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From |
Joe Canner <jcanner1@jhmi.edu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: Re: st: Re: cutoff point for ROC curve |

Date |
Tue, 15 Oct 2013 13:50:57 +0000 |

Mike, As was discussed yesterday in a different thread, you can use -roccomp- to compare and plot multiple ROCs. For example: . logit outcome predictor1 . lroc . predict xb_predictor1 if e(sample), xb . logit outcome predictor2 . lroc . predict xb_predictor2 if e(sample), xb . roccomp outcome xb_predictor1 xb_predictor2, graph summary Of course, you can also compare/plot more than two ROCs as desired; just repeat the -logit-, -lroc-, -predict- sequence. Regards, Joe Canner Johns Hopkins University School of Medicine -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Michael Stewart Sent: Tuesday, October 15, 2013 9:42 AM To: statalist Subject: Re: Re: st: Re: cutoff point for ROC curve Dear Steve and Clyde, Thank you very much for your time and advice. I have one additional question and I was hoping to get advice. If I have multiple models, these there a way to draw multiple ROC curves in one graph , for better demonstration of the predictive abilities of different models. Thank you again for your time and effort. -- Thank you , Yours Sincerely, Mike On Mon, Oct 14, 2013 at 5:55 PM, Clyde Schechter <clyde.schechter@gmail.com> wrote: > I would advise Michael Stewart not to seek some arbitrary formula for > the optimal cut-off point. He doesn't say what is being classified, > but regardless, the substantive issue is the trade-off between two > types of misclassification errors: false negatives and false > positives. Both types of error have consequences, usually different. > To find an optimal cut-point requires assigning a loss to each type of > error and then expressing the expected loss in terms of sensitivity, > specificity and prevalence of the attribute being identified by the > classification. Then you pick the cut-off which minimizes the > expected loss. > > My practical experience with this process is that people are often > reluctant to quantify the losses associated with each type of error, > because the losses are often of a qualitatively different nature. For > example, a missed diagnosis may lead to loss of life, whereas a false > positive diagnosis may lead to unnecessary surgery. How does one > assign values to those? Not easily. > > So it feels more comfortable to seize on some simple formula, such as > the sum of sensitivity and specificity. Nevertheless, if you don't > really quantify and compare the losses associated with each type of > error, applying some arbitrary formula will give you only the > illusion, not the reality, of optimality. One is simply optimizing an > arbitrary quantity that bears no relation to the matter at hand. > > Clyde Schechter > Dept. of Family & Social Medicine > Albert Einstein College of Medicine > Bronx, New York, USA > * > * 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/ . * * 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/ * * 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/

**References**:**Re: Re: st: Re: cutoff point for ROC curve***From:*Clyde Schechter <clyde.schechter@gmail.com>

**Re: Re: st: Re: cutoff point for ROC curve***From:*Michael Stewart <michaelstewartresearch@gmail.com>

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