >
> > > chris
> >
> > Hmm, but Ricardo is interested in comparing the
> referral rates between
> > practices and that command isn't going to do that.
> >
> > Presumably each physician belongs to only one
> practice. Ricardo is
> > concerned that referral rates may vary between
> physicians WITHIN a
> > practice, i.e. that observations on patients seen
> by different physicians
> > within the same practice are not independent - or
> put another way, there
> > may be more variation between physicians in the
> same practice than would be
> > expected by chance if all physicians in a practice
> had identical referral
> > rates. This means there may be overdispersion. I
> can't see that -clogit-
> > is useful as there's no matching and there's no
> information on differences
> > between practices within physicians so a
> fixed-effects model is no use.
> >
> > The simplest analysis is:
> > . logistic referred practice
> > However this ignores the possibility of
> overdispersion and is therefore
> > likely to be anticonservative (P-value too small).
> >
> > To allow for the within-physician correlation you
> could use -xtlogit- to
> > fit a model with a between-physician variance
> component. Either a
> > random-effects model:
> > . xtlogit referred practice, i(doctorid) or
> > Or a population-averaged model:
> > . xtlogit referred practice, i(doctorid) or pa
> > Both are likely to give similar results though the
> second is less likely to
> > run into numerical problems.
> >
> > Another approach would be to use -glm- with the
> -scale- option to fit a
> > logistic regression model with a multiplicative
> overdispersion factor. To
> > do this in Stata you need to first -collapse- the
> data to give grouped
> > binomial data:
> > . collapse (sum) referred (count)
> npatients=referred, by(practice doctorid)
> > . glm referred practice, family(binomial
> npatients) scale(x2) eform
> >
> > This gives the same point estimate as the
> -logistic- command above but
> > increases the standard error to allow for the
> overdispersion. I'd be
> > surprised if the results were much different to
> the -xtlogit- analyses
> > above. If they do i'd probably prefer the
> -xtlogit- model.
> >
> > Ricardo's idea about computing rates for each
> physician and weighting
> > appropriately would be yet another possibility
> (esp. if followed by a
> > variance-stabilising transformation) but would be
> an 'old-fashioned'
> > approach as modern software makes the more correct
> analyses above easier
> > than summarising, weighting, transforming...
> >
> > Hope this helps,
> > Roger.
> >
> ----------------------------------------------------
> > Roger Harbord
> mailto:[email protected]
> > Department of Social Medicine, University of
> Bristol
> >
> > > On Mon, 8 Sep 2003, Ricardo Ovaldia wrote:
> > >
> > >> Dear all,
> > >>
> > >> I have received only one reply to my post
> (please see
> > >> my original post below), so either of two
> things is
> > >> possible: (1) there is not a good answer or (2)
> my
> > >> question was no clear. So let me ask a slightly
> > >> different question.
> > >>
> > >> Can I simply take the number of referrals at
> one
> > >> practice and divide by the number of patients
> seen at
> > >> that practice (referral rate for practice 1)
> and
> > >> compare it to the rate of the other practice
> without
> > >> taking in account that some physicians within
> practice
> > >> may refer more than others? Or, should I
> compute the
> > >> referral rate for each physician, and then
> combine
> > >> these rates within practice by weighting them
> some
> > >> how? If so how can I do this in Stata? what
> kind of
> > >> weights should I use?
> > >>
> > >> Thank you,
> > >> Ricardo.
> > >>
> > >>
> > >>
> > >> --- Ricardo Ovaldia <[email protected]> wrote:
> > >> > Dear all,
> > >> >
> > >> > We are interested in comparing the referral
> rates
> > >> > for
> > >> > two physician practices. The fist group has
> 20
> > >> > physicians and the second group has 22
> physicians.
> > >> > Each physician saw a variable number of
> similar
> > >> > patients some saw as few as 4 and others as
> many as
> > >> > 43. The physician either referred the patient
> to a
> > >> > specialist or did not.
> > >> >
> > >> > If I compute the referral rate for each
> physician
> > >> > group and compare them, I concerned because
> the
> > >> > observations are not independent. I suspect
> that
> > >> > some
> > >> > doctors are more likely to refer patients
> than
> > >> > other.
> > >> > I though that I could use �clogit- to
> group by
> > >> > doctor�s ID:
> > >> >
> > >> > . clogit referred practice, group(doctorid)
> > >> >
> > >> > This did not work because the
> �practice-
> > >> variable
> > >> > does
> > >> > not vary within doctor ID, so it is dropped
> from the
> > >> > model (omitted due to no within-group
> variance).
> > >> >
> > >> > Any suggestions will be greatly appreciated.
> > >> >
> > >> > Thank you,
> > >> > Ricardo.
> > >> >
> > >> >
> > >> > =====
> > >> > Ricardo Ovaldia, MS
> > >> > Statistician
> > >> > Oklahoma City, OK
> > >> >
> > >> > __________________________________
> > >> > Do you Yahoo!?
> > >> > Yahoo! SiteBuilder - Free, easy-to-use web
> site
> > >> > design software
>
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