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

 From "JVerkuilen (Gmail)" To statalist@hsphsun2.harvard.edu Subject Re: st: Odd ratio / relative risk in logistic regression Date Tue, 9 Apr 2013 08:36:53 -0400

```On Tue, Apr 9, 2013 at 12:06 AM, Ching Wong
>

<snip>

In general I'm dubious of screening by univariate statistics before
running a model, but I guess if you need to do that David Hoaglin's
advice is spot-on: Don't set the criterion for inclusion too high.

> 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

Note that it's unusual for there to be such a substantial discrepancy
between Pearson chi square and deviance, which makes me think there's
something up with this model.

> 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?

-help binreg- gives the options, of which there are several including
odds ratio.

I think one could make the case for using -glm- pretty much all the
time. Next time I teach categorical I intend to push -glm- as the "one
stop shop". That's not quite true but it's pretty close and the fact
that the post-estimation is consistent is a big plus.
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```