Chapter 8 slides

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Is the association causal, or are
there alternative explanations?
Epidemiology matters: a new introduction to methodological foundations
Chapter 8
Seven steps
1.
Define the population of interest
2.
Conceptualize and create measures of exposures and health
indicators
3.
Take a sample of the population
4.
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 together
7.
Assess the extent to which the result matters, is externally valid,
to other populations
Epidemiology Matters – Chapter 1
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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?
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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?
Epidemiology Matters – Chapter 8
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1. Exposure causes disease
2. Complicating causes
3. Causal thinking in populations
4. Epidemiologic studies and assessing causes
5. Summary
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1. Exposure causes disease
2. Complicating causes
3. Causal thinking in populations
4. Epidemiologic studies and assessing causes
5. Summary
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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 exposure
2. Hold all other characteristics of the person, place and time
constant
3. Change only the exposure and observe then if the health
indicator changes
This is, of course, impossible
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Non-diseased
Diseased
Non-exposed
Exposed
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Observing individuals
under simultaneous conditions
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Observing individuals
under simultaneous conditions
Person 1: exposure causal
Person 2: exposure not causal
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1. Exposure causes disease
2. Complicating causes
3. Causal thinking in populations
4. Epidemiologic studies and assessing causes
5. Summary
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Why would an exposure be causal for Person 1 but not
causal for Person 2?
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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
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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?
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Previous example
Exposure of interest E
Component causes of sufficient
cause A,B,C - without E
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Previous example
Exposure of interest E
Component causes of sufficient
cause A,B,C - without E
Person 2 gets disease regardless of exposure E
These additional causes complicate causal inference
Epidemiology Matters – Chapter 8
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1. Exposure causes disease
2. Complicating causes
3. Causal thinking in populations
4. Epidemiologic studies and assessing causes
5. Summary
Epidemiology Matters – Chapter 8
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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
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Group comparison, example
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Group comparison, example
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Group comparison, example
Excess cases of
disease due to
causal effect of
the exposure on
the outcome
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Causal association?
1. Exposure causes disease
2. Complicating causes
3. Causal thinking in populations
4. Epidemiologic studies and assessing causes
5. Summary
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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?
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Distribution of additional causes
To assess comparability we need to know about the distribution
of other causes of disease between exposed and unexposed
groups
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Comparing groups
Epidemiologic study #1
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Comparing groups
Epidemiologic study #1
Epidemiologic study #2
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Comparing groups
Epidemiologic study #1
Epidemiologic study #2
Even distribution of dots across exposure conditions
Exposure conditions are comparable
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Comparing groups
Epidemiologic study #1
Epidemiologic study #2
Even distribution of dots across exposure conditions
Uneven distribution of dots across exposure conditions
Exposure conditions are comparable
These exposure conditions are not comparable
Epidemiology Matters – Chapter 8
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Causal association?
1. Exposure causes disease
2. Complicating causes
3. Causal thinking in populations
4. Epidemiologic studies and assessing causes
5. Summary
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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’
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Seven steps
1.
Define the population of interest
2.
Conceptualize and create measures of exposures and health
indicators
3.
Take a sample of the population
4.
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 together
7.
Assess the extent to which the result matters, is externally valid,
to other populations
Epidemiology Matters – Chapter 1
31
epidemiologymatters.org
Epidemiology Matters – Chapter 1
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