# st: RE: Roctab and binomial exact confidence interval

 From "Feiveson, Alan H. (JSC-SK311)" To "statalist@hsphsun2.harvard.edu" Subject st: RE: Roctab and binomial exact confidence interval Date Fri, 30 Oct 2009 07:57:12 -0500

Garry

I have had a similar situation arise - i.e. when there is perfect separation in a small sample, there is no direct way to get a sampling error estimate of the ROC area. In this case, the reported confidence interval is bogus. As an alternative, I fit a model to the data and used simulation, drawing samples of size (109 in your case), did the classification on each sample and looked at the empirical distribution of the ROC area.

AL Feiveson

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Garry Anderson
Sent: Friday, October 30, 2009 3:42 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: Roctab and binomial exact confidence interval

Dear Statalist,

webuse hanley
gen ratingm5 = rating
replace ratingm5 = rating - 5 if disease==0
roctab disease ratingm5,binomial

ROC                    -- Binomial Exact --
Obs       Area     Std. Err.      [95% Conf. Interval]
--------------------------------------------------------
109     1.0000       0.0000        0.00023     0.05006

How does one interpret the 95% CI of 0.00023 to 0.05006 when the ROC
area is 1.00?

I have seen a dataset (n=47, 15 +ve) where the ROC area was 1.00 and I
wish to determine the lower 95%CI.

Cheers, Garry

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