Epid 600 Class 9 Confounding

EPID 600; Class 9 Confounding University of Michigan School of Public Health

1

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

Coffee drinkers

Mean course grade

None

72

A little

77

Some

84

A lot

92

Coffee

Course grade 2

Course grade

Coffee Studying

Causal association Non-causal association

3

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

4

Counterfactual thinking Observed

Sick

Counterfactual (parallel universe)

Healthy

5

Counterfactual thinking Observed

Sick

Counterfactual (parallel universe)

Sick

6

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

7

Violation of exchangeability, i.e., confounding Study

Truth Disease

Exposed

Counterfactual exposed if they were not exposed

Exposed

Not exposed

8

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

9

Is urbanicity a risk factor for developing coronary heart disease? Incidence of CHD

Urban

9 per 1000

Non-urban

7 per 1000

10

Age and cities

Urban

Rural

Old

20%

5%

Middle-age

40%

45%

Young

40%

50%

11

Age and CHD incidence

CHD incidence per 1,000

Old

15

Middle-age

10

Young

5

12

Urban

CHD incidence Age distribution Causal association Non-causal association 13

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

14

Is drinking alcohol a risk factor for lung cancer? Lung cancer

Drinkers

90/100,000

Non-drinkers

80/100,000

15

Alcohol

Lung cancer Smoking Causal association Non-causal association 16

General rule A variable is a confounder if 1.  It is a risk factor for the outcome 2.  It is associated with the exposure

17

Schematically...

EXPOSURE

OUTCOME

OBSERVED ASSOCIATION

18

Schematically...

EXPOSURE

OUTCOME

“THIRD VARIABLE” 19

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...

OR =

88 / 62 68 / 82

=

88*82 62*68

= 1.71

21

Is a confounder responsible for the observed association?

? MALE GENDER

MALARIA

22

Is a confounder responsible for the observed association?

? MALE GENDER

MALARIA

OUTDOOR OCCUPATION 23

Two questions to be asked... Is working outdoors a risk factor for malaria? Is male gender associated with working outdoors?

24

Is working outdoors a risk factor for malaria? Cases

Controls

Mostly Outdoor

63

18

Mostly Indoor

87

132

150

150

42% of the cases work mostly outdoors 12% of the controls work mostly outdoors OR =

63 / 87 18 / 132

=

63 * 132 87*18

=

5.3 25

Is working outdoors associated with being male?

Males

Females

Total

% Male

Mostly Outdoor

68

13

81

84%

Mostly Indoor

88

131

219

40%

Those who work outdoors are more likely to be male than those who work indoors. 26

Back to the questions Is working outdoors a risk factor for malaria? Is male gender associated with working outdoors?

27

Back to the questions Is working outdoors a risk factor for malaria? YES Is male gender associated with working outdoors? YES

28

Male

Malaria

Outdoor occupation

Causal association Non-causal association 29

So... Working outdoors may be a confounder of the observed relation between male gender and malaria... But is it?

30

One question to be asked... Is there still a relationship between male gender and malaria when we account for potential confounders?

31

Stratification 1. 

Hold occupation (the potential confounder) constant

2. 

Examine the relationship of interest within strata of occupation (i.e., indoors vs. outdoors)

32

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

Mostly Outdoor Occupation Cases

Controls

Males

53

15

Females

10

3

Total

63

18

OR =

53*3 10*15

=

1.06

Mostly Indoor Occupation Cases

Controls

Males

35

53

Females

52

79

Total

87

132

OR =

35*79 52*53

= 1.00

33 Adapted from Szklo & Nieto, 1999

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

34

Summary Crude OR: 1.7 Outdoor work adjusted odds ratio: 1.0

35

Back to the question... Is there still a relationship between male gender and malaria when we account for potential confounders?

36

Back to the question... Is there still a relationship between male gender and malaria when we account for potential confounders?

NO

37

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

38

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

39

Assess measure of association within strata Are stratum specific measures same?

Yes

No

Crude measure=stratum specific?

Yes

No

Apparently unconfounded Confounding Report crude measure report measure adjusted for confounding

Next lecture

Caveat 1: Remember that confounding is not an “all-ornone” phenomenon; therefore association does not have to “go away”, simply “change” Caveat 2: All this is assuming no bias 40

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.

41 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

Height and suicide

Participants in the NordTrøndelag Health Study

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

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

Participants followed up until December 31, 2002.

42 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

Height and suicide: total cohort

43 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

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

Height
quar,le


Hazard
ra,o
 (adjusted
for
age
 and
sex)


Lowest
quar,le


1.00


Quar,le
2


1.01


Quar,le
3


0.65


Highest
quar,le


0.69


Height
per
SD
(sex
 specific)


0.83


44 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

Height and suicide: subsample with confounder information

45 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

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

46 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

Handling confounding Analysis Stratification Adjustment Restriction Design Matching Randomization (experimental) studies

47

Stratification: another example

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

?

Lung cancer

Smoking

Is alcohol consumption related to lung cancer in SMOKERS? Is alcohol consumption related to lung cancer in NON-SMOKERS? 48

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

49

Standardization, particularly age standardization, has historically been very important for this purpose How would the incidence rates of CHD between urban and nonurban areas compare if both areas had the same age-distribution? Crude incidence Urban

Non-urban

9 per 1000

7 per 1000

?

?

50

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)

51

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

Gender

?

Malaria

Outdoor Work 52

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

53

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

54

A detail A variable is a confounder if: 1. 

It is a risk factor of the outcome

2. 

It is associated with the exposure

55

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 56

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

57