Post on 02-Jun-2018
8/10/2019 Bias Penelitian
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Evaluating the Role of Bias
8/10/2019 Bias Penelitian
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Bias slide #2
Definition of Bias
Bias is a systematic error that results in anincorrect (invalid) estimate of the measure ofassociation
A. Bias can create spurious association whenthere really is none (bias away from the null)
B. Bias can mask an association when there
really is one (bias towards the null)
C. Bias is primarily introduced by the
investigator or study participants
8/10/2019 Bias Penelitian
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Bias slide #3
Definition of Bias(contd)
D. Bias does not mean that the investigator isprejudiced.
E. Bias can arise in all study types: experimental,
cohort, case-control
F. Bias occurs in the design and conduct of a
study. It can be evaluated but not fixed in the
analysis phase.
G. Two main types of bias are selection and
observation bias.
8/10/2019 Bias Penelitian
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Bias slide #4
Selection Bias
A. Results from procedures used to select
subjects into a study that lead to a result
different from what would have been obtained
from the entire population targeted for study
B. Most likely to occur in case-control or
retrospective cohort because exposure andoutcome have occurred at time of study
selection
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Bias slide #5
Selection Bias in a Case-Control Study
A. Occurs when controls or cases are more
(or less) likely to be included in study if
they have been exposed -- that is,
inclusion in study is not independent of
exposure
8/10/2019 Bias Penelitian
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Bias slide #6
Selection Bias in a Case-Control Study
B. Result: Relationship between exposure and
disease observed among study participants is
different from relationship between exposure and
disease in individuals who would have been
eligible but were not included.
The odds ratio from a study that suffers from
selection bias will incorrectly represent therelationship between exposure and disease in the
overall study population
8/10/2019 Bias Penelitian
7/27
Question: Do PAP smears prevent cervical cancer? Casesdiagnosed at a city hospital. Controls randomly sampledfrom household in same city by canvassing the
neighborhood on foot. True relationship:CervicalCancerCases
Controls
Had PAPsmear
100 150
Did nothave PAP
smear
150 100
Total 250 250
OR = (100)(100) / (150)(150) = .44 There is a 54% reduced risk of
cervical cancer among women who had PAP smears vs. women
who did not. (40% of cases had PAP smears versus 60% of controls)
8/10/2019 Bias Penelitian
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Bias slide #8
Recall: Cases from the hospital and controls
come from the neighborhood around the
hospital.
Now for the bias: Only controls who were at
home at the time the researchers camearound to recruit for the study were actually
included in the study. Women at home were
less likely to work and less likely to have
regular checkups and PAP smears.
Therefore, being included in the study as a
control is not independent of the exposure.
8/10/2019 Bias Penelitian
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Bias slide #9
Cervical
CancerCases
Controls
Had PAP
smear
100 100
Did not
have PAPsmear
150 150
Total 250 250
OR = (100)(150) / (150)(100) = 1.0
There is no association between PAP smears and the
risk of cervical cancer. Here, 40% of cases and 40% of
controls had PAP smears.
The resulting data are as follows:
8/10/2019 Bias Penelitian
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Bias slide #10
Ramifications of using women who were at
home during the day as controls:
These women were not representative of
the whole study population that produced
the cases. They did not accuratelyrepresent the distribution of exposure in
the study population that produced the
cases, and so they gave a biased estimateof the association.
8/10/2019 Bias Penelitian
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Bias slide #11
Selection Bias in a Cohort Study
Selection bias occurs when selection of
exposed and unexposed subjects is notindependent of outcome (so, it can only
occur in a retrospective cohort study)
8/10/2019 Bias Penelitian
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Bias slide #12
Selection Bias in a Cohort Study
Example:
A retrospective study of an occupational exposure
and a disease in a factory setting.
The exposed and unexposed groups are enrolled on
the basis of prior employment records.The records are old, and many are lost, so the
complete cohort working in the plant is not available
for study.
If people who did not develop disease and were
exposed were more likely to have their records lost,
then there will be an overestimate of association
between the exposure and the disease.
8/10/2019 Bias Penelitian
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Bias slide #13
Diseased Non-
diseased
Total
Exposed 50 950 1000
Un-
exposed
50 950 1000
RR = (50/1000) / (50/1000) = 1.00
True relationship, if all records were
available
8/10/2019 Bias Penelitian
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Bias slide #14
Diseased Non-diseased Total
xposed 50 750 800
n-exposed 50 950 1000
RR = (50/800) / (50/1000) = 1.25
If more records were lost in this category (exposed
subjects who did not get the disease), the bias would be
even greater.
200 records were lost, all among exposed who
did not get the disease
8/10/2019 Bias Penelitian
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Bias slide #15
Selection Bias: What are the solutions?
Little or nothing can be done to fix this biasonce it has occurred.
You need to avoid it when you design andconduct the study by, for example, using thesame criteria for selecting cases andcontrols, obtaining all relevant subject
records, obtaining high participation rates,and taking in account diagnostic and referralpatterns of disease.
8/10/2019 Bias Penelitian
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Bias slide #16
Observation Bias
An error that arises from systematicdifferences in the way information onexposure or disease is obtained from the
study groups
Results in participants who areincorrectly classified as either exposed orunexposed or as diseased or notdiseased
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Bias slide #17
Observation Bias
Occurs after the subjects have entered
the study
Several types of observation bias: recall
bias, interviewer bias, loss to follow up,
and differential and non-differentialmisclassification
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Bias slide #18
Observation Bias
Recall bias - People with disease
remember or report exposures differently
(more or less accurately) than those
without disease.
Can result in over- or under-estimate ofmeasure of association.
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Bias slide #19
Observation Bias
Solutions: Use controls who are
themselves sick; use standardized
questionnaires that obtain complete
information, mask subjects to study
hypothesis
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Classic recall bias:Cases underreport exposure
TRUTH OBSERVEDSTUDY DATA
Case Control Case Control
Exposed 40 20 Exposed 30 20
Unexposed 60 80 Unexposed 70 80
Total 100 100 100 100Odds Ratio:
2.7
Odds Ratio:
1.7
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Bias slide #21
Observation Bias
Interviewer bias- Systematic differencein soliciting, recording, interpretinginformation.
Can occur whenever exposureinformation is sought when outcome isknown (as in case-control), or whenoutcome information is sought whenexposure is known (as in cohort study).
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Bias slide #22
Observation Bias
Interviewer bias
Solutions: mask interviewers to study
hypothesis and disease or exposure
status of subjects, use standardized
questionnaires or standardized methods
of outcome (or exposure) ascertainment
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Bias slide #23
Observation Bias Loss to follow up - A concern in cohort and
experimental studies if people who are lost tofollow up differ from those that remain in the
study.
Bias results if subjects lost differ from those that
remain with respect to both the outcome and
exposure.
Solution: Since that information cannot be known,
you must achieve high and equal rates of follow
up for the exposed and unexposed groups.
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Bias slide #24
Misclassification - Subjects exposure or
disease status is erroneously classified.
Two types of misclassification: non-differential
and differential. We will cover only the more
common form: non-differential
misclassification.
Observation Bias
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Bias slide #25
Non-differential misclassification
Inaccuracies with respect to disease
classification are independent of exposure.
Or, inaccuracies with respect to exposure are
independent of disease. Will bias towards the
null if the exposure is has two categories.
Non-differential misclassification makes the
groups more similar.
Observation Bias
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Bias slide #26
Misclassification-
Example: Study of vaginal spermicides and
congenital disorders (Jick et al., 1981).
Solutions: Use multiple measurements,
most accurate source of information
Observation Bias
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Bias slide #27
When interpreting study results,
ask yourself these questions
Given conditions of the study, could bias
have occurred?
Is bias actually present? Are consequences of the bias large enough
to distort the measure of association in an
important way? Which direction is the distortion?Is it
towards the null or away from the null?