Clinical Epidemiology Bootcamp

Clin Epi Boot Camp
R5 review 2014
H. Lindsay MD MPH FRCPC

Outline
Studies: Exposures and outcomes
Odds vs. risks
Odds ratios and risk ratios
ARR/RRR/NNT
Sensitivity / specificity
PPV/NPV

Hierarchy of Studies
Systematic
Reviews
RCTs
Cohort Studies
Case-Control Studies
Case Reports, Case Series
Editorials, Expert Opinion

Exposure and Outcome
What are the exposures and outcomes in the
following examples?
Crash2 trial
5000 followed to see if they developed heart
disease and looked at if they smoked
20 pts with MS questioned about lead paint in
their house

Exposure and Outcome
Crash2 trial
Exposure (Treatment): TXA
Outcome: Mortality
5000 followed to see if they developed heart disease
and looked at if they smoked
Exposure: smoking
Outcome: Heart disease
20 pts with MS questioned about lead paint in their
house
Exposure: lead paint
Outcome: MS

Cross Sectional
Defined by outcomes and exposures
determined at same point in time
Attitudes of ED physicians towards homeless
Drug-assisted intubation by EMS providers
(often surveys)

Case-Control
Groups defined by outcomes
Compare children who LWBS from ED to those
who didn’t and compare wait times
Look at cases of ketamine sedation including
those involving laryngospasm and consider
predictors

Cohort
Groups defined by exposures
Framingham: 5000 pts followed to see if they
developed heart disease, asked about smoking,
activity, cholesterol
Following pts with varying features of TIA to see
who develops stroke

RCT
Groups defined by exposures
Only differs from cohort in that:
Exposure is introduced (C)
Groups are randomized (R)

Cohort
Non-Randomized
Defined
Population
Exposed
Disease
No
Disease
Non-
exposed
Disease
No
Disease

RCT
Randomized
Defined
Population
Treatment
Disease
No
Disease
Control
Disease
No
Disease

2 X 2 Tables
Outcome (+) Outcome (-) Totals
Exposure (+)
(Experimental
group)
a b a+b
Exposure (-)
(Control
group)
c d c+d
Totals a+c b+d a+b+c+d

2 X 2 Tables
Outcome (+) Outcome (-) Totals
Exposure (+)
(Experimental
group)
a b a+b
Exposure (-)
(Control
group)
c d c+d
“The truth always rises to the
top”

45 ED physicians worked over the last month, 25
on days, and 20 on nights. A total of 15 EPs who
worked day shifts got gastro, and 5 EPs who
worked night shifts got gastro.
What is the outcome?
What is the exposure?

Fill in the 2X2 table.

Gastro (+) Gastro (-) Totals
Day shift (+) 15 25
Day shift (-) 5 20
Totals 45

Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45

Of 75 EM residents, 65 of them pass the exam.
10 people were at this lecture, and 9 of them
were among the ones who passed.

Pass (+) Pass (-) Totals
Lecture (+) 9 10
Lecture (-)
Totals 65 75

Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75

2000 pts with severe bleeding from trauma were
randomized to get TXA or not – 1000 in each
group. 500 pts in total died, and 75 of them had
received TXA.

received TXA.
Death (+) Death (-) Totals
TXA (+) 75 1000
TXA (-) 1000
Totals 500 2000

received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000

Risk
Risk is a proportion
Risk is a probability of something occurring
Risk represents incidence
Number of outcomes occurring out of all possible
outcomes
Flip a coin 10 times: heads occur 5 times, do not
occur 5 times
Risk of heads is 5/10 or ½ or 50%.

Risk
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
What is the risk of getting gastro on day shift?
What is the risk of someone on night shift getting gastro?

Risk
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
Number of outcomes occurring on day shift = 15
Number of total possible outcomes occurring on day shift =
25
Risk = 15/25 or 60%

Risk
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
Number of outcomes occurring on night shift = 5
Number of total possible outcomes occurring on night shift =
20
Risk = 5/20 or 25%

Odds
Odds are a ratio
Number of outcomes occurring vs. outcomes not
occurring
Less intuitive
Flip a coin 10 times: heads occur 5 times, do not
occur 5 times
Odds are 5:5 or 1:1

Odds
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
What are the odds of someone on day shift getting gastro?
What are the odds of someone on night shift getting gastro?

Odds
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
Number of outcomes occurring = 15
Number of outcomes not occurring = 10
Odds = 15:10 = 3:2 or 1.5

Odds
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
Odds = 5:15= 1:3 or .33

Risk vs. Odds
Risk
What is the risk
(probability) of
drawing a diamond
from a deck of
cards?
Odds
What are the odds
of drawing a
diamond from a
deck of cards?

Risk vs. Odds
Risk
What is the risk
(probability) of
drawing a diamond
from a deck of
cards?
13 diamonds out of
52 cards (or 1 out of
4)
13/52 = 25%
Odds
What are the odds
of drawing a
diamond from a
deck of cards?
13 diamonds to 39
non-diamonds (or 1
to 3)
13:39 = 1:3 odds

Risk
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
What is the risk of passing if you went to the lecture?
What is the risk of passing if you didn’t go to the lecture?

Risk
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
Number of passes if you went to lecture = 9
Number of total possible passes if you went to lecture = 10
Risk = 9/10 or 90%

Risk
Number of passes if you didn’t go to lecture = 56
Number of total possible passes if you didn’t go to lecture =
65
Risk = 56/65 or 86%
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75

Risk
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
What is the risk of dying among those who got TXA?
What is the risk of dying among those who didn’t get TXA?

Risk
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
Number of deaths with TXA = 75
Number of total possible deaths with TXA = 1000
Risk = 75/1000 or 7.5%

Risk
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
Number of deaths without TXA = 425
Number of total possible deaths without TXA = 1000
Risk = 425/1000 or 42.5%

Odds
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
What are the odds of someone at the lecture passing?
What are the odds of someone not at the lecture passing?

Odds
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
Odds = 9:1

Odds
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
Odds = 56:9 or 6.2:1

Odds
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
What are the odds of someone who got TXA dying?
What are the odds of someone who didn’t get TXA dying?

Odds
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
Odds = 75:925 or 3:37 or 0.08:1

Odds
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
Odds = 425:925 or 17:37 or 0.46:1

Risk Ratios and Odds
Ratios
Looks at strength of association
We know there’s a risk of gastro on day shift –
how much more than from night shift?
You are more likely to pass after a lecture – how
much more likely?
You are less likely to die with TXA in trauma –
how much less?

Risk Ratio
Risk ratio = relative risk (same thing) = RR
Observational Studies:
Relative risk = risk in exposed
risk in nonexposed
Experimental Studies:
Risk of event in experimental group = a/a+b =
EER
Risk of event in control group = c/c+d = CER
Relative risk = EER/CER

Risk Ratio
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
What is the relative risk?

Risk Ratio
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
Risk of gastro in day shift: 15/25 = .6
Risk of gastro in night shifts: 5/20 = .25
Risk ratio = .6/.25 = 2.4

Risk Ratio
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
ED physicians on day shift were 2.4 X more likely
to get gastro than those on night shift.

Odds Ratio
Odds ratio = OR
Observational Studies:
Odds ratio = odds in exposed
odds in nonexposed
Experimental Studies:
Odds of event in experimental group = a/b
Odds of event in control group = c/d
Odds ratio = a/b / c/d
* if you’re interested, this comes out to the cross-
product, or
a x d / b x c

Odds Ratio
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
What is the odds ratio?

Odds Ratio
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
Odds of gastro in day shift: 15/10 = 1.5
Odds of gastro in night shifts: 5/15 = 0.33
Odds ratio = 1.5/.33 = 4.5

Odds Ratio
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
Odds ratio = 15/10 / 5/15 = 15 X 15 / 5 X 10 = 4.5

Odds Ratio
Day shift (+) 15 10 25
Day shift (-) 5 15 20
Totals 20 20 45
The odds of getting gastro from day shift were 4.5:1
compared to night shift

Risk Ratio
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
What is the risk ratio?

Risk Ratio
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
Risk in exposed (event) group = 9/10 = .9
Risk in non-exposed (control) group = 56/65 = .86
Risk Ratio = 0.9/0.86 = 1.05

Odds Ratio
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
Odds in exposed (event) group = 9:1 = 9
Odds in non-exposed (control) group = 56:9 = 6.2
Odds Ratio = 9/6.2 = 1.45
or:
9X9 / 56X1 = 1.45

Risk
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000

Risk
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
Risk in exposed (event) group = 75/1000 = .075
Risk in non-exposed (control) group = 425/1000 = .425
Risk Ratio = .075 / 0.425 = 0.18

Odds
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
Odds in exposed (event) group = 75:925 = 0.08
Odds in non-exposed (control) group = 425:575 = 0.74
Odds Ratio = 0.08/0.74 = 0.11
or:
75X575 / 425X925 = 0.11

ARR
Absolute risk reduction = difference in event
rates
ARR = risk difference
ARR = CER – EER
If the control group has an outcome rate of 15%,
and the treatment/exposure group has an
outcome rate of 10%, what is the ARR?

ARR
Absolute risk reduction = difference in event rates
ARR = risk difference
ARR = CER – EER
If the control group has an outcome rate of 15%, and
the treatment/exposure group has an outcome rate of
10%, what is the ARR?
ARR = 15%-10% = 5%

NNT
Number needed to treat = the number of patients
needed to treat to prevent one bad outcome
NNT = 1 / ARR
Highly tied to ARR
If the absolute risk reduction is 10% - ie. The control
group has deaths in 20% of patients, and the
treatment group has deaths in 10% of patients, then
how many patients would you have to treat to prevent
one death?

NNT
Number needed to treat = the number of patients
needed to treat to prevent one bad outcome
NNT = 1 / ARR
Highly tied to ARR
If the absolute risk reduction is 10% - ie. The control
group has deaths in 20% of patients, and the
treatment group has deaths in 10% of patients, then
how many patients would you have to treat to prevent
one death?
NNT = 1/.1 = 10

ARR
Of 2000 residents at a school, half of them had
their call cut in half. By the end of residency, 18
of them had dropped out, 8 of whom had had the
reduction in call.
Draw the 2X2 table

ARR
reduction in call.
Dropped out Didn’t drop out Totals
Call reduced (+) 8 992 1000
Call not
reduced
10 990 1000
Totals 18 1982 2000

ARR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
What are the event rates (risk)?

ARR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
EER = 8/1000 = 0.008
CER = 10/1000 = 0.01

ARR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000

ARR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
RR = 8/1000 / 10/1000 = 0.8

ARR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
What is the absolute risk reduction?

ARR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
ARR = CER – EER = 0.01-0.008 = 0.002 or
0.2%

ARR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
What is the NNT?

ARR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
NNT = 1/ARR = 1/0.002 = 200

RRR
Relative risk reduction = a proportion comparing
the risk between different groups
RRR = CER-EER / CER
RRR = ARR / CER

RRR
Relative risk reduction = a proportion comparing the
risk between different groups
Intuitively easy to understand but tells you nothing
about the actual risk of the outcome
Eg. Of all 5th year ED residents in the country who
took the Kingston course, say 1% of people failed the
exam, and of those who didn’t take it, 2% failed.
RRR = CER – EER / CER = 1%/2% = 50%
So the relative risk reduction is 50%! 2% to 1% -
that’s cutting the risk in half! If you didn’t take the
Kingston course, you have a 50% greater risk of
failing!

RRR
Relative risk reduction = a proportion comparing the
risk between different groups
Intuitively easy to understand but tells you nothing
about the actual risk of the outcome
But the absolute risk reduction is only 1%. If you only
start out with a miniscule risk of failing, relative risk
reductions are deceiving.

RRR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
What is the RRR?

RRR
reduction in call.
Call not
reduced
10 990 1000
Totals 18 1982 2000
RRR = CER-EER / CER
= 10/1000 – 8/1000 / 10/1000
= .002 / .01 = 0.2
= 20%

ARR/RRR
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
What is the relative risk reduction?
What is the NNT?

ARR/RRR
Lecture (+) 9 1 10
Lecture (-) 56 9 65
Totals 65 10 75
RRR = CER-EER/CER = 56/65 - 9/10 / 56/65 = .86-.9/.86 = 0.047 =
4.7%
ARR = CER-EER = 56/65 - 9/10 = 0.04 = 4%
NNT = 1/ARR = 25

ARR/RRR
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
What is the relative risk reduction?
What is the NNT?

ARR/RRR
received TXA.
TXA (+) 75 925 1000
TXA (-) 425 575 1000
Totals 500 1500 2000
RRR = CER-EER/CER = 425/1000 – 75/1000 / 425/1000 = .35/.425 =
0.82 = 82%
ARR = CER-EER = 425/1000 - 75/1000 = 0.35 = 35%
NNT = 1/ARR = 1/.35 = 2.9

Diagnostic Tests
Sensitivity and specificity are measures of a test
and do not change with the patient population

Diagnostic Tests
Disease (+) Disease (-) Totals
Test (+) a b a+b
Test (-) c d c+d
“The truth always rises to the
top”

Sens and Spec
Sensitivity is about the population that has
disease.
Test (+) a b
Test (-) c d
Totals a+c b+d
Sens = a/ a+c

Sens and Spec
Sensitivity and specificity are measures of a test and
do not change with the patient population
Sensitivity is about the population that has disease.
Sensitivity = ability of the test to correctly identify
those who have disease
Sensitivity = the proportion of patients with disease
who will have a positive test
“given a patient with disease, what is the probability
of a positive test?”

Sens and Spec
Specificity is about the population that does not
have disease.
Test (+) a b
Test (-) c d
Totals a+c b+d
Spec = d/ b+d

Sens and Spec
Sensitivity and specificity are measures of a test and
do not change with the patient population
Specificity is about the population that does not have
disease.
Specificity = ability of the test to correctly identify
those who do not have disease
Specificity = the proportion of patients without
disease who will have a negative test
“given a patient without disease, what is the
probability of a negative test?”

PPV and NPV
PPV and NPV
PPV and NPV are far more clinically relevant
Why?
Like us, they start with a test result and tell us the
likelihood of disease given that test result
They are highly influenced by prevalence and
change with patient populations

PPV and NPV
PPV is about the population that has a positive
test
Test (+) a b a+b
Test (-) c d c+d
Totals a+c b+d
PPV = a/ a+b

PPV and NPV
PPV is about the population that has a positive
test
PPV = the proportion of patients who test
positive who actually have disease
PPV = given a positive test, the likelihood that
this patient actually has disease
“given a positive test, what is the probability of
having disease?”

PPV and NPV
NPV is about the population that has a negative
test
Test (+) a b a+b
Test (-) c d c+d
Totals a+c b+d
NPV = d/ c+d

PPV and NPV
NPV is about the population that has a negative
test
NPV = the proportion of patients who test
negative who actually do not have disease
NPV = given a negative test, the likelihood that
this patient actually does not have disease
“given a negative test, what is the probability of
not having disease?”

Diagnostic Tests
An ED physician tests the use of u/s to detect
appendicitis in the ED. Of 100 patients, 45 of
them ultimately have appendicitis on CT. U/S
correctly identified 30, but there were also 20
false positives.
Test (+) a b
Test (-) c d
Totals a+c b+d

Diagnostic Tests
them ultimately have appendicitis on CT. U/s
false positives.
Test (+) 30 20 50
Test (-) 15 35 50
Totals 45 55 100
What is the sensitivity?
What is the specificity?

Diagnostic Tests
false positives.
Test (+) 30 20 50
Test (-) 15 35 50
Totals 45 55 100
Sens = 30/45 = 67%
Spec = 35/55 = 64%

Diagnostic Tests
false positives.
Test (+) 30 20 50
Test (-) 15 35 50
Totals 45 55 100
What is the PPV?
What is the NPV?

Diagnostic Tests
false positives.
Test (+) 30 20 50
Test (-) 15 35 50
Totals 45 55 100
PPV = 30/50 = 60%
NPV = 35/50 = 70%

Diagnostic Tests
A general surgery resident decides to take this
same test and validate it. The test still has a
sensitivity of 67%, and specificity of 64%, but in
the 50 pts he sees, 45 of them end up having
appendicitis.
Test (+)
Test (-)
Totals
Fill in the 2X2 table

Diagnostic Tests
appendicitis.
Test (+) 30 2 32
Test (-) 15 3 18
Totals 45 5 50
What is the PPV?
What is the NPV?

Diagnostic Tests
appendicitis.
Test (+) 30 2 32
Test (-) 15 3 18
Totals 45 5 50
PPV = 30/32 = 94%
NPV = 3/18= 17%

Diagnostic Tests
A patient is really worried that he might have Ebola.
His naturopath did a test that has a 98% sensitivity
for Ebola, and it came back positive. However, you
look up this test and find it has a 5% specificity, and
the prevalence of Ebola in the region is 0.01%
Test (+)
Test (-)
Totals
Given this positive test, what is the likelihood of
this patient having disease?

Diagnostic Tests
A patient is really worried that he might have Ebola.
His naturopath did a test that has a 98% sensitivity
for Ebola, and it came back positive. However, you
look up this test and find it has a 5% specificity, and
the prevalence of Ebola in the region is 0.01%
Test (+) 98 949905 950003
Test (-) 2 49995 49997
Totals 100 999900 1000000
PPV = 98/950003 = 0.01%

Likelihood Ratios
Ratio between the probability of observing the
result in a patient with disease and the probability
of observing the result in a patient without
disease
Advantages:
Combine sens and spec
Can calculate probability of disease for an
individual pt
Can be calculated for several levels of test or
finding

(+) Likelihood Ratios
Ratio between the probability of having a positive
test in a patient with disease and the probability
of having a positive test in a patient without
disease
LR (+) = sens / (1-spec) = TP/FP
> 10 greatly increase probability of disease (rule
in)
<0.1 greatly decreases probability of disease
(rule out)
(eg CT in appendicitis – LR(+) = 37, highly useful
for ruling in disease)

(-) Likelihood Ratios
Ratio between the probability of having a negative
test in a patient with disease and the probability of
having a negative test result in a patient without
disease
LR (-) = 1-sens / spec = FN/TN
> 10 greatly increase probability of disease (rule in)
<0.1 greatly decreases probability of disease (rule
out)
(eg. D-dimer – LR(-) is 0.05, useful for negative
result. LR(+) is around 2.4 – not useful for positive
result)

Likelihood Ratios
2 options to use likelihood ratio to calculate post-
test probability
1. Convert to pre-test odds, multiply by LR, convert
post-test odds to probability
2. Use Fagan nomogram

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