Transcript THREE CONCEPTS ABOUT THE RELATIONSHIPS OF VARIABLES IN RESEARCH • CONFOUNDING • MEDIATION • EFFECT MODIFICATION, INTERACTION OR MODERATION.

THREE CONCEPTS ABOUT
THE RELATIONSHIPS OF
VARIABLES IN RESEARCH
• CONFOUNDING
• MEDIATION
• EFFECT MODIFICATION,
INTERACTION OR MODERATION
THINKING ABOUT THE WAYS IN
WHICH VARIABLES MAY BE
RELATED ILLUMINATES BIAS
AND CONFOUNDING
ILLUSTRATION OF
CONFOUNDING
• Diabetes is associated with
hypertension.
• Does diabetes cause hypertension?
• Does hypertension causes diabetes?
Or is it possible that diabetes and
hypertension share a common
antecedent?
Thus while an exposure may cause a
disease, another way in which exposure
and disease may be related is if both
variables are caused by FACTOR X. For
hypertension and diabetes, Factor X might
be obesity
X
(obesity)
B (diabetes)
A (hypertension)
If we had concluded that diabetes caused
hypertension, whereas, in fact, they had no
true causal relationship, we would say that:
THE RELATIONSHIP BETWEEN
HYPERTENSION AND DIABETES IS
CONFOUNDED BY OBESITY. OBESITY
WOULD BE TERMED A CONFOUNDING
VARIABLE IN THIS RELATIONSHIP.
Another important truism:
CONFOUNDERS ARE TRUE CAUSES OF
DISEASE, WHEREAS BIASES ARE
ARTEFACTS
MEDIATION AND
CONFOUNDING
Not every factor that is associated with
both the exposure and the disease is a
confounding variable. Such a factor
could be a MEDIATING VARIABLE.
A mediator is also associated with both
the independent and dependent
variables, but is part of the causal chain
between the independent and dependent
variables.
FAILURE TO DISTINGUISH A
CONFOUNDER FROM A MEDIATOR IS ONE
OF THE COMMONEST ERRORS IN
EPIDEMIOLOGY. THESE TWO KINDS OF
VARIABLES CANNOT BE DISTINGUISHED
ON STATISTICAL GROUNDS. THEY CAN
ONLY BE SEPARATED FROM EACH OTHER
BASED ON AN UNDERSTANDING OF THE
TOTAL DISEASE PROCESS.
To make this distinction clear, lets see how
we set about to CONTROL FOR
confounding in epidemiological research.
APPROPRIATE CONTROL
FOR CONFOUNDING
HYPOTHESIS: There is an
association between an exposure
(coffee drinking) and a disease
(myocardial infarction), but we
wonder whether cigarette smoking
could be a confounder of this
relationship.
STEP 1. IS THERE AN ASSOCIATION?
Heavy coffee drinking is statistically associated
with higher rates of myocardial infarction. Is
coffee then a cause of myocardial infarction?
STEP 2. IDENTIFY POTENTIAL
CONFOUNDERS:
Could cigarette smoking be a confounder?
STEP 3. IS THE POTENTIAL CONFOUNDER
ASSOCIATED WITH THE EXPOSURE?
Heavy coffee drinking is associated with higher
rates of smoking. Smoking fulfills one criterion
for potential confounding.
STEP 4. IS THE POTENTIAL CONFOUNDER
ASSOCIATED WITH THE DISEASE OF
INTEREST?
Smoking is associated with higher rates of
myocardial infarction. Smoking fulfills the
second criterion for potential confounding.
STEP 5. WHAT HAPPENS WHEN WE
CONTROL FOR CIGARETTE SMOKING?
Adjustment for cigarette smoking
eliminates the association of heavy coffee
drinking and myocardial infarction. The
association is explained by the fact that
more coffee drinkers are also smokers
CONCLUSION: COFFEE
DRINKING IS NOT A CAUSE
OF MYOCARDIAL
INFARCTION
INAPPROPRIATE CONTROL
FOR CONFOUNDING
HYPOTHESIS: There is an
association between an exposure
(obesity) and a disease (myocardial
infarction), but we wonder whether
cholesterol level could be a
confounder of this relationship.
STEP 1. IS THERE AN ASSOCIATION?
Obesity is statistically associated with higher
rates of myocardial infarction. Is obesity then
a cause of myocardial infarction?
STEP 2. IDENTIFY POTENTIAL
CONFOUNDERS
Could cholesterol level be a confounder?
STEP 3. IS THE POTENTIAL CONFOUNDER
ASSOCIATED WITH THE EXPOSURE?
Obesity and cholesterol level are associated.
STEP 4. IS THE POTENTIAL
CONFOUNDER ASSOCIATED WITH
THE DISEASE OF INTEREST?
Cholesterol level is associated with
higher rates of myocardial infarction.
STEP 5. WHAT HAPPENS WHEN WE
CONTROL FOR CHOLESTEROL
LEVEL?
Adjustment for cholesterol
eliminates the association of obesity
and myocardial infarction.
CONCLUSION: WE SHOULD NOT
CONCLUDE THAT OBESITY IS NOT A
REAL CAUSE OF MYOCARDIAL
INFARCTION, BECAUSE CHOLESTEROL
LEVEL MAY BE PART OF THE PATHWAY
FROM OBESITY TO MYOCARDIAL
INFARCTION. CONTROLLING FOR A
PART OF THE CAUSAL PATHWAY IS
OVER-CONTROL.
SUMMARY OF HOW A THIRD
VARIABLE CAN RELATE TO
TWO OTHER VARIABLES
(EXPOSURE AND DISEASE)
A. IT CAN BE A CONFOUNDING VARIABLE
CONFOUNDER
EXPOSURE
DISEASE
B. IT CAN BE A MEDIATING VARIABLE
(SYNONYM: INTERVENING VARIABLE)
EXPOSURE
MEDIATOR
DISEASE
AN EXPOSURE THAT PRECEDES A MEDIATOR
IN A CAUSAL CHAIN IS CALLED AN
ANTECEDENT VARIABLE.
Example:
African-American babies are smaller
than white babies. Smaller babies have
higher mortality. Controlling for birth
weight reduces or eliminates the
differences between the ethnic groups
in infant mortality. Does this mean that
Ethnicity is not important in infant
mortality? No, because birth weight is
part of the causal pathway from ethnicity
to infant mortality. It is a mediator.
C. IT CAN BE A MODERATOR VARIABLE
(SYNONYMS: INTERACTING OR EFFECTMODIFYING VARIABLE)
MODERATOR
EXPOSURE
DISEASE
A moderator variable is one that moderates or
modifies the way in which the exposure and
the disease are related. When an exposure has
different effects on disease at different values
of a variable, that variable is called a modifier.
Examples:
• Aspirin protects against heart attacks, but only
in men and not in women. We say then that
gender moderates the relationship between
aspirin and heart attacks, because the effect is
different in the different sexes. We can also
say that there is an interaction between sex
and aspirin in the effect of aspirin on heart
disease.
• In individuals with high cholesterol levels,
smoking produces a higher relative risk of
heart disease than it does in individuals with
low cholesterol levels. Smoking interacts with
cholesterol in its effects on heart disease.
AN EXAMPLE OF
INTERACTION OR EFFECT
MODIFICATION
A study finds that there is no
relationship, in infants < 2,000g at
birth, between multiple birth status
(i.e. being a singleton or a twin) and
the risk of mortality (Paneth et al,
American J of Epidemiology,
1982;116:364-375).
ODDS RATIO FOR MORTALITY IN
SINGLETONS (COMPARED TO TWINS)
UNADJUSTED = 1.06
ADJUSTED FOR BIRTHWEIGHT = 1.02
However, this odds ratio conceals
interesting information. It turns out that
there is indeed a relationship between
plurality and mortality, in the following
way:
BIRTHWEIGHT
501-750G
751-1000G
1001-1250G
1251-1500G
1501-1750G
1751-2000G
ODDS FOR
MORTALITY
IN SINGLETONS
0.58
0.65
0.91
1.09
2.45
1.94
Clearly, under 1250g mortality is lower in
singletons, above 1250g it is higher in
singletons. These effects in opposite
directions canceled each other out. This
reversal of RR’s is unusual - usually
interaction accentuates a relative risk
that is present at all values.
The test for interaction is that the ODDS
RATIO (or other measure of association)
changes substantially according to
different values of a third variable.
HOW RANDOM MISCLASSIFICATION CAN
SOMETIMES PRODUCE A TYPE 1 ERROR
1. RANDOM MISCLASSIFICATION OF
A CONFOUNDER
If a confounding variable is randomly
misclassified, and then the exposuredisease relationship is stratified (or
controlled) for this confounder, a
spurious association can be
produced. This usually requires that
the confounding variable be very
strongly related to the exposure.
Example: Cigarette smoking and coffee
drinking are associated. Since more
coffee drinkers are smokers, more
coffee drinkers recorded as nonsmokers are really smokers than are
non-coffee drinkers recorded as nonsmokers. As a result, coffee drinkers
can be found in some studies to have
higher rates of lung cancer, even after
smoking is controlled.
2. RANDOM MISCLASSIFICATION
ALONG AN EXPOSURE GRADIENT
• If an exposure has a strong association with
disease only above a certain threshold,
random misclassification of that exposure is
likely to produce a dose-response relationship.
(Although this phenomenon surely occurs, I
have never seen a clear demonstration of it in
epidemiology.)
• If cigarette smoking only produced lung cancer
in two-pack a day smokers, the data would
likely show some effect in one-pack a day
smokers, because more of the two-pack a day
smokers are likely to be misclassified as onepack a day smokers than as non-smokers.
CHECKLIST FOR BIAS AND
CONFOUNDING
•
•
•
•
•
Choice and framing of study question
Choice of study population source
Participation of study population
Baseline assessments of participants
Subsequent assessments of data from or
about participants
– Exposure data
– Outcome data
• Analysis of data
• Publication of data
Adapted from Bhopal, 2002, p. 73