st: Modelling two binary outcomes that are not mutually exclusive
Date
Sat, 27 Nov 2004 14:59:25 +0000
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)
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