Is the association causal, or are there alternative explanations?

Epidemiology matters: a new introduction to methodological foundations

Chapter 8

2Epidemiology Matters – Chapter 1

Seven steps

1. Define the population of interest2. Conceptualize and create measures of exposures and health

indicators3. Take a sample of the population4. Estimate measures of association between exposures and health

indicators of interest

5. Rigorously evaluate whether the association observed suggests a causal association

6. Assess the evidence for causes working together7. Assess the extent to which the result matters, is externally valid, to

other populations

3Epidemiology Matters – Chapter 8

Inferential thinking, chapter 7

In Chapter 7 we asked a conceptual (counterfactual) question:

Would the disease have occurred when and how it did without the exposure, or without the amount of exposure that occurred, the timing of exposure, or within the context of multiple exposures?

4Epidemiology Matters – Chapter 8

Inferential thinking, chapter 8

In Chapter 8 we ask a pragmatic question:

Does the association that we measure in our data reflect the amount of excess disease that occurred due to the effects of the exposure, or could there be alternative explanations for the study findings other than a causal explanation?

5Epidemiology Matters – Chapter 8

1. Exposure causes disease

2. Complicating causes

3. Causal thinking in populations

4. Epidemiologic studies and assessing causes

5. Summary

6Epidemiology Matters – Chapter 8

1. Exposure causes disease

2. Complicating causes

3. Causal thinking in populations

4. Epidemiologic studies and assessing causes

5. Summary

7Epidemiology Matters – Chapter 8

When does exposure cause disease?

A counterfactual test to see if an exposure is a cause would require us to:1. Take the same person observed over the same time period,

once with the exposure and once without the exposure2. Hold all other characteristics of the person, place and time

constant3. Change only the exposure and observe then if the health

indicator changesThis is, of course, impossible

8Epidemiology Matters – Chapter 8

Non-diseased Diseased

Non-exposed Exposed

9Epidemiology Matters – Chapter 8

Observing individualsunder simultaneous conditions

Epidemiology Matters – Chapter 8 10

Observing individualsunder simultaneous conditions

Person 1: exposure causal

Person 2: exposure not causal

11Epidemiology Matters – Chapter 8

1. Exposure causes disease

2. Complicating causes

3. Causal thinking in populations

4. Epidemiologic studies and assessing causes

5. Summary

12Epidemiology Matters – Chapter 8

Why would an exposure be causal for Person 1 but not

causal for Person 2?

13Epidemiology Matters – Chapter 8

Complicating causes

Many sufficient cause sets can produce particular health indicators

The exposure of interest may be part of only one particular sufficient cause set; there are other sufficient causes that also produce the health indicator of interest

14Epidemiology Matters – Chapter 8

Complicating causes, an example

Disease X has two sufficient causes

1. A, B, and C

2. E, F, and G

Individual exposed to A, B, C, F, and G

Will get the disease

Completes sufficient cause 1 (A, B, and C)

Now exposed to E

Completes sufficient cause 2 (E, F, and G)

Exposure to E is not causal for this individual because she would have gotten the disease regardless given

exposure to A, B, and C

Therefore if E is exposure of interest we need to consider A, B, and C as other causes of disease

How can we visualize individuals with component causes not included in sufficient causal structure of E?

Epidemiology Matters – Chapter 8 15

Previous exampleExposure of interest E

Component causes of sufficient cause A,B,C - without E

Epidemiology Matters – Chapter 8 16

Previous exampleExposure of interest E

Component causes of sufficient cause A,B,C - without E

Person 2 gets disease regardless of exposure EThese additional causes complicate causal inference

17Epidemiology Matters – Chapter 8

1. Exposure causes disease

2. Complicating causes

3. Causal thinking in populations

4. Epidemiologic studies and assessing causes

5. Summary

18Epidemiology Matters – Chapter 8

Causal thinking in populations

Remember that epidemiological studies investigate groups of people

Therefore, our causal thinking applies to groups of individuals with multiple sufficient causes

We are interested in understanding the number of excess cases of disease that can be removed if we remove a particular cause

19Epidemiology Matters – Chapter 8

Group comparison, example

20Epidemiology Matters – Chapter 8

Group comparison, example

21Epidemiology Matters – Chapter 8

Group comparison, example

Excess cases of disease due to causal effect of the exposure on the outcome

22Epidemiology Matters – Chapter 8

Causal association?

1. Exposure causes disease

2. Complicating causes

3. Causal thinking in populations

4. Epidemiologic studies and assessing causes

5. Summary

23Epidemiology Matters – Chapter 8

Epidemiologic study design

It is impossible to observe the same people over the same period with and without

exposure

Instead we use group comparison of exposed and unexposed groups, often observed

in parallel over a similar time period

Ideally we want the unexposed group in an epidemiologic study to represent the

experience of exposed group had they not been exposed

However, what can complicate this approach is if there are imbalances in the

comparability of these groups allowing there to be different causes in each group

It is therefore essential to know how comparable these groups are to each other,

i.e., how close is the unexposed group to what we would expect the exposed group

to resemble if they were not exposed?

24Epidemiology Matters – Chapter 8

Distribution of additional causes

To assess comparability we need to know about the distribution

of other causes of disease between exposed and unexposed

groups

25Epidemiology Matters – Chapter 8

Comparing groupsEpidemiologic study #1

26Epidemiology Matters – Chapter 8

Comparing groupsEpidemiologic study #1 Epidemiologic study #2

27Epidemiology Matters – Chapter 8

Comparing groupsEpidemiologic study #1 Epidemiologic study #2

Even distribution of dots across exposure conditions

Exposure conditions are comparable

28Epidemiology Matters – Chapter 8

Comparing groupsEpidemiologic study #1 Epidemiologic study #2

Uneven distribution of dots across exposure conditions

These exposure conditions are not comparable Even distribution of dots across exposure conditions

Exposure conditions are comparable

29Epidemiology Matters – Chapter 8

Causal association?

1. Exposure causes disease

2. Complicating causes

3. Causal thinking in populations

4. Epidemiologic studies and assessing causes

5. Summary

30Epidemiology Matters – Chapter 8

Non-comparability

To replicate a counterfactual paradigm we want to observe the same

group at same time with the only variable changing being exposure

This is infeasible. Instead we compare groups of people and aim to keep

the distribution of all other variables equal between the groups

Failure to achieve this results in group ‘non-comparability’

31Epidemiology Matters – Chapter 1

Seven steps

1. Define the population of interest2. Conceptualize and create measures of exposures and health

indicators3. Take a sample of the population4. Estimate measures of association between exposures and health

indicators of interest

5. Rigorously evaluate whether the association observed suggests a causal association

6. Assess the evidence for causes working together7. Assess the extent to which the result matters, is externally valid, to

other populations

32Epidemiology Matters – Chapter 1

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