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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.

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Presentation on theme: "How can we mitigate against non- causal associations in design and analysis? Epidemiology matters: a new introduction to methodological foundations Chapter."— Presentation transcript:

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

2 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 12

3 1.Randomization 2.Matching 3.Stratification 4.Sources of non-comparability 5.Summary Epidemiology Matters – Chapter 103

4 1.Randomization 2.Matching 3.Stratification 4.Sources of non-comparability 5.Summary Epidemiology Matters – Chapter 104

5 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 5Epidemiology Matters – Chapter 10

6 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? 6Epidemiology 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 7Epidemiology Matters – Chapter 10

8 Randomization  Creates comparability between groups  Removes individual’s ability to choose exposure status 8Epidemiology 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 9Epidemiology Matters – Chapter 10

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 10Epidemiology 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 11Epidemiology Matters – Chapter 10

12 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 12Epidemiology Matters – Chapter 10

13 13 Non-diseased Diseased Non-exposed Exposed

14 Study 1 14Epidemiology Matters – Chapter 10

15 Study 1, interpretation 15 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 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 16Epidemiology Matters – Chapter 10

17 Impact of saturated fat intake 17 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 Epidemiology Matters – Chapter 10

18 Impact of saturated fat intake 18 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 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 19Epidemiology Matters – Chapter 10

20 Study 2 20Epidemiology Matters – Chapter 10

21 Study 2 - interpretation 21 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 Study 1 vs Study 2 22 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 23 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 Epidemiology Matters – Chapter 10

24 Study 2, impact of saturated fat intake 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 1.Equipoise and ethics 2.Complication and intention to treat analysis, 3.Placebos and placebo effects, and the 4.Importance of blinding 25Epidemiology 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 26Epidemiology Matters – Chapter 10

27 1.Randomization 2.Matching 3.Stratification 4.Sources of non-comparability 5.Summary Epidemiology Matters – Chapter 1027

28 Matching 1.Why and how to match 2.Analyzing matched pair data 28Epidemiology Matters – Chapter 10

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

30 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 30Epidemiology Matters – Chapter 10

31 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 31Epidemiology Matters – Chapter 10

32 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 32Epidemiology Matters – Chapter 10

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

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

35 35Epidemiology Matters – Chapter 10 Male low fish oil Male high fish oil Female low fish oil Female high fish oil MaleFemaleTotal Low fish oil91827 High fish oil71623 Total Matching to control non-comparability

36 Matching pairs, sex 36Epidemiology Matters – Chapter 10 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

37 Matching pairs, sex 37Epidemiology Matters – Chapter 10

38 Analyzing matched pair data 38Epidemiology Matters – Chapter 10

39 Analyzing matched pair, example 39Epidemiology Matters – Chapter 10 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.

40 1.Randomization 2.Matching 3.Stratification 4.Sources of non-comparability 5.Summary Epidemiology Matters – Chapter 1040

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

42 Stratification 1.Why and how to stratify 2.Interpreting stratified analyses 42Epidemiology 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 43Epidemiology Matters – Chapter 10

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

45 Stratification Stratification removes effects of non-comparable variable on an exposure-outcome relation by limiting the variance on that outcome 45Epidemiology 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 46Epidemiology Matters – Chapter 10

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

48 Stratification example smokers 48Epidemiology Matters – Chapter 10 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 = There is an increased risk of esophageal cancer among heavy alcohol consumers, even in the subpopulation of individuals who all smoke.

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 49Epidemiology Matters – Chapter 10

50 Non-comparability through stratification 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 50Epidemiology 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 51Epidemiology 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 52Epidemiology Matters – Chapter 10

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

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

55 Smoking and depression example 55Epidemiology Matters – Chapter 10

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

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

58 Smoking and depression sex association 58Epidemiology Matters – Chapter 10 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

59 Smoking and depression stratified analysis, men 59Epidemiology Matters – Chapter 10 Among men, those who smoke have 1.5 times the risk of depression compared to those who do not smoke, over 5 years.

60 Smoking and depression stratified analysis, women 60Epidemiology Matters – Chapter 10 Among women, those who smoke have 1.49 times the risk of depression compared to those who do not smoke, over 5 years.

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 61Epidemiology Matters – Chapter 10

62 1.Randomization 2.Matching 3.Stratification 4.Sources of non-comparability 5.Summary Epidemiology Matters – Chapter 1062

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

64 Sources of non-comparability 1.Factors in the causal pathway are not non- comparable variables 2.Factors that are consequences of exposure and outcome 64Epidemiology 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 65Epidemiology 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 66Epidemiology 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 67Epidemiology Matters – Chapter 10

68 1.Randomization 2.Matching 3.Stratification 4.Sources of non-comparability 5.Summary Epidemiology Matters – Chapter 1068

69 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 169

70 epidemiologymatters.org 70Epidemiology Matters – Chapter 1


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