Lecture 1

Question 1

sensitivity

Answer 1

• the extent to which the test is accurate for those who have the disease in question
• the proportion of people with the disease who have a positive test for the disease
• when deciding whether to use the test or not
• avoiding false negative errors
• TP/(TP+FN)

Question 2

specificity

Answer 2

• the extent to which the test is accurate for those who do not have the disease in question
• the proportion of people without the disease who have a negative test.
• when deciding where to use the test or not
• avoiding false positive errors
• TN/(TN+FP)

Question 3

positive predictive value

Answer 3

• the extent to which a patient’s positive test indicates the presence of disease
• TP/(TP+FP)

Question 4

negative predictive value

Answer 4

• the extent to which a patient’s negative test indicates absence of disease
• TN/(TN+FN)

Question 5

when should a sensitive test be used?

Answer 5

• should be chosen when there is an important penalty for missing a disease
• Sensitive tests are also helpful during the early stages of a diagnostic workup, when several diagnoses are being considered, to reduce the number of possibilities.

Question 6

when should a specific test be used?

Answer 6

• are useful to confirm (or “rule in”) a diagnosis that has been suggested by other data.
• particularly needed when false-positive results can harm the patient physically, emotionally, or financially

Question 7

cutoff point

Answer 7

• the point on the continuum between normal and abnormal

Question 8

receiver operator characteristic (ROC) curve

Answer 8

• Another way to express the relationship between sensitivity and specificity for a given test is to construct a curve
• constructed by plotting the true-positive rate (sensitivity) against the false-positive rate (1 — specificity) over a range of cutoff values.
• shows how severe the trade-off between sensitivity and specificity is for a test and can be used to help decide where the best cutoff point should be

Question 9

tests that discriminate well on ROC curve

Answer 9

• crowd toward the upper left corner of the ROC curve

Question 10

tests that perform less well on ROC curve

Answer 10

• have curves that fall closer to the diagonal running from lower left to upper right.

Question 11

best cutoff point on ROC curve

Answer 11

• the best cutoff point is at or near the “shoulder” of the ROC curve, unless there are clinical reasons for minimizing either false negatives or false positives

Question 12

the overall accuracy of a test on ROC curve

Answer 12

• can be described as the area under the ROC curve; the larger the area, the better the test

Question 13

most common way to circumvent test between sensitivity and specificity.

Answer 13

The most common way is to use the results of several tests together, as discussed later in this chapter.

Question 14

predictive value

Answer 14

• the probability of disease given the results of the test

Question 15

accuracy

Answer 15

• summarizes the overall value of the test

Question 16

prevalence

Answer 16

• prior probability

- the probability of disease before the test result is known.

Question 17

the more sensitive a test is

Answer 17

• The more sensitive a test is the better will be its negative predictive value

Question 18

the more specific a test is

Answer 18

• more specific the test is, the better will be its positive predictive value

Question 19

diagnostic tests are most helpful when

Answer 19

• diagnostic tests are most helpful when the presence of disease is neither very likely nor very unlikely.

Question 20

physicians can increase the yield of diagnostic tests how

Answer 20

physicians can increase the yield of diagnostic tests by applying them to demographic groups known to be at higher risk for a disease.