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From |
Clyde Schechter <[email protected]> |

To |
[email protected] |

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

Date |
Mon, 14 Oct 2013 14:55:17 -0700 |

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/

**Follow-Ups**:**Re: st: Re: cutoff point for ROC curve***From:*Phil Clayton <[email protected]>

**Re: Re: st: Re: cutoff point for ROC curve***From:*Michael Stewart <[email protected]>

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