B5.073 Colon Cancer Screening/B1.019 Screening and Test Performance

Question 1

how do you maximize sensitivity/specificity?

Answer 1

plot 1-spec vs. sense

area under curve as close to 1 as possible

Question 2

what is the formula for NNT?

Answer 2

calculate absolute risk reduction

1/ARR = NNT

Question 3

aims of cancer screening

Answer 3

reduce cancer related mortality and morbidity

detect precursors or early disease

Question 4

risks of cancer screening

Answer 4

1. test itself: exposures, complications
2. false positives
3. overdiagnosis: unnecessary treatment
4. cost

Question 5

benefits of cancer screening

Answer 5

reduced morbidity
reduced mortality
psychological reassurance
health maintenance
quality of life

Question 6

what is lead time bias

Answer 6

identification of disease earlier in natural course, but screening has no impact on disease course
die at same age despite earlier detection

Question 7

what is length bias

Answer 7

long preclinical phase is earlier to detect

screened individuals are more likely to have milder or slower developing diseases

Question 8

what is overdiagnosis

Answer 8

diagnosed cancers that would not go on to cause symptoms/death given their natural course

Question 9

essential features of a disease been screened for

Answer 9

disease common or of unusual severity
known natural history and lag phase between detection and irreversible outcomes (detectable pre-clinical phase)
disease is treatable

Question 10

essential features of a screening test

Answer 10

acceptable to public
risk/benefit profile favorable
economically and technically feasible
accurate and repeatable
acceptable levels of sens and spec

Question 11

what is sensitivity

Answer 11

ability of a test to correctly identify those who have disease

• worry about false positives
• few false negatives

Question 12

SnNOUT

Answer 12

when sensitivity is high, a negative result can rule out disease
most negatives are true negatives

Question 13

what is specificity

Answer 13

ability of a test to correctly identify those who do not have a disease

• worry about false negatives
• few false positives

Question 14

SpIN

Answer 14

when specificity is high, a positive result can rule in disease
most positives true positives

Question 15

how to calculate sensitivity

Answer 15

probability of a test being positive given that disease is present
SN = TP/ (TP+FN)
first column in 2x2 table

Question 16

how to calculate specificity

Answer 16

probability of a test being negative given the disease is not present
SP = TN/ (TN+FP)
second column in 2x2 table

Question 17

perfect sensitivity

Answer 17

detects all cases

no false negatives

Question 18

perfect specificity

Answer 18

rules out all non-cases

no false positives

Question 19

what is positive predictive value

Answer 19

probability that disease is present given test is positive
PPV = TP/(TP+FP)
first row on 2x2 table

Question 20

what is negative predictive value

Answer 20

probability that disease is not present given that the test is negative
NPV = TN/(TN+FN)
second row on 2x2 table

Question 21

sens/spec vs. ppv/npv

Answer 21

sens/spec: how likely is a test to rule in or rule out disease, given whether or not disease is present
ppv/npv: how likely is a person to have or not have disease given the test result

Question 22

how is prevalence related to predictive value

Answer 22

screening more efficient id directed at patients with high prevalence
PPV and NPV depend on prevalence of disease in population unlike sens and spec

Question 23

accuracy

Answer 23

ability of a test to identify who have disease and who does not
-sense and spec

Question 24

precision

Answer 24

results of a test are consistent each time the rest is conducted

Question 25

prevalence

Answer 25

pretest probability

probability of disease before test result is known

Question 26

predictive value

Answer 26

posttest probability

probability of disease after test result known

Question 27

likelihood ratios

Answer 27

LR = likelihood of a test result when disease is present/ likelihood of a test result when disease is absent