Download Confounding: An Introduction

Confounding:
An Introduction
Philip la Fleur, RPh MSc(Epidem)
Deputy Director, Center for Life Sciences
[email protected]
Epidemiology Supercourse
Astana, July 2012
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
Review: Why Randomize?
Emerg Med J 2003;20:164-168
Tadalafil Therapy for Pulmonary Arterial Hypertension (PAH). Circul 2009;119:2894
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 investigated
2. Must be independently associated with the
outcome being investigated
3. Not be in the causal pathway between exposure
and outcome.
Higher versus Lower Positive End-Expiratory Pressures
in Patients with the Acute Respiratory Distress Syndrome
NEJM 2004;351:327-36
Understanding Confounding and Adjusting for
Confounding; Qualitative Demonstration
• Treatment Group
(N=100)
– 80 young
– 20 old
• Control Group
N=100
– 20 young
– 80 old
Result =
Treatment
(apparently) Worked!
• Treatment Group
– 80 young
– 20 old
The Truth:
RR of Treatment = 1.0
Risk of Death in Young = 10%
Risk of Death in Old = 20%
• Control Group
– 20 young
– 80 old
Overall Analysis (all patients)
Dead
Alive
Total
Treated
8+4 = 12
88
100
Control
2+16 = 18
82
100
Total
30
170
200
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
How do we solve this problem?
• Young Patients
– Treatment
– Control
• Old Patients
– Treatment
– Control
All Subjects
Dead
Alive
Total
Treated
12
18
100
Control
18
82
100
Total
30
170
200
Young Subjects
Old Subjects
Dead
Alive
Total
Dead
Alive
Total
Treated
8
72
80
Treated
4
16
20
Control
2
18
20
Control
16
64
80
Total
10
90
100
Total
20
80
100
Risk in Treatment Group: 8/80 = 0.1
Risk in Treatment Group: 4/20 = 0.2
Risk in Control Group: 10/100 = 0.1
Risk in Control Group: 16/80 = 0.2
Relative Risk = 1.0
Relative Risk = 1.0
Higher versus Lower Positive End-Expiratory Pressures
in Patients with Acute Respiratory Distress Syndrome
NEJM 2004;351:327-36
Definition of a Confounder
• For a variable to be a confounder it should meet three
conditions:
1.
2.
3.
The factor must be associated with the exposure being investigated
Must be independently associated with the outcome being
investigated
Not be in the causal pathway between exposure and outcome.
EXPOSURE
(Truck Driving)
OUTCOME
(Lung Cancer)
CONFOUNDER
(Smoking)
Example: Do we have a confounder?
Oral Contraceptive
Use
Cervical Cancer
Age at first intercourse
= CONFOUNDER?
Example: Do we have a confounder?
Used OC
Never used OC
Cases
450
300
Controls
200
250
Odds Ratio = 1.9
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
Is it a Confounder? Test #1
1. The factor must be associated with the exposure being
investigated
2. Must be independently associated with the outcome
being investigated
3. Not be in the causal pathway between exposure and
outcome.
Cervical Cancer
Oral Contraceptive Use
?
Age at First
Intercourse
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 intercourse
50% of those who used OC had an early age of intercourse
Is it a Confounder? Test #2
1. The factor must be associated with the exposure being
investigated
2. Must be independently associated with the outcome
being investigated
3. Not be in the causal pathway between exposure and
outcome.
Cervical Cancer
Oral Contraceptive Use
Age at First
Intercourse
?
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
Is it a Confounder? Test #3
1. The factor must be associated with the exposure being
investigated
2. Must be independently associated with the outcome
being investigated
3. Not be in the causal pathway between exposure and
outcome.
Cervical Cancer
Oral Contraceptive Use
Age at First
Intercourse
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
The End
End
References/Bibliography
1.
2.
3.
4.
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
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):33743 (and associated lecture by G. Guyatt)
Streiner GR, Norman DL, PDQ Epidemiology. 2nd Ed. BC Decker Inc. 1998