CONFOUNDING DEFINITION: A third variable (not the exposure or outcome variable of interest) that distorts the observed relationship between the exposure

CONFOUNDING

DEFINITION: A third variable (not the exposure or outcome variable of interest) that distorts the observed relationship between the exposure and outcome.

• Confounding is a confusion of effects that is a nuisance and should be controlled for if possible.

• Age is a very common source of confounding.

CONFOUNDING

CRITERIA FOR A CONFOUNDING FACTOR:

1. Must be a risk factor (or protective factor) for the disease of interest.

2. Must be associated with the exposure of interest (e.g. unevenly distributed between the exposure groups).

3. Must not be an intermediate step in the causal pathway between the exposure and outcome

CONFOUNDINGE D

CF

Confounding ISpresent

E ?CF DConfounding

NOTpresent

CONFOUNDING

What factor might confound the association between birth order and Down’s syndrome?

0

0.0005

0.001

0.0015

0.002

0.0025

1 2 3 4 5

Hypothetical probabilityof Down’s syndrome

Birth Order of Child

CONFOUNDING

0

0.0005

0.001

0.0015

0.002

0.0025

1 2 3 4 5

Hypothetical probabilityof Down’s syndrome

Birth Order of ChildMeanAge of Mothers

Age1 < Age2 < Age3 < Age4 < Age5

CONFOUNDINGHypothesis: High alcohol consumption is associated

with stomach cancer (case-control study)

D+ D-

E+ 62 35 97

E- 68 95 163

130 130 260

OR = (62 / 68) / (35 / 95)

OR = 2.47

•The odds of being exposed to high alcohol consumptionappear to 2.47 times higher for stomach cancer cases as compared to controls

•The risk of stomach cancer is estimated to be 2.47 times higher in persons with high alcohol consumption as compared to persons without high alcohol consumption

CONFOUNDING

But what about smoking?

Perhaps the cases were more likely to be smokers than the control subjects since heavy consumers of alcohol may also be likely to be smokers.

In other words, maybe high alcohol consumption has little to do with the risk of stomach cancer independent of smoking.

CONFOUNDING

D+ D-

E+ 18 20 38

E- 42 80 122

60 100 160

NON-SMOKERS SMOKERS

D+ D-

E+ 44 15 59

E- 26 15 41

70 30 100

OR = ????? OR = ?????

Is there evidence that smoking confounds the relationship between alcohol consumption and stomach cancer?

CONFOUNDING

D+ D-

E+ 18 20 38

E- 42 80 122

60 100 160

NON-SMOKERS SMOKERS

D+ D-

E+ 44 15 59

E- 26 15 41

70 30 100

OR = (18 / 42) / (20 / 80)

OR = 1.71

OR = (44 / 26) / (15 / 15)

OR = 1.69

Is there evidence that smoking confounds the relationship between alcohol consumption and stomach cancer?

CONFOUNDING

In general:

If Strata 1 OR < Crude OR > Strata 2 OR

or

If Strata 1 OR > Crude OR < Strata 2 OR

then confounding is present.

CRUDE

ORCA = 2.47

STRATA 1

ORNS = 1.71

STRATA 2

ORSM = 1.69

CONFOUNDING

A more direct way to evaluate confounding is toaggregate the strata-specific point estimates toobtain a standardized (adjusted) estimate

(Unit #6)

CRUDE

ORCA = 2.47

STRATA 1

ORNS = 1.71

STRATA 2

ORSM = 1.69

Confounding by Indication

● Often occurs in “pharmaco-epidemiology.”

When evaluating the effect of a particular drug, many times people who take the drug differ from those who do not according to the medical indication for which the drug is prescribed.

This means there may be differences in disease severity or other risk factors between the study groups, introducing a bias known as “confounding by indication.”

CONFOUNDING

Which of the following are likely to be confounding factors?

Hypothesis: Caffeine intake is associated with heart disease

Factor Low Intake High IntakeCurrent Smoker (%) 12% 27%

Age (mean years) 36.3 37.1

Body Mass Index (mean) 28.4 24.3

Regular Exercise (%) 24% 14%

Female Gender (%) 43% 41%

Type A personality (%) 16% 28%

Hypertension (%) 9% 16%

CONFOUNDINGHypothesis: Caffeine intake is associated with heart

disease

Factor Low Intake High IntakeCurrent Smoker (%) 12% 27%

Age (mean years) 36.3 37.1

Body Mass Index (mean) 28.4 24.3

Regular Exercise (%) 24% 14%

Female Gender (%) 43% 41%

Type A personality (%) 16% 28%

Hypertension (%) 9% 16%

Which of the following are likely to be confounding factors?

Evaluating Associations

In summary, to declare with confidence that a “valid” statistical association exists:

* Chance must be considered to be an unlikelyexplanation for the findings

* Sources of bias have been considered andruled out (or taken into account)

* Confounding has been evaluated and ruled out (or taken into account)


Note: Keep in mind that even if chance, bias, and confounding have been sufficiently ruled out (or taken into account), it does not necessarily mean that the valid association observed is causal.

The observed association may simply be a coincidence.coincidence.

(i.e. In the last 10, years, incidence rates for prostate cancer have increased, as have sales of SUVs and plasma TV screens).


A “valid” statistical association implies “Internal Validity”

Internal Validity: The results of an observationare correct for the particular group being studied

What about “external validity”?

Do the results of the study apply (“generalize”) to people who were not in it (e.g. the target population)?


External Validity (Generalizability)

* Some valid associations exist only withinparticular subgroups

* Internal validity must always be the primary objective since an invalid result cannot be generalized

* Thus, internal validity should never becompromised in an attempt to achievegeneralizability

Evaluating Causal Associations

CAUSATION: A philosophical concept mergedwith practical guidelines

* The presence of a valid statistical associationdoes not imply causality

* A judgment of causality must be made in thepresence of all available information, andreevaluated with each new finding

* Different criteria and philosophical views have been proposed to assess causality

Evaluating Causal Associations

The spectrum of the causal proposition:

credibility0 <-------------------------------------------------------> 100%

0 - 30 credibility: worthy of research study30 - 70 credibility worthy of public health policy70 - 90 credibility: almost an established fact> 90 credibility: proven fact

Smoking --> lung cancer: 98% credibility

Evaluating Causal AssociationsSufficient Cause: A set of minimal conditions thatinevitably produce disease

Component Cause: An individual cause of disease present within one or more sufficient causes

SufficientCause I

SufficientCause II

SufficientCause III

U

A B

U

A E

U

B E

SufficientCause I

SufficientCause II

SufficientCause III

U

A B

U

A E

U

B E

* Factor (cause) U is a “necessary” cause since itmust be present for disease to occur

* Individually, neither factors A, B, or E are“necessary” causes since disease can occur

without any one of them.

* UAB, UAE, and UBE are “sufficient” causes

SufficientCause I

SufficientCause II

SufficientCause III

U

A B

U

A E

U

B E

EXAMPLE:

Accounts for50% of dx cases



If we can prevent any of the factors:U = 100% reduction in disease occurrenceA = 80% reduction in disease occurrenceB = 70% reduction in disease occurrenceE = 50% reduction in disease occurrence

SufficientCause I

SufficientCause II

SufficientCause III

U

A B

U

A E

U

B E

EXAMPLE:




Hypothetical Example:U = Genotype susceptible ( “necessary”) to the diseaseA = Exposure to infectious agent B = Other chronic conditionE = Psychological status

SufficientCause I

SufficientCause II

SufficientCause III

U

A B

U

A E

U

B E

For biologic effects, most and sometimes all of thecomponents of a sufficient cause are unknown

In our ignorance of these hidden causal components,we classify people according to measured causal riskindicators, and then assign the average risk observed within a class to persons within the class

DIFFERENT PHILOSOPHIES OF CAUSAL INFERENCE

* CONSENSUS: (Thomas Kuhn - 1962)The consensus of the scientific communitydetermines what is considered accepted and whatis refuted.

* FALSIFICATION: (Karl Popper - 1959)Scientific hypotheses can never be proved orestablished as true. Therefore, science advances bya process of elimination (falsification)

* INDUCTIVE-ORIENTED CRITERIA (Hill - 1965)Employ a common set of criteria to attempt todistinguish causal from non-causal associations

HILL’S CAUSAL CRITERIA

1. Strength of the association:

Pro: The stronger the association, the less likely therelationship is due merely to some unsuspectedor uncontrolled confounding variable

Con: Strong but non-causal associations are commonExample: Non-causal relation between Downsyndrome and birth rank, which is confoundedby maternal age

Con: Ratio measures (e.g. RR) may be comparativelysmall for common exposures and diseases(e.g. smoking and cardiovascular disease)


2. Biologic credibility of the hypothesis:

Pro: A known or postulated biologic mechanism by which the exposure might reasonably alter the risk of developing the disease is intuitively appealing

Con: Plausibility is often based on prior beliefs rather than logic or actual data

Con: What is considered biologically plausible at any given time depends on the current state of knowledge

HILL’S CAUSAL CRITERIA3. Consistency of the findings

Pro: Due to the “inexact” nature of epidemiologicinvestigations, evidence of causality is

persuasive when several studies conducted by different investigators at different times yield similar results

Con: Some effects are produced by their causes only under unusual circumstances

Con: Studies of the same phenomenon can be expected to yield different results simply because they differ intheir methods and from random errors.


4. Temporal Sequence

Pro: By definition, a cause of disease must precede onset of the disease.

Con: The existence of an appropriate time sequence can be difficult to establish (e.g. lifestyle factors are likely to be altered after the first symptoms of a disease occur). Confounding by indication may also occur for transient exposures.


5. Dose-Response Relationship

Pro: Logically, most harmful exposures could be expected to increase the risk of disease in a gradient fashion

(e.g. if a little is bad, a lot should be worse)

Con: Some associations show a single jump (threshold) rather than a monotonic trend

Con: Some associations show a “U” or “J” shaped trend (e.g. alcohol consumption and mortality)

SUMMARY OF EVALUATING CAUSALITY

Multiple philosophies exist for evaluating causality. None are definitive.

The set of causal criteria offered by Hill are useful, but are also saddled with reservations and exceptions.

Always keep an open mind when evaluating evidence from epidemiologic studies.

SUMMARY OF EVALUATING CAUSALITY

Medewar (1979)

“I cannot give any scientist of any age

better advice than this: the intensity of the

conviction that a hypothesis is true has no

bearing on whether or not it is true.”

Documents

CONFOUNDING DEFINITION: A third variable (not the exposure or outcome variable of interest) that distorts the observed relationship between the exposure