[R] Fw: Logistic regresion - Interpreting (SENS) and (SPEC)
Robert W. Baer, Ph.D.
rbaer at atsu.edu
Mon Oct 13 22:41:03 CEST 2008
----- Original Message -----
From: "Frank E Harrell Jr" <f.harrell at vanderbilt.edu>
To: "John Sorkin" <jsorkin at grecc.umaryland.edu>
Cc: <r-help at r-project.org>; <dieter.menne at menne-biomed.de>;
<p.dalgaard at biostat.ku.dk>
Sent: Monday, October 13, 2008 2:09 PM
Subject: Re: [R] Fw: Logistic regresion - Interpreting (SENS) and (SPEC)
> John Sorkin wrote:
>> Perhaps I was not clear in my previous Email message. Sensitivity and
>> specificity do tell us about the quality of a test in that given two
>> tests the one with higher sensitivity will be better at identifying
>> subjects who have a disease in a pool who have a disease, and the more
>> sensitive test will be better at identifying subjects who do not have a
>> disease in a pool of people who do not have a disease. It is true that
>> positive predictive and negative predictive values are of greater utility
>> to a clinician, but as you know these two measures are functions of
>> sensitivity, specificity and disease prevalence. All other things being
>> equal, given two tests one would select the one with greater sensitivity
>> and specificity so in a sense they do measure the "quality" of a clinical
>> test - but not, as I tried to explain the quality of a statistical model.
> That is not very relevant John. It is a function of all those things
> because those quantities are all deficient.
> I would select the test that can move the pre-test probability a great
> deal in one or both directions.
Of course, this quantity is known as a likelihood ratio and is a function of
sensitivity and specificity. For 2 x 2 data one often speaks of postive
likelihood ratio and negative likelihood ratio, but for multi-row
contingency table one can define likelihood ratios for a series of cut-off
points. This has become a popular approach in evidence-based medicine when
diagnostic tests have continuous rather than binary outputs.
>> You are of course correct that sensitivity and specificity are not truly
>> "inherent" characteristics of a test as their values may change from
>> population-to-population, but paretically speaking, they don't change all
>> that much, certainly not as much as positive and negative predictive
> They change quite a bit, and mathematically must change if the disease is
> not all-or-nothing.
>> I guess we will disagree about the utility of sensitivity and specificity
>> as simplifying concepts.
>> Thank you as always for your clear thoughts and stimulating comments.
> And thanks for yours John.
>> among those subjects with a disease and the one with greater specificity
>> will be better at indentifying John David Sorkin M.D., Ph.D.
>> Chief, Biostatistics and Informatics
>> University of Maryland School of Medicine Division of Gerontology
>> Baltimore VA Medical Center
>> 10 North Greene Street
>> GRECC (BT/18/GR)
>> Baltimore, MD 21201-1524
>> (Phone) 410-605-7119
>> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>>>>> Frank E Harrell Jr <f.harrell at vanderbilt.edu> 10/13/2008 2:35 PM >>>
>> John Sorkin wrote:
>>> Jumping into a thread can be like jumping into a den of lions but here
>>> goes . . .
>>> Sensitivity and specificity are not designed to determine the quality of
>>> a fit (i.e. if your model is good), but rather are characteristics of a
>>> test. A test that has high sensitivity will properly identify a large
>>> portion of people with a disease (or a characteristic) of interest. A
>>> test with high specificity will properly identify large proportion of
>>> people without a disease (or characteristic) of interest. Sensitivity
>>> and specificity inform the end user about the "quality" of a test. Other
>>> metrics have been designed to determine the quality of the fit, none
>>> that I know of are completely satisfactory. The pseudo R squared is one
>>> such measure.
>>> For a given diagnostic test (or classification scheme), different
>>> cut-off points for identifying subject who have disease can be examined
>>> to see how they influence sensitivity and 1-specificity using ROC
>>> I await the flames that will surely come my way
>> John this has been much debated but I fail to see how backwards
>> probabilities are that helpful in judging the usefulness of a test. Why
>> not condition on what we know (the test result and other baseline
>> variables) and quit conditioning on what we are trying to find out
>> (disease status)? The data collected in most studies (other than
>> case-control) allow one to use logistic modeling with the correct time
>> Furthermore, sensitivity and specificity are not constants but vary with
>> subjects' characteristics. So they are not even useful as simplifying
>>> John David Sorkin M.D., Ph.D.
>>> Chief, Biostatistics and Informatics
>>> University of Maryland School of Medicine Division of Gerontology
>>> Baltimore VA Medical Center
>>> 10 North Greene Street
>>> GRECC (BT/18/GR)
>>> Baltimore, MD 21201-1524
>>> (Phone) 410-605-7119
>>> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>>>>>> Frank E Harrell Jr <f.harrell at vanderbilt.edu> 10/13/2008 12:27 PM >>>
>>> Maithili Shiva wrote:
>>>> Dear Mr Peter Dalgaard and Mr Dieter Menne,
>>>> I sincerely thank you for helping me out with my problem. The thing is
>>>> taht I already have calculated SENS = Gg / (Gg + Bg) = 89.97%
>>>> and SPEC = Bb / (Bb + Gb) = 74.38%.
>>>> Now I have values of SENS and SPEC, which are absolute in nature. My
>>>> question was how do I interpret these absolue values. How does these
>>>> values help me to find out wheher my model is good.
>>>> With regards
>>>> Ms Maithili Shiva
>>> I can't understand why you are interested in probabilities that are in
>>> backwards time order.
>>>>> Subject: [R] Logistic regresion - Interpreting (SENS) and (SPEC)
>>>>> To: r-help at r-project.org Date: Friday, October 10, 2008, 5:54 AM
>>>>> Hi I am working on credit scoring model using logistic
>>>>> regression. I havd main sample of 42500 clentes and based on
>>>>> their status as regards to defaulted / non - defaulted, I
>>>>> have genereted the probability of default.
>>>>> I have a hold out sample of 5000 clients. I have calculated
>>>>> (1) No of correctly classified goods Gg, (2) No of correcly
>>>>> classified Bads Bg and also (3) number of wrongly classified
>>>>> bads (Gb) and (4) number of wrongly classified goods (Bg).
>>>>> My prolem is how to interpret these results? What I have
>>>>> arrived at are the absolute figures.
> Frank E Harrell Jr Professor and Chair School of Medicine
> Department of Biostatistics Vanderbilt University
> R-help at r-project.org mailing list
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