Confounding: An Introduction

Epidemiology Supercourse Astana, July 2012

Philip la Fleur, RPh MSc(Epidem)Deputy Director, Center for Life Sciencesplafleur@kazcan.com

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 investigated2. 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 Pressuresin 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

Result =

• Control Group N=100– 20 young– 80 old

Treatment (apparently) Worked!

• 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)

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

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

Higher versus Lower Positive End-Expiratory Pressuresin 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. 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)

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 #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

?

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

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

?

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 #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

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

EndThe End

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