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CONFOUNDERS
DALAM PENELITIAN
MK. EPIDEMIOLOGI GIZI
DEPT. GIZI MASYARAKAT, FEMA, IPB
2014
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Association between birth order and Down Syndrome
Data from Stark and Mantel (1966)
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Association between maternal age and Down Syndrome
Data from Stark and Mantel (1966)
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Association between maternal age and Down Syndrome,
stratified by birth order
Data from Stark and Mantel (1966)
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Bias
Confounding
Random error / chance
The three major threats to
internal validity:
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Causal Inference
One of the most important aspects
in clinical research is the inferencethat an association between an
exposure and outcome represents
a cause-effect relationshipSalah satu aspek yang paling
penting dalam penelitian klinis
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7
Criteria for causal inference
1. Strength of the association2. Consistency - replication
3. Specificity of the association
4. Temporality
5. Biological gradient
6. Plausibility7. Coherence
Kekuatan asosiasi
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Confounders
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Confounding is confusion, or mixing,
of effects; the effect of the exposure ismixed together with the effect of
another variable, leading to bias
"Confounding kebingungan, ataupencampuran, efek; efek paparan
dicampur bersama-sama dengan
pengaruh variabel lain, yang mengarah
untuk bias "Rothman KJ. Epidemiology. An introduction. Oxford: Oxford University Press, 2002
Latin: confundere = to mix together
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Confounder
It occurs when there is a confounder, which isassociated with both exposure and diseaseindependently. Hal ini terjadi ketika adaperancu, yang berhubungan dengan kedua
paparan dan penyakit secara mandiri.Exposure Disease
Confounder
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1. A confounder must be causally or non-causallyassociated with the exposurein the source population (studybase) being studied; 1. perancu harus kausal atau non-kausal yang berhubungan dengan paparan pada populasi
sumber (studi dasar) sedang dipelajari;
C
E
2. A confounder must be a causal risk factor (or a surrogatemeasure of a cause) for the diseasein the unexposedcohort; and 2. confounder harus menjadi faktor risikopenyebab (atau ukuran pengganti sebab a) untuk penyakitdalam kelompok tidak terpapar; dan
3. A confounder must not be an intermediate cause(not anintermediate step in the causal pathway between theexposure and the disease)
C
D
C DE X
A factor is a confounder if 3 criteriaare met:
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Why concern about confounding?
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Confounding pulls the observed association away
from the true associationIt can either exaggerate/over-estimatethe true
association (positive confounding), Example:
ORtrue= 1.0ORobserved= 3.0
or
It can hide/under-estimatethe true association
(negative confounding), Example:
ORtrue= 3.0
ORobserved= 1.0
Direction of Confounding
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Coffee CHD
Smoking
Examples of confounding
Smoking is correlated with coffee drinkingand a risk factor CHD even for those who
do not drink coffee
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Coffee
CHDSmoking
Confounding ?
Coffee drinking may be correlated with smokingbut is not a risk factor in non-smokers
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Alcohol Lung Cancer
Smoking
Confounding
Smoking is correlated with alcohol consumptionand a risk factor LC even for those who do not
drink alcohol
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Confounding Example
Case-control to study to examine theeffect of alcohol used on lung cancer
Lung Ca No lung Ca
Alcohol 90 60
No Alcohol 60 90
OR for Ca =
(a x d)/(b x c) =
(90 x90)/(60 x 60) =
2.25
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Confounding Example
Alcohol No
Alcohol
Smokers 120 30
Non
smokers
30 120
OR for alcohol andsmoking=(120x120)/(30x30) = 16.0
OR for lung cancer andsmoking=(100x100)/(50x50) = 4.0
Therefore smoking is related
to both lung cancer andalcohol use and thus maybea confounder
Lung Ca No Lung
Ca
Smokers 100 50
Nonsmokers
50 100
C f di E l
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Confounding Example
OR for lung cancer and
alcoholIn smokers =
(80x10)/(20x40) = 1.0
In non-smokers =
(10x80)/(20x40) = 1.0
So its safe to drink alcohol
(but not smoke) if youre
worried about lung cancer
Smokers Non-
smokersCa No
Ca
Ca No
Ca
Alcohol 80 40 10 20
No
Alcohol
20 10 40 80
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How to control confounders?
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Controlling for Confounding
1. Study design
Randomization
Restriction
Matching
2. Analysis
StratificationMultivariable analysis
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I. Confounding: study design
1. RandomizationA. Evenly distributes known and
unknown confounders
B. This is really why everyoneconsiders RCTs the gold standard
study design
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Exposure Disease (outcome)
Confounder
Randomization breaks any links
between treatment and prognostic factors
E D
CRandomization
X
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Randomization
Only for intervention studies
Definition: random assignment of study subjects toexposure categories
To control/reduce the effect of confounding variablesabout which the investigator is unaware (i.e. both
known and unknown confounders get distributed evenlybecause of randomization)
Randomization does not always eliminate confounding
Covariate imbalance in small trials
Misdistribution of potentially confounding variablesafter randomization
C f di d d i
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Confounding: study design
2. Restriction
Limits entrance into the study to
individuals who fall within a specified
or categories of the confounder
e.g., only including smokers in your study
on alcohol use and lung cancer
Obviously must know ahead of time whatis a confounder
Restriction
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Restriction
The distribution of the potential confounding factors doesnot vary across exposure or disease categories
An investigator may restrict study subjects to onlythose falling with specific level(s) of a confoundingvariable
Advantages of restriction
straightforward, convenient, inexpensive (but, reducesrecruitment!)
Disadvantages of restriction
Limits number of eligible subjects
Limits ability to generalize the study findings Residual confounding
Impossible to evaluate the relationship of interest atdifferent levels of the confounder
C f di t d d i
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Confounding: study design3. Matching
Match cases and controls on known confounders(one or more)
Example - have one smoker in the controls forevery smoker in the cases
Makes it harder to identify controls but may beuseful when the confounder would otherwise bevery rare in one of the groups (increasesstatistical power)
Involves selection of a comparison group that isforced to resemble the index group with respectto the distribution of one or more potentialconfounders
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Matching
Matching is commonly used in case-controlstudies
Match on strong confounder
Types: Pair (individual) matching
Frequency matching
The use of matching usually requires
special analysis techniques (e.g. matchedpair analyses and conditional logisticregression)
II Confounding: control at the analysis stage
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Confounding is one type of bias that can be adjustedin the analysis (unlike selection and information
bias)
Options at the analysis stage:Stratification
Multivariate methods
To control for confounding in the analyses,confounders must be measured in the study
II. Confounding: control at the analysis stage
Confounding: Adjusted in the Analysis
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Confounding: Adjusted in the Analysis
Stratified analysis
Like we did above with smokers vs. non-smokersand looking at differences between the ORs of thetwo groups
Cant be done easily if you have lots ofconfounders
Multivariable analysisVery commonly done
Not without its issues as well
How many confounders to include
Which to include
Which model of analysis
M lti i te A l sis
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Multivariate Analysis
Stratified analysis works best only in the presence of
1 or 2 confounders
If the number of potential confounders is large,
multivariate analyses offer the only real solution
Can handle large numbers of confounders(covariates) simultaneously
Based on statistical regression models
E.g. logistic regression, multiple linear regressionAlways done with statistical software packages
Control of confounding
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Control of confounding
hard to control unknown risk factors
These methods can control only known
potential confounders.
Only random assignment of exposurecan control for unknown potential
confounders.
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Whichever method you choose, you
have to know potential confoundersreported in previous studies.
Literature searching is important
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Common confounders
Age -- e.g., exposed persons are older
Sex -- e.g., more exposure in men
Risk factors - more exposed persons (orunexposed) smoke(-), exercise(+), eat
vegetables(+), use drugs(-), . . .
Effective control of confounding
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Effective control of confounding
requires:
Knowing the causal pathwaysKnowing all relevant causal factors
Measuring all relevant causal factors
accurately
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Crude vs. Adjusted Effects
Crude:does not take into account the effect of the confounder
Adjusted:
accounts for the confounderMantel-Haenszel method estimator
Multivariate analyses (e.g. logistic regression)
Confounding is likely when:
RRcrude =/= RRadjustedORcrude =/= ORadjusted
Stratified Analysis
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Crude 2 x 2 table
Calculate Crude OR (or RR)
Stratify by Confounder
Calculate ORs
for each stratum
If stratum-specific ORs are similar,
calculate adjusted OR (e.g. MH)
Crude
Stratum 1 Stratum 2
If Crude OR =/= Adjusted OR,
confounding is likely
If Crude OR = Adjusted OR,
confounding is unlikely
ORCrude
OR1 OR2
Stratified Analysis
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Conclusion
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ConclusionBias is a systematic error in collecting or
interpreting dataBias is a flaw in design and cannot be
analyzed away
Confounders are extraneous factors that
distort the relationship between theexposure and the outcome
Confounders may be adjusted away if theyare measured
Confounding can sometimes be preventedby proper study design
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Control confounding at the designingstage
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Strategy Advantages Disadvantages
Specification
Include onlynon-smokers.
Easily understood Limits generalizability
May limit sample size
Matching
Match smoking
status of casesand controls
Useful for
eliminating
influence of strong
constitutional
confounders like age
and sex
Decision to match must
be made when designing
and can have irreversible
adverse effects on analysis
Time consuming
Can not analyze
associations of matched
variables with the outcome
Control confounding at the analysisstage
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Strategy Advantages Disadvantages
Stratification
Conduct analysis
separately for
smokers and non-
smokers.
Easily understood
Reversible
May be limited by
sample size for eachstratum
Difficult to control
for multiple
confoundersStatistical
adjustment
Conduct
multivariate analysis
controlling
(adjusting) for
smoking status.
Multiple
confounders can be
controlled.
Reversible
Need advanced
statistical techniques
Results may be
difficult to understand