Dr. Rufaidah Dabbagh
MBBS, MPH
Dr. Armen Torchyan
MD, MPH
CMED 304
Family and Community Medicine Department

To understand bias and how it can be
minimized
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Recognize different types of bias
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Understand concept of confounding
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Learn how to think of possible confounding
factors for associations between exposures
and outcomes
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Types of Bias:
◦ Selection bias
◦ Information bias
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Information bias can lead to
“misclassification” of your study participants.
What could cause misclassification?
Why misclassification of disease status?
•Incorrect Diagnosis
•Limited knowledge
•Diagnostic process complex
•Inadequate access to technology
•Laboratory error
•Disease subclinical
•Detection bias (e.g. more thorough exam in exposed)
•Subject Self report
•Incorrect recall
•Reluctant to be truthful
•Records incorrectly coded in data-base
Available at: http://www.sph.emory.edu/activepi/Instructors/Kevin_MSword/Lesson_9boh.htm. Accessed
on Oct 18, 2011.
Why misclassification of exposure status?
•Imprecise measurement
•Subject Self report
•Interviewer bias
•Incorrect coding of exposure data
Available at: http://www.sph.emory.edu/activepi/Instructors/Kevin_MSword/Lesson_9boh.htm. Accessed
on Oct 18, 2011.


Two general surgeons wanted to measure the
association between eating healthy
vegetables and having gall stones. They
recruited patients in their hospital and
formed two groups of participants: gall stone
group, non-gall stone group.
They interviewed individuals from each group
to inquire about the previous intake of fruits
and vegetables over the past 5 years.


Reducing non-response
Reducing loss to follow
up
Increasing sample size
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You and your colleagues would like to
conduct a study to see the prevalence of
substance abuse among high school students
living in Riyadh.
You randomly chose your sample from a
number of schools in Riyadh and distributed
your questionnaires.
What type of bias is this study be subject to?
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You were conducting a study on the
association between duration of
breastfeeding and malnutrition.
You randomly select a group of mothers
attending the MMR immunization clinic and
ask them about the pattern and duration of
breastfeeding they had for their children.
What type of bias might this study be subject
to?

Differential misclassification:
◦ When the misclassification results in exposure is
incorrectly more in cases than in controls. Or vice
versa; like one group has a lot more incorrect
information than the other group

Non-differential misclassification:
◦ When the misclassification is not related to
exposure status or disease status. And is occurring
at the same proportion in both groups; e.g. if 20%
of cases are classified incorrectly on exposure in
cases and about 20% in controls too
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
An obstetrician wanted to study the
association between congenital
malformations and history of infections
during pregnancy.
He interviewed women (a group who
delivered children with malformations, and a
group of women with normal children). He
asked about history of all types of infections
during pregnancy.
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After finishing the interviews, he also wanted to
go through the women's’ medical records, in
order to minimize recall bias.
He discovered that women who had a baby with
malformation tended to remember all infections
during pregnancy more than the mothers with
normal babies.
What kind of misclassification is this?
Available at: http://ebp.lib.uic.edu/applied_health/files/images/random_cry.gif. Accessed on Oct 18, 2011.
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A case-control study was conducted to
investigate the association between artificial
sweetener and bladder cancer. Controls were
selected from a group of people diagnosed with
obesity related conditions. It is well known that
obesity related conditions are associated with an
increased likelihood of using artificial sweetener.
Could the association between artificial
sweetener and bladder cancer be confounded by
any external factors?


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Obesity related conditions
Artificial sweetener --- Bladder Cancer
Unhealthy lifestyle (consumption of
other artificial preservatives & carcinogens)
Can you think of any other factors?
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A study was done to explore the association
between birth order and Down syndrome. It
was found with increasing birth order, there
was also an increase in the occurrence of
Down syndrome.
The prevalence of Down syndrome was
6/1000 live births at the first birth and
16/1000 live births, for birth of 5 or greater.
Affected babies per 1000 live births
18
16
14
12
1st Child
10
2nd Child
8
3rd Child
6
4th Child
4
5th Child
2
0
Birth Order
Kennith J. Rothman, Epidemiology and
introduction, p 102
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Mother’s age
Birth order ------------ Down syndrome
Because mother age and birth order are highly correlated,
we expect that mothers who give birth to their fifth baby
might be considerably older than mothers giving birth to
first baby. We also know that the risk of Down syndrome
increases with maternal age.
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A study was done to examine the association
between Caffeine and Breast cancer. The
following data was obtained:
Caffeine
Breast Cancer
No breast cancer
Yes
30
18
No
70
82
Total
100
100
What are the odds of caffiene intake in cases
compared to controls?OR= 30x82 = 1.95
70x18
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The investigators thought that the calculated OR
was confounded by the effect of age. They
stratified participants according to age:
Age <40
Age ≥ 40
Caffeine
Ca
No Ca
Caffeine
Ca
No Ca
Yes
5
8
Yes
25
10
No
45
72
No
25
10
Total
50
80
Total
50
20
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OR=? OR= 5x 72 = 1
OR=?

What do you conclude?
Age confounds the association
between caffeine intake and
breast cancer
45x8
OR= 25x10 = 1
25x10
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Nurse’s Health study; a cohort study was
done to determine the association between
oral contraceptive use and ovarian cancer.
The following data were obtained:
Oral
contraceptive
Ovarian Ca
No ovarian Ca
Total
Yes
350
200
550
No
125
200
325
Total
475
400
875
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What is the crude RR?
RR= 350÷550 = 1.65
125÷325
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The investigators thought that the calculated Risk ratio might
be confounded by the effect of smoking. Data were stratified
according to smoking status and relationship was studied :
Smokers
Non-smokers
OCP
Ovarian
Ca
No ovarian
Ca
Total
OCP
Ovarian
Ca
No ovarian
Ca
Total
Yes
95
5
100
Yes
298
152
450
No
150
25
175
No
100
50
150

RR=?
RR= 95÷100 = 1.1
150÷175
RR=?
RR= 298÷450 = 0.99
100÷150
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http://stattrek.com/ap-statistics-2/surveysampling-bias.aspx
Thank you