Outline

1. What does causal inference entail?2. Using directed acyclic graphs

a. DAG basicsb. Identifying confoundingc. Understanding selection bias

3. Causal perspective on effect modificationa. Brief recap of effect modification (EM)b. Linking EM in our studies to realityc. Types of interactiond. Causal interaction / EM

1. Sufficient cause model (“causal pies”)2. Potential outcomes model (“causal types”)

e. Choosing which measure of interaction to estimate and report

4. Integrating causal concepts into your research

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

• Conditioning on a variable means that you divideyour data into strata based on that variable (alsocalled “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

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

• 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

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

Outcome

Selection factor

• Conditioning on a selection factor that is a collider will induce an association between exposure andoutcome, or alter the true association between exposure and outcome

Exposure

Selection bias

7

Drug A

CVD

Retained in study (not lost to follow-‐up)

Selection bias

8

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-‐fold increase 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

• Selection factor is visiting the medical practice where study recruitment is being carried out

• Alters any true association between hormone therapy and endometrial cancer

Hormonereplacement

therapy

Endometrial cancer

Visit medical

practice, recruited

for study

Selection bias

80

11

1. Define research hypothesis– Your hypothesis can include possible effect modification– Determine to what extent you aim to make causal inferences

using your data2. Determine study design (trial, cohort, etc.)3. Draw a DAG

a. Identify potential confoundersb. Choose which variables to measure

4. Analyze your dataa.b.

c.

Control for confounders identified in step 3Assess effect modification on the additive or multiplicative scaleMake statistical inferences

5. Make scientific inferences about your hypothesis

Causal inference in your research

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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-‐control studies

3. Read more in Rothman’s Modern Epidemiology Causal Diagrams chapter (3rd edition or later)

DAGs are a powerful tool!