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Re: st: diagt and accuracy


From   "heg2001@med.cornell.edu" <heg2001@med.cornell.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: diagt and accuracy
Date   Thu, 9 Jun 2005 13:57:16 -0400

My original inclination was to use proportion correctly identified, ie,
(true positives + true negatives )/total population
A biostatistician told me that "accuracy" (as definted in my earlier note)
includes prevalence of disease, an I hadn't heard that before. I think the
biostatistician believed a weighted average, or accounting for the
population I have (compared to the population in which the test was
developed, actually not too different from mine), was important.
Originally I wanted to have ROC area (as calculated in -diagt-), but I was
told that with a dichotomous test ROC area wasn't relevant, ie, I didn't
have different cutpoints at which to operate the test. That's when she
mentioned accuracy defined with prevalence.

In fact, ROC area doesn't account for prevalence either...
Thanks for your comments.

Heather

Original Message:
-----------------
From: Ronán Conroy rconroy@rcsi.ie
Date: Thu, 09 Jun 2005 16:42:31 +0100
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: diagt and accuracy


Heather Gold wrote:

> Oops, the first line was cut off.
> I meant to write that diagt defines accuracy as 
> (sensitivity+specificity)/2.
>
The help file says

 The ROC (Receiver Operating Characteristic curve) area
is (for a simple test) the average of sensitivity and specificity.

- not the same thing. -diagt- doesn't provide accuracy in its output.

> >Has anyone heard of accuracy defined as
> >(prevalence*sensitivity+(1-prevalence)*specificity) ? This is like a
> >weighted average that incorporates prevalence and might be helpful 
> with a
> >dichotomous diagnostic test (ie, rather than ROC). If so, is there a
> >standard depending on one's field, eg, medicine?
> >
>

There are two sorts of questions you can ask about a test: ones that 
relate to its ability to identify people and ones that relate to the 
probability that a result is correct.

Sensitivity/specificity are the proportion of people with/without the 
condition who are correctly identified. Positive/negative predictive 
values are the proportion of positive/negative tests which are correct. 
These latter depend on prevalence, while sensitivity and specificity do not.

It follows that there are two ways of conceptualising overall test 
performance - proportion of people correctly identified and proportion 
of tests that are correct. In each case, however, this comes out as the 
sum of true positives and true negatives over the total N and so is 
independent of prevalence.

What did you have in mind that the adjustment would accomplish?

-- 

Ronan M Conroy (rconroy@rcsi.ie) 
Senior Lecturer in Biostatistics 
Royal College of Surgeons 
Dublin 2, Ireland 
+353 1 402 2431 (fax 2764) 
-------------------- 
Just say no to drug reps 
http://www.nofreelunch.org/

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