how can we mitigate against non- causal associations in design and analysis? epidemiology matters: a...
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How can we mitigate against non-causal associations in design and
analysis?
Epidemiology matters: a new introduction to methodological foundations
Chapter 10
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 10
1. Randomization
2. Matching
3. Stratification
4. Sources of non-comparability
5. Summary
4Epidemiology Matters – Chapter 10
1. Randomization
2. Matching
3. Stratification
4. Sources of non-comparability
5. Summary
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Comparability
Exposed and unexposed should be comparable on all factors associated with the disease other than the exposure
One way to ensure this comparability is to randomize the exposure
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ComparabilityWhat is wrong with non-comparability? Consider an example: Study: 5,000 smokers and 5,000 non-smokers are followed for
10 years After 10 years, the smokers have 3.0 times the risk of motor
vehicle crash fatality compared with non-smokers Are you comfortable reporting that smoking causes motor
vehicle crash fatality?
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Comparability, an example Study: 5,000 smokers and 5,000 non-smokers are followed for
10 years After 10 years, the smokers have 3.0 times the risk of motor
vehicle crash fatality compared with non-smokers Are you comfortable reporting that smoking causes motor
vehicle crash fatality? Individuals who choose to smoke are more likely to engage in
other behaviors with adverse consequences for health
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Randomization
Creates comparability between groups Removes individual’s ability to choose exposure
status
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Randomized Control Trial, RCT
Sample from population (purposive) Assign individuals to be exposed or unexposed Follow population forward to determine who
develops outcome
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The goal of RCT
We want our comparison groups to be “different” on just our main exposure that
we are studying in relation to some outcome
AND the “same” on all the other important
covariates
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Why does randomization control for non-comparability? Example
Two investigators conduct two separate studies Exploring effects of regular cardiovascular exercise on
incidence of cardiovascular disease Population is post-menopausal women Hypothesis: exercise is protective against
cardiovascular disease
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Example, study 1
Purposive sample of 80 post-menopausal women with no
history of cardiovascular disease
Asks women if they engage in ≥ 30 minutes of regular
cardiovascular exercise ≥ 3 times/week (regular exercise
compared to non-regular exercise)
Follows groups for five years
Count women in each group who have a cardiovascular event
Assume no losses to follow-up
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Non-diseased Diseased
Non-exposed Exposed
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Study 1
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Study 1, interpretation
Those who exercise have approximately 0.5 times the risk of cardiovascular disease compared with those who do not exercise.
There are approximately 20 fewer cases of cardiovascular disease per every 100 people who exercise compared with those
who do not exercise.
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Study 1,validity
Women who choose to exercise regularly may be more likely to be non-smokers, eat a more healthy diet, take multivitamins, etc.
We do not know whether the exercise had any causal effect on their cardiovascular health
In fact, the women who exercise had much lower average daily saturated fat intake than the non-exercisers
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Impact of saturated fat intake
Exerciser with high saturated fat intake
Non-exerciser with high saturated fat intake
Exerciser without high saturated fat intake
Non-exerciser without high saturated fat intake
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Impact of saturated fat intake
9 dotted people (high fat consumers) among 40 exercisers
Total prevalence = 22.5% of high fat consumption among the exercisers
18 dotted people (high fat consumers) among the 40 non-exercisers
Total prevalence = 45% of high fat consumption among the non-
exercisers
There is a greater proportion of high fat consumers among the non-exercisers
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Example, study 2 Purposive sample of 80 post-menopausal women with no
history of cardiovascular disease Randomly assigns women to engage in ≥ 30 minutes of
regular cardiovascular exercise ≥ 3 times/week (regular exercise compared to non-regular exercise)
Follows groups for five years Counts women in each group who have a cardiovascular
event Assume no losses to follow-up or noncompliance
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Study 2
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Study 2 - interpretation
Risk of cardiovascular disease among those randomized to exercise is 14.3% less than the risk among those randomized to not exercise.
We expect 10 fewer cases per 100 individuals exposed compared with the unexposed.
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Study 1 vs Study 2
Study 1 risk ratio = 0.5 and risk difference = -0.2 Study 2 risk ratio = 0.86 and risk difference = -0.1
Therefore, the effect is weaker in Study 2 than the effect in Study 1.
Why?
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Study 2, impact of saturated fat intake
Exerciser with high saturated fat intake
Non-exerciser with high saturated fat intake
Exerciser without high saturated fat intake
Non-exerciser without high saturated fat intake
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Study 2, impact of saturated fat intake
12 dotted people (high fat consumers) among 40 exercisers
Total prevalence = 30% of high fat consumption among the exercisers
12 dotted people (high fat consumers) among the 40 non-exercisers
Total prevalence = 30% of high fat consumption among the non-exercisers
There is the same proportion of excess high fat consumers among both groups
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Limitations to randomization
1. Equipoise and ethics2. Complication and intention to treat analysis, 3. Placebos and placebo effects, and the 4. Importance of blinding
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Randomization, summary
When randomization works, all factors that would differ between two
groups who got to choose their exposure status are, on average, evenly
distributed between the groups
This includes all known risk factors for the outcome and a myriad unknown
or difficult to measure
Because they are evenly distributed across the groups, factors cannot
affect the study estimates
Randomized trials are a powerful way to achieve comparability between
exposed and unexposed groups on both known and unknown factors that
cause the outcome
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1. Randomization
2. Matching
3. Stratification
4. Sources of non-comparability
5. Summary
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Matching
1. Why and how to match2. Analyzing matched pair data
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Matching
Randomization often unethical and infeasible Matching controls non-comparability where
randomization is impossible
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Matching
Participants matched on potential sources of non-comparability
Matching is a common way to control for non-comparability in design stage
In a cohort study, exposed individuals are matched to ≥ 1 unexposed individuals on ≥ 1 factor(s) of interest
In a case control study, diseased individuals are matched to a sample of disease free individuals
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Matching, example
Research question: Is low regular consumption of fish oil associated with development of depression?
Sample 25 individuals with a first diagnosis of depression recruited
from local mental health treatment center 25 individuals with no history of depression from
community surrounding mental health treatment center
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Matching, example
Concerned about sex as a potential source of non-comparability Women more likely to develop depression compared with
men Women on average have more nutritious diets and more
likely to supplement diets with fish oil Other potential sources of non-comparability to worry about
(though we are not necessarily matching on) are age, alcohol and cigarette use, socio-economic factors
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Matching, example
Each time we select a case from the treatment center, we select one or more controls of the same sex
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Matching to control non-comparability
Male low fish oil
Male high fish oil
Female low fish oil
Female high fish oil
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Male low fish oil
Male high fish oil
Female low fish oil
Female high fish oil
Male Female Total
Low fish oil 9 18 27
High fish oil 7 16 23
Total 16 34 50
Matching to control non-comparability
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Matching pairs, sex
Male low fish oil
Male high fish oil
Female low fish oil
Female high fish oil
Each pair is identical with respect to the matched factors
Sample had 50 individuals
Sample now has 25 matched pairs
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Matching pairs, sex
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Analyzing matched pair data
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Analyzing matched pair, example
Interpretation: Individuals who do not consume fish oil are 2.0 times as likely
to develop depression as individuals who consume fish oil, controlling for sex.
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1. Randomization
2. Matching
3. Stratification
4. Sources of non-comparability
5. Summary
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Control of non-comparability
Design stage Randomization Matching
Analysis stage Stratification
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Stratification
1. Why and how to stratify2. Interpreting stratified analyses
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Control of non-comparability in theanalysis stage
Collect data on variables that might contribute to non-comparability
Our ability to control for non-comparability in analysis stage is only as good as the quality of measures of variables contributing to non-comparability
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Is a potential factor related to non-comparability associated with the exposure and the outcome?
Control of non-comparability in theanalysis stage
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Stratification
Stratification removes effects of non-comparable variable on an exposure-outcome relation by limiting the variance on that outcome
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Stratification, exampleExamine relation between alcohol consumption and esophageal cancer among two groups
Non-smokers Among individuals who have never smoked a cigarette in their lives,
what is the relation between heavy alcohol consumption and esophageal cancer?
Smoking cannot confound the effect estimate because no individual in this subgroup has engaged in any smoking
Smokers Among smokers (presumably around the same duration and average
amount), were those who are heavy alcohol consumers more likely to develop esophageal cancer?
Smoking cannot confound the estimate because everyone is a smoker
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Stratification examplenon-smokers
Conditional probability of esophageal cancer among heavy alcohol consumers = 1/6 or 16.7%
Conditional probability of esophageal cancer among not heavy alcohol consumers = 1/16 or 6.3%
Risk ratio = 16.7/ 6.3 = 2.65
Risk difference = 16.7– 6.3 = 10.4
Interpretation: There is an increased risk of esophageal cancer among heavy alcohol consumers, even in the subpopulation of individuals who do not smoke.
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Stratification examplesmokers
Conditional probability of esophageal cancer among heavy alcohol consumers = 21/31, or 67.7%.
Conditional probability of esophageal cancer among not heavy alcohol consumers = 7/27 or 25.9%
Risk ratio = 67.7 / 25.9 = 2.61
Risk difference = 67.7 – 25.9 = 41.8
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There is an increased risk of esophageal cancer among heavy alcohol consumers, even in the subpopulation of individuals who all smoke.
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Stratification, example There is an increased risk of esophageal cancer among heavy
alcohol consumers, even in the subpopulation of individuals who do not smoke
There is an increased risk of esophageal cancer among heavy alcohol consumers, even in the subpopulation of individuals who all smoke
Therefore, even when we limit variance on the possible source of non-comparability (i.e., smoking) there still remains an increased risk of esophageal cancer among heavy alcohol drinkers
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Non-comparability throughstratification
1. Careful and rigorous measurement of potential non-comparable
variables is key to control for non-comparability in data analysis
2. Before stratification, always check that potential non-comparable
variables are associated with exposure and outcome under study
3. If a variable is not associated with both exposure and outcome,
then stratifying or otherwise controlling for that variable will not
change the estimate of the effect of exposure on outcome
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Non-comparability, another example
Example: cigarette smoking and depression
Rate of depression higher among cigarette smokers than among
non-smokers
Hypothesized that smoking can impact neurotransmitters in the
brain that impact negative mood and emotion
How could sex be a potential source of non-comparability in this
association?
Men are more likely than women to be smokers
Men are less likely to experience depression compared with women
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Smoking and depressionexample
Population of interest is adults in general population Purposive sample of 80 individuals with no history of
depression Assess smoking status at baseline Follow over 5 years to see how many develop depression Assume no individuals were lost to follow-up
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Smoking and depressionexample
Female smoker
Female non-smoker
Male smoker
Male non-smoker
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Smoking and depressionexample
Male smoker
Male non-smoker
Female smoker
Female non-smoker
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Smoking and depressionexample
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Smoking and depressionexample interpretation
Over five years, smokers had 1.04 times the risk of developing depression
compared with nonsmokers, and 1.05 times the odds. There are 10 excess cases
of depression among the smoking group per 100 persons over the course of 5
years (risk difference).
But what about sex?
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Smoking and depressionsex association
Smoking and sex
73% of men are smokers
38.3% of women are smokers
Men are more likely than women to be smokers
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Smoking and depressionsex association
Smoking and sex
73% of men are smokers
38.3% of women are smokers
Men are more likely than women to be smokers
Depression and sex
15% of men are depressed
53.2% of women are depressed
Men are less likely to have depression than women
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Smoking and depressionstratified analysis, men
Among men, those who smoke have 1.5 times the risk of depression compared to those
who do not smoke, over 5 years.
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Smoking and depressionstratified analysis, women
Among women, those who smoke have 1.49 times the risk of depression compared to those
who do not smoke, over 5 years.
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Smoking and depressionstratified analysis, interpretation
Smoking was not associated with depression in original, crude analysis
Stratifying by sex, smoking is associated with the development of depression
Sex obscured the association between smoking and depression
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1. Randomization
2. Matching
3. Stratification
4. Sources of non-comparability
5. Summary
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Is every variable that is associated with exposure and
outcome a potential source of non-comparability?
No
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Sources of non-comparability
1. Factors in the causal pathway are not non-comparable variables
2. Factors that are consequences of exposure and outcome
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Factors in causal pathway
Factors that are on the causal pathway of interest between the exposure and outcome do not contribute to non-comparability
If we control for them, we will obstruct the ability to observe the true effects of the exposure on the outcome
Factors on the causal pathway of interest should not be controlled
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Factors in causal pathway, example
Interested in prenatal exposure to tobacco smoke and offspring growth restriction during puberty
Hypothesize that prenatal exposure to tobacco causes low birth weight, and then this low birth weight causes growth restriction in puberty
Should not control for birth weight
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What if we do control for birth weight through stratification?
Among offspring with low birth weight, we would find that exposure to tobacco smoke is unrelated to offspring growth restriction We restricted analysis to only those with the intermediary
outcome of interest - low birth weight Among offspring with normal birth weight, we would not find
an association between the exposure and outcome We restricted analysis to only those without the
intermediary outcome – low birth weight
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1. Randomization
2. Matching
3. Stratification
4. Sources of non-comparability
5. Summary
69Epidemiology 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
70Epidemiology Matters – Chapter 1
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