1. Epidemiology matters: a new introduction to methodological foundations
How can we mitigate against non-causal associations in design and analysis?
Epidemiology matters: a new introduction to methodological foundations
Chapter 10

2. Epidemiology Matters – Chapter 1
Seven steps
Define the population of interest
Conceptualize and create measures of exposures and health indicators
Take a sample of the population
Estimate measures of association between exposures and health indicators of interest
Rigorously evaluate whether the association observed suggests a causal association
Assess the evidence for causes working together
Assess the extent to which the result matters, is externally valid, to other populations
Epidemiology Matters – Chapter 1

3. Epidemiology Matters – Chapter 10
Randomization
Matching
Stratification
Sources of non-comparability
Summary
Epidemiology Matters – Chapter 10

4. Epidemiology Matters – Chapter 10
Randomization
Matching
Stratification
Sources of non-comparability
Summary
Epidemiology Matters – Chapter 10

5. Epidemiology Matters – Chapter 10
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
Epidemiology Matters – Chapter 10

6. Epidemiology Matters – Chapter 10
Comparability
What 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?
Epidemiology Matters – Chapter 10

7. 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
Epidemiology Matters – Chapter 10

8. Epidemiology Matters – Chapter 10
Randomization
Creates comparability between groups
Removes individual’s ability to choose exposure status
Epidemiology Matters – Chapter 10

9. Randomized Control Trial, RCT
Sample from population (purposive)
Assign individuals to be exposed or unexposed
Follow population forward to determine who develops outcome
Epidemiology Matters – Chapter 10

10. Epidemiology Matters – Chapter 10
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
The reason we randomize then is we ultimately want our comparison group ………
Epidemiology Matters – Chapter 10

11. 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
Epidemiology Matters – Chapter 10

12. Epidemiology Matters – Chapter 10
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
Epidemiology Matters – Chapter 10

13. Epidemiology Matters – Chapter 10
Non-diseased
Diseased
Non-exposed
Exposed
Epidemiology Matters – Chapter 10

14. Epidemiology Matters – Chapter 10
Study 1
Epidemiology Matters – Chapter 10

15. Epidemiology Matters – Chapter 10
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.
Epidemiology Matters – Chapter 10

16. Epidemiology Matters – Chapter 10
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
Epidemiology Matters – Chapter 10

17. Impact of saturated fat intake
Exerciser with high saturated fat intake
Exerciser without high saturated fat intake
Non-exerciser with high saturated fat intake
Non-exerciser without high saturated fat intake
Epidemiology Matters – Chapter 10

18. 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
Epidemiology Matters – Chapter 10

19. Epidemiology Matters – Chapter 10
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
Epidemiology Matters – Chapter 10

20. Epidemiology Matters – Chapter 10
Study 2
Epidemiology Matters – Chapter 10

21. 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.
Epidemiology Matters – Chapter 10

22. Epidemiology Matters – Chapter 10
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?
Epidemiology Matters – Chapter 10

23. Study 2, impact of saturated fat intake
Exerciser with high saturated fat intake
Exerciser without high saturated fat intake
Non-exerciser with high saturated fat intake
Non-exerciser without high saturated fat intake
Epidemiology Matters – Chapter 10

24. 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
Epidemiology Matters – Chapter 10

25. Limitations to randomization
Equipoise and ethics
Complication and intention to treat analysis,
Placebos and placebo effects, and the
Importance of blinding
Epidemiology Matters – Chapter 10

26. 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
Epidemiology Matters – Chapter 10

27. Epidemiology Matters – Chapter 10
Randomization
Matching
Stratification
Sources of non-comparability
Summary
Epidemiology Matters – Chapter 10

28. Epidemiology Matters – Chapter 10
Matching
Why and how to match
Analyzing matched pair data
Epidemiology Matters – Chapter 10

29. Epidemiology Matters – Chapter 10
Matching
Randomization often unethical and infeasible
Matching controls non-comparability where randomization is impossible
Epidemiology Matters – Chapter 10

30. Epidemiology Matters – Chapter 10
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
Epidemiology Matters – Chapter 10

31. Epidemiology Matters – Chapter 10
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
Epidemiology Matters – Chapter 10

32. Epidemiology Matters – Chapter 10
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
Epidemiology Matters – Chapter 10

33. Epidemiology Matters – Chapter 10
Matching, example
Each time we select a case from the treatment center, we select one or more controls of the same sex
Epidemiology Matters – Chapter 10

34. Matching to control non-comparability
Male low fish oil
Female low fish oil
Male high fish oil
Female high fish oil
Epidemiology Matters – Chapter 10

35. Matching to control non-comparability
Male
Female
Total
Low fish oil 9 18 27
High fish oil 7 16 23 34 50
Male low fish oil
Female low fish oil
Male high fish oil
Female high fish oil
Epidemiology Matters – Chapter 10

36. Epidemiology Matters – Chapter 10
Matching pairs, sex
Male low fish oil
Female low fish oil
Male high 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
Epidemiology Matters – Chapter 10

37. Epidemiology Matters – Chapter 10
Matching pairs, sex
Thus, in the upper left-hand corner we have six pairs in which both the depressed and non-depressed individual were low consumers of fish oil. Four of these pairs were women (no dots), and two of the pairs were men (dots). In the upper right-hand corner we have 10 pairs in which the depressed individual was a low consumer of fish oil and the non-depressed individual was a high consumer of fish oil. The bottom left contains the pairs in which the non-depressed individuals were low consumers of fish oil and the depressed individual was not, and the bottom right contains pairs in which both case and control were high consumers of fish oil.
Epidemiology Matters – Chapter 10

38. Analyzing matched pair data
Epidemiology Matters – Chapter 10

39. 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.
Epidemiology Matters – Chapter 10

40. Epidemiology Matters – Chapter 10
Randomization
Matching
Stratification
Sources of non-comparability
Summary
Epidemiology Matters – Chapter 10

41. Control of non-comparability
Design stage
Randomization
Matching
Analysis stage
Stratification
Epidemiology Matters – Chapter 10

42. Epidemiology Matters – Chapter 10
Stratification
Why and how to stratify
Interpreting stratified analyses
Epidemiology Matters – Chapter 10

43. Control of non-comparability in the analysis 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
Epidemiology Matters – Chapter 10

44. Control of non-comparability in the analysis stage
Is a potential factor related to non-comparability associated with the exposure and the outcome?
Epidemiology Matters – Chapter 10

45. Epidemiology Matters – Chapter 10
Stratification
Stratification removes effects of non-comparable variable on an exposure-outcome relation by limiting the variance on that outcome
Epidemiology Matters – Chapter 10

46. Stratification, example
Examine 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
Epidemiology Matters – Chapter 10

47. Stratification example non-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.
Epidemiology Matters – Chapter 10

48. Stratification example smokers31
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 – = 41.8
In summary, heavy alcohol consumption is associated with esophageal cancer both among a subpopulation of individuals who smoke, and a subpopulation who do not smoke. Smoking cannot have any effect on the outcome within these subpopulations, because there is no variance (i.e., all individuals within the subpopulation engage in the same amount of smoking behavior). By stratifying our analysis on a third variable that we believe is a cause non-comparability, we can obtain estimates of the exposure-disease relation that are not confounded by that third variable.
There is an increased risk of esophageal cancer among heavy alcohol consumers, even in the subpopulation of individuals who all smoke.
Epidemiology Matters – Chapter 10

49. 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
Epidemiology Matters – Chapter 10

50. Non-comparability through stratification
Careful and rigorous measurement of potential non-comparable variables is key to control for non-comparability in data analysis
Before stratification, always check that potential non-comparable variables are associated with exposure and outcome under study
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
Epidemiology Matters – Chapter 10

51. 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
Epidemiology Matters – Chapter 10

52. Smoking and depression example
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
Epidemiology Matters – Chapter 10

53. Smoking and depression example
Female smoker
Male smoker
Female non-smoker
Male non-smoker
Epidemiology Matters – Chapter 10

54. Smoking and depression example
Male smoker
Female smoker
Male non-smoker
Female non-smoker
Epidemiology Matters – Chapter 10

55. Smoking and depression example
Epidemiology Matters – Chapter 10

56. Smoking and depression example 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?
Epidemiology Matters – Chapter 10

57. Smoking and depression sex association
Smoking and sex
73% of men are smokers
38.3% of women are smokers
Men are more likely than women to be smokers
Epidemiology Matters – Chapter 10

58. Smoking and depression sex 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
Epidemiology Matters – Chapter 10

59. Smoking and depression stratified 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.
Epidemiology Matters – Chapter 10

60. Smoking and depression stratified 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.
Epidemiology Matters – Chapter 10

61. Smoking and depression stratified 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
Epidemiology Matters – Chapter 10

62. Epidemiology Matters – Chapter 10
Randomization
Matching
Stratification
Sources of non-comparability
Summary
Epidemiology Matters – Chapter 10

63. Epidemiology Matters – Chapter 10
Is every variable that is associated with exposure and outcome a potential source of non-comparability? No
Epidemiology Matters – Chapter 10

64. Sources of non-comparability
Factors in the causal pathway are not non-comparable variables
Factors that are consequences of exposure and outcome
Epidemiology Matters – Chapter 10

65. 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
Epidemiology Matters – Chapter 10

66. 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
Epidemiology Matters – Chapter 10

67. 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 
Epidemiology Matters – Chapter 10

68. Epidemiology Matters – Chapter 10
Randomization
Matching
Stratification
Sources of non-comparability
Summary
Epidemiology Matters – Chapter 10

69. Epidemiology Matters – Chapter 1
Seven steps
Define the population of interest
Conceptualize and create measures of exposures and health indicators
Take a sample of the population
Estimate measures of association between exposures and health indicators of interest
Rigorously evaluate whether the association observed suggests a causal association
Assess the evidence for causes working together
Assess the extent to which the result matters, is externally valid, to other populations
Epidemiology Matters – Chapter 1

70. Epidemiology Matters – Chapter 1
epidemiologymatters.org
Epidemiology Matters – Chapter 1