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

[Dr Murray Finkelstein <murray.finkelstein@utoronto.ca>]

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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