# Lecture 1 Flashcards

1
Q

sensitivity

A
• 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)
2
Q

specificity

A
• 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)
3
Q

positive predictive value

A
• the extent to which a patient’s positive test indicates the presence of disease
• TP/(TP+FP)
4
Q

negative predictive value

A
• the extent to which a patient’s negative test indicates absence of disease
• TN/(TN+FN)
5
Q

when should a sensitive test be used?

A
• 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.
6
Q

when should a specific test be used?

A
• 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
7
Q

cutoff point

A
• the point on the continuum between normal and abnormal
8
Q

A
• 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
9
Q

tests that discriminate well on ROC curve

A
• crowd toward the upper left corner of the ROC curve
10
Q

tests that perform less well on ROC curve

A
• have curves that fall closer to the diagonal running from lower left to upper right.
11
Q

best cutoff point on ROC curve

A
• 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
12
Q

the overall accuracy of a test on ROC curve

A
• can be described as the area under the ROC curve; the larger the area, the better the test
13
Q

most common way to circumvent test between sensitivity and specificity.

A

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

14
Q

predictive value

A
• the probability of disease given the results of the test
15
Q

accuracy

A
• summarizes the overall value of the test
16
Q

prevalence

A
• prior probability

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

17
Q

the more sensitive a test is

A
• The more sensitive a test is the better will be its negative predictive value
18
Q

the more specific a test is

A
• more specific the test is, the better will be its positive predictive value
19
Q

diagnostic tests are most helpful when

A
• diagnostic tests are most helpful when the presence of disease is neither very likely nor very unlikely.
20
Q

physicians can increase the yield of diagnostic tests how

A

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