Frank, you bring up some good points as to why sens and spec are not useful. Backwards-time backwards-information-flow probabilities/ transposed conditionals resonate most with me.
In literature, sens and spec are described as being properties of the test, independent of prevalence - this is advantage I usually see of sens and spec over PPV and NPV. This does not make much sense to me. It seems to me that if the test is developed and validated using an appropriate population, then the predictive statistics don’t need to be independent of prevalence. Could someone explain why this is or is not really an advantage?
I think a ROC-like curve with an AUC statistic would be more useful if the two axes were PPV and NPV. Why don’t I ever see this used?