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# st: Testing equality of coefficients in different logistic models

 From Sara Mottram To statalist@hsphsun2.harvard.edu Subject st: Testing equality of coefficients in different logistic models Date Wed, 13 Sep 2006 16:38:17 +0100

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 (I am basing my analysis plan on a fairly out of date paper, which suggests that binary regressions are the only way to choose where non-proportionality is needed as there were no programs able to do this at the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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