1. Causality in clinical epidemiology. Some
factors which could hamper causal inference
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Clinical Epidemiology LUMC

2. New concept in epidemiology: DAGs
• What are DAGS?
• How can they be used in epidemiologic research?
• Are they of any practical use? Example: the NEO study
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3. What is a causal DAG?
• DAG: Directed Acyclic Graph
• “Directed” : an arrow indicates a causal relation
E D
• E (exposure) causes D (disease)
• “Acyclic”: no variable can causes itself
• Using some simple rules, DAGs can help detecting
confounding and selection bias
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4. DAG rule 1: two variables are associated if they are
connected by a (non blocked) path
1. Example 1:
E B D
• E and D are associated
Unhealthy obesity Osteoarthritis
diet
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5. Example 2
C
E D
• E and D are associated: there is a path from E via C to D
• But there is no causal relation between E and D
•  CONFOUNDING
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6. Confounding in DAGS
C
?
E D
• The common cause C confounds the relation between E and D.
• DAG language: there is a ‘backdoor path’ from van E via C to D
• Backdoor path from E to D: path starts at E, against the flow(up to a
cause of E) to C, than proceed to D
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7. DAG rule 2: backdoor paths confound. Observed effects
differ from causal effects
C
?
E D
• E-C-D is backdoor path
C
E D
• E-C-D is NOT a backdoor path (there is a direct effect from E on
D and an indirect effect via C)
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8. DAG rule 3: paths are blocked by conditioning/
stratification
C
Oke !
?
E D
• Stratification/adjusting for C closes the backdoor path E-C-D
• We indicate stratification by a box
• If all backdoor path are closed, the causal effect of E on D can be
estimated
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9. Problem: colliders
Physical activity Obesity gene
A B
D Obesity
• A and B are both independent causes of D
• D is called a collider, because two directed arrows collide at D
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10. DAG rule 4: a collider blocks a path
A B
D
• The path from A via D to B is blocked because D is a collider
• A and B are independent
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11. Stratification on a collider
Physical activity Obesity gene
Obesity
• Consider group of obese subjects. What do you expect from a
obese person without obesity genes?
• Probably a couch potato
• Association between physical activity and obesity genes within
obese subjects
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12. DAG rule 5: conditioning on a colider induces
dependency (opens a path)
A B
D
Selection bias : conditioning on colliders
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13. NEO (Netherlands Epidemiology of Obesity) study
• Ralph Rippe, Martin den Heijer, Renée den Mutsert
• Aim: Investigate disease pathways in overweight or obesity
• Population-based, prospective cohort, age 45-65 with
oversampling of obese subjects
• Obese: BMI > 27 (n ≈ 5994)
• Normal (n ≈ 900)
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14. Questions
• 1) When is selection on BMI a problem?
• 2) if so, how can we solve it?
• DAGs help to think about these questions
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15. Different scenarios with selection/stratification on BMI
1. BMI is cause of exposure
B E D
•No backdoor paths, no collider bias
•No problems
2. BMI is risk factor for the disease, independent of E
E
D
B
•No backdoor paths, no collider bias
•No problems
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16. Different types of selection/stratification on BMI
3. E is a cause of B, and affects D. U is another cause of B,
and affects D
E
B D
U
•Selection on B (collider) creates a backdoor path E-U-D
•Related to index event bias
•Solution 1. Block path E-U-D, by conditioning on U
•Solution 2: use control group, adjust by inverse probability
weighting
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17. Different types of selection/stratification on BMI
4. B is an intermedair
E B D
•Estimating direct effect of E still possible: the effect of E on D
in subjects with overweight
•Estimating indirect effect/total effects , control group is needed
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18. Conclusion
• DAGs are a very useful tool in the design and analysis of
studies
• Confounding: existence of common causes, backdoor paths
• Selection bias: conditioning on a collider
• NEO study: find optimal way to obtain causal effects in
situation 3 and 4 (efficient ways of weighting observations)
• Incorporate that BMI is proxy for “obesity”
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