Interpreting Data Regarding Diagnostics Testing

Question 1

what is a gold standard?

Answer 1

a test that is considered to be consistently correct and to which other tests can be compared
–> best test available for any given condition

ex: autopsy results to determine disease presence
biopsy to determine malignancy
culture results to determine presence of a specific bacteria

Question 2

what is reliablity

Answer 2

level of agreement between repeated measures of the same variable

synonymous with repeatability, test-retest reliability, and reproducibility

Question 3

level of agreement between repeated measures of the same variability is:

Answer 3

reliability, repeatability, test-retest reliability, reproducibility

Question 4

what is validity?

Answer 4

the extent to which a test actually rests what it claims to test

Question 5

the extent to which a test actually rests what it claims to test is

Answer 5

validity

Question 6

what are the quantitative measures of validity?

Answer 6

sensitivity, specificity, predictive values, and likelihood ratios

Question 7

sensitivity, specificity, predictive values, and likelihood ratios are quantitative measures of:

Answer 7

validity

Question 8

what is specificity of a test?

Answer 8

its ability to detect people who do not have a disease
-true positives)
=TN (true negative) /(TN+FP or total without the disease)

Question 9

what is sensitivity of a test?

Answer 9

its ability to detect people who do have a disease
-true negatives
=TP (True positives) / (TP+FN or total with the disease)

Question 10

TN/ TN+FP

Answer 10

specificity - detecting true positives

Question 11

TP/TP+FN

Answer 11

sensitivity - detecting true negatives

Question 12

why are sensitivity and specificity useful

Answer 12

-describes the quality of the test

Question 13

what is the positive predictive value

Answer 13

likelihood that a person with a positive result actually has the disease
-answers what percentage of the positive results actually have the disease

=TP/TP+FP (true positives over all positives)

Question 14

what is the negative predictive value

Answer 14

likelihood that a person with a negative result actually does not have the disease

=TN/TN+FN (true negatives over all negatives)

Question 15

=TP/TP+FP (true positives over all positives)

Answer 15

positive predictive value

Question 16

=TN/TN+FN (true negatives over all negatives)

Answer 16

negative predictive value

Question 17

prevalence:

Answer 17

=TP+FN/total

true results over total results

Question 18

describe the relationship between PPV/NPV and prevalence

Answer 18

PPV and NPV both depend on characteristics of test itself AND the prevalence of disease in a population
–> prevalence of disease dramatically affects PPV and NPV

Question 19

why are PPV and NPV useful?

Answer 19

• more clinically useful than sensitivity and specificity because they depend both on the characteristics of the test and the prevalence of the disease in a population
• can answer the question how likely is it that a patient has or doesnt have the disease theyre being tested for

Question 20

explain SNOUT

Answer 20

highly sensitive tests have very few false negatives (FN) therefore when a sensitive test is negative it can rule out a disease

Question 21

explain SPIN

Answer 21

highly specific tests have very few false positives (FP) therefore when a specific test is positive it tends to rule in a disease

Question 22

what is likelihood ratio (LR)?

Answer 22

value calculated by combining sensitivity and specificity of a test

Question 23

the value calculated by combining sensitivity and specificity of a test is

Answer 23

the likelihood ratio

Question 24

what does a positive LR mean?

Answer 24

=sensitivity / 1-specificity
test can rule in a disease
the higher the number, the more effective it is at ruling in a disease

Question 25

what does a negative LR mean

Answer 25

=1-sensitivity/specificity
test can rule out a disease
the lower the number, the more effective it is at ruling out a disease

Question 26

what is an ROC curve

Answer 26

plots the true positive rate against the false positive rate for different cutoffs of a diagnostic test

Question 27

a highly specific test would generate:

Answer 27

a lot of false negatives
-would capture all the people without the disease, but this includes the tail of the group with the disease so this would give a lot of false negatives

Question 28

a highly sensitive test would generate:

Answer 28

a lot of false positives

-would identify all the people with a disease but in doing so would include people without the disease