Which statement best describes negative predictive value?

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Negative predictive value is the probability that the disease is absent given a negative test result. It tells you how confident you can be that someone who tests negative truly does not have the disease. The value is calculated as the number of true negatives divided by all negative results (true negatives plus false negatives). This measure is especially high when the disease is uncommon in the population, because most people who test negative are truly disease-free. It also depends on test sensitivity, since higher sensitivity reduces false negatives and therefore increases NPV. So the statement that describes negative predictive value is the one that states the likelihood the disease is absent given a negative test.