If the zeros are identifiable from some other information - e.g.,
hospital costs will be 0 if the patient isn't hospitalized or in this
case we would know the visits to a specialist is 0 if the patient is
only seen for physical exams, etc. - then a two-part model might work.
In this case one uses two models: one for the number of visits if visits
are >0 and one (a logistic) for distinguishing 0 vs. non-zero. I have a
few papers in Statistics in Medicine in 2001 that may be helpful.
I would emphasize that in this case, some of the 0 visits to a
specialist are in patients who should have seen a specialist but didn't.
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Maarten buis
Sent: Monday, March 16, 2009 2:08 PM
To: stata list
Subject: RE: st: Zero-truncated Negative Binomial convergence
--- Emily Wilson wrote:
> I am having trouble running a zero-truncated negative binomial
> regression. The dependent variable is: # of visits to a
> specialist physician in the past year, and the distribution is
> something like:
> 0 visits ~= 93,000
> 1 visit ~= 15,000
> 2 visits ~= 1,000
> 3 visits ~= 500
The zero truncated distribution assumes that there are no zeros.
In your case you definately do have zeros. I would start with a
regular -poisson-, and than I might worry about excess zeros,
for which you can look at the zero inflated poisson (-zip-), the
negative binomial (-nbreg-), and zero inflated negative binomial
(-zinb-). There is a nice discussion of these models and how to
choose between them in this book:
http://www.stata-press.com/books/regmodcdvs.html
Hope this helps,
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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