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RE: st: reporting odds for N categories

 From "Nick Cox" <[email protected]> To <[email protected]> Subject RE: st: reporting odds for N categories Date Thu, 22 Sep 2005 21:53:01 +0100

```In May's last example, a log scale works well too:

#d ;
twoway   rcap lb ub alc
|| connected odds alc,
legend(off) sort
title("Odds for age group 45-54")
ysc(log)
yla(.1 "0.1" .2 "0.2" .5 "0.5" 1 2, ang(h)) ;
#d cr

As the plot is then of log odds, this is also a logit scale,
but with labelling in terms of odds.

(Do tell me if there is any biostatistical solecism
in my suggestion!)

Nick
[email protected]

May Boggess

> On Thursday morning, Dan asked about odds and odds ratios:
> >
> > I have a cross sectional dataset which reports the health
> outcomes and
> > covariate information for patients belonging to Z clinics.
> >
> > I am trying to compare the performance (ie. % of patients
> in a clinic
> > that failed health outcome) for each of the Z clinics, but
> can not find
> > a way that I can produce results suitable for graphing from
> either the
> > logistic or tabodds commands, as the reference group is, by
> default, the
> > clinic with the lowest numeric ID.
> >
> > How can I set the reference category to the mean, so that I
> can graph
> > the odds of failing, controlling for A and B, for each of
> the Z clinics
> > in the dataset?
> >
>
> Dan has asked a few different things, but here I'll just
> concentrate on
> one aspect of his question: how to graph odds.
>
> One can use the epitab command -tabodds- to calculate odds.
> For example:
>
>   clear
>   webuse bdesop
>   expand freq
>   tabodds case alc
>   di  "   p= " 40/(40+386)
>   di  "odds= " (40/(40+386))/(1- 40/(40+386))
>
> The last two lines remind us how odds are calculated: odds=p/(1-p).
> We can use a logistic model to obtain the same results. The
> reason to do
> that is that we can get the probabilities, and thus odds, as
> variables
> in the dataset:
>
>   clear
>   webuse bdesop
>   expand freq
>   tabodds case alc
>   xi:logit case i.alc
>   predict p
>   gen odds=p/(1-p)
>   tabdisp alc, c(p odds)
>
> Now -twoway- can be used to create a graph:
>
>   twoway scatter odds alc
>
> In order to be able to compare odds, we need confidence intervals as
> well.  I find the easiest way is to use -predictnl- instead of
> -predict-, since it can calculate the confidence intervals as well:
>
>   clear
>   webuse bdesop
>   expand freq
>   tabodds case alc
>   xi:logit case i.alc
>   predictnl odds=predict(p)/(1-predict(p)), ci(lb ub)
>   label var odds "odds"
>   tabdisp alc, c(odds lb ub)
>
> Notice that in the intervals we produce this way are close to but not
> exactly the same as those that -tabodds- calculates (which are from
> Clayton and Hills "Statistical Models in Epidemiology" (1993) p. 169).
>
> Again I can use -twoway- to get a graph:
>
> twoway rcap lb ub alc || connected odds alc, legend(off) sort
>
> This is essentially the same as the graph we can obtain from
> -tabodds-
> using the -ciplot- option. Why go to all this trouble then?
>
> So we can go a little further. In Dan's case he needs to
> adjust for some
> covariates. In example dataset I have agegrp to adjust for:
>
>   clear
>   webuse bdesop
>   expand freq
>   tabodds case alc
>   xi:logit case i.alc agegrp
>   predictnl odds=predict(p)/(1-predict(p)), ci(lb ub)
>   label var odds "odds"
>   tabdisp alc agegrp, c(odds)
>
> I can now choose what I would like to graph, but for one
> example I will
> graph the odds for age group 45-54:
>
> clear
> webuse bdesop
> expand freq
> tabodds case alc
> xi:logit case i.alc agegrp
> predictnl odds=predict(p)/(1-predict(p)), ci(lb ub)
> label var odds "odds"
> tabdisp alc agegrp, c(odds)
>
> keep if age==3
> #d;
> twoway   rcap lb ub alc
>    || connected odds alc,
>    legend(off) sort
>    title("Odds for age group 45-54");
> #d cr

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