Bias and Confounding
The function of epidemiology research is in part, to examine the relationship between
exposure and outcome. To this effect various studies are conducted gathering data,
analysing the data and interpreting the data.
Bias is any trend in the collection, analysis, interpretation, publication or review of
data that can lead to conclusions that are systematically different from the truth. Also
deviation of results or interference’s from the truth, or processes leading to such
deviation are bias.
Bias can occur during any stage of a study:

during the literature review of the study question

during the selection of the study sample

during the measurement of exposure and outcome

during the analysis of data

during the interpretation of the analysis

during the publication of the results
Various forms of bias had been described and defined. Most of them however can
be categorised in one of three general types:

Selection bias

Information bias

Confounding bias
Some biases are specific to a particular type of analytical study whereas others can
be found in all basic study designs (cross-sectional, case control and cohort).
Selection bias
Selection bias can occurs in the design phase of studies. It may also occur during
the execution of study when some subjects are included and not others, based on
the procedures used to select subjects. Errors in the estimation of effect happens
when characteristics of the subjects selected for the study are systematically different
from those in the target population, a distortion of the measured effect will then result.
Many varieties of selection bias have been described. Admission bias,
prevalence/incidence bias detection bias, volunteer bias and loss to follow-up bias
are common forms of this type of bias. Admission bias occurs when case control and
cross sectional studies are done exclusively in hospital settings where the population
studied not accurately reflects the target population.
Prevalence/incidence bias happens when mild or asymptomatic cases as well as
fatal short disease episodes are missed when studies are performed late in disease
process. Volunteer bias occurs when those who volunteer to participate in a study
differ systematically with regard to either exposure or disease status from those who
did not volunteer.
The common element of such biases is that the relation between exposure and
disease is different for those who participate in study and those who would be
theoretically eligible for the study but do not participate.
Selection bias is a theoretical possibility whenever correlates of the outcome capable
of influencing study participation are existent in some individuals at the beginning of
the study. These correlates may be unmeasured or even unrecognised by the
investigator.
Information (observation) bias
Information bias occurs in the data collection stage of studies. It happens when
estimated effect is distorted either by an error in measurement or by misclassifying
the subject for exposure and/or outcome variables.
The most common types of information bias include interviewer bias, questionnaire
bias, recall bias, diagnostic suspicion bias and exposure suspicion bias.
Interviewer bias results when systematic differences occur in the soliciting, recording,
or interpreting of information from study subjects. Questionnaire bias results when
leading questions or other flaws in questionnaire result in a difference in accuracy
between compared groups. Recall bias happens e.g. when people, having had
adverse health outcomes, remember and report past exposure differently from those
who did not experience any adverse health outcome.
Confounding bias (confounding)
Confounding is essentially a mixing of effects that occurs when a factor (confounder)
associated with the exposure of interest is also associated with development of the
disease or outcome of interest independently of exposure. Therefore, a distorted
estimate of the exposure effect results because the exposure effect is mixed with the
effect of extraneous variables.
A confounder must be predictive of disease occurrence independent of its
association with the exposure of interest, but cannot be an intermediate in the casual
chain of association between exposure and disease development. The confounding
variable can effect the association between exposure and disease positively or
negatively; the distorted estimate resulting from confounding can overestimate or
underestimate the true effect or even change the apparent direction of effect.
To be confounding, and extraneous variable must have the following characteristics:

It must be a risk factor for disease

It must be associated with the exposure under study in the population studied

It must not be an intermediate step in the casual path between the exposure
and the disease
In epidemiological studies an investigator would like to minimise both systematic
error (bias) and random error (chance). Reducing systematic errors lead to an
increase in the validity of the study, while reducing random errors increase the power
of the study. Knowledge of systematic error (bias0 therefore become an important
issue in epidemiological studies.
By careful use of proper technique in the design, data collection, and analysis stages
bias can be prevented or minimised.
________________
Hendrik Vermooten
TABLE 4
Prevention of Selection Bias Study Designs
Study Designs
Type of Selection
Cross Sectional CaseRetrospective Prospective
Bias
Control
Cohort
Cohort
Berkson’s
1. Avoid
1. Use
NA
NA
selecting
population
subjects from
based case
hospitals
and
population
based
control
Prevalence/incidence 1. Include non-surviving
NA
NA
subject in the study through
proxy interviews
2. Use
incident
cases
Detection
NA
1. Case and
controls
should be
restricted to
patients
who have
1. exposed and unexposed
under gone subjects should be under
identical
identical disease detection
detection
manoeuvres
Membership
1. Difficult to prevent in these four designs
2. Use multiple comparison
cohorts
Healthy worker effect NA
NA
1. Use working cohorts
for comparison
2. 2. Use multiple
comparison cohorts
Volunteer
1. Use repeated contacts or questionnaire to achieve
response rate of at least 80%
2. Compare respondents with a sample of
nonrespondents
Loss to follow-up
NA
NA
1. Maintain a high follow-up
rate
TABLE 5
Prevention of information Bias in Basic Study Design
Study Design
Type of Bias
information
Interview
Cross sectional
Case-Control
Retrospective
Prospective
Cohort
Cohort
1. “Binding” of the interviewer with respect to the study
hypothesis
2. Use a trained and experienced interviewer
Interinterviewer
1. Use only one interviewer in the study
2. Train interviewers according to standard protocols
3. Use the same interviewer for study and comparison groups
4. Discard data from incompetent interviewers
Questionnaire
1. Careful wording to avoid leading questions
2. Pretest questionnaire several times
3. Use dummy question to conceal hypothesis
4. Offer categorized values for subjects to select instead of
requesting specific values
Recall
1. Difficult to prevent. May be
NA
NA
measured by asking questions
whose answers may be checked
against records
Diagnostic
1. Both exposed and nonsuspicion
1. Difficult to prevent
exposed groups should be
observed using comparable
methods
Exposure
1. Difficult to
1. Both cases
NA
NA
suspicion
prevent
and controls
should be
observed
using
comparable
methods
TABLE 6
Prevention of confounding Bias in Cross-Sectional and Matched and Unmatched
Case-Control and Cohort
Case- Control
Cross-
Matched
Unmatched
Cohort
Matched
Unmatched
sectional
Although control covariates ensures
Crude estimate Same as case
unbiasedness, unnecessary control for
is always
control
nonconfouding covariates always reduces power
unbiased
unmatched
of the study
To maximize both validity and power, an
investigator should always perform analyses
controlling (adjusted estimates) and not
controlling (crude estimates) for the covariate(s)
If both estimates are similar, then the crude
estimate is unbiased and should be adopted on
power considerations.
If both estimates are not similar then the adjusted
estimate, which is the only unbiased one should
be used
studies