[R] Fw: Logistic regresion - Interpreting (SENS) and (SPEC)

[Gabor Grothendieck]

On Mon, Oct 13, 2008 at 11:47 PM, Frank E Harrell Jr
<f.harrell at vanderbilt.edu> wrote:
> Gabor Grothendieck wrote:
>>
>> On Mon, Oct 13, 2008 at 11:21 PM, Frank E Harrell Jr
>> <f.harrell at vanderbilt.edu> wrote:
>>>
>>> cryan at binghamton.edu wrote:
>>>>
>>>> I recall a concept of Snout:  sensitivity that is high enough to
>>>> essentially rule out the presence of disease.  And Spin:  specificity
>>>> that
>>>> is high enough to essentially rule in the presence of disease.
>>>>
>>>> So perhaps the below is backwards?  The higher the sensitivity, the
>>>> greater the NPV?  And the higher the specificity, the
>>>
>>> greater the PPV?
>>> Why should we care when we can directly estimate Prob(disease | test
>>> results
>>> and risk factors)?
>>
>> Sensitivity and specificity are functions of the test only but ppv is
>> also a function
>> of the disease prevalence.   Just change the prevalence and the ppv
>> changes
>> whereas sensitivity and specificity are invariant.
>
> Gabor,
>
> That's a very common belief but it turns out not to be true.  See references
> from my earlier post.  Sensitivity and specificity are only invariant in you
> don't analyze how they vary.
>
> Also, much research does not understand what prevalence really means. It
> actually could be argued to not be a scientific quantity as its meaning
> depends on unspecified mixtures of subjects.

Its the number of diseased patients in the population divided by the
total population
considered.

Suppose we want to compare the PSA test for prostate cancer to some other
new diagnostic.  We want a measure of the test itself, not of the population.
We would like the numbers to be the same in Japan and North America even
though the prevalence of prostate cancer varies widely between them.

>
>>
>> If our aim is to assess a test one wants a measure that only measures the
>> test
>> itself.
>
> There is no such measure.  The performance of a test depends on the type of
> patient being tested as well as other things.
>

There is no such thing as a normal distribution since if you get enough
data you will find discrepancies but that does not mean that for all practical
purposes that there is no normal distributions.

Sensitivity and specificity are generally used to compare tests, not patients.