The basic idea of diagnostic test interpretation is to calculate the probability a patient has a disease under consideration given a certain test result. A 2 by 2 table is used as a mneumonic device. Be sure to label the table with the test results on the left side and the disease status on top as shown here:
Disease
present 
Disease
absent 

Test
positive 
True
positives 
False
positives 
Test
negative 
False
negative 
True
negatives 
Specificity is the proportion of patients without disease who test negative. In probability notation: P(T^{}D^{}) = TN / (TN + FP).
Pretest Probability is the estimated likelihood of disease before the test is done. It is the same thing as prior probability and is often estimated. If a defined population of patients is being evaluated, the pretest probability is equal to the prevalence of disease in the population. It is the proportion of total patients who have the disease. In probability notation: P(D^{+}) = (TP+FN) / (TP+FP+TN+FN).
Sensitivity and specificity describe how well the test discriminates between patients with and without disease. They address a different question than we want answered when evaluating a patient, however. What we usually want to know is: given a certain test result, what is the probability of disease? This is the predictive value of the test.
Predictive value of a positive test is the proportion of patients with positive tests who have disease. In probability notation: (D^{+}T^{+}) = TP / (TP+FP). This is the same thing as posttest probability of disease given a positive test. It measures how well the test rules in disease.
Predictive value of a negative test is the proportion of patients with negative tests who do not have disease. In probability notation: (D^{}T^{}) = TN / (TN+FN). It measures how well the test rules out disease. Notice that this is not the same as posttest probability of disease given a negative test which is one minus the predicitive value of a negative test.