On Friday, November 26, 2021 at 1:56:03 PM UTC-5, Rich Ulrich wrote:
> On Thu, 25 Nov 2021 12:36:29 -0800 (PST), Cosine <ase...@gmail.com
> > We would use more than one metrics to test the significance of a
> > study. The most often used ones are sensitivity (SE) and specificity
> > (SP). However, this pair would be affected by the disease prevalence.
> > In contrast, the positive/negative predictive values (P/NPVs) are not
> > affected by the prevalence.
> I would not say it that way. The PPV is /based on/ the prevalence.
> It assumes a single value for the prevalence. The rarer the condition,
> the more likely the Positive is False.
> If you know the prevalence pretty well, you should use it for
> your description. "Testing the significance" uses the same set
> of numbers, same 2x2 table (I presume) for SE and SP, so you
> don't expect more power from one than for the other. It should
> be the same test.
> If you are interested in tests across the whole ROC curve, you
> test the curve. If you are interested in some specific prevalence,
> you test at that value.
The equations in this BMJ Stats Note (Altman & Bland, 1994) show how prevalence is related to PPV and NPV:
Note as well that nowadays, some authors are using the following terms for the predictive values:
I like this terminology and notation better than PPV/NPV, because it makes it clear that it is the *test result*, not the predictive value, that is either positive or negative.