EPID 600; Class 9 Confounding

University of Michigan School of Public Health

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Is drinking coffee causally associated with doing well in EPID 600?

Coffee Course grade

Coffee drinkers Mean course grade

None 72

A little 77

Some 84

A lot 92

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Coffee Course grade

Studying

Non-causal association

Causal association

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Summary…

Those who study drink more coffee Studying is associated with a higher course grade in EPID600 So, the apparent association between coffee drinking and doing better on this course is explained by the different studying habits of those who drink coffee and those who do not

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Sick

Healthy

Observed

Counterfactual (parallel universe)

Counterfactual thinking

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Sick

Sick

Observed

Counterfactual (parallel universe)

Counterfactual thinking

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Confounding

Confounding centrally is a violation of exchangeability, that is, when the presence of variables make the counterfactual scenarios non-comparable When confounding occurs the apparent effect of exposure on outcome is distorted because the effect of an extraneous factor is mistaken for an actual exposure effect The concept of confounding is central to epidemiology If we want to make inferences regarding causation we need to take into account the possibility of confounding Confounding not an “all or none” phenomenon

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Violation of exchangeability, i.e., confounding

Truth Study

Exposed Counterfactual exposed if they were not exposed

Exposed Not exposed

Disease

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Confounding

When a non-causal association between a given exposure and an outcome is observed as a result of the influence of a third variable, that third variable is usually designated a confounding variable or confounder

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Is urbanicity a risk factor for developing coronary heart disease?

Incidence of CHD

Urban 9 per 1000

Non-urban 7 per 1000

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Age and cities

Urban Rural

Old 20% 5%

Middle-age 40% 45%

Young 40% 50%

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Age and CHD incidence

CHD incidence per 1,000

Old 15

Middle-age 10

Young 5

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Urban CHD incidence

Age distribution

Non-causal association

Causal association

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Summary...

In urban areas you have more older people Older people have a higher incidence of coronary heart disease than younger people So, the apparent association between living in urban areas and CHD is explained by the different age distribution between urban and non-urban areas

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Is drinking alcohol a risk factor for lung cancer?

Lung cancer

Drinkers 90/100,000

Non-drinkers 80/100,000

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Alcohol Lung cancer

Smoking

Non-causal association

Causal association

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General rule

A variable is a confounder if

1. It is a risk factor for the outcome

2. It is associated with the exposure

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Schematically...

EXPOSURE OUTCOME

OBSERVED ASSOCIATION

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Schematically...

EXPOSURE OUTCOME

“THIRD VARIABLE”

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Example: male gender as a risk factor for malaria

Cases Controls Total

Male 88 68 156

Female 62 82 144

Total 150 150 300

Adapted from Szklo & Nieto, 1999 20

Example continued...

88 / 62

68 / 82 OR = =

88*82

62*68 = 1.71

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Is a confounder responsible for the observed association?

MALE GENDER MALARIA ?

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Is a confounder responsible for the observed association?

MALE GENDER MALARIA

OUTDOOR OCCUPATION

?

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Two questions to be asked...

Is working outdoors a risk factor for malaria? Is male gender associated with working outdoors?

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Is working outdoors a risk factor for malaria?

42% of the cases work mostly outdoors

12% of the controls work mostly outdoors

Cases Controls

63 18 87 132

Mostly Outdoor

Mostly Indoor 150 150

OR = 18 / 132

63 / 87 =

87*18

63 * 132 = 5.3

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Is working outdoors associated with being male?

Males

68

88

Females

13

131

Mostly Outdoor

Mostly Indoor

Total

81

219

% Male

84%

40%

Those who work outdoors are more likely to be male than those who work indoors.

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Back to the questions

Is working outdoors a risk factor for malaria?

Is male gender associated with working outdoors?

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Back to the questions

Is working outdoors a risk factor for malaria? YES Is male gender associated with working outdoors? YES

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Male Malaria

Outdoor occupation

Non-causal association

Causal association

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So...

Working outdoors may be a confounder of the observed relation between male gender and malaria...

But is it?

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One question to be asked...

Is there still a relationship between male gender and malaria when we account for potential confounders?

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Stratification

1.  Hold occupation (the potential confounder) constant 2.  Examine the relationship of interest within strata of

occupation (i.e., indoors vs. outdoors)

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Adapted from Szklo & Nieto, 1999

Cases

Males 53 15

Females 10 3

Controls

Total 63 18

Mostly Outdoor Occupation

OR = 10*15

53*3 = 1.06

Cases

Males 35 53

Females 52 79

Controls

Total 87 132

Mostly Indoor Occupation

OR = 52*53

35*79 = 1.00

Stratified analyses of the association between gender and malaria according to whether individuals work mainly outdoors or indoors

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The principle...

IF working outdoors did NOT explain the relationship between male gender and malaria, then men SHOULD have higher risk of malaria whether they worked outdoors or not

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Summary

Crude OR: 1.7

Outdoor work adjusted odds ratio: 1.0

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Back to the question...

Is there still a relationship between male gender and malaria when we account for potential confounders?

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Back to the question...

Is there still a relationship between male gender and malaria when we account for potential confounders?

NO

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So...

Working outdoors is a confounder of the observed relation between male gender and the risk of malaria.

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Summary: is a covariate a confounder?

QUESTION 1: Is it associated with exposure? QUESTION 2: Is it causally associated with outcome?

YES

STEP 1: Calculate Crude Measure of Association STEP 2: Calculate Measure of Association within strata of potential confounder

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Assess measure of association within strata

Are stratum specific measures same?

No Yes

Yes No

Confounding report measure

adjusted for confounding

Apparently unconfounded Report crude measure

Next lecture Crude measure=stratum specific?

Caveat 1: Remember that confounding is not an “all-or-none” phenomenon; therefore association does not have to “go away”, simply “change”

Caveat 2: All this is assuming no bias

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Are taller people happier?

It is believed that mental illness is shaped by factors throughout the life course Some hypothesize that childhood illness, socioeconomic adversity, and diet may play a role in the development of mental illness as an adult In the absence of direct measures for these childhood , variables, investigators have looked at birthweight, height, and BMI as proxies. In Norway a prospective cohort of 74,332 men and women was used to investigate the association of height, and body mass index, with suicide, anxiety, and depression.

Bjerkeset et al. Association of adult body mass index and height with anxiety, depression, and suicide in the general population. Am J Epidemiol. 2007; 167(2):193-202

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1984– 1986: (HUNT 1) participants aged 20 years or more

How many suicides occur? What is the relative height of those who commit suicide?

Height and suicide

Participants in the Nord-Trøndelag

Health Study

Participants followed up until December 31,

2002.

Bjerkeset et al. Association of adult body mass index and height with anxiety, depression, and suicide in the general population. Am J Epidemiol. 2007; 167(2):193-202

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Height and suicide: total cohort

Bjerkeset et al. Association of adult body mass index and height with anxiety, depression, and suicide in the general population. Am J Epidemiol. 2007; 167(2):193-202

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Height and suicide

Adjusted only for age and sex, it appears that increasing height is in fact related to a lower risk of suicide

Heightquar,le Hazardra,o(adjustedforage

andsex)Lowestquar,le 1.00Quar,le2 1.01Quar,le3 0.65Highestquar,le 0.69HeightperSD(sexspecific)

0.83

Bjerkeset et al. Association of adult body mass index and height with anxiety, depression, and suicide in the general population. Am J Epidemiol. 2007; 167(2):193-202

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Height and suicide: subsample with confounder information

Bjerkeset et al. Association of adult body mass index and height with anxiety, depression, and suicide in the general population. Am J Epidemiol. 2007; 167(2):193-202

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Height and suicide: un-confounded

After adjusting for education, marital status, smoking history, frequency of alcohol use, physical activity, vigor, nervousness, calmness, cheerfulness, and frequency of using tranquilizers, the association is highly attenuated

Height quartile Hazard ratio (adjusted for age and sex)

Lowest quartile 1.00 Quartile 2 1.17 Quartile 3 0.84 Highest quartile 0.97 Height per SD (sex specific)

0.97

Bjerkeset et al. Association of adult body mass index and height with anxiety, depression, and suicide in the general population. Am J Epidemiol. 2007; 167(2):193-202

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Handling confounding

Analysis Stratification Adjustment Restriction

Design Matching Randomization (experimental) studies

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Stratification: another example

Look at the association within strata of the confounder (i.e., holding the confounding constant)

Is alcohol consumption related to lung cancer in SMOKERS?

Is alcohol consumption related to lung cancer in NON-SMOKERS?

Alcohol Lung cancer

Smoking

?

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Adjustment

Use a statistical technique to estimate what the association would be IF the confounder was not associated with the exposure

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Standardization, particularly age standardization, has historically been very important for this purpose

Urban Non-urban

Crude incidence

9 per 1000 7 per 1000

? ?

How would the incidence rates of CHD between urban and non-urban areas compare if both areas had the same age-distribution?

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Standardization

Takes into account different underlying population structures, i.e., accounts for possible confounding due to different other variables Typically applied to age Essentially calculates weights against another population to make different rates comparable Allows direct comparison of mortality data in different populations, controlling for age (but not other potential confounders, of course)

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Matching in case control studies

For each case of malaria that works outdoors, choose a control that works outdoors For each case of malaria that works indoors, choose a control that works indoors Make cases and controls similar in terms of where they work and take potential confounder into account in analysis 1 of the 2 conditions for confounding is NOT met

Outdoor Work

Malaria Gender ?

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Aside...advantages and disadvantages of matching

Advantages Some control over confounding in a ‘relatively straightforward’ way If we match on strong confounders we may increase study power

Disadvantages Difficult to find controls if matching on too many variables Variables used in matching cannot be assessed as either independent or dependent variables If we match on variables related to exposure, we are overmatching Need to use special techniques to analyze matched data

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Randomization in experimental studies

“Assign” people at random to alcohol intake vs. no alcohol intake On average proportion of smokers will be similar in both groups 1 of the 2 conditions for confounding is NOT met

Alcohol Lung Cancer

Smoking

?

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A detail

A variable is a confounder if: 1.  It is a risk factor of the outcome 2.  It is associated with the exposure

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A detail...

Diet Heart disease

Cholesterol

?

Cholesterol is a RESULT of the exposure Cholesterol is then NOT a confounder However (and here’s where it gets tricky), cholesterol can be both a mediator and a confounder

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Concluding confounding

Confounding is a violation of exchangeability; the extent to which we introduce error (bias) in our study relates to how well we deal with the confounding

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