Transcript Bias - www.chnri.org

Bias
• Defined as any systematic error in a study that
results in an incorrect estimate of association
between exposure and risk of disease.
• To err is human. Most rigorously designed
investigations will have potential for one or more
types of biases relating to the manner in which the
subjects are selected, the way in which the
information is obtained, reported or interpreted
• Unlike confounding, the effect of bias can not be
easily evaluated, nor can bias be controlled at the
stage of analysis
Bias
• All sources of bias must be anticipated at
the stage of planning of the study, and steps
taken to minimize their influence at the time
of collection of data
• Since such errors may occur nevertheless,
their role in explaining the observed
association must be carefully evaluated
Types of bias
• Selection bias
• Observation (information bias)
Selection bias
• It occurs when the patients included in the study
are not representative of all eligible subjects
• A common source of error in case-control studies
and retrospective cohort studies, but also seen in
prospective cohort studies
• In the first two types of studies the exposure and
disease are already present in the cases and hence
a large scope for introduction of bias
• If selection of cases and controls is based on
different criteria, and if these in turn are related to
exposure status, bias will result
Example
• Hospital based case control study relating
thromboembolism to OC use: Hospital
admissions occurred more frequently
amongst OC users with disease than nonusers (referral bias) Colleagues referred
those women who had TE and were OC
users to the doctor who was testing the
hypothesis
Another example
• Estrogen use and the risk of uterine cancer:
Women using estrogens experience uterine
bleeding more often and hence get investigated
more often than non-users of estrogen
• Refusal or non-response in either study group also
introduces a selection bias. If the response rate is
different in cases and controls, and response rate is
also related to exposure status, then bias will be
introduced in any observed association between
exposure and disease
Case control studies
• What represents an unbiased control group is the
most difficult question.
• Control group should be one whose subjects
would have been included as cases, had they had
the disease. If the control group comes from a
different section of community, the sampling of
controls will be biased
• Subject unwilling or subject not selected by the
investigator: if this is linked to the exposure status,
bias will be introduced.
Observation (information) bias
• It results from systematic differences in the
way data on exposure and outcome are
obtained from various study groups
• Recall bias: arises when individual with
disease remember past exposures more
vividly than the non-diseased or those with
exposures report subsequent events in a
different manner from those not so exposed
Observation (information) bias
• Interviewer bias: systematic difference in
soliciting, recording or interpreting
information from study participants. It can
occur with all study designs. It is however, a
major problem with case control studies
when the interviewer is not blinded to the
disease status.
Observation (information) bias
• Follow up losses: When the persons lost to follow
up differ from those who remain in the cohort,
with respect to both exposure and outcome, any
observed association will be biased
• Misclassification bias: incorrect categorization
with respect to exposure or disease status:
Random or non-differential: any true association
will be diluted or be difficult to detect
• Differential misclassification is a more serious
problem and can influence the association in either
direction
Information bias
• Lead time bias: arising due to early
detection of disease by using screening
tests.
• Example: cancer prostate diagnosed by PSA
screening, or colon cancer diagnosed by
routine colonoscopy
Control of bias: study design
• Use of hospital controls in case control studies
• Use well defined populations to minimize loss
during follow up
• Objective and uniform criteria for assessment of
exposure and disease in both study groups
• Blinding of interviewers, rigorous training
• Use of clearly written protocols
• Use of standard techniques for errors or missing
data
• Using dummy variables for exposure assessment