This type of question is popular. Definitions of other terms
are given below.
*Negative predictive value* :The fraction of people with negative
tests who actually don't have the condition.

*Likelihood ratio*: If you have a positive test, how many times
more likely are you to have the disease? If the likelihood ratio equals
6.0, then someone with a positive test is six times more likely to have
the disease than someone with a negative test. The likelihood ratio equals
sensitivity/(1.0-specificity).

The sensitivity, specificity and likelihood ratios are properties of
the test. The positive and negative predictive values are properties of
both the test and the population you test. If you use a test in two populations
with different disease prevalence, the predictive values will be different.
A test that is very useful in a clinical setting (high predictive values)
may be almost worthless as a screening test. In a screening test, the prevalence
of the disease is much lower so the predictive value of a positive test
will also be lower.