5.3.1 causal em

1. Outline
1. What does causal inference entail?
2. Using directed acyclic graphs
a. DAG basics
b. Identifying confounding
c. Understanding selection bias
3. Causal perspective on effect modification
a. Brief recap of effect modification (EM)
b. Linking EM in our studies to reality
c. Types of interaction
d. Causal interaction / EM
1. Sufficient cause model (“causalpies”)
2. Potential outcomes model (“causal types”)
e. Choosing which measure of interaction to estimate and report
4. Integrating causal concepts into your research

2. 2
Understanding selection bias with
DAGS
• A “selection factor” is a variable that influences
whether or not each individual’s data ends up in the
analysis
• For example
– Decides to participate in study
– Loss to follow-‐up
– Competing causes (of death)
• In DAGs, colliders can indicate possible selection bias

3. • Conditioning on a variable means that you divide
your data into strata based on that variable (also
called “controlling for” or “adjusting for”)
• Represented by putting a box around the variable –
example of conditioning on a collider
Review: Colliders
A B
C
3

4. 4
Selection bias
• Selection bias is equivalent in DAG terminology to
“conditioning on a collider”
• Selection bias occurs when both the exposure and
outcome affect whether or not an individual is
included in the study data

5. • Conditioning on a collider can change the association
(induce an association, alter a true association)
between the two ancestor variables (A and B)
A
C
B A
C
B
Truth
5
Selection bias
Selection bias

6. Outcome
Selection factor
• Effectively you analyze your study data within one stratum of
any selection factor
– You only have data on those who participated
– You only have data on those retained in the study (not lost to follow-‐
up)
• If exposure and outcome both cause the selection factor then
you are “conditioning on a collider”
Exposure
Selection bias
6

7. Outcome
Selection factor
• Conditioning on a selection factor that is a collider
will induce an association between exposure and
outcome, or alter the true association between
exposure and outcome
Exposure
Selection bias
7

8. Drug A
CVD
Retained in study (not
lost to follow-‐up)
Selection bias
8

9. 9
An example from 1970’s epidemiology:
• Several case-‐control studies of postmenopausal hormone
therapy and endometrial cancer
• Enrolled women visiting medical practices
• Studies reported a 10-‐foldincrease in risk of cancer
among women taking hormone therapy
• Was the increase in risk causal, or was it a result of bias?
– Women with endometrial cancer may have been more likely to
have symptoms that led them to visit doctor
– Women taking postmenopausal hormone therapy may have
been more likely to have symptoms that led them to visit doctor
Selection bias

10. • Selection factor is visiting the medical practice where
study recruitment is being carried out
• Alters any true association between hormone
therapy and endometrial cancer
Hormone
replacement
therapy
Endometrial
cancer
Visit medical
practice, recruited
for study
Selection bias
80

11. 1
1
1. Define research hypothesis
– Your hypothesis can include possible effect modification
– Determine to what extent you aim to make causal inferences
using your data
2. Determine study design (trial, cohort, etc.)
3. Draw a DAG
a. Identify potential confounders
b. Choose which variables to measure
4. Analyze your data
a.
b.
c.
Control for confounders identified in step 3
Assess effect modification on the additive or multiplicative
scale
Make statistical inferences
5. Make scientific inferences about your hypothesis
Causal inference in your research

12. 1
2
1. Today we talked about identifying confounding and
selection bias with DAGs
2. DAGs can also be used to identify:
1. Survivor bias
2. Residual confounding
3. Bias caused by missing data
4. How to estimate indirect or direct effects
5. Bias in matched case-‐controlstudies
3. Read more in Rothman’s Modern Epidemiology
Causal Diagrams chapter (3rd edition or later)
DAGs are a powerful tool!