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st: Modelling two binary outcomes that are not mutually exclusive


From   Ron�n Conroy <[email protected]>
To   [email protected]
Subject   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 ([email protected]) 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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