Review

Question 1

CER (Control Event Rate)

Answer 1

of people that had the event/# of people in the control group

Question 2

EER (Experimental Event Rate

Answer 2

of people that had the event/# of people in the experimental group

Question 3

ARR (Absolute Risk Reduction)

Answer 3

CER - EER (control event rate - experimental event rate)

Question 4

if ARR = 0.074, control group = coumadin, experimental group = ceprotex, then you can interpret it as:

Answer 4

“ If 100 people were treated with Ceprotex and 100 people were treated with coumadin, 7.4 fewer people in the Ceprotex group would suffer a stroke over a 5 year period”

Question 5

RRR (Relative Risk Reduction)

Answer 5

RRR = (CER - EER)/CER

Question 6

if RRR = 0.50, control group = coumadin, experimental group = ceprotex,
then you can interpret it as:

Answer 6

“Compared to coumadin, Ceprotex is associated with a 50% relative risk reduction in the 5-year incidence of stroke.”

Question 7

NNT (Number Needed to Treat)

Answer 7

NNT = 1/ARR = 1/(CER - EER)

Question 8

If NNT = 1/0.074 = 13.5:

Answer 8

“Approximately 14 people need to be treated with Ceprotex compared to coumadin to prevent one stroke”

Question 9

ARI (Absolute Risk Increase)

Answer 9

EER - CER (experimental event rate - control event rate)

Question 10

NNH (Number Needed to Harm)

Answer 10

NNH = 1/ARI

Question 11

if we calculate NNH = 1/0.015 = 67:

Answer 11

“If 67 people are treated with Ceprotex instead of coumadin, 1 extra person will suffer gastrointestinal bleeding”

Question 12

deductive reasoning

Answer 12

○ hypothesis testing
○ comparing current case against a pattern
○ does the patient have pulmonary embolism?

Question 13

inductive reasoning

Answer 13

○ differential diagnosis
○ find a pattern
○ what does the patient have?

Question 14

When assessing a new diagnostic test:

Answer 14

○ use new test among the patient who would receive test normally in clinical practice
○ compare to appropriate reproducible gold standard, which should also be performed on all patients that would normally receive the test in clinical practice

Question 15

2x2 table

Answer 15

Disease + Disease -
Test + true positive false positive
Test - false negative true negative

Question 16

Sensitivity

Answer 16

true positive rate
the number of people that test positive and have the disease out of all the people that have the disease
You know patient has the disease. What is the chance that the patient will test positive?
A/(A+C)

Question 17

Specificity

Answer 17

true negative rate
the number of people that test negative and do not have the disease out of all the people that do not have the disease
You know the patient does not have the disease. What is the chance that the patient will test negative?
D/(D+B)

Question 18

positive predictive value (PPV)

Answer 18

The number of people that actually have the disease among the people that test positive for the disease
You know the patient has tested positive. What is the chance that the patient has the disease?
A/(A+B)

Question 19

negative predictive value (NPV)

Answer 19

the number of people that don’t have the disease among the people that test negative for the disease
You know the patient has tested negative. What is the chance that the patient does not have the disease?
D/(C+D)

Question 20

Effect of Prevalence

Answer 20

○ no effect on sensitivity or specificity
○ higher prevalence: increases PPV and decreases NPV
○ lower prevalence: decreases PPV and increases NPV

Question 21

likelihood ratio

Answer 21

people with a given result among people with disease / people the the same result among people without the disease

Question 22

likelihood ratio (positive)

Answer 22

sensitivity / (1 - specificity)

A/A+C / B/B+D

Question 23

likelihood ratio (negative)

Answer 23

(1 - sensitivity) / specificity

C/A+C / D/B+D

Question 24

Comparing likelihood ratios and predictive values

Answer 24

○ both look at what it means to the patient to receive a given test
○ predictive values depends on the prevalence
○ likelihood ratios are derived from sensitivity and specificity which are not affected by prevalence

Question 25

receiver operator curve

Answer 25

○ can be used to determine the optimal cutoff for the distinction between a positive and a negative test result ○ plot of sensitivity against 1 - specificity or true positive rate versus false positive rate ○ built by plotting the sensitivity and 1-specificity values that were calculated when using varied cutoffs ○ slope of a tangent to ROC at any given point is the ratio of sensitivity/(1-specificity) or LR ○ goal: optimize sensitivity and specificity ○ optimal cutoff is the one closest to the upper left hand corner of the graph

Question 26

Bayes Theorem: underlying concept

Answer 26

it is a method for evaluating new information in conjunction with prior information most helpful when pretest probability is intermediate

Question 27

Bayes Theorem: general form

Answer 27

Prior odds hypothesis x Bayes Factor = Final (Posterior) odds of hypothesis

Question 28

Bayes Theorem: form for diagnostic tests

Answer 28

Pre-test odds of disease x likelihood ratio = Post-test odds of disease

Question 29

Odds if p = 0.5 if p = 0.33

Answer 29

Odds = probability/(1 – probability) then odds = 0.5/ (1-0.5) = 1:1 then odds = 0.33/ (1-0.33) = 1:2

Question 30

Probability if odds = 3:1 if odds = 1:4

Answer 30

Probability = odds in favor/total odds = odds in favor/(odds in favor + odds against) then p = 3/ (3+1) = 0.75 then p = 1/(1+4) = 0.2

Question 31

Pretest odds of disease is determined by

Answer 31

clinical risk factors for the disease, “subjective impression”

Question 32

The pre-test odds are multiplied by _ to get post test odds

Answer 32

likelihood ratio

Question 33

if pretest probability = .33 then pretest odds = _ (probability = _) if LR = 10 then posttest odds = _ covert posttest odds to probability: _

Answer 33

1:2, 0.5 0.5 x 10 = 5 (5:1) 5/(5+1) = .8

Question 34

population vs sample

Answer 34

population: all the members of a particular group sample: use a small subset of individuals to draw conclusions about the larger population ● relevant because may not be feasible to measure entire population ● infer information about population from sample results ● random selection to best reflect composition of population

Question 35

mean

Answer 35

sum(x) / n

Question 36

median

Answer 36

middle number when ordered from smallest to largest (or vice versa)

Question 37

mode

Answer 37

most often represented number

Question 38

standard deviation

Answer 38

square root of: sum(x - mean)^2 / (n-1) | square root of variance

Question 39

variance

Answer 39

find distance between each value and mean add of the squares of the distances (to prevent canceling of positives by negatives) divide the sum of squares by the number of values

Question 40

range

Answer 40

largest value - smallest value

Question 41

interquartile range

Answer 41

values corresponding to the 25th and 75th percentile

Question 42

frequency

Answer 42

how often a value occurs in a data set

Question 43

standard error of the mean

Answer 43

standard deviation / square root of sample size