false negative observations

480 true negative observations

400 persons with X

600 persons without X

Positive predictive value = —a— = -true positives (360)-x 100 = 75%

Thus, 3 out of 4 of the persons with positive observations really have the disease, and 1 out of 4 does not.

By a similar calculation, you can determine the probability that a negative observation is a true negative. The results here are reasonably reassuring to the involved patient:

Negative predictive value = —d— = —true negatives (480)— x 100 = 92%

As prevalence of the disease in a population diminishes, however, the predictive value of a positive observation diminishes remarkably, while the predictive value of a negative observation rises further. In Example 2, in a second population, B, of 1000 people, only 1% have disease X. Now there are only 10 cases of X and 990 people without X. If this population is screened with the same observation, which has a 90% sensitivity and an 80% specificity, here are the results:

Example 2. Prevalence of Disease X = 1%

Disease X Present Absent


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Blood Pressure Health

Blood Pressure Health

Your heart pumps blood throughout your body using a network of tubing called arteries and capillaries which return the blood back to your heart via your veins. Blood pressure is the force of the blood pushing against the walls of your arteries as your heart beats.Learn more...

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