Gabor Grothendieck wrote:
On Mon, Oct 13, 2008 at 11:47 PM, Frank E Harrell Jr
<[EMAIL PROTECTED]> wrote:
Gabor Grothendieck wrote:
On Mon, Oct 13, 2008 at 11:21 PM, Frank E Harrell Jr
<[EMAIL PROTECTED]> wrote:
[EMAIL PROTECTED] 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.
True, but researchers who attempt to adjust various estimators for
prevalence in other populations tend to mix conditional and
unconditional estimates.
When strong risk factors exist I find the concept of prevalence not very
useful, just as I don't want to know the prevalence of pregnancy in the
entire population.
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.
I'm not clear on the analogy.
Sensitivity and specificity are generally used to compare tests, not patients.
That's for sure.
There is one "pure" quantity although it doesn't measure absolute yield
of the test: the adjusted odds ratio.
Cheers,
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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