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# st: Odd ratio / relative risk in logistic regression

 From Ching Wong To statalist@hsphsun2.harvard.edu Subject st: Odd ratio / relative risk in logistic regression Date Tue, 9 Apr 2013 13:36:49 +0930

```Hi,

My analysis involves two steps:

1. Chi-square testing:
I did a few chi-sqare testing with different variables.
-tab grade var 3, ch2 etc.
Basesd on the result of the chi-sqaure testings, the variables which
are significant (i.e. p<0.05) will then put into the logistic
regression.

2. logistic regression:
I put the command as followings:
- binreg grade var1 var3 var4 etc.
And I have got the following output.

Iteration 1:   deviance =  113.0721
Iteration 2:   deviance =  92.10798
Iteration 3:   deviance =  87.45499
Iteration 4:   deviance =  86.88055
Iteration 5:   deviance =  86.86395
Iteration 6:   deviance =  86.86393
Iteration 7:   deviance =  86.86393
Generalized linear models                          No. of obs      =       297
Optimization     : MQL Fisher scoring              Residual df     =       294
(IRLS EIM)                      Scale parameter =         1
Deviance         =  86.86392755                    (1/df) Deviance =  .2954555
Pearson          =  311.8670508                    (1/df) Pearson  =  1.060772
Variance function: V(u) = u*(1-u/1)                [Binomial]
Link function    : g(u) = ln(u/(1-u))              [Logit]
BIC             = -1587.093
------------------------------------------------------------------------------
|                 EIM
grade |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
var1 |   2.955512   1.066853     2.77   0.006     .8645186    5.046506
var4|   .4058033    1.07797     0.38   0.707     -1.70698    2.518587
_cons |  -4.464928   .6125685    -7.29   0.000    -5.665541   -3.264316
------------------------------------------------------------------------------

In this case, I can tell var 1 is significant in the logistic
regression model, since it has a p-value =0.006. However, how can I
find out the odd ratio or the relative risk of this model? Did I use
the wrong command?

Thanks.

Regards,

Wong
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```