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SV: st: ROC-curves

 From Ragnhild Bergene Skråstad <[email protected]> To "[email protected]" <[email protected]> Subject SV: st: ROC-curves Date Thu, 17 Oct 2013 17:00:41 +0000

```thank you so much for you help!
best regards
Ragnhild BS
________________________________________
Fra: [email protected] [[email protected]] p&#229; vegne av Marta Garcia-Granero [[email protected]]
Sendt: 15. oktober 2013 12:23
Til: [email protected]
Emne: Re: st: ROC-curves

Hi Ragnhild:

Since I did not see any replies,  I'm giving you some ideas (the ones I
use):

a) You can get the 95%CI for the AUC using bootstrapping:

program bootroc, rclass
quietly logit "your model goes here"
quietly lroc, nograph
return scalar area=r(area)
end

bootstrap roc=r(area), reps(1000) : bootroc
stat bootstrap, all

b) you can save the predicted probabilities using  "predict double prob,
xb". Then you can use -rocreg- with "roc(1-spec value)" option. Also,
the predicted probabilities can be used with other roc commands that
give 95%CI with different methods (Hanley, deLong...)

Anyway, I'm sure that others can give more detailed explanations, and
better ideas than these ones.

HTH,
Marta Garcia-Granero

El 14/10/2013 18:54, Ragnhild Bergene Skråstad escribió:
> Hi!
> I investigate how diffrent tests, in combination, can predict a given
outcome.
>
> I have made a logistic model with the command "logistic" and plotted
the ROC-curve with the command "lroc". This cave me the ROC-curve and
the AUC. I wonder:
> - how can I get the 95 % CI for this AUC?
> and
> - I would like to get the sensitivity at a given fixed false-positive
rate. Do I have to get all the coordinates on the ROC curve and identify
the one at the FPR at interest- and if so, how do I do that, or is it a
direct way to do this?
> best wishes
> Ragnhild B Skråstad

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