Intermediate methods in observational epidemiology 2008
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Intermediate methods in observational epidemiology 2008. Confounding - I. OBSERVATION VS. EXPERIMENT. Confounding variable :. Absent, mortality = 10%. Present, mortality = 50%. Observational (n= 2000). Experimental (n=2000). 1300. 700. 1300. 700. No intervention. Intervention.

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Intermediate methods in observational epidemiology 2008

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Intermediate methods in observational epidemiology 2008

Intermediate methods in observational epidemiology 2008

Confounding - I


Intermediate methods in observational epidemiology 2008

OBSERVATION VS. EXPERIMENT

Confounding variable:

Absent, mortality = 10%

Present, mortality = 50%

Observational

(n= 2000)

Experimental

(n=2000)

1300

700

1300

700

No intervention

Intervention

Intervention

No intervention

800

200

500

500

650

350

650

350

Deaths

65

175

65

175

50

250

180

300

240

240

Mortality

18%

30%

24%

24%

80

100


Intermediate methods in observational epidemiology 2008

(DUAL) ASSOCIATION OF CONFOUNDING VARIABLE WITH BOTH OUTCOME AND INDEPENDENT VARIABLE

Less common in the group that undergoes the intervention

Confounding variable

Increased mortality (“outcome”)


Intermediate methods in observational epidemiology 2008

Confounding variable:

Absent, mortality = 10%

No intervention

Intervention

Present, mortality = 50%

50

No. Deaths:

80

250

100

One of the solutions to eliminate confounding: stratify

30%

Mortality:

18%

800

200

500

500

180

300


Israeli study see kahn sempos pp 105

A variable is only

a confounder if dual

association is present

?

MI

BP

Age

Israeli Study, see Kahn & Sempos, pp. 105

OR= 1.88

  • Is the association causal?

    • Is it due to a third (confounding) variable (e.g., age)?


Intermediate methods in observational epidemiology 2008

Does age meet the criteria to be a confounder?

Yes

OR= 3.0

OR= 3.4


Intermediate methods in observational epidemiology 2008

(DUAL) ASSOCIATION OF AGE WITH BOTH SYSTOLIC HYPERTENSION AND MYOCARDIAL INFARCTION

Increased odds of systolic hypertension (“exposure”)

Age

Increased odds of myocardial infarction (“outcome”)


Intermediate methods in observational epidemiology 2008

CONFOUNDING EFFECT

… and not in the causality pathway between exposure and outcome:

Exposure

Confounder

Outcome

Exposure

Confounder

Outcome


Blood pressure mi risk

Odds Ratios not homogeneous

Blood Pressure MI Risk

0.9

1.9

NO

  • Is it appropriate to calculate an adjusted OR?

Assumption when doing adjustment: Homogeneity of odds ratios (no multiplicative interaction).


Ways to assess if confounding is present

Ways to assess if confounding is present:

Strategy 1:Does the variable meet the criteria to be a confounder (relation with exposure and outcome)?

Strategy 2: If the effect of that variable (on exposure and outcome) is controlled for (e.g., by stratification or adjustment) does the association change?


Ways to control for confounding

Ways to control for confounding

  • During the design phase of the study:

    • Randomized trial

    • Matching

    • Restriction

  • During the analysis phase of the study:

    • Stratification

    • Adjustment

      • Stratified methods

        • Direct method

        • Mantel-Haenszel adjustment of Odds Ratios

      • Regression methods


Matching in case control studies

Matching in Case-Control Studies


Matching in a case control study

Matching in a Case-Control Study

  • Objective: To achieve comparability between cases and controls with regard to confounding variables

  • Technique: For each case, choose a control without the case disease, of the same or similar age, at same service, same sex, etc.


Example of matched case control study

Example of Matched Case-Control Study

  • Cases: aplastic anemia seen in Baltimore from 1978-80

  • Controls: patients with non-hematologic/nonmalignant disorders, matched to cases on age (± 5 years), sex, ethnic background and hospital of admission

  • Hypothesis: subclinical HBV is associated with Aplastic Anemia


Matched case control study

42 yr old black woman

40 yr old white male

57 yr old white woman

55 yr old white woman

48 yr old black men

44 yr old AA woman- diab.

37 yr old white male- MI

60 yr old white woman- AP

55 yr old white woman- lupus

49 yr old AA men- meningioma

Matched case-control study

Cases of Aplastic Anemia

Controls (Patients)*

(*Admitted to the same hospital as index case

with other diseases)


Intermediate methods in observational epidemiology 2008

Pairs of Cases and Controls Individually Matched by Age and Sex


Intermediate methods in observational epidemiology 2008

Pairs of Cases and Controls Individually Matched by Age and Sex


Intermediate methods in observational epidemiology 2008

Pairs of Cases and Controls Individually Matched by Age and Sex


Intermediate methods in observational epidemiology 2008

Pairs of Cases and Controls Individually Matched by Age and Sex


Intermediate methods in observational epidemiology 2008

Pairs of Cases and Controls Individually Matched by Age and Sex


Intermediate methods in observational epidemiology 2008

Pairs of Cases and Controls Individually Matched by Age and Sex


Intermediate methods in observational epidemiology 2008

Odds Ratio for Matched Case-Control Studies

Favors hypothesis

Against hypothesis

= 4/2= 2.0


Intermediate methods in observational epidemiology 2008

Risk Factors for Brain Tumors in Subjects Aged <20 years: A Case-Control Study (Gold et al, Am J Epidemiol 1979;109:309-19)

  • Exploratory study of risk factors for brain tumors

  • Subjects < 20 yrs old

  • Cases: primary malignant brain tumors in Baltimore in 1965-75

  • Normal controls: chosen from birth certificates on file, and matched on cases by sex, date of birth (±1 year) and race

  • Interviews with parents of children


Risk factors for brain tumors birthweight

Exposed: 3629+ g

Unexposed: <3629 g

Cases’ birth weight

3629+ g

<3629 g

Controls’ birthweight

3629+ g

8

7

<3629 g

18

38

Risk Factors for Brain Tumors: Birthweight

Odds Ratio= 18/7= 2.6

(Gold et al, Am J Epidemiol 1979;109:309-19)


A few notes on matching

A few notes on “Matching”

  • Most frequently used in case-control studies

  • Frequency vs. individual matching

  • Advantages:

    • Intuitive, easy to explain

    • Guarantees certain degree of comparability in small studies

    • Efficient (if matching on a strong confounder)

    • Particularly useful when outpatients are studied, and sample size is relatively small (e.g., <100 cases and <100 controls)

      • Example: Case-control study of risk factors for emphysema:

        • For each newly diagnosed case of emphysema seen in an Outpatient Unit, select the next (control) patient without diabetes, with an age ± 2 years, of the same sex, and educational status

  • Disadvantages:

    • Costly, usually logistically complicated

    • Inefficient if matching on a weak confounder

    • Questionable representativiness of control group (limits its use for other case-control comparisons)

    • Cannot study the matching variable (and additive interaction)

    • Possibility of residual confounding


Further issues for discussion

Further issues for discussion

  • Types of confounding

  • Confounding is not an “all or none” phenomenon

  • Residual confounding

  • Confounder might be a “constellation” of variables or characteristics

  • Considering an intermediary variable as a “confounder” for examining pathways

  • Statistical significance and confounding


Types of confounding

Types of confounding

  • Positive confounding

    When the confounding effect results in an overestimation of the effect (i.e., the crude estimate is further away from 1.0 than it would be if confounding were not present).

  • Negative confounding

    When the confounding effect results in an underestimation of the effect (i.e., the crude estimate is closer to 1.0 than it would be if confounding were not present).


Intermediate methods in observational epidemiology 2008

Type of confounding:

PositiveNegative

3.0

x

TRUE, UNCONFOUNDED

5.0

OBSERVED, CRUDE

3.0

x

2.0

0.4

x

0.3

x

0.4

0.7

3.0

X ?

0.7

1

0.1

10

Relative risk


Intermediate methods in observational epidemiology 2008

  • Confounding is not an “all or none” phenomenon

    A confounding variable may explain the whole or just part of the observed association between a given exposure and a given outcome.

    • Crude OR=3.0 … Adjusted OR=1.0

    • Crude OR=3.0 … Adjusted OR=2.0

  • Residual confounding

    Controlling for one of several confounding variables does not guarantee that confounding be completely removed. Residual confounding may be present when:

    - the variable that is controlled for is an imperfect surrogate of the true confounder,

    - other confounders are ignored,

    • the units of the variable used for adjustment/stratification are too broad

    • the confounding variable is misclassified

  • The confounding variable may reflect a “constellation” of variables/characteristics

    • E.g., Occupation (SES, physical activity, exposure to environmental risk factors)

    • Healthy life style (diet, physical activity)


  • Intermediate methods in observational epidemiology 2008

    Residual Confounding: Relationship Between Natural Menopause and Prevalent CHD (prevalent cases v. normal controls), ARIC Study, Ages 45-64 Years, 1987-89


    Intermediate methods in observational epidemiology 2008

    • Confounding is not an “all or none” phenomenon

      A confounding variable may explain the whole or just part of the observed association between a given exposure and a given outcome.

      • Crude OR=3.0 … Adjusted OR=1.0

      • Crude OR=3.0 … Adjusted OR=2.0

  • Residual confounding

    Controlling for one of several confounding variables does not guarantee that confounding be completely removed. Residual confounding may be present when:

    - the variable that is controlled for is an imperfect surrogate of the true confounder,

    - other confounders are ignored,

    • the units of the variable used for adjustment/stratification are too broad

    • the confounding variable is misclassified

  • The confounding variable may reflect a “constellation” of variables/characteristics

    • E.g., Occupation (SES, physical activity, exposure to environmental risk factors)

    • Healthy life style (diet, physical activity)


  • Intermediate methods in observational epidemiology 2008

    • Treating an intermediary variable as a confounder (i.e., ignoring “the 3rd rule”)

      Under certain circumstances, it might be of interest to treat an hypothesized intermediary variable acting as a mechanism for the [risk factor-outcome] association as if it were a confounder (for example, adjusting for it) in order to explore the possible existence of additional mechanisms/pathways.


    Intermediate methods in observational epidemiology 2008

    exposure

    Obesity

    Hypertension

    confounder

    Mortality

    outcome

    Confounding factor or part of the chain of causality?

    Example: relationship of obesity to mortality

    Scenario 1: The relationship of obesity to mortality is confounded by hypertension, i.e., the relationship is statistical but not causal


    Intermediate methods in observational epidemiology 2008

    exposure

    Obesity

    Hypertension

    Mortality

    outcome

    Confounding factor or part of the chain of causality?

    Example: relationship of obesity to mortality

    Scenario 2: The relationship of obesity to mortality is causal and mediated by hypertension

    mediator


    Intermediate methods in observational epidemiology 2008

    exposure

    Obesity

    Hypertension

    mediator

    Mortality

    outcome

    Confounding factor or part of the chain of causality?

    Example: relationship of obesity to mortality

    Scenario 3: In addition to being mediated by hypertension, the causal relationship of obesity to mortality is direct


    Intermediate methods in observational epidemiology 2008

    exposure

    Obesity

    Obesity

    Other mechanisms, e.g., diabetes

    Hypertension

    mediator

    Mortality

    Mortality

    outcome

    Confounding factor or part of the chain of causality?

    Example: relationship of obesity to mortality

    Scenario 4: In addition to being mediated by hypertension, the causal relationship of obesity to mortality is mediated by other mechanisms


    Intermediate methods in observational epidemiology 2008

    exposure

    Obesity

    Obesity

    Other mechanisms, e.g., diabetes

    Hypertension

    mediator

    Mortality

    Mortality

    outcome

    Confounding factor or part of the chain of causality?

    Example: relationship of obesity to mortality

    The different scenarios are not mutually exclusive!


    Obesity and mortality

    Obesity and Mortality


    Intermediate methods in observational epidemiology 2008

    Obesity and Mortality

    For positive associations (exposures associated with a RR> 1.0):


    Statistical significance as criteria to assess the presence of confounding

    BAD IDEA!

    If the confounder is strongly associated with the exposure, even a small difference between cases and controls (not statistically significant because of limited sample size) may still induce confounding… and vice versa

    Case-cont

    Confounder

    ?

    Exposure

    Statistical significance as criteria to assess the presence of confounding

    E.g., a confounder might be ruled out in a case-control study solely because there is no statistically significant difference in the levels of the confounder comparing cases and controls.

    E.g., Study of menopause as predictor of myocardial infarction. Even a small difference in age between cases and controls (e.g., 1 year, NS) may result in confounding due to the strong association between age and “exposure” (menopause).


    Intermediate methods in observational epidemiology 2008

    Example: Menopause as a risk factor

    Odds Ratio= 60/40 ÷ 50/50 = 1.5

    % post-menopausal

    Age (years)

    55

    56


    Intermediate methods in observational epidemiology 2008

    controls

    cases

    Odds Ratio= 60/40 ÷ 50/50 = 1.5

    % post-menopausal

    Age (years)

    55

    56


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