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# Re: st: ROC-curves

 From Marta Garcia-Granero <[email protected]> To [email protected] 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)
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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