Epidem. Inf. (1988). 101. 459-472 459 Printed in Great Britain
Confounding: An Introduction Epidemiology Supercourse Astana, July 2012 Philip la Fleur, RPh...
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Confounding: An Introduction
Epidemiology Supercourse Astana, July 2012
Philip la Fleur, RPh MSc(Epidem)Deputy Director, Center for Life [email protected]
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Objectives• Review why randomization is used and how it can
minimize confounding• Understand how to identify a confounder• Understand the fundamental logic underlying
adjusted analyses
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Review: Why Randomize?
Emerg Med J 2003;20:164-168
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Tadalafil Therapy for Pulmonary Arterial Hypertension (PAH). Circul 2009;119:2894
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Definition of a Confounder
• For a variable to be a confounder it should meet three conditions: 1. The factor must be associated with the exposure
being investigated2. Must be independently associated with the
outcome being investigated 3. Not be in the causal pathway between exposure
and outcome.
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Higher versus Lower Positive End-Expiratory Pressuresin Patients with the Acute Respiratory Distress Syndrome
NEJM 2004;351:327-36
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Understanding Confounding and Adjusting for Confounding; Qualitative Demonstration
• Treatment Group (N=100)– 80 young– 20 old
Result =
• Control Group N=100– 20 young– 80 old
Treatment (apparently) Worked!
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• Treatment Group– 80 young– 20 old
• Control Group– 20 young– 80 old
The Truth:RR of Treatment = 1.0Risk of Death in Young = 10%Risk of Death in Old = 20%
Dead Alive Total
Treated 100
Control 100
Total 200
8+4 = 12 88
2+16 = 18 82
30 170
Overall Analysis (all patients)
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Calculate Relative Risk
Dead Alive Total
Treated 12 88 100
Control 18 82 100
Total 30 170 200
Risk of Dying in Treated: 12/100 = 0.12
Risk of Dying in Control: 18/100 = 0.18
Relative Risk of Dying in Treated Compared to Control = 0.12/0.18 = 0.67
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How do we solve this problem?
• Young Patients– Treatment– Control
• Old Patients– Treatment– Control
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Dead Alive Total
Treated 8 72 80
Control 2 18 20
Total 10 90 100
Dead Alive Total
Treated 4 16 20
Control 16 64 80
Total 20 80 100
Young Subjects Old Subjects
Risk in Treatment Group: 8/80 = 0.1 Risk in Treatment Group: 4/20 = 0.2
Dead Alive Total
Treated 12 18 100
Control 18 82 100
Total 30 170 200
All Subjects
Risk in Control Group: 10/100 = 0.1
Relative Risk = 1.0
Risk in Control Group: 16/80 = 0.2
Relative Risk = 1.0
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Higher versus Lower Positive End-Expiratory Pressuresin Patients with Acute Respiratory Distress Syndrome
NEJM 2004;351:327-36
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Definition of a Confounder• For a variable to be a confounder it should meet three
conditions: 1. The factor must be associated with the exposure being investigated2. Must be independently associated with the outcome being
investigated 3. Not be in the causal pathway between exposure and outcome.
EXPOSURE(Truck Driving)
OUTCOME(Lung Cancer)
CONFOUNDER(Smoking)
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Example: Do we have a confounder?
Oral Contraceptive Use
Cervical Cancer
Age at first intercourse = CONFOUNDER?
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Example: Do we have a confounder?
Used OC Never used OC
Cases 450 300
Controls 200 250
Odds Ratio = 1.9
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Example: Do we have a confounder?
Age at first intercourse was < 20 years
Age at first intercourse was 20+ years
Used OC Never Used OC Used OC Never Used OC
Cases 400 200 50 100
Controls 100 50 100 200
Estimated Odds Ratio
= 1.0 = 1.0
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Is it a Confounder? Test #11. The factor must be associated with the exposure being
investigated2. Must be independently associated with the outcome
being investigated 3. Not be in the causal pathway between exposure and
outcome.
Oral Contraceptive Use Cervical Cancer
Age at First Intercourse
?
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Is it a Confounder? Test #1
Exposure
Confounder
Used OC Never Used OC
Age at first intercourse <20 years 100 (50%) 50 (20%)
Age at first intercourse 20+ years 100 (50%) 200 (80%)
Total 200 (100%) 250 (100%)
20% of those who never used OC had an early age of intercourse50% of those who used OC had an early age of intercourse
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Is it a Confounder? Test #21. The factor must be associated with the exposure being
investigated2. Must be independently associated with the outcome
being investigated 3. Not be in the causal pathway between exposure and
outcome.
Oral Contraceptive Use Cervical Cancer
Age at First Intercourse
?
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Is it a Confounder? Test #2
Confounder
Age at first intercourse <20 years
Age at first intercourse 20+ years
Cases 600 150
Controls 150 300
Odds Ratio = 8.0
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Is it a Confounder? Test #31. The factor must be associated with the exposure being
investigated2. Must be independently associated with the outcome
being investigated 3. Not be in the causal pathway between exposure and
outcome.
Oral Contraceptive Use Cervical Cancer
Age at First Intercourse
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Confounding
Relative Risk in the entire population
Relative Risk in young people
Relative Risk in old people
Adjusted Relative Risk
Scenario 1 No confounding 3.0 3.0 3.0 3.0
Scenario 2 Confounding 3.0 2.0 2.0 2.0
Scenario 3 Confounding 1.9 1.0 1.0 1.0
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EndThe End
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References/Bibliography1. Last JM. A Dictionary of Epidemiology, 4th ed. Oxford University Press, 20012. Guyatt G et al. Users’ Guides to the Medical Literature, 2nd ed. McGraw Hill,
20083. Kennedy CC et al Tips for Teachers of EBM: Adjusting for Prognostic Imbalances
(Confounding variables) in studies of therapy or harm. J Gen Int Med 23(3):337-43 (and associated lecture by G. Guyatt)
4. Streiner GR, Norman DL, PDQ Epidemiology. 2nd Ed. BC Decker Inc. 1998