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| From | "Seed, Paul" <paul.seed@kcl.ac.uk> |
| To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
| Subject | Re: st: ROC-curves |
| Date | Fri, 18 Oct 2013 08:23:12 +0000 |
On 14/10/2013 18:54, Ragnhild Bergene Skråstad wrote: > Hi! > I investigate how different 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 The simplest way to get CI for a roc curve following logistic regression is to use -predict- and -roctab-: * Start Stata commands * logistic outcome <predictors> capture drop pred predict pred roctab outcome pred * End Stata commands * * outcome and <predictors> are replaced as appropriate. Much quicker and less trouble than bootstrapping. To find the appropriate cutpoint for a given sensitivity you can use -centile- with -if- centile pred if outcome == 1, centile(90) Likewise for specificity centile pred if outcome == 0, centile(10) Best wishes, Paul T Seed, Women's Health, KCL * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/