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Case-control study 3: Bias and confounding and analysis

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Title: Case-control study 3: Bias and confounding and analysis


1
Case-control study 3Bias and confoundingand
analysis
  • Preben Aavitsland

2
Contents
  • Monday 1
  • Design Case-control study as a smarter cohort
    study
  • The odds ratio
  • Tuesday 2
  • Choosing cases and controls
  • Power calculation
  • Wednesday
  • Case-control studies in outbreaks
  • Thursday 3
  • Bias and confounding
  • Matching
  • Analysis

3
Summary of the case-control study
  • Study causal effects of exposures (risk factors,
    preventive factors) on disease
  • Define cases
  • Find source population
  • Select controls that are representative of source
    population
  • Ask cases and controls the same questions about
    exposures
  • Compare exposure ratios between cases and
    controls, OR a/b / c/d

4
Calculating the odds ratio (OR)
  • Cross product ratio ad / bc a/b / c/d

5
Can we believe the result?
Having a dog
TBE
OR RR 4.5
6
What can be wrong in the study?
  • Random error
  • Results in low precision of the epidemiological
    measure ? measure is not precise, but true
  • 1 Imprecise measuring
  • 2 Too small groups

Systematic errors( bias) Results in low
validity of the epidemiological measure ? measure
is not true 1 Selection bias 2 Information
bias 3 Confounding
7
Random errors
8
Systematic errors
9
Errors in epidemiological studies
Error
Random error (chance)
Systematic error (bias)
Study size
10
Random error
  • Low precision because of
  • Imprecise measuring
  • Too small groups
  • Decreases with increasing group size
  • Can be quantified by confidence interval

11
Estimation
  • When we measure OR, we estimate a point estimate
  • Will never know the true value
  • Confidence interval indicates precision or amount
    of random error
  • Wide interval ? low precision
  • Narrow interval ? high precision
  • OR 4.5 (2.0 10)

12
OR and confidence interval
  • Shows magnitude of the causal effect
  • Shows direction of the effect
  • OR gt 1 ? increases risk (risk factor)
  • OR gt 1 ? decreases risk (preventive factor)
  • Shows the precision around the point estimate
  • Condition no systematic errors
  • Forget about p-values! No advantages.

13
Larger study ? narrower interval
Use Episheet
14
Systematic error
  • Does not decrease with increasing sample size
  • Selection bias
  • Information bias
  • Confounding

15
Selection bias
  • Error because the association exposure ?
    diseaseis different for participants and
    non-participants in the study
  • Errors in the
  • procedures to select participants
  • factors that influence participation

16
Examples of selection bias
  • Self-selection bias
  • Healthy worker effect
  • Non-response
  • Refusal
  • Loss to follow-up

17
Can we believe the result?
Having a dog
TBE
OR IRR 4.5
ORad / bc
Cases were interviewed in the hospital. Controls
were interviewed by phone to their home in the
evening. But then, many dog-owners would be
walking their dog
18
Preventing selection bias
  • Same selection criteria
  • High response-rate
  • High rate of follow-up

19
Information bias
  • Error because the measurement of exposure or
    diseaseis different between the comparison
    groups.
  • Errors in the
  • procedures to measure exposure
  • procedures to diagnose disease

20
Examples of information bias
  • Diagnostic bias
  • Recall bias
  • Researcher influence

21
Can we believe the result?
Having a dog
TBE
OR IRR 4.5
ORad / bc
Cases were so eager to find an explanation for
their disease that they included their
neighbours dog when they were asked whether they
had a dog
22
Misclassification
True
Differential
Non-differential
23
Non-differential misclassification
  • Same degree of misclassification in both cases
    and controls
  • OR will be underestimated
  • True value is higher
  • If no causal effect found, ask
  • Could it be due to non-differential
    misclassification?

24
Preventing information bias
  • Clear definitions
  • Good measuring methods
  • Blinding
  • Standardised procedures
  • Quality control

25
Confunding - 1
Mixing of the effect of the exposure on disease
with the effect of another factor that is
associated with the exposure.
Eksposure
Disease
Confounder
26
Confounding - 2
  • Key term in epidemiology
  • Most important explanation for associations
  • Always look for confounding factors

Surgeon
Post op inf.
Op theatre I
27
Criteria for a confounder
1 A confounder must be a cause of the disease (or
a marker for a cause) 2 A confounder must be
associated with the exposure in the source
population 3 A confounder must not be affected by
the exposure or the disease
Umbrella
Less tub.
2
1
Class
3
28
Downs syndrome by birth order
29
Find confounders
Second, third and fourth child are more often
affected by Downs syndrome.
Many children
Downs
Maternal age
30
Downs syndrome by maternal age
31
Downs syndrome by birth order and maternal age
groups
32
Find confounders
The Norwegian comedian Marve Fleksnes once
stated I am probably allergic to leather because
every time I go to bed with my shoes on, I wake
up with a headache the next morning.
Sleep shoes
Headache
Alcohol
33
Find confounders
A study has found that small hospitals have
lower rates of nosocomial infections than the
large university hospitals. The local politicians
use this as an argument for the higher quality of
local hospitals.
Small hosp
Few infections
Well patients
34
Controlling confounding
  • In the design
  • Restriction of the study
  • Matching
  • In the analysis
  • Restriction of the analysis
  • Stratification
  • Multivariable regression

35
Restriction
Restriction of the study or the analysis to a
subgroup that is homogenous for the possible
confounder. Always possible, but reduces the size
of the study.
Umbrella
Less tub.
Lower class
Class
36
Restriction
We study only mothers of a certain age
Many children
Downs
35 year old mothers
37
Matching
Selection of controls to be identical to the
cases with respect to distribution of one or more
potential confounders.
Many children
Downs
Maternal age
38
Disadvantages of matching
  • Breaks the rule Control group should be
    representative of source population
  • Therefore Special matched analysis needed
  • More complicated analysis
  • Cannot study whether matched factor has a causal
    effect
  • More difficult to find controls

39
Why match?
  • Random sample from source population may not be
    possible
  • Quick and easy way to get controls
  • Matched on social factors Friend controls,
    family controls, neighbourhood controls
  • Matched on time Density case-control studies
  • Can improve efficiency of study
  • Can control for confounding due to factors that
    are difficult to measure

40
Should we match?
  • Probably not, but may
  • If there are many possible confounders that you
    need to stratify for in analysis

41
Stratified analysis
  • Calculate crude odds ratio with whole data set
  • Divide data set in strata for the potential
    confounding variable and analyse these separately
  • Calculate adjusted (ORmh) odds ratio
  • If adjusted OR differs (gt 10-20) from crude OR,
    then confounding is present and adjusted OR
    should be reported

42
Stratification
43
Multivariable regression
  • Analyse the data in a statistical model that
    includes both the presumed cause and possible
    confounders
  • Measure the odds ratio OR for each of the
    exposures, independent from the others
  • Logistic regression is the most common model in
    epidemiology

44
Controlling confounding
  • In the design
  • Restriction of the study
  • Matching
  • In the analysis
  • Restriction of the analysis
  • Stratification
  • Multivariable methods

45
What can be wrong in the study?
  • Random error
  • Results in low precision of the epidemiological
    measure ? measure is not precise, but true
  • 1 Imprecise measuring
  • 2 Too small groups

Systematic errors( bias) Results in low
validity of the epidemiological measure ? measure
is not true 1 Selection bias 2 Information
bias 3 Confounding
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