|From||Dr Murray Finkelstein <email@example.com>|
|Subject||Re: st: Modelling two binary outcomes that are not mutually exclusive|
|Date||Sat, 27 Nov 2004 10:41:12 -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?