Transcript confounding vriable

(www)
confounding variable
 also known as extraneous variables or
intervening variables
 confounding variables “muddy the waters”
 alternate causal factors or contributory factors
which unintentionally influence the results of an
experiment, but aren’t the subject of the study
Cases
syndromsby
byBirth
birthOrder
order
Cases of
of Down
Down Syndrome
Cases per 100 000
live births
180
160
140
120
100
80
60
40
20
0
1
2
3
Birth order
4
5
EPIET
(www)
Cases of Down Syndrome by Age Groups
Cases of Down Syndrom by age groups
Cases per 1000
900
100000 live 800
births
700
600
500
400
300
200
100
0
< 20
20-24
25-29
30-34
35-39
40+
Age groups
EPIET
(www)
Cases Syndrome
of Down syndrom
Cases of Down
by Birth Order
by birth
order
and mother's
age
and
Maternal
Age
Cases per 100000
1000
900
800
700
600
500
400
300
200
100
0
1
2
3
Birth order
4
5
<2
0
30
-34
25
-29
20
-24
40
+
35
-39
e
Ag
ps
u
o
gr
EPIET
(www)
Confounding


A third factor which is related to both
exposure and outcome, and which
accounts for some/all of the observed
relationship between the two
Confounder not a result of the exposure
 e.g., association between child’s birth rank
(exposure) and Down syndrome
(outcome); mother’s age a confounder?
 e.g., association between mother’s age
(exposure) and Down syndrome
(outcome); birth rank a confounder?
mediating variable



a.k.a. moderating, intervening, intermediary, or mediating
variables
a 2nd or 3rd variable that can increase or decrease the
relationship between an independent and a dependent variable.
for example, whether listeners are persuaded more by the
quality or quantity of arguments is moderated by their degree of
involvement in an issue.
Confounding
To be a confounding factor, two conditions must be met:
Exposure
Outcome
Third variable
Be associated with exposure
- without being the consequence of exposure
Be associated with outcome
- independently of exposure (not an intermediary)
Confounding
Birth Order
Down Syndrome
Maternal Age
Maternal age is correlated with birth
order and a risk factor even if birth order
is low
Confounding ?
Maternal Age
Down Syndrome
Birth Order
Birth order is correlated with maternal age
but not a risk factor in younger mothers
Confounding
Coffee
CHD
Smoking
Smoking is correlated with coffee
drinking and a risk factor even for those
who do not drink coffee
Confounding ?
Smoking
CHD
Coffee
Coffee drinking may be correlated with
smoking but is not a risk factor in nonsmokers
Confounding
Alcohol
Lung Cancer
Smoking
Smoking is correlated with alcohol
consumption and a risk factor even for
those who do not drink alcohol
Confounding ?
Smoking
CHD
Yellow fingers
Not related to the outcome
Not an independent risk factor
Confounding ?
Diet
CHD
Cholesterol
On the causal pathway
Confounding
Imagine you have repeated a positive finding of birth order
association in Down syndrome or association of coffee drinking
with CHD in another sample. Would you be able to replicate it?
If not why?
Imagine you have included only non-smokers in a study and
examined association of alcohol with lung cancer. Would you
find an association?
Imagine you have stratified your dataset for smoking status in
the alcohol - lung cancer association study. Would the odds
ratios differ in the two strata?
Imagine you have tried to adjust your alcohol association for
smoking status (in a statistical model). Would you see an
association?
Confounding
Imagine you have repeated a positive finding of birth order
association in Down syndrome or association of coffee drinking
with CHD in another sample. Would you be able to replicate it?
If not why?
You would not necessarily be able to replicate the
original finding because it was a spurious association
due to confounding.
In another sample where all mothers are below 30 yr,
there would be no association with birth order.
In another sample in which there are few smokers,
the coffee association with CHD would not be
replicated.
Confounding
Imagine you have included only non-smokers in a study and
examined association of alcohol with lung cancer. Would you
find an association?
No because the first study was confounded. The
association with alcohol was actually due to smoking.
By restricting the study to non-smokers, we have
found the truth. Restriction is one way of preventing
confounding at the time of study design.
Confounding
For confounding to occur, the confounders should be
differentially represented in the comparison groups.
Randomisation is an attempt to evenly distribute
potential (unknown) confounders in study groups. It
does not guarantee control of confounding.
Matching is another way of achieving the same. It
ensures equal representation of subjects with known
confounders in study groups. It has to be coupled with
matched analysis.
Restriction for potential confounders in design also
prevents confounding but causes loss of statistical
power (instead stratified analysis may be tried).
Confounding
Randomisation, matching and restriction can be tried at
the time of designing a study to reduce the risk of
confounding.
At the time of analysis:
Stratification and multivariable (adjusted) analysis can
achieve the same.
It is preferable to try something at the time of designing
the study.
Effect Modification
In an association study, if the strength of the
association varies over different categories of a third
variable, this is called effect modification. The third
variable is changing the effect of the exposure.
The effect modifier may be sex, age, an environmental
exposure or a genetic effect.
Effect modification is similar to interaction in statistics.
There is no adjustment for effect modification. Once it
is detected, stratified analysis can be used to obtain
stratum-specific odds ratios.
HOW TO CONTROL FOR CONFOUNDERS?

IN STUDY DESIGN…



RESTRICTION of subjects according to potential
confounders (i.e. simply don’t include confounder in study)
RANDOM ALLOCATION of subjects to study groups to
attempt to even out unknown confounders
MATCHING subjects on potential confounder thus assuring
even distribution among study groups
HOW TO CONTROL FOR CONFOUNDERS?

IN DATA ANALYSIS…




STRATIFIED ANALYSIS using the Mantel Haenszel method to
adjust for confounders
IMPLEMENT A MATCHED-DESIGN after you have collected
data (frequency or group)
RESTRICTION is still possible at the analysis stage but it
means throwing away data
MODEL FITTING using regression techniques
WILL ROGERS' PHENOMENON
Assume that you are tabulating survival for patients with a certain type of
tumor. You separately track survival of patients whose cancer has metastasized
and survival of patients whose cancer remains localized. As you would expect,
average survival is longer for the patients without metastases. Now a fancier
scanner becomes available, making it possible to detect metastases earlier.
What happens to the survival of patients in the two groups?
The group of patients without metastases is now smaller. The patients who are
removed from the group are those with small metastases that could not have
been detected without the new technology. These patients tend to die sooner
than the patients without detectable metastases. By taking away these
patients, the average survival of the patients remaining in the "no metastases"
group will improve.
What about the other group? The group of patients with metastases is now
larger. The additional patients, however, are those with small metastases.
These patients tend to live longer than patients with larger metastases. Thus
the average survival of all patients in the "with-metastases" group will
improve.
Changing the diagnostic method paradoxically increased the average survival of
both groups! This paradox is called the Will Rogers' phenomenon after a quote
from the humorist Will Rogers ("When the Okies left California and went to
Oklahoma, they raised the average intelligence in both states"). (www)
See also Festenstein, 1985 (www)