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Now you are ready to make your calculations:

Now you are ready to make your calculations:

a true positive observations (95)

d true negative observations (90)

a true positive observations (95)

a + b total positive observations (95 + 10)

d true negative observations (90)

c + d total negative observations (90 + 5)

Now return to the table. The vertical red bars designate sensitivity (a/a + c) and specificity (d/b + d), and the horizontal red bars designate positive predictive value (a/a + b) and negative predictive value (d/c + d). The data displayed indicate that the hypothetical test has excellent test characteristics. The sensitivity and specificity of the test are both over 90%, as are the positive and negative predictive values. Such a test would be clinically useful for assessing a disease or condition in your patient.

Note that the predictive value of a test or observation depends heavily on the prevalence of the condition within the population studied. Prevalence is the proportion of people in a defined population at any given point in time who have the condition in question. When the prevalence of a condition is low, the positive predictive value of the test will fall. When the prevalence is high, the sensitivity, specificity, and positive predictive value are high, and the negative predictive value approaches zero. To work further on these relationships, turn to the Appendix, Prevalence and Predictive Value, on pp. 801-802 to practice making the calculations described.

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