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EPID 600; Class 8Bias
University of Michigan School of Public Health
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Bias
Systematic error in the design, conduct or analysis of a
study that results in a mistaken estimate of an exposures
effect on disease
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Bias
Systematic error in the design, conduct or analysis of a
study that results in a mistaken estimate of an exposures
effect on disease
Wrong study design!Wrong sampling strategy!
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Bias
Systematic error in the design, conduct or analysis of a
study that results in a mistaken estimate of an exposures
effect on disease
Problems in enrollment of cases, of controls!Loss to follow-up!
Poor collection of data!
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Bias
Systematic error in the design, conduct oranalysis of a
study that results in a mistaken estimate of an exposures
effect on disease
Wrong modeling assumptions!
Miscategorization of variables!
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Rothman KJ. Epidemiology: An Introduction. Oxford, 2002. 6
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Evaluating bias
1. Why did it occur?2. What effect does it have on the observed association?3. What can be done to control for bias in this study and to
prevent it in future studies?
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Types of (important) bias
1. Selection biasError in selection of study participants
2. Information biasErrors in procedures for gathering relevant information
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1. Selection bias
Systematic error in selecting subjects into one or more of
the study groups, such as cases and controls, or exposed
and unexposed
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Study question
Does coffee drinking cause pancreatic cancer?
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Selection Bias: in a case-control study
Cases: patients hospitalized with a diagnosis of
pancreatic cancer
Controls: patients hospitalized for other reasons by the
same gastroenterologist who had hospitalized the case
Results: found a strong relationship between coffee
drinking and pancreatic cancer
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What happened?
POPULATION
Yes No
Yes
NoCoffee
Cancer
Yes No
Coffee
Persons who do not drink
coffee are more likely to be
controls
Cancer
Yes
No
STUDY SAMPLE
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Study question
Is there a relation between occupational exposure to
asbestos and lung cancer?
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Selection Bias: in a cohort study
Exposed: workers who handle asbestos (100%
participation)
Unexposed: workers in other areas of the factory who
agree to participate
(50% participation)
Results: found NO relationship between asbestos and
lung cancer
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What happened?
POPULATION
Yes No
Yes
No
Asbestos
Cancer
Yes No
UNEXPOSED workers who
participate are those at high risk
for lung cancer, so unexposed
with disease are over-
represented
Cancer
No
STUDY SAMPLEAsbestos
Yes
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2. Information Bias
Systematic error in obtaining information regarding
subjects in the study
Examples: bias in recall, in collecting data, in interview,in reporting
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Study question
Is perinatal infection associated with a risk of congenital
malformation?
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Information Bias in a case-control study:
Example 1
Cases: newborns with congenital malformations
Controls: healthy newborns
Results: found a strong relationship between mothers
recall of infection during pregnancy and malformation
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What happened?
Recall bias
Parents of children with congenital malformations were
more likely to report infection during pregnancy thanparents of children without congenital malformations
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What happened?
POPULATION
Yes No
Yes
No
Yes No
Infection
during
pregnancy
Yes
No
Infection
during
pregnancy
Congenital
Malformation
Congenital
Malformation
STUDY SAMPLE20
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What happened?
POPULATION
Yes No
Yes
No
Yes No
Infection
during
pregnancy
Yes
No
Infection
during
pregnancy
Congenital
Malformation
Congenital
Malformation
Misclassification of unexposed as
exposed is more common in cases
than in controls DIFFERENTIAL
MISCLASSIFICATION
STUDY SAMPLE21
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What happened?
POPULATION
Yes No
Yes
No
Yes No
Infection
during
pregnancy
Yes
No
Infection
during
pregnancy
Congenital
Malformation
Congenital
Malformation
Misclassification of unexposed as
exposed is more common in cases
than in controls DIFFERENTIALMISCLASSIFICATION
STUDY SAMPLE22
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What if there is misclassification and it
is similar in both cases and controls ?
Case Non-Case
InfectionYes
No
Non-differential misclassification
Usually biases estimate of association towards 1 (the null)
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Toward the null
1
0.5
2
3
0
the null
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Study question
Is smoking associated with an increased risk of myocardial
infarction (MI) ?
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Information Bias in a case-control study:
Example 2
Cases: hospitalized cases of MI in elderly adults
Controls: elderly adults, randomly selected from the
community, who have never been hospitalized for MI
Results: found a weak relationship between smoking and
MI
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What happened?
Many true cases of MI are misclassified as non-cases, and
are included in the controls (they were not hospitalized and
had no symptoms)
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What happened?
POPULATION
Yes No
Yes
No
Yes No
Smoke Yes
No
Smoke
Myocardial
Infarction
Myocardial
Infarction
Misclassification of cases as controls
is similar in smokers and non-
smokers NON-DIFFERENTIALMISCLASSIFICATION
STUDY SAMPLE28
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What happened?
POPULATION
Yes No
Yes
No
Yes No
Smoke Yes
No
Smoke
Myocardial
Infarction
Myocardial
Infarction
Misclassification of cases as
controls is similar in smokers and
non-smokers NON-DIFFERENTIAL
MISCLASSIFICATION
STUDY SAMPLE29
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What happened?
POPULATION
Yes No
Yes
No
Yes No
Smoke Yes
No
Smoke
Myocardial
Infarction
Myocardial
Infarction
Misclassification of cases as
controls is similar in smokers and
non-smokers NON-DIFFERENTIAL
MISCLASSIFICATION
STUDY SAMPLE30
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Study question
Is use of oral contraceptives (OC) associated with an
increased risk of venous thrombophlebitis (blood clots)?
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Information Bias: in a cohort study
Exposed: women who use OC
Unexposed: women who do not use OC
Results: found a strong relationship between OC use
and thrombophlebitis
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What happened?
Detection bias (also called surveillance bias)
Women who are on oral contraceptives are more likely to
receive a diagnosis of thrombophlebitis
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What happened?
POPULATION
Yes No
Yes
No
Yes No
OC
Use
Yes
No
OC
Use
Thrombophlebitis
STUDY SAMPLE
Thrombophlebitis
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What happened?
POPULATION
Yes No
Yes
No
Yes No
OC
Use
Yes
No
OC
Use
Thrombophlebitis
Misclassification of non-disease as
disease is different in exposed
and unexposed persons
DIFFERENTIAL
MISCLASSIFICATION
STUDY SAMPLE
Thrombophlebitis
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What happened?
POPULATION
Yes No
Yes
No
Yes No
OC
Use
Yes
No
OC
Use
Thrombophlebitis
Misclassification of non-disease as
disease is different in exposed
and unexposed persons
DIFFERENTIAL
MISCLASSIFICATION
STUDY SAMPLE
Thrombophlebitis
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Putting numbers to the differential vs. non-
differential examples, 1
POPULATION
Yes No
Yes
No
Yes No
OC
Use
Yes
No
OC
Use
Thrombophlebitis
Misclassification of non-disease as
disease is different in exposed
and unexposed personsDIFFERENTIAL MISCLASSIFICATION
RESULTING IN BIAS AWAY FROM
THE NULL
STUDY SAMPLE
Thrombophlebitis
50
50 100
25
70
50 100
5
REAL OR =
(100*50)/
(50*25)=4
BIASED OR =
(100*70)/
(50*5)=2837
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Putting numbers to the differential vs. non-
differential examples, 2
POPULATION
Yes No
Yes
No
Yes No
Infection
during
pregnancy
Yes
No
Infection
during
pregnancy
Congenital
Malformation
Congenital
Malformation
Misclassification of unexposed as
exposed is more common in cases
than in controls DIFFERENTIAL
MISCLASSIFICATION RESULTINGIN BIAS AWAY FROM THE NULL
STUDY SAMPLE
50
50 100
25
REAL OR =
(100*50)/
(50*25)=4
75
25 100
25
BIASED OR =
(100*75)/
(25*25)=1238
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25
75
Putting numbers to the differential vs. non-
differential examples, 3
POPULATION
Yes No
Yes
No
Yes No
Exposure
Yes
No
Exposure
Disease
Disease
Misclassification of exposed as
unexposed is more common in cases
than in controls DIFFERENTIAL
MISCLASSIFICATION RESULTING IN
BIAS TOWARDS THE NULL
STUDY SAMPLE
50
50 100
25
REAL OR =
(100*50)/
(50*25)=4100
25
BIASED OR =
(100*25)/
(25*75)=1.339
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Accuracy of weight/height reports
Obesity is acknowledged as a critical health probleminternationally
Studies often use reported (as opposed to measured) data to
estimate the prevalence of overweight and obesity at the
population levelThere have been investigations regarding the truth of these
reported values in adults and adolescents; the validity of
parent-reported weight and height was studied by a team in
Canada.
Dubois and Girad. Accuracy of maternal reports of pre-schoolers weights and heights as estimates of BMI values. Int J Epid. 2007; 36: 132-138.
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Height/weight reports
2) Within 3 months,
childrens weight
and height were
directly measured
1) Mothers asked to
report on height and
weight of children
aged 4
3) Investigators
examined the
prevalence of
obesity based on
reported valuesversus
prevalence of
obesity based on
measured values
Dubois and Girad. Accuracy of maternal reports of pre-schoolers weights and heights as estimates of BMI values. Int J Epid. 2007; 36: 132-138.
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Height/weight reports
The cohort: 4-year old children in 2002, who were part of a regional
stratified sample of children born in Quebec in 1998
Height/Weight report: One care-giver, usually the mother, reported height
and weight to an interviewer; the caregiver was not told that subsequent
measurement would be taken.
Interviewers made sure that mothers recalled these values rather than
measuring them on the spot
Height and weight measurement: Within three months of the interview,
nutritionists followed a standardized protocol and measured height and
weight of children
Dubois and Girad. Accuracy of maternal reports of pre-schoolers weights and heights as estimates of BMI values. Int J Epid. 2007; 36: 132-138.
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Height/weight report: is it the same for
all?
SES Reported Measured
Highest 1 1
Middle 1.8 1.7
Lowest 2.2 1.9
Odds ratios among boys:
BMI>95th Percentile
Is any group of people
consistently over-
reporting BMI of children?
Dubois and Girad. Accuracy of maternal reports of pre-schoolers weights and heights as estimates of BMI values. Int J Epid. 2007; 36: 132-138.
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Height/weight reports
In this figure, the measuredweight is 17 kg for a 51-
month-old child who is 1.03m
tall. This child ranks at the
71st percentile if the child is a
girl and at the 65th percentile if
the child is a boy.
If the mother reports the
weight as being 2 kg less than
the actual value, the child
would be classified as beingbelow the 15th percentile.
Dubois and Girad. Accuracy of maternal reports of pre-schoolers weights and heights as estimates of BMI values. Int J Epid. 2007; 36: 132-138.
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Height/weight report: findings
Heights were reported more accurately than weights (there was no
difference in the means of reported vs. measured heights)
A greater proportion of mothers overestimated boys weights; a
greater proportion of lower SES mothers misreport
12% of the children were classified as overweight based on thereported data; 9% were classified as overweight using measured
data 3% overestimation of overweight in this population
Dubois and Girad. Accuracy of maternal reports of pre-schoolers weights and heights as estimates of BMI values. Int J Epid. 2007; 36: 132-138.
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Special biases
Non-respondent bias
Persons who do not participate in a particular study may be
different than those who do
e.g., in telephone surveys, women are more likely to
answer surveys than are men; if the exposure of interest is
differentially distributed between women and men and if
gender is associated with the outcome of interest bias willresult
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Other special biases
Unmasking (detection signal) bias
Membership bias
Diagnostic suspicion bias
Exposure suspicion biasRecall bias
Family information bias
Neyman bias
Berkson bias
etc
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Evaluating Bias
1. Why did it occur?2. What effect does it have on the observed association?3. What can be done to control for bias in this study, and
to prevent it in future studies?
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Preventing Bias
Careful attention to sampling
Minimize non-response
Standardization of measurements
Training and quality controlBlinding
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