# st: OR results of mhodds vs. logistic

 From Suzy To statalist@hsphsun2.harvard.edu Subject st: OR results of mhodds vs. logistic Date Fri, 26 May 2006 09:14:37 -0400

Dear Statalisters:
I think this might be more of a statistical question rather than a Stata question. I would like to know why the odds ratio estimate, as an approximation to the odds ratio
for a one unit increase in x1 (x1 is continuous) for the mhodds command is so different than the logistic output from the command below. FYI, x2 is continuous as well. Both x1 and x2 are linear in the logit.
Suzy

OR results for x1 controlling for x2 are different for the mhodds command and the logistic command below:

mhodds Y x1 x2 by(x3)

-------------------------------------------------------------------------------
Y | Odds Ratio chi2(1) P>chi2 [95% Conf. Interval]
----------+--------------------------------------------------------------------
1 | 1.154021 21.50 0.0000 1.08622 1.22606
2 | 1.095894 5.87 0.0154 1.01764 1.18017
3 | 1.073977 3.63 0.0566 0.99799 1.15574
4 | 1.308015 5.38 0.0203 1.04258 1.64103
5 | 1.004919 0.00 0.9552 0.84669 1.19271
-------------------------------------------------------------------------------

by x3, sort : logistic Y x1 x2 [the output right here is my summary of the results obtained, not the actual output]

-------------------------------------------------------------------------------
Y | Odds Ratio chi2(1) P>chi2 [95% Conf. Interval]
----------+--------------------------------------------------------------------
1 | 1.187428 5.32 0.000 1.114544 1.265078
2 | 1.141416 3.56 0.000 1.061168 1.227733
3 | 1.127073 3.46 0.001 1.053328 1.205982
4 | 1.198436 2.16 0.031 1.01679 1.412532
5 | 1.055348 0.76 0.445 .9191339 1.211748
-------------------------------------------------------------------------------

by x3, sort : logistic Y x1 x2 [this is the actual output for x3]

------------------------------------------------------------------------------
-> x3 = 1

Logistic regression Number of obs = 2862
LR chi2(2) = 130.25
Prob > chi2 = 0.0000
Log likelihood = -493.86326 Pseudo R2 = 0.1165

------------------------------------------------------------------------------
Y | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 1.18209 .039921 4.95 0.000 1.10638 1.262982
x2 | 1.051794 .0060071 8.84 0.000 1.040086 1.063634
------------------------------------------------------------------------------

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