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

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
CONFOUNDING
E
D
Confounding IS
present
CF
Confounding
NOT
present
E
?CF
D
CONFOUNDING
Hypothetical probability
of Down’s syndrome
0.0025
0.002
0.0015
0.001
0.0005
0
1
2
3
4
5
Birth Order of Child
What factor might confound the association
between birth order and Down’s syndrome?
CONFOUNDING
Hypothetical probability
of Down’s syndrome
0.0025
0.002
0.0015
0.001
0.0005
0
1
2
3
4
5
Birth Order of Child
Mean
Age of
Age1 < Age2 < Age3 < Age4 < Age5
Mothers
CONFOUNDING
Hypothesis: High alcohol consumption is associated with
stomach cancer (case-control study)
E+
E-
D+
62
68
130
D35
95
130
97
163
260
OR = (62 / 68) / (35 / 95)
OR = 2.47
•The odds of being exposed to high alcohol
consumption
appear 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
NON-SMOKERS
E+
E-
D+
D-
18
42
60
20
80
100
OR = ?????
SMOKERS
38
122
160
E+
E-
D+
44
26
70
D15
15
30
59
41
100
OR = ?????
Is there evidence that smoking confounds
the relationship between alcohol
consumption and stomach cancer?
CONFOUNDING
NON-SMOKERS
E+
E-
D+
D-
18
42
60
20
80
100
SMOKERS
38
122
160
OR = (18 / 42) / (20 / 80)
OR = 1.71
E+
E-
D+
44
26
70
D15
15
30
59
41
100
OR = (44 / 26) / (15 / 15)
OR = 1.69
Is there evidence that smoking confounds
the relationship between alcohol
consumption and stomach cancer?
CONFOUNDING
CRUDE
STRATA 1
STRATA 2
ORCA = 2.47
ORNS = 1.71
ORSM = 1.69
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.
CONFOUNDING
CRUDE
STRATA 1
STRATA 2
ORCA = 2.47
ORNS = 1.71
ORSM = 1.69
A more direct way to evaluate confounding is to
aggregate the strata-specific point estimates to
obtain a standardized (adjusted) estimate
(Unit #6)
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
Hypothesis: Caffeine intake is associated with heart disease
Which of the following are likely to be confounding factors?
Factor
Low Intake High Intake
Current 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%
CONFOUNDING
Hypothesis: Caffeine intake is associated with heart disease
Which of the following are likely to be confounding factors?
Factor
Low Intake High Intake
Current 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%
Evaluating Associations
In summary, to declare with confidence that a
“valid” statistical association exists:
*
Chance must be considered to be an unlikely
explanation for the findings
*
Sources of bias have been considered and
ruled out (or taken into account)
*
Confounding has been evaluated and ruled
out (or taken into account)
Evaluating Associations
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.
(i.e. In the last 10, years, incidence rates for
prostate cancer have increased, as have sales
of SUVs and plasma TV screens).
Evaluating Associations
A “valid” statistical association implies “Internal
Validity”
Internal Validity: The results of an observation
are 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)?
Evaluating Associations
External Validity (Generalizability)
*
Some valid associations exist only within
particular subgroups
*
Internal validity must always be the
primary objective since an invalid result
cannot be generalized
*
Thus, internal validity should never be
compromised in an attempt to achieve
generalizability
Evaluating Causal Associations
CAUSATION:
A philosophical concept merged
with practical guidelines
*
The presence of a valid statistical association
does not imply causality
*
A judgment of causality must be made in the
presence of all available information, and
reevaluated with each new finding
*
Different criteria and philosophical views
have been proposed to assess causality
Evaluating Causal Associations
The spectrum of the causal proposition:
credibility
0 <-------------------------------------------------------> 100%
0 - 30 credibility:
worthy of research study
30 - 70 credibility worthy of public health policy
70 - 90 credibility:
almost an established fact
> 90 credibility:
proven fact
Smoking --> lung cancer: 98% credibility
Evaluating Causal Associations
Sufficient Cause: A set of minimal conditions that
inevitably produce disease
Component Cause: An individual cause of disease
present within one or more sufficient causes
Sufficient
Cause I
Sufficient
Cause II
Sufficient
Cause III
U
U
U
A
B
A
E
B
E
Sufficient
Cause I
Sufficient
Cause II
Sufficient
Cause III
U
U
U
A
B
A
E
B
E
*
Factor (cause) U is a “necessary” cause since it
must 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
EXAMPLE:
Sufficient
Cause I
Sufficient
Cause II
Sufficient
Cause III
U
U
U
A
B
Accounts for
50% of dx cases
A
E
Accounts for
30% of dx cases
B
E
Accounts for
20% of dx cases
If we can prevent any of the factors:
U = 100% reduction in disease occurrence
A = 80% reduction in disease occurrence
B = 70% reduction in disease occurrence
E = 50% reduction in disease occurrence
EXAMPLE:
Sufficient
Cause I
Sufficient
Cause II
Sufficient
Cause III
U
U
U
A
B
Accounts for
50% of dx cases
A
E
Accounts for
30% of dx cases
B
E
Accounts for
20% of dx cases
Hypothetical Example:
U = Genotype susceptible ( “necessary”)
to the disease
A = Exposure to infectious agent
B = Other chronic condition
E = Psychological status
Sufficient
Cause I
Sufficient
Cause II
Sufficient
Cause III
U
U
U
A
B
A
E
B
E
For biologic effects, most and sometimes all of the
components of a sufficient cause are unknown
In our ignorance of these hidden causal
components,we classify people according to
measured causal risk
indicators, 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 community
determines what is considered accepted and what
is refuted.
FALSIFICATION: (Karl Popper - 1959)
Scientific hypotheses can never be proved or
established as true. Therefore, science advances by
a process of elimination (falsification)
INDUCTIVE-ORIENTED CRITERIA (Hill - 1965)
Employ a common set of criteria to attempt to
distinguish causal from non-causal associations
HILL’S CAUSAL CRITERIA
1.
Strength of the association:
Pro: The stronger the association, the less likely the
relationship is due merely to some unsuspected
or uncontrolled confounding variable
Con: Strong but non-causal associations are common
Example: Non-causal relation between Down
syndrome and birth rank, which is confounded
by maternal age
Con: Ratio measures (e.g. RR) may be comparatively
small for common exposures and diseases
(e.g. smoking and cardiovascular disease)
HILL’S CAUSAL CRITERIA
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 CRITERIA
3.
Consistency of the findings
Pro: Due to the “inexact” nature of epidemiologic
investigations, 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.
HILL’S CAUSAL CRITERIA
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.
HILL’S CAUSAL CRITERIA
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.”