[I also thought giving it the title “guns don’t kill people, people with a poor understanding of Bayes theorem do…”]
One of THE most useful thing you can learn when it comes to reading a paper is to understand the negative predictive value of a test. Both what it means and what it doesn’t mean
In general the NPV is used in a way that makes a test seem better than it really is.
A lot of tests we commonly use (like say D-dimer) have quoted NPVs of 98% or so. Which sounds great. If the test is negative in a certain bunch of patients similar to the one that the test was developed for then there’s a98% they don’t have the disease. That’s great isn’t it?
The key is in the italics. If the test was developed in a population of patients where only 5% had the disease then the NPV will be very different if applied to a population where 50% have the disease.
How do you get an NPV?
What do you need to know about that calculation?
Specificity and sensitivity are a factor of the test itself and should be constant whereas the prevalence varies from population to population.
To save you working it out the NPV will go down as the prevalence of the disease increases.
So if your quoted NPV of 98% was worked out in a population of only 5% prevalence then if you apply the test to a population prevalence of 50% then you can’t expect the same NPV.
[As an aside if you’re trying to rule out a disease in a population with a disease prevalence of 50% then you sure as hell better be using a VERY sensitive test if you want to lower your post-test probability sufficiently]
A much more useful test characteristic in our line of work is high sensitivity. A d-dimer probably is more in the range of low 90s when it comes to sensitivity which is why if you’re working up someone with a pre-test probability in the 50% range it’s kind of a useless test.
Now as usual I’m sharing my own ignorance here, but does anyone know of an occasion where you’d genuinely want to know the NPV of a test?