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# Re: st: Odd ratio / relative risk in logistic regression

 From Srinivas To statalist@hsphsun2.harvard.edu Subject Re: st: Odd ratio / relative risk in logistic regression Date Tue, 9 Apr 2013 04:28:57 -0700 (PDT)

```Hi
after the variable list insert ,or
rgds
dr.srinivasan
--- On Mon, 4/8/13, Ching Wong <ching.y.wong@student.adelaide.edu.au> wrote:

> Subject: st: Odd ratio / relative risk in logistic regression
> To: statalist@hsphsun2.harvard.edu
> Date: Monday, April 8, 2013, 9:06 PM
> 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
> 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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```