Epidemiology Homework due Sat 7-2015 - Please write in short essay format in word doc

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Confounding is a mixing of effects of extraneous factors (confounders) with the effect of the exposure of interest. The association between exposure and disease is distorted because it is mixed with the effect of another factor that is also associated with the disease. A confounder is therefore an alternative explanation for the observed association between an exposure and disease. The result of confounding is to distort the true association either towards or away from the null. Many epidemiologists refer to confounding as a type of bias.

Example: Who can run faster, men or women? Exposure: gender Outcome: speed Hypothesis: The average running speed of men is faster than the average running speed of women. All men and women in one town invited to participate in a road race. On race day, both men and women come and race. The average running time for the men is faster than the women. CONCLUSION: Men run faster than women, because of their gender. But wait! Someone notices that women with young children did not race. In fact, women who ran the race were, on average, older than men who ran. For example, the average age of women was 50 years while the average age of men was 25 years. CONCLUSION: Perhaps men were faster not because of their gender, but because they were younger. Another race is held, this time making sure that the ages in the two groups (men and women) are comparable. In other words, the men and women have same distribution of ages. Race result: Once again, men are faster. CONCLUSION: Controlling for age, men are still faster than women. But wait! Someone points out that the men are, on average, taller than the women. CONCLUSION: Perhaps men were faster not due to their gender, but because their legs are longer. Another race is held, this time making sure both heights and ages in the two groups (men and women) are comparable. Race result: Once again, men are faster. CONCLUSION: Men are faster than women, regardless of age or height. But wait! Someone points out that 50% of the women had hair longer than their shoulders, and only 5% of the men did! CONCLUSION: Long hair made the women run slower? (Is this a reasonable conclusion?) The point of this exercise is to demonstrate that there are in fact often many alternative explanatory factors for the association between an exposure and an outcome. Properly considering potential confounding factors is an important part of any epidemiologic analysis.

Criteria for confounding Let’s review the meaning of association. If a characteristic is associated with disease, then the risk of disease is different among people with the characteristic compared to those without. If the

CONFOUNDING

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characteristic is associated with exposure, then the distribution of the characteristic is different among people with the exposure compared to people without exposure (unbalanced between groups). In general, for a characteristic to be a confounder, it must be associated with both the outcome and the exposure under study. (Think about the race example above: Why would age and height be reasonable explanations, but not hair length?) There are 3 major criteria which must be satisfied for a factor to be a confounder:

1) Confounder is a risk factor for the outcome, independent of exposure 2) Confounder is associated with exposure 3) Confounder is not in the causal pathway between exposure and disease.

We will now explore each of these criteria in more detail. 1. Association with the outcome A confounder can be a risk factor, preventive, or marker for a cause of disease. It must be associated with disease independent of exposure. (This is why the arrow in the diagram above shows the confounder pointing directly to the outcome, and not in between the exposure and outcome.) Another way to say a confounder is independently associated with outcome is that the association between the confounder and outcome is present regardless of the exposure (whether or not a person is exposed or unexposed is irrelevant). To figure this out, we examine the association in the unexposed population, since being exposed doesn’t matter. Example: The road race again. Age and height are associated with speed regardless of gender. Taller people (both men and women) have greater speed. Younger people (both men and women) have greater speed. 2. Association with exposure We have already discussed the need for comparability between the exposed and unexposed groups in experimental and cohort studies. Recall the purpose of randomization in an experimental study, and the rules for selection of the comparison group in a cohort study -- we want the groups being compared to be similar with respect to all factors other than the exposure of interest. A variable can only be a confounder if it is different between compared groups. In order to satisfy this criterion, the prevalence of the factor (i.e., its frequency) must greater among the exposed group versus the unexposed group. This means the confounder is associated with exposure. In a cohort study, we examine this association among the entire study population (because the population is defined based on exposure status). In a case-control study, this association is only examined among the controls (because controls represent the exposure distribution in the source population). Example: In a cohort study, smoking is a confounder of the effect of occupational exposures to dyes on bladder cancer. Smoking is associated with dye exposure, because workers tend to smoke more than non-workers do.

Exposure Outcome (Disease)

Confounder

Exposure Outcome (Disease)

Confounder

Dye exposure

Bladder Cancer

Smoking

Dye exposure

Bladder Cancer

Smoking

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3. Not in the causal pathway A factor cannot be a confounder if it is a step in the causal chain or pathway between exposure and disease: Notice on the first diagram above, the confounder arrows go directly to exposure or directly to the outcome, but not in between exposure and disease. Occupational dye exposure does not “cause” smoking, which then leads to bladder cancer – so we say it is not on the causal pathway in between exposure and disease. This means that smoking may be a confounder. An example of a non-confounder identified using this criterion is that moderate alcohol consumption increases serum HDL levels which, in turn, decrease the risk of heart disease. HDL level is an intermediate step in this causal chain, and therefore not a confounder. Instead, it is something interesting that helps us understand the disease mechanism. Example of confounded data Let’s examine a hypothetical cohort study of dietary fat and heart disease. The hypothesis is that people with a high fat diet are more likely to develop heart disease. Exposure: high fat diet; Outcome: heart disease; Confounder: gender. Our total population consists of 1000 exposed and 1000 unexposed individuals, and we calculate an overall or crude risk ratio: Heart

Disease No Heart Disease

Total

High fat diet

600 400 900

Low fat diet 400 700 1100

2000

Moderate alcohol

Heart disease

Serum HDL

Moderate alcohol

Heart disease

Serum HDL

Dye exposure Bladder cancerX Smoking

RR= 600/900 =1.8

400/1100

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Interpretation: Individuals with a low fat diet are nearly two times as likely to develop heart disease during the study period. Alternatively, we could also say those individuals are 100% more likely to develop heart disease. You know enough about epidemiology now to understand that the risk of disease is often not the same for all individuals. Let’s see if the risk is the same for both men and for women. To do this, we separate the population by gender. We call this process stratification. Stratification allows us to examine the association within categories (strata) of the confounding variable. In other words, stratification restricts the populations to a specific characteristic which we suspect is a confounder. We must then calculate RRs and interpret them separately for each stratum, calling these the adjusted estimates. Note that we have just divided the population into 2 strata, men and women. The total population is still 2000 people, but notice that there are 1000 women and 1000 men. Women Men

Heart Disease

No Heart

Disease

Heart Disease

No Heart

Disease High fat

diet 40 160 200

High fat diet

560 240 700

Low fat diet

160 640 800 Low fat

diet 240 60 300

1000 RR = 1.0 1000

RR= 40/200 =1.0

160/800

Interpretation: Among men, there is no greater risk of heart disease in the high fat versus low fat diet group. Among women, there is no greater risk of disease in the high fat versus low fat diet group. Does this make sense? The crude (overall) estimate suggested a twofold increased risk among the exposed relative to the unexposed, but the adjusted estimates suggest there is no association. This was the effect of confounding by gender - it created the appearance of an association when there was not actually one. Why might this be true? To find out, let’s walk through our 3 criteria for confounding: Criterion 1) Confounder must be associated with outcome, independent of exposure. Is gender associated with heart disease, independent of dietary fat?

Exposure Disease

Gender

Exposure Disease

Gender

RR= 560/700 =1.0

240/300

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How do we know if a factor is related to the outcome, independent of exposure? We ask the question: even among the unexposed (low fat diet), is the factor (gender) associated with the outcome (heart disease)? To answer this question we can look at the relationship between the confounder and the disease in the unexposed population. In other words, even if you are not exposed, the factor must still be associated with disease to be a confounder. Unexposed population – Low fat diet

Heart Disease

No Heart

Disease

Male 240 60 300

Female 160 640 800

1100

(Note: These numbers come from the gender-stratified tables above – prove it to yourself!) Interpretation: Among people with a high fat diet, the risk of heart disease for men is four times greater than for women. The criterion that the confounder be associated with the outcome, independent of exposure, is satisfied. Criterion 2) Confounder must be associated with exposure. Is gender associated with dietary fat? To answer this question, we need to compare the relative proportion of men and women exposed – in other words, calculate the prevalence ratio of exposure for men versus women. We do another stratified analysis to measure the association, by looking at the RR of exposure for males to females: High fat

diet Low fat

diet

Male 700 300 1000

Female 200 800 1000

2000

RR= 240/300 =4.0

160/800

Proportion of men exposed: 700/1000=0.7 or 70% Proportion of women exposed: 200/1000=0.2 or 20%

PR= 700/1000 =3.5

200/1000

Proportion of diseased men: 240/300=0.8 or 80% Proportion of diseased women: 160/800=0.2 or 20%

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Interpretation: The prevalence of exposure (high fat diet) is 3.5 times greater for men then for women. The criterion that the confounder must be associated with exposure is satisfied. If there were equal proportions of exposed men and women, we could not say that gender is associated with exposure – because the PR produced from this table would be 1.0. Criterion 3) Confounder cannot be in the causal pathway. Exposure to a high fat diet is not likely to precede or cause gender. Therefore, gender is NOT on the exposure – disease pathway, and the third criterion is also met. (Remember, the criterion is that the confounder is NOT on the pathway. If you think it IS on the pathway, the criterion is NOT satisfied. It’s a common mistake to mix this up!) CONCLUSION: We suspected confounding by gender, because the crude association suggested a relationship between high fat diet and heart disease, but the gender-stratified results showed no association. Walking through the 3 criteria for confounding confirmed that gender is in fact a confounder, because all 3 criteria were satisfied. (Note: It’s all or nothing – if 2 criteria are satisfied, this is not enough.) Using this same example, smoking is another likely confounder for this same association. Walk through the criteria for confounding: Smoking is 1) a risk factor for heart disease, regardless of whether diet is high or low fat, 2) individuals with a high fat diet may be more likely to smoke, and 3) smoking is not on the causal pathway between high fat diet and heart disease.

Identifying confounders Usually, risk factors for disease are potential confounders, such as lifestyle and behavioral factors including diet, smoking, alcohol consumption, etc., obesity, gender, age, genetic factors, as well as socio- demographic factors (income, education level, access to health care, etc.). Epidemiologists often rely on existing knowledge of disease to identify possible confounders before conducting a study, so that they can try to collect information on these confounders. Still, it is not possible to identify all possible confounders, or even to measure all known confounders. This results in residual confounding, because the exposure-disease association may still be distorted by either known or unknown confounders. How do we know if a factor is a confounder in your data or not? One way is to compare the crude and stratified measures of association, as above. If they differ appreciably, then this factor is a likely confounder. Confounding can cause the crude estimate to be either an overestimate or an underestimate of the true association (i.e., toward or away from the null value of 1.0). Controlling for confounders Confounding tends to result in a biased estimate of the true exposure–outcome association. If we collect information on the confounder, then we can usually remove its effect. Because confounders may bias our association and the interpretation of study results, we should control for them. Confounding factors are nuisance variables because they get in the way of the relationship we want to observe, and we need to remove their influence in order to observe the true association. We have done this already in Module 2 with age-standardization.

High fat diet Heart diseaseX Gender

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There are two major strategies to control for confounding - in either the design phase or the analysis phase of a study. To control for confounding in either phase you must have information on the variables that are potential confounders. This is true for all methods of controlling confounding except for randomization. Controlling for confounders during the design phase Methods to control for confounding at the design phase of a study include randomization, restriction, and matching. These decisions are made before the study is conducted, in order to best control for these nuisance factors.

• Randomization: In Module 3 you learned about randomization in the context of experimental studies. With a large enough sample size, randomization is likely to control for both known and unknown confounders.

• Restriction: The investigator can restrict the inclusion criteria for study subjects and limit

entrance to individuals who fall within a specified category of the confounder. In the road race example, you could restrict the race to people in a certain age range (say, 25-30) or to people in given height range. By restricting the race, there cannot be major differences in age or height between exposed and unexposed groups. (Remember, a variable can only be a confounder if it is different between compared groups.) The advantage of restriction is that it is straightforward, convenient, and less expensive than collecting information for a larger group. However, restricting limits our ability to generalize study findings to other populations. For example, if we only allowed individuals aged 25-30 to race, we cannot make effective guesses about how 50-60 year olds (or some other age group) might have performed if they had raced.

• Matching: Here the investigator selects study subjects so that the potential confounders are

distributed in an identical manner among the exposed and unexposed groups (cohort study) or among the cases and controls (case control study). An exposed subject of specific characteristics is matched to an unexposed subject who shares those same characteristics. Example: Matching in cohort study of exercise and heart attack. Exposed group: exercisers, Unexposed group: non-exercisers. Confounders to be matched are age, gender, and smoking status; since we know these are strong risk factors for heart attack and are also likely associated with exposure (people who exercise tend to be nonsmokers). The matching on age doesn’t have to be exact, but should be close (+ or – a couple of years).

Exposed subject Unexposed subject

45 year old female, nonsmoker, exerciser 45 year old female, nonsmoker, non-exerciser 50 year old male, nonsmoker, exerciser 50 year old male, nonsmoker, non-exerciser 41 year old female, smoker, exerciser 40 year old female, smoker, non-exerciser

Controlling for confounders in the analysis In addition to these methods to control for confounding in the design of a study, we can also control for confounding using analysis methods. We have demonstrated stratification in many of the tables above.

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Because the RRs are calculated for each strata of a factor separately, we can say we control for confounding by the factor via stratification.

Example: Case control study of oral contraceptive use and risk of heart attack. Age is a confounder.

Case Control OC use Yes 39 24

No 114 154

Crude OR= 2.2

Stratified analysis Age < 40 years Age ≥ 40 years

Case Control Case Control

OC use Yes 21 17 Yes 18 7

No 26 59

No 88 95

OR = 2.8 OR = 2.8

Note each stratum is like a restricted analysis, because there is a narrow range of the confounder. The stratum-specific ORs (2.8) differ from the crude OR (2.2) by about 25%. This difference indicates that there is confounding by age. There are some limitations of stratification. It is difficult to control for many variables simultaneously because a large number of strata will be generated relative to the number of study subjects. There are also other methods for controlling for confounding in the analysis, such as multivariate regression and other types of statistical approaches. Summary of confounding

• Is a mixing of effect between and exposure, an outcome, and a third variable known as a confounder

• Considered a nuisance which must be eliminated • Studies may have a small, moderate or large degree of confounding • Confounding can either exaggerate or minimize the true association • Epidemiologists have developed methods to control confounding in the design and analysis of

studies Effect modification Let’s say we are evaluating a potential confounder and conduct a stratified analysis, as above. If the strata-specific estimates differ from the crude, we say that confounding was present. But what if the stratum-specific estimates are appreciably different from each other? This is called effect modification.

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Effect modification occurs when the association between exposure and disease varies by levels of a third variable. In other words, the presence/absence/or level of one risk factor influences the association between some other risk factor and the disease. The diagram shows the relationship between exposure and disease for a factor with only 2 strata (such as gender).

Example: In a cohort study, it appears that gender influences the association between dietary fat and heart disease (the association between dietary fat and heart disease is different for women than for men). Generally, effect modification reflects biological interactions. In this example, estrogen or other sex hormones which differ between genders may interact with dietary fat and alter its effect on the heart.

A stratified analysis of dietary fat and heart disease shows the effect modification. Notice that the stratum-specific RRs differ from the crude (demonstrating confounding) as well as from each other (demonstrating effect modification).

Crude Stratum-specific Men Women HD+ HD- HD+ HD- HD+ HD-

E+ 600 300

E+ 200 50

E+ 400 250

E- 400 700

E- 200 550

E- 200 150

RR= (600/900)/(400/1100)=1.8 RR=(200/250)/(200/750) = 3.0 RR=(400/650)/(200/350)=1.08 Effect modification can improve our understanding about the association between exposure and disease. For example, if men and women respond differently to dietary fat, we could use this information to target certain preventive dietary measures for men with respect to heart disease. Another example: The relation between body mass index (a measure of obesity) and breast cancer varies according to menopausal status. Among pre-menopausal women, higher BMI decreases risk. Among post-menopausal women, higher BMI increases (or does not affect) risk. Let’s reflect on the difference between effect modification and confounding. We want to describe and understand effect modification, since it helps improve our understanding of disease mechanisms and possible differences in preventive treatment between groups. Confounding, on the other hand, is a

Exposure Outcome (Disease)

Effect modifier

Exposure Outcome (Disease)

Effect modifier

Dietary fat Heart disease

Gender

Dietary fat Heart disease

Gender

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nuisance and we need to control for it or eliminate it since it biases our measure of association. Since we use stratification to observe both effect modification and confounding, once we identify effect modification by a factor, we can stop our evaluation of confounding by that same factor (since stratification will deal with confounding anyway).