st: Testing equality of coefficients in different logistic models

[Sara Mottram <s.mottram@cphc.keele.ac.uk>]

I have an ordinal response with three levels and I am hoping eventually 
to fit an ordinal regression model, possible a partial proportional odds 
model using -gologit2-. However, to explore my data and get an idea of 
where I may and may not have proportionality of odds, I have created two 
dichotomies and would like to fit two binary logistic regression models, 
testing for the equality of the slope coefficients, much as the 
-autofit- option does in -gologit2-.
i.e. in the output below, is 1.86 equal to 1.96?

. logistic mstair_1 edu
------------------------------------------------------------------------------
   mstair_1 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. 
Interval]
-------------+----------------------------------------------------------------
        edu |   1.861951   .0870595    13.29   0.000     1.698903    
2.040648
------------------------------------------------------------------------------

. logistic mstair_2 edu
------------------------------------------------------------------------------
   mstair_2 | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. 
Interval]
-------------+----------------------------------------------------------------
        edu |   1.960462   .1124576    11.74   0.000     1.751988    
2.193743
------------------------------------------------------------------------------

I suspect that there is an Stata command to carry out this test, but I 
have been unable to find it, either in the manuals or in the FAQ. From 
the manual, I get the impression that the Wald test performed by -test- 
can compare coefficients within the same model or compare all of the 
coefficients in two models?

I would also value opinions on whether there is any value in performing 
two binary regressions, given that -gologit2- has an automatic fitting 
procedure in -autofit-. (I am basing my analysis plan on a fairly out of 
date paper, which suggests that binary regressions are the only way to 
choose where non-proportionality is needed as there were no programs 
able to do this at the time).

Best wishes
Sara

--
Sara Mottram	
Research Assistant: Biostatistics
Primary Care Musculoskeletal Research Centre
Primary Care Sciences
Keele University
Staffordshire, ST5 5BG
Tel:  01782 584711
Fax:  01782 583911 
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