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Re: st: ROC-curves

From   Marta Garcia-Granero <>
Subject   Re: st: ROC-curves
Date   Tue, 15 Oct 2013 12:23:11 +0200

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)

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...)

Take a look at this page:

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

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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