Transcript Slide 1

Causation and Causality
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Cause
A
B
Result
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Enabling
factor
Precipitating
factor
Predisposing
factor
Reinforcing
factor
Causation
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OBSERVED ASSOCIATION
Could it be due to selection or measurement bias?
NO
Assessing the
relationship
between a
possible cause
and an outcome
Could it be due to confounding?
NO
Could it be a result of chance?
PROBABLY NOT
Could it be causal?
Apply guidelines and make judgment
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Association of causation (Bradford Hill Criteria)
Temporal relation
Does the cause precede the effect?
Plausibility
Is the association consistent with other knowledge?
(mechanism of action; evidence from experimental animals)
Consistency
Have similar results been shown in other studies?
Strength
What is the strength of the association between the cause and
the effect? (relative risk)
Dose-response relationship
Is increased exposure to the possible cause associated with
increased effect?
Reversibility
Does the removal of a possible cause lead to reduction of
disease risk?
Study design
Is the evidence based on a strong study design?
Judging the evidence
How many lines of evidence lead to the conclusion?
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Causation in social determinant
•
•
•
•
Individualism
Reductionism
Mono-causality
Legitimacy of social inequalities
Armstrong 1999
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Causality method
Individual
inductive
deductive
refer
infer
Population
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A number of factors probably affect the likelihood that
a notifiable disease will be reported:
1.
The clinical severity of the condition
2.
Whether the affected individual consults a physician
3.
The type of physician consulted (eg, private vs public provider, generalist vs
specialist)
4.
Any social stigma associated with the condition
5.
Level of interest in the condition among clinicians
6.
The physician's knowledge of reporting requirements
7.
Existence of an adequate definition of the condition for surveillance purposes
8.
Availability and utilization of appropriate diagnostic laboratories
9.
Availability of effective disease control measures
10. Interests and priorities of local and state health officials
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"Causal inference in epidemiology is
better viewed as an exercise in measurement
of an effect rather than as criterion-guided
process for deciding whether an effect is
present or not.“
Rothman & Greenland 2005
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OR =
2.25
34/40
36/60
24/60
6/40
18/20
OR = .6
2/20
OR = .6
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Bias
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Bias
• Deviation from the truth
• Arises from some aspect(s) of study design or
conduct
• Can serve to incorrectly estimate
– Occurrence of disease
– Existence (or absence) of an association
– Strength of an association
• Can be random / non-random
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RANDOM BIAS
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Random Bias
• Random deviation from the truth
• Incorrect assessment of exposure / outcome
– Continuous:
Incorrect measurement
– Binary / Categorical: Incorrect categorisation
• May result from
– Poor instruments / tests
– Data-entry error
– Subject error
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Random Bias
• Is misclassification a problem?
– Example 1
Schoolbag
weight (kg)
LBP
No LBP
RR (95%CI)
Light
37 (16.2%)
192
1.0
Medium
51 (20.0%)
204
1.2 (0.8-1.9)
Heavy
45 (18.4%)
199
1.1 (0.7-1.8)
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Random Bias
• Is misclassification a problem?
– Example 2
Conduct
problems
LBP
No LBP
RR (95%CI)
Low
60 (15.9%)
317
1.0
Medium
58 (17.6%)
271
1.1 (0.8-1.6)
High
50 (25.5%)
146
1.6 (1.1-2.3)
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Random Bias (Summary)
• Random misclassification <50%
– Decreases likelihood of observing an effect
– Bias findings towards the null
• Increases likelihood of Type II error (falsely accepting H0)
– Serves to underestimate any association
• Random misclassification >50%
– Mathematically possible to model the effect
– Improves the accuracy of the magnitude of your
effect estimate
• But not the direction!
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Random Bias (Summary)
• Cannot be controlled for in the analysis
• Can be minimised (at the design stage) by use
of accurate, effective, and efficient
instruments
– Sensitivity / Specificity
– Validity / Reliability
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NON-RANDOM BIAS
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Non-Random Bias
• Systematic deviation from the truth
• Can increase or decrease an effect estimate
• Study design can (help to) eliminate
• Can be a problem, but may be useful
– Can be investigated
– Size of increase or decrease can be estimated
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Non-Random Bias
• Selection bias
– Concerned about who is in your study
• Information bias
– Concerned about the information you elicit from
your subjects
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Selection Bias
• How is the study population selected?
• Concern
– Selected subjects are systematically different to
subjects not selected
. . . with respect to the relationship under examination
• Sources
– Volunteer bias
– Referral bias
– Healthy-worker effect
– Non-response (follow-up)
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Selection Bias – Volunteer Bias
• To estimate the prevalence of dementia in the UK
population, in persons aged 70-80yrs
– Study 1
• Newspaper advertisement. Respondents to attend research unit
• Prevalence: 1.5%
– Study 2
• Subjects selected from GP age/sex register. Postal questionnaire
• Prevalence: 3.5%
• Potential effect of volunteer bias
– Underestimate of disease prevalence
– Might the effect be an overestimate?
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Selection Bias – Referral Bias
• To examine the association between smoking
and duodenal ulcer
– Cases:
Selected from specialist GE clinic
– Controls:
Selected from general population
– Conclusion (1)
• Smoking increases the odds of duodenal ulcer
– Conclusion (2)
 In persons with duodenal ulcers, smoking increases symptom severity . . .
and therefore likelihood of attending GE clinic
• Potential effect of referral bias (here)
– To increase the strength of the observed association
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“Healthy Worker” Effect
• Typically, but not exclusively, in occupational
environments
• Example:
– Case-control study
LBP
No LBP
Builders
23
154
Chi2: 4.59; p=0.03
Site Offices
14
42
OR: 0.4 (0.2-0.9)
• Conclusions
– Builders experience protection?
– Healthy worker effect?
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Non-Participation Bias
• Most frequent cause for concern in large-scale
epidemiological surveys
– Non-participation
– Loss to follow-up
• Systematic differences between participants and
non-participants
– With respect to the relationship under examination
• Typical non-participants
– Young / Male / Ethnic minorities
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Selection Bias – What to Do
• If possible
– Prevent it
• At the very least
– Take steps to minimise it
• And perhaps
– Estimate its effect
• What effect(s) might it have had on your study?
• How might this change the results?
• Does this change the conclusions?
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Minimising Selection Bias
• Be aware
– Potential sources of selection bias
• Equal opportunity for participation and follow-up
– Cases / Controls
– Exposed / Unexposed groups
– Intervention / Control groups
• Tactics for high participation / follow-up rates
– Reminders / Postcards / Phone calls
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Questionnaire
plus language choice
Language
request
Response
Non-response
Questionnaire
Questionnaire
Questionnaire
plus language
choice
plus language choice
plus language choice
New
questionnaire
Response
Non-response
Language
request
Response
Non-response
Language
Language
New
request
questionnaire
request
Response
Response
Response
Non-response
Non-response
Questionnaire
Non-response
plus language choice
Language
request
Response
New
New
questionnaire
questionnaire
Response
New
questionnaire
Response
Response
Not at home
Refusal
Non-response
Phone call
Not at home
Response
Response
Visit
Non-response
Non-response
Non-response
Refusal
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Assessing Selection Bias
• Demographic approach / Alternative data
– What information is available on your non-responders?
– Where did you get sample from?
– Can you examine response by age / sex / occupation / etc?
• Examine “reluctant” responders
Prevalence
Wave 1
15%
15%
Wave 2
14%
12%
Wave 3
15%
10%
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Potential Effect of Non-response
• Study: to estimate the prevalence of Raynauds
Phenomenon in schoolchildren
– Subjects:
– Response rate:
903 children aged 12-15yrs
80% (183 non-responders)
• What is potential effect of non-response bias?
RP
No RP
Prevalence
95%CI
Original
107
613
14.9%
12.3-17.5%
-
107
796
11.8%
9.7-14.0%
Over: ~25%
290
613
32.1%
29.1-35.2%
Under: ~50%
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Non-Random Bias
• Selection bias
– Concerned about who is in your study
• Information bias
– Concerned about the information you elicit from
your subjects
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Information Bias
• What information are you getting from subjects?
• Concern
– Are there systematic differences in what is being
collected, between study groups?
– Does each subject have an equal chance of providing
the same information?
• Sources
– Observer bias
– Attention bias
– Surveillance bias
– Recall bias
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Information Bias – Observer Bias
• Interviewer knowledge may influence structure
of questions
• Preconceived expectations of study outcome
• Study methods may change over time
• Different investigators may examine different
subjects
• Times / locations of interviews may vary
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Minimising Observer Bias
• Standardised techniques / instruments / etc
– Thorough training of data collection staff
– Test agreement between interviewers / instruments
• Use objective measurements where possible
• Where possible, researchers should be
– Randomly allocated to subjects
– Blind to study question
– Blind to case / control status
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Information Bias – Attention Bias
• “Hawthorn” Effect
– Western Electric Co., Illinois
• People may respond differently if they think
they know what is being studied
• Potential effect:
– ↑↓ prevalence of disease
– ↑↓ relationship under examination
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Minimising Attention Bias
• Mask true study question from participants
– Ethics
– Informed consent
– “Health” study
• Collect information about several outcomes
– Difficult in a case-control study
• Collect information about several exposures
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Information Bias – Surveillance
• Tendency to examine more closely those with
outcome of interest
– Case-control study to examine the association between
occupational asbestos exposure and lung cancer
– Temptation to follow cases
• Tendency to follow more closely (or for longer) those
with exposure of interest
– Randomised controlled trial to examine CBT for low back
pain
– Temptation to follow treatment group
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Minimising Surveillance Bias
• Ensure identical methodological procedures
for all study participants
• Where possible, blind researchers
– To study question
– To case / control status
– To exposure / non-exposure status
– To treatment / non-treatment group
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Information Bias – Recall Bias
• Major concern where exposure data
measured retrospectively
– Cross-sectional study
– Case-control studies
• Concern
– Differential recall between cases and controls
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Information Bias – Recall Bias
Odds Ratio: Chronic Widespread Pain
20
Hospitalisation
Operation
10
8
6
4
2
1
.8
.6
.4
.2
N
Y
Y
N
Y
Y
Self-report
GP records
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Minimising Recall Bias
• Minimise period of recall (if possible)
• Measure exposure data objectively
– Medical notes
– Third-party verification of exposure information
– Triangulation of measurements
• Do you have to conduct a retrospective study?
– Prospective (e.g. National Childhood Development Study)
– Retrospective (e.g. Pre-eclampsia and Hypertension)
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Bias – Summary
• Concerned with the internal validity of a study
– i.e. the extent to which, within the subjects studied,
the results are true
• Deviation of results, or inferences, from the
truth
– Or, processes leading to such deviation
• Results from some aspect(s) of study design or
conduct
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Bias – Summary
• Can serve to incorrectly estimate
– Occurrence of disease
– Existence (or absence) of an association
– Strength of an association
• Random bias
– Random misclassification
– Biases results towards null hypothesis (until >50%)
• Non-random bias
– Can bias results towards, or away, from null hypothesis
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Bias – Summary
• Can be prevented by design
– Use of accurate instruments
– Volunteer bias
e.g. pre-select potential
participants
– Recall bias
e.g. prospective design
– Non-response bias
e.g. high response rate
– Loss to follow-up
e.g. high response rate
– Observer bias
e.g. blinding of researchers
• Equal importance (equal treatment)
– Cases / Controls
– Exposed / Unexposed individuals
– Treatment / Non-treatment groups
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Bias – Summary
• Can be estimated
• Cannot be overcome by analysis
• May be useful
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Any Questions?
• Random bias
– Random misclassification
• Non random bias
– Selection
•
•
•
•
Volunteer bias
Referral bias
Healthy-worker effect
Non-response (follow-up)
– Information bias




Observer bias
Attention bias
Surveillance bias
Recall bias
– Note: this lists are not exhaustive!
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Confounding
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What is Confounding?
• Confusing, or mixing, of effects
• Two (or more) different explanations for trends
in the data cannot be differentiated
• You observe a relationship
Exposure
Outcome
– Could this be due to another exposure?
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Down’s Syndrome
18
Cases per 1000 live births
16
14
12
10
8
6
4
2
0
1
2
3
4
5+
Birth order
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Down’s Syndrome
90
Cases per 1000 live births
80
70
60
50
40
30
20
10
0
<20
20-24
25-29
30-34
35-39
40+
Maternal age (yrs)
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Down’s Syndrome
100
Cases per 1000 live births
90
80
70
60
50
40
30
20
Birth order
3
4
5+
40+
2
35-39
1
<20
20-24
0
25-29
30-34
10
Maternal age
(yrs)
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Association between A and B
X
• They are unrelated
A
• A causes B
A
B
• B causes A
B
A
• 3rd Variable C
A
C
B
B
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Is “C” a Confounder?
• C is only associated with A
X
• C is only associated with B
X
• C is associated with both A and B
?
• C is on the path between A and B
X
Exposure (A)
Variable C
Outcome (B)
Variable C
Variable C
Variable C
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Coffee and Myocardial Infarction
• Hypothesis
– Drinking coffee causes an increase in the risk of
myocardial infarction
Coffee
?
?
M.I.
?
Smoking
• Conclusion
Step 6
1
2
3
4
5
– What
Smoking
Is
Could
there
the
happens
potential
cigarette
smoking
an
is association?
a confounding
to
be
confounder
smoking
the
a path
association
confound
variable?
variable
associated
between
the relationship?
withcoffee
the outcome?
exposure?
drinking and
if we
for smoking?
– MI
Effect
of adjust
non-adjustment:
over-estimation of effect
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Obesity and Myocardial
Infarction
• Hypothesis
– Obesity causes in increase in the risk of myocardial
infarction
Obesity
M.I.
Cholesterol
• However
Query6
Step
– What
Cholesterol
Could
happens
cholesterol
is ato
path
the
confound
variable,
association
this
notassociation?
between
a confounding
obesity
variable
and MI if we
– adjust
Shouldfor
notcholesterol?
adjust for cholesterol
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Smoking and Low Birth Weight
• Hypothesis
– Smoking increases the risk of having a baby of low
birth weight
Smoking
Low weight
Maternal age
• Conclusion
Query6
Step
– Maternal
Could happens
What
maternal
age isto
age
a confounding
theconfound
association
this
variable
between
association?
smoking and low
if we adjust for
maternal age? of effect
– birth
Effectweight
of non-adjustment:
under-estimation
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Properties of a Confounder
• A confounder must be
– Associated with the exposure
– Associated with the outcome
• A confounder must not be
– A path variable
• A confounder may
– Increase any observed effect
– Decrease any observed effect
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Properties of a Path Variable
• Must be on the same “path” as exposure and
outcome
Obesity
M.I.
Cholesterol
• But needn’t be between them
Obesity
M.I.
Cholesterol
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How to Control for Confounding
• Prevent it
– Restriction
– Randomisation
– Matching
• Assess it
– Stratification
– Standardisation
– Adjustment
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Preventing Confounding
• Restriction
– Specify restricted inclusion criteria
– Study will be homogeneous for the potential confounder
• Randomisation
– Randomise participants based on the exposure of interest
– Potential confounders evenly distributed between groups
• Matching
– Match subjects based on (potential) confounding variable
– Potential confounder evenly distributed between groups
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Preventing Confounding
• Hypothesis
Aspirin
↓DVT
• Potential confounder
– Smoking status
• Methods to prevent confounding
– Restriction
– Randomisation
– Matching
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Preventing Confounding
• Hypothesis
Aspirin
↓DVT
• Restriction
– Include only non-smokers
• Advantages
• Disadvantages
– Any observed effect should
not be confounded by
smoking
– Smoking
 Error in measurement
 Misclassification
– Residual confounding
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Preventing Confounding
• Hypothesis
Aspirin
↓DVT
• Randomisation
– Randomise subjects
• Advantages
• Disadvantages
– Any observed effect should
not be confounded by
smoking
– Nor any other variable
– Errors in randomisation
– Smoking
 Error in measurement
 Misclassification
– Residual confounding
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Preventing Confounding
• Hypothesis
Aspirin
↓DVT
• Matching
– If cases smoke, match to a smoker; likewise nonsmokers
• Advantages
• Disadvantages
– Any observed effect should
not be confounded by
smoking
– Smoking
 Error in measurement
 Misclassification
– Residual confounding
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Disadvantages of Prevention
• Restriction
– Harder to achieve desired sample size
– Limits generalisability
– Increasingly difficult with >1 potential confounder
• Randomisation
– Limited use outside RCTs
• Matching
– Limited use outside case-control studies
– Increasingly difficult with >1 potential confounder
– Effects of matched variables cannot be examined
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How to Control for Confounding
• Prevent it
– Restriction
– Randomisation
– Matching
• Assess it
– Stratification
– Standardisation
– Adjustment
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Assessing Confounding
• Stratification
– Assess exposure-outcome relationship
independently for different confounder strata
• Standardisation
– Model exposure-outcome relationship weighted by
the potential confounder
• Statistical adjustment
– Model exposure-outcome relationship while
controlling for potential confounders
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Stratification
• Separate (stratify) analysis into sub-groups
– Choose sub-groups (strata) based on value of
potential confounding variables
• Analogy
– Conducting several restricted studies within the
same sample population
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Stratification – Example
• Study: to examine whether breast feeding
decreases the risk of infant gastroenteritis
Breast feeding
Gastroenteritis
• We know that
↑ Soc-Ec group
↑ Breast-feeding
↑ Soc-Ec group
↑ Hygiene
↓ Over-crowding
↑ Hygiene
↓ Over-crowding
↓ Gastroenteritis
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Stratification – Example
• Is breast feeding is protective of gastroenteritis?
– Cohort Study
Breast-fed
Hospital admission for
gastroenteritis
Yes
No
No
77 (22%)
274
RR: 1.3
Yes
89 (17%)
443
(1.0-1.8)
• Query: relationship with socio-economic group
S-Ec group
Low
82 (24%)
254
RR: 1.6
High
85 (15%)
472
(1.2-2.2)
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Stratification – Example
• Stratify analysis by socio-economic group
Hospital admission for
gastroenteritis
Breast-fed
(High S-Ec)
Breast-fed
(Low S-Ec)
Yes
No
No
24 (17%)
118
RR: 1.1
Yes
60 (15%)
344
(0.7-1.8)
No
53 (26%)
152
RR: 1.1
Yes
28 (23%)
95
(0.8-1.6)
• Risk the same in both groups
– Association is confounded by socio-economic group
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Stratification – Presenting Results
• Presenting crude results = misleading
– (Non) breast-feeding and gastroenteritis
– RRcrude
1.3 (1.0-1.8)
• Present stratified results
– RRHigh S-Ec group
– RRLow S-Ec group
1.1 (0.7-1.8)
1.1 (0.8-1.6)
• Present summary result
– Mantel-Haenszel method
– RRM-H
1.1 (0.8-1.5)
– See: Silman and Macfarlane – Chapter 18
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Stratification – Pros and Cons
• Advantages
– Can compute summary estimate of effect
– Flexible and reversible
– Can choose which potential confounders to examine after
data collection
• Disadvantages
– Number of strata is limited by the sample size needed for
each stratum
– Increasingly difficult with >1 potential confounder
– Difficult to incorporate confounders as continuous
variables
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Assessing Confounding
• Stratification
– Assess exposure-outcome relationship
independently for different confounder strata
• Standardisation
– Model exposure-outcome relationship weighted by
the potential confounder
• Statistical adjustment
– Model exposure-outcome relationship while
controlling for potential confounders
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Standardisation
• Adjust risk in exposed group to that which
would have been observed had they had the
same confounder distribution as the unexposed
group
• Compute effect measure using conventional
methods
– Relative risk
– Odds ratio
– Etc
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Standardisation – Example
• Conduct crude analysis
Exposure
Disease
Person years
Incidence
Yes
15
500
3%
No
38
2500
1.5%
• Crude Risk Ratio
– Ratio of crude incidence
Incidenceexposed
Incidencenon-exposed
=
3.0
1.5
=
2.0
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Standardisation – Example
• Could this relationship be confounded by age?
Exposed Group
Age
Non-exposed Group
Number
Proportion
Number
Proportion
25-44
100
0.20
900
0.36
45-64
180
0.36
820
0.33
65+
220
0.44
780
0.31
Total
500
1.0
2500
1.0
Age
Disease
Person years
Incidence
25-44
6
994
0.6%
45-64
14
986
1.4%
65+
33
967
3.3%
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Standardisation – Example
• Crude Risk Ratio
– Ratio of crude incidence
Incidenceexposed
Incidencenon-exposed
=
3.0
1.5
=
2.0
• Standardised Risk Ratio
– Ratio of age-standardised incidence
Age-adjusted Incidenceexposed
Incidencenon-exposed
=
??
1.5
=
??
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Standardisation – Example
• Standardisation
– Examine the age-specific incidence in the exposed group
– Examine age profile in the non-exposed group
– Multiply, to produce age-specific values for exposed group
– Sum, to produce weighted incidence for exposed group
Exposed Group
Unexposed Group
Disease
Person
years
Inc. (%)
Number
Proportion
Weighted
incidence
(IncW)
24-44
1
100
1.0
900
0.36
0.36
45-64
4
180
2.2
820
0.33
0.73
65+
10
220
4.5
780
0.31
1.40
[Crude incidence = 3.0%]
Total
1.0
ΣIncW=2.5%
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Standardisation – Example
• Crude Risk Ratio
– Ratio of crude incidence
Incidenceexposed
Incidencenon-exposed
=
3.0
1.5
=
2.0
• Standardised Risk Ratio
– Ratio of age-standardised incidence
Age-adjusted Incidenceexposed
Incidencenon-exposed
=
2.5
1.5
=
1.7
• Conclusion
– Exposure associated with a 70% increase in risk, not
100%
BMA Medical College and Vajira Hospital
Standardisation
• Advantages
– Can compute summary estimate of effect
– Flexible and reversible
– Can choose which potential confounders to examine
after data collection
• Disadvantages
– Increasingly difficult with >1 potential confounder
– Difficult to incorporate confounders as continuous
variables
BMA Medical College and Vajira Hospital
Assessing Confounding
• Stratification
– Assess exposure-outcome relationship
independently for different confounder strata
• Standardisation
– Model exposure-outcome relationship weighted by
the potential confounder
• Statistical adjustment
– Model exposure-outcome relationship while
controlling for potential confounders
BMA Medical College and Vajira Hospital
Statistical Adjustment
• Model the relationship of interest, mathematically
– Adjust for variance in the potential confounding
variable(s)
• Simple relationship
– Analysis of relationship
– Statistically adjusted analysis of relationship
Variation
in
Exposure
Exposure
Variation
Outcome
in
Outcome
Variation in potential confounder
BMA Medical College and Vajira Hospital
Statistical Adjustment
• Study
– To examine association between breast-feeding and
infant gastroenteritis
– Crude (unadjusted) model
Poisson regression
Log likelihood = -441.94016
Number of obs
LR chi2(1)
Prob > chi2
Pseudo R2
=
=
=
=
883
3.00
0.0831
0.0034
-----------------------------------------------------------------------------gastro |
IRR
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------breast |
1.31131
.2040887
1.74
0.082
.9665549
1.779033
------------------------------------------------------------------------------
BMA Medical College and Vajira Hospital
Statistical Adjustment
• Study
– To examine association between breast-feeding and
infant gastroenteritis
– Adjusting for socio-economic group
Poisson regression
Log likelihood = -435.22429
Number of obs
LR chi2(2)
Prob > chi2
Pseudo R2
=
=
=
=
874
9.71
0.0078
0.0110
-----------------------------------------------------------------------------gastro |
IRR
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------breast |
1.136832
.1909347
0.76
0.445
.8179641
1.580005
socec |
1.531345
.2566646
2.54
0.011
1.102568
2.126868
------------------------------------------------------------------------------
BMA Medical College and Vajira Hospital
Statistical Adjustment
• Study
– To examine association between breast-feeding and
infant gastroenteritis
– Adjusting for socio-economic group and over-crowding
Poisson regression
Log likelihood = -430.45422
Number of obs
LR chi2(5)
Prob > chi2
Pseudo R2
=
=
=
=
874
19.25
0.0017
0.0219
-----------------------------------------------------------------------------gastro |
IRR
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------breast |
1.159983
.1952895
0.88
0.378
.8339633
1.613452
socec |
1.517063
.2544418
2.48
0.013
1.092044
2.107498
crowding_2 |
1.267779
.3345957
0.90
0.369
.755774
2.126645
crowding_3 |
1.880479
.4896574
2.43
0.015
1.128819
3.132656
crowding_4 |
1.903118
.6026854
2.03
0.042
1.023068
3.540193
------------------------------------------------------------------------------
BMA Medical College and Vajira Hospital
Statistical Adjustment
• Advantages
– Can choose which potential confounders to examine
after data collection
– Easy to incorporate >1 potential confounder
– Continuous variables can be fully used (with care)
– Flexible and reversible
• Disadvantages
– Relevant co-variables must have been measured
– Results may be hard to understand
– Will produce the best mathematical model
BMA Medical College and Vajira Hospital
CONFOUNDING – SUMMARY
BMA Medical College and Vajira Hospital
Confounding – Summary
• Describes an association that is true but is
misleading
• To be a confounder a variable must
– Be associated with the outcome of interest; and
– Be associated with the exposure of interest
• To be a confounder a variable must not
– Be on the path
• Upstream or downstream from the exposure
BMA Medical College and Vajira Hospital
Confounding – Summary
• Confounding
– May increase or decrease the magnitude of any
observed effect
– Can be prevented in design
• Restriction
• Randomisation
• Matching
– Can be overcome in analysis
• Stratification
• Standardisation
• Statistical adjustment (multivariable models)
– Is not black and white!
BMA Medical College and Vajira Hospital
Confounding – Extra
• A potential confounder may be
– More than one variable
– But it’s only possible to adjust for variables you have
measured!
• Residual confounding: potential effect of
– All unexamined variables
– All unmeasured variables
– All extra variance in poorly measured variables
• Very important to measure all potential confounders
BMA Medical College and Vajira Hospital
Any Questions?
• Confounding
• Path variables
• Preventing confounding
– Restriction
– Randomisation
– Matching
• Assessing confounding
– Stratification
– Standardisation
– Multivariable analysis
BMA Medical College and Vajira Hospital