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

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

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

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

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

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

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

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

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

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

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

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

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

Epidemiology Matters – Chapter 10 Study 1 Epidemiology Matters – Chapter 10

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

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

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

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

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

Epidemiology Matters – Chapter 10 Study 2 Epidemiology Matters – Chapter 10

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analyzing matched pair data Epidemiology Matters – Chapter 10

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

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

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

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

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

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

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

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

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

Stratification example smokers 31 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 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

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

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

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

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

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

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

Smoking and depression example Epidemiology Matters – Chapter 10

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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