Evaluating the diagnosis

Question 1

Sensitivity

Answer 1

• the usefulness of a test in the context of truly diseased population
• a sensitive test will pick up the bases with even small amounts of evidence
• makes more false positive inclusions
• highly sensitive tests are preferred if a diagnosis should not be missed but over-diagnosis is not harmful
• SN OUT: highly sensitive test if NEGATIVE helps to rule OUT a disease

Question 2

Specificity

Answer 2

• the usefulness of a test in the context of the non-diseased population
• highly specific tests will pick up cases onli if definitive evidence is noted
• highly sensitive tests make more galse negative exclusions
• highly specific tests are preferred if missing some cases is not bad but wrongly labeling is bad/costly
• SP In: highly specific test if POSITIVE helps to rule IN a disease

Question 3

Sensitivity calculation

Answer 3

True positive/total diseased

A/A+C

Question 4

Specificity calculation

Answer 4

True negative/total non-diseased

D=B+D

Question 5

Accuracy

Answer 5

=all correct ‘hits’/total hits

= (true positive+ true negative)/total population

Question 6

Receiver operator curve

Answer 6

• ROC curve
• useful in choosing between two diagnostic tests of different sensitivty and specificity rates or choosing a cut off point for making a diagnosis

Question 7

ROC curve

Answer 7

• plot the true positive rates (sensitivity) on Y axis and corresponding false positive rates (1- specificity) on the X axis
• cut-off point in the curved point
• when comparing two test curves the one with the curve closer to the left upper corner is the better screening test

Question 8

Area under the ROC curve

Answer 8

-a measure of test accuracy

Question 9

Likelihood ratios

Answer 9

-more useful than specificity and sensitivity calculations as they give us a single measure to tell us how much more likely a positive or negative test result is to have come from someone with a disease than someone without it

Question 10

Likelihood ratio for a positive test

Answer 10

LR+= likelihood of testing positive rightly/wrongly

• (A/A+C))/(B/B+D)
• sensitivity/1-spec

Question 11

Likelihood ratio of a negative test

Answer 11

LR-= likelihood of testing negative rightly/wrongly

• (C/A+C)/(D/B+D)
• 1-sens/spec

Question 12

LR+ values

Answer 12

• usually >1 for most tests
• if over 10 then you can use
• if below 0.1 then don’t use

Question 13

Pretest probability

Answer 13

• prevalence in the studied population

- (A+C)/(A+B+C+D)

Question 14

A

Answer 14

True positive

Question 15

B

Answer 15

False positive

Question 16

C

Answer 16

False negative

Question 17

D

Answer 17

True negative

Question 18

Likelihood of a diagnosis

Answer 18

• depends on the prevalence or prior probability of the disease before applying a test
• one cannot multiply the probability by the likelihood ratio
• probabilities need to be converted to odds before the likelihood ratio can be used

Question 19

Using pre-test odds to calculate post-test probability

Answer 19

1. obtain pretest probability (A+C)/(A+B+C+D)
2. convert pretest probability to pretest odds (A+C)/(B+D)
3. convert pretest odds to post test odds - Post test odds= LR x Pretest odds
4. convert post-test odds to post test probability- posttest odds/1+ postest odds

Question 20

Using Bayesian nomogram (Fagan) to calculate post-test probability

Answer 20

1. obtain pretest probability
2. obtain likelihood ration (LR+/LR-)
3. draw a line using straight edge accross the two values available
4. post-test probability is the value obtained on the other side of the nomogram

Question 21

Positive predictive value

Answer 21

• True positive/Total test positive

- can apply for individual patients results and informs the chance of having the disease if tested positive

Question 22

Negative predictive value

Answer 22

• True negative/total test negative
• informs the chance of not having the disease if tested negative
• decreases with increasing prevalence