Transcript Measures of Effect: An Introduction

Measures of Effect:
An Introduction
Philip la Fleur, RPh MSc(Epidem)
Deputy Director, Center for Life Sciences
[email protected]
Epidemiology Supercourse
Astana, July 2012
Come to Ottawa,
Canada and get
“Out and About”
Canadian Agency for Drugs and
Technologies in Health (www.cadth.ca)
Emerg Med J 2003;20:164-168
Objectives
Understand how to calculate and interpret and articulate
measures of effect and morbidity
1. 2x2 Table
•
•
•
•
Risk
Odds
Relative Risk
Odds Ratio
2. Relative Risk Reduction and Absolute Risk Reduction
3. Number needed to Treat and Number needed to Harm
Summary
Outcome
Outcome
Total
Exposure
Yes
No
Yes
a
b
a+b
No
c
d
c+d
Total
a+c
b+d
N
1. 2x2 Table
Outcome
Outcome
Total
Exposure
Yes
No
Yes
a
b
a+b
No
c
d
c+d
Total
a+c
b+d
N
Risk
•
•
•
•
Probability that an event will occur (Last 2001)
E.g. that a person will die within one year
Risk in Exposed = a/(a+b)
Risk in unexposed, “Baseline risk” = c/(c+d)
Outcome
Outcome
Total
Exposure
Yes
No
Yes
a
b
a+b
No
c
d
c+d
Total
a+c
b+d
N
Odds
• The ratio of the probability of occurrence of an
event to that of non-occurrence
• E.g. odds of smokers developing a chronic cough
• Odds in Exposed = a/b
• Odds in unexposed, “Baseline odds” = c/d
Outcome
Outcome
Total
Exposure
Yes
No
Yes
a
b
a+b
No
c
d
c+d
Total
a+c
b+d
N
Risk Versus Odds
Risk
Odds
0.80
4.0 ⌂⌂⌂⌂/⌂
0.67
2.0 ⌂⌂/⌂
0.50
1.0 ⌂/⌂
0.20
0.10
0.25 ⌂/⌂⌂⌂⌂
0.11 ⌂/⌂⌂⌂⌂⌂⌂⌂⌂⌂
Conversion:
Odds = Risk/(1-Risk)
Risk = Odds / (1 + Odds)
Would you swim here?
Develop a Question: PICO
Population: children under 5 years of age
Intervention (exposure):
Comparator (control):
Swimming in the Ishim River
Not swimming in the Ishim river
Outcome: Otitis Media
Question: What is the risk that a child
under 5 will develop an ear infection
after swimming in the Ishim river?
Odds Ratio
Outcome
Outcome
Total
Exposure
Yes
No
Yes
a
b
a+b
No
c
d
c+d
Total
a+c
b+d
N
Odds Ratio
Ear
Infection
Ear
Infection
Total
Swimming
in Ishim
Yes
No
Yes
40
60
100
No
5
95
100
Total
45
155
200
Odds of getting infection for swimmers: 40/60 = 0.67
Odds of getting infection for non-swimmers: 5/95 = 0.052
Relative Risk
Outcome
Outcome
Total
Exposure
Yes
No
Yes
a
b
a+b
No
c
d
c+d
Total
a+c
b+d
N
Relative Risk
Ear
Infection
Ear
Infection
Total
Swimming
in Ishim
Yes
No
Yes
40
60
100
No
5
95
100
Total
45
155
200
Risk of getting infection for swimmers: 40/100 = 0.4
Risk of getting infection for non-swimmers: 5/100 = 0.05
OR versus RR Key Messages
• Odds and Odds Ratios are difficult to
conceptualize but statisticians prefer them in
some situations because of their
mathematical properties
• Odds Ratios always exaggerate the relative
risk, but when baseline risk is low (e.g. <10%),
the OR approximates the relative risk
• Relative Risk is a more intuitive measure and is
becoming more common in medical literature
Objectives
Understand how to calculate and interpret and articulate
measures of effect and morbidity
1. 2x2 Table
•
•
•
•
Risk
Odds
Relative Risk
Odds Ratio
2. Relative Risk Reduction and Absolute Risk Reduction
3. Number needed to Treat and Number needed to Harm
2. Relative Risk Reduction
and
Absolute Risk Reduction
Objectives
• Learn how to interpret risk of events in the
control (baseline group) and intervention
groups (treatment group) from published
studies.
• Understand the concepts of relative risk
reduction and absolute risk reduction and
how they usually differ from one population
to another.
Trial 1: High Risk Patients
– New drug for acute myocardial
infarction to reduce mortality
– First studied in a high risk
population:
• 40% mortality at 30 days among
untreated
• e.g., elderly, heart failure,
anterior wall infarction
Ref: http://www.cche.net/usersguides/ebm_tips.asp
Trial 1: High Risk Patients
– New drug for acute myocardial
infarction to reduce mortality
– First studied in a high risk
population:
• 40% mortality at 30 days among
untreated
• e.g., elderly, heart failure,
anterior wall infarction
• 30% mortality among treated
– How would you describe the
effect of the new drug?
Ref: http://www.cche.net/usersguides/ebm_tips.asp
Trial 1: High Risk Patients
Ref: http://www.cche.net/usersguides/ebm_tips.asp
Trial 2: Low Risk Patients
– New drug for acute myocardial
infarction to reduce mortality
– Later studied in a lower risk
population:
• 10% mortality at 30 days among
untreated
• e.g., younger, uncomplicated
inferior wall infarction
Ref: http://www.cche.net/usersguides/ebm_tips.asp
Trial 2: Low Risk Patients
– New drug for acute myocardial
infarction to reduce mortality
– Later studied in a lower risk
population:
• 10% mortality at 30 days among
untreated
• e.g., younger, uncomplicated
inferior wall infarction
• 7.5% mortality among treated
– How would you describe the
effect of the new drug?
Ref: http://www.cche.net/usersguides/ebm_tips.asp
Trial 2: Low Risk Patients
Ref: http://www.cche.net/usersguides/ebm_tips.asp
Summary Points for Relative Risk
Reduction and Risk Difference
• Relative risk reduction is often more
impressive than absolute risk reduction.
• The lower the risk in the control group, the
larger the difference between relative risk
reduction and absolute risk reduction.
Objectives
Understand how to calculate and interpret and articulate
measures of effect and morbidity
1. 2x2 Table
•
•
•
•
Risk
Odds
Relative Risk
Odds Ratio
2. Relative Risk Reduction and Absolute Risk Reduction
3. Number needed to Treat and Number needed to Harm
3. Number Needed to Treat
Number Needed to Harm
Objectives
• Learn how to calculate Number Needed to
Treat (NNT) from an estimate of risk
difference.
• Increase awareness of the range of NNTs
associated with common interventions.
Definitions
• Number Needed to Treat (NNT):
– Number of persons who would have to receive
an intervention for 1 to benefit.
• Number Needed to Harm(NNH):
– Number of persons who would have to receive
an intervention for 1 to be experience a adverse
event.
Calculating NNT
If a disease has a mortality of 100% without
treatment and therapy reduces that mortality to 50%,
how many people would you need to treat to prevent
1 death?
Estimate NNT
CER%
How many 60-year-old patients with mild
hypertension would you have to treat with
diuretics for 5 years to prevent 1 stroke?
How many people with myocardial
infarction would you have to treat with ßblockers for 2 years to prevent 1 death?
How many people with acute myocardial
infarction would you have to treat with
streptokinase to prevent 1 person from
dying in the next 5 weeks?
Ref: http://www.cche.net/usersguides/ebm_tips.asp
EER%
ARR%
NNT
Estimate NNT
CER%
How many 60-year-old patients with
hypertension would you have to treat with
diuretics for 5 years to prevent 1 death?
How many people with myocardial infarction
would you have to treat with ß-blockers for 2
years to prevent 1 death?
How many people with acute myocardial
infarction would you have to treat with
streptokinase to prevent 1 person from
dying in the next 5 weeks?
Ref: http://www.cche.net/usersguides/ebm_tips.asp
EER%
ARR%
NNT
Estimate NNT
CER%
How many 60-year-old patients with
hypertension would you have to treat with
diuretics for 5 years to prevent 1 death?
How many people with myocardial
infarction would you have to treat with ßblockers for 2 years to prevent 1 death?
How many people with acute myocardial
infarction would you have to treat with
streptokinase to prevent 1 person from dying
in the next 5 weeks?
Ref: http://www.cche.net/usersguides/ebm_tips.asp
EER%
ARR%
NNT
Calculation
NNT= 100/ARR (where ARR is %)
or
NNT= 1/ARR (where ARR is proportion)
NNH= 100/ARI (where ARI is %)
Or
NNH = 1/ARI (where ARI is proportion)
NNTs from Controlled Trials
Population: hypertensive 60-year-olds
Therapy: oral diuretics
Outcome: stroke over 5 years
Control
Event %
Treatment
Event %
Absolute
Risk
Reductio
n%
NNT
2.9
1.9
1
100
Population: myocardial infarction
Therapy: ß-blockers
Outcome: death over 2 years
9.8 7.3 2.5 40
Population: acute myocardial infarction
Therapy: streptokinase (thrombolytic)
Outcome: death over 5 weeks
12 9.2
Ref: http://www.cche.net/usersguides/ebm_tips.asp
2.8
36
Population: hypertensive 60-year-olds
Outcome: stroke over 5 years
Depiction of Results in Control Group
Ref: http://www.nntonline.net/
Population: hypertensive 60-year-olds
Outcome: stroke over 5 years
Depiction of Results in Treatment Group
Ref: http://www.nntonline.net/
Bottom Line
• It is easy to mis-estimate baseline risk and
effects of therapy
• NNT is easily calculated from the absolute
risk reduction (ARR)
• Awareness of threshold NNT can help
anticipate the risk reduction to look for in a
therapy.
Summary
Outcome
Outcome
Total
Exposure
Yes
No
Yes
a
b
a+b
No
c
d
c+d
Total
a+c
b+d
N
References/Slide Sources
1.
2.
3.
4.
5.
6.
Last JM. A Dictionary of Epidemiology, 4th ed. Oxford University Press, 2001
Guyatt G et al. Users’ Guides to the Medical Literature, 2nd ed. McGraw Hill,
2008
Guyatt G. Tips for Teachers of Evidence Based Medicine. Lecture on Odds and
Risk.
Grimes and Schulz. Making Sense of Odds and Odds Ratios. Obs & Gyn
2008(111):423-6
Some Slides for Risk Reduction and NNT are from: Alexandra Barratt, Peter C.
Wyer, Rose Hatala, Thomas McGinn, Antonio L. Dans, Sheri Keitz, Virginia Moyer,
Gordon Guyatt, Robert Hayward, for the EBM Teaching Tips Working Group
(www.cche.net)
Smiley Diagrams from: Dr. Chris Cates EBM Website: www.nntonline.net