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Re: st: Modelling two binary outcomes that are not mutually exclusive


From   Dr Murray Finkelstein <murray.finkelstein@utoronto.ca>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Modelling two binary outcomes that are not mutually exclusive
Date   Sat, 27 Nov 2004 10:34:00 -0500

little suspect since symptoms of depression and anxiety often appear together (and the pharmaceutical companies make a big deal of medications to treat these "comorbidities"). If you had continuous measures, you could generate a Depression-Anxiety Index. Failing that, I would favour a third category for the "mixed" disorder.

Ronán Conroy wrote:


I have two binary outcomes, measured in a patient population (anxiety and depression). For various reasons, I suspect that a number of patient characteristics predict depression but not anxiety.

If the two diagnoses were mutually exclusive, all would be well. I could use multinomial logistic regression and compare the coefficients. However, there is about a 20% overlap. Is this a Known Problem? I could model the overlap category as a third outcome, and show that the coefficients were similar to those for depression alone and different to those for anxiety alone, but this is slicing the sample a little thin - there are just 8 people with both disorders. (This approach actually works, sort of, given the small numbers, so I'm on the right track from the theory point of view.)

Any suggestions out there?

--

|~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
| Murray M Finkelstein PhD MD CCFP | Department of Family and Community Medicine | University of Toronto Mt Sinai Hospital, Suite 413 600 University Avenue Toronto, Ontario Canada M5G 1X5 Phone: 416-326-7879
FAX: 416-326-7761 E-mail: murray.finkelstein@utoronto.ca

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