st: Modelling two binary outcomes that are not mutually exclusive

[Ronán Conroy <rconroy@rcsi.ie>]

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?

--

Ronan M Conroy (rconroy@rcsi.ie) 
Senior Lecturer in Biostatistics 
Royal College of Surgeons 
Dublin 2, Ireland 
+353 1 402 2431 (fax 2764) 
-------------------- 
Just say no to drug reps 
http://www.nofreelunch.org/

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