Exercise is a confounding variable in this study. It is associated with both occupation (coal miners exercise less than farm workers) and lung capacity, but it is not on the causal pathway between occupation and lung capacity.

1. BIAS &
CONFOUNDERS
DR QURAT-UL-AIN
ASSISTANT PROFESSOR/ HOD
COMMUNITY DENTISTRY DEPARTMENT
HBS MEDICAL & DENTAL COLLEGE

2. LEARNING OBJECTIVES:
YOU WILL LEARN HOW TO UNDERSTAND
AND DIFFERENTIATE COMMONLY USED
TERMINOLOGIES IN EPIDEMIOLOGY,
BIAS & CONFOUNDING.

3. WHAT IS BIAS?
• Bias may be defined as any systematic
error in an epidemiological study that
results in an incorrect estimate of the true
effect of an exposure on the outcome of
interest

5. BIAS
The interpretation of study findings or
surveys is subject to debate, due to the
possible errors in measurement which
might influence the results.

6. BIAS
• The effect of bias will be an estimate either above
or below the true value, depending on the
direction of the systematic error.
• The magnitude of bias is generally difficult to
quantify, and limited scope exists for the
adjustment of most forms of bias at the analysis
stage.
• As a result, careful consideration and control of
the ways in which bias may be introduced during
the design and conduct of the study is essential in
order to limit the effects on the validity of the
study results.

7. TYPES OF BIAS IN EPIDEMIOLOGICAL STUDIES
• Two Categories:
1. Information Bias
2. Selection Bias.

8. 1-INFORMATION BIAS
• Information bias is any systematic difference from
the truth that arises in the collection, recall,
recording and handling of information in a study,
including how missing data is dealt with.
• It is a probable bias within Observational Studies,
particularly in those with retrospective designs, but
can also affect Experimental Studies.

9. TYPES OF INFORMATION BIAS
Major types of information bias are;
1. Observer Bias,
2. Recall Bias
3. Reporting Bias.

10. TYPES OF INFORMATION BIAS
• Observer bias:
May be a result of the investigator’s prior
knowledge of the hypothesis under
investigation or knowledge of an
individual's exposure or disease status.
Such information may influence the way
information is collected, measured or
interpretation by the investigator for each
of the study groups.

12. OBSERVER BIAS:
• For example,
in a trial of a new medication to treat
hypertension, if the investigator is aware
which treatment arm participants were
allocated to, this may influence their
reading of blood pressure
measurements.
Observers may underestimate the blood
pressure in those who have been
treated, and overestimate it in those in
the control group.

13. OBSERVER BIAS CAN BE REDUCED OR
ELIMINATED BY:
1. Ensuring that observers are well trained.
2. Screening observers for potential biases.
3. Having clear rules and procedures in place for the
experiment.
4. Making sure behaviors are clearly defined.
5. Setting a time frame for: collecting data, for the
duration of the experiment, and for experimental
parts.

14. RECALL (OR RESPONSE) BIAS
• Recall bias may result in either an underestimate or overestimate of the
association between exposure and outcome.
• In a Case-control Study data on exposure is collected retrospectively.
• The quality of the data is therefore determined to a large extent on the
patient's ability to accurately recall past exposures.
• Recall bias may occur when the information provided on exposure varies
between the cases and controls.

15. RECALL BIAS
• For example an individual with the
outcome (cold) under investigation
(case) may report their exposure
experience differently than an individual
without the outcome (control) under
investigation.
• Case= yes I was sneezed on
• Control= can not remember any
sneezing

16. RECALL BIAS
• Methods to minimise recall bias include:
• Collecting exposure data from work or medical
records.
• Blinding participants to the study hypothesis.

17. REPORTING BIAS
• In reporting bias,
individuals may selectively
suppress or reveal
information, for similar
reasons (for example,
around smoking history).
• Reporting bias can also
refer to selective outcome
reporting by study
authors.

18. 2. SELECTION BIAS
• Selection bias occurs when there is a systematic
difference between either:
• Those who participate in the study and those
who do not (affecting generalisability) or
• Those in the treatment arm of a study and
those in the control group (affecting
comparability between groups).

19. 2. SELECTION BIAS
• There are differences in the
characteristics between study
groups, and those
characteristics are related to
either the exposure or
outcome under investigation.
• Selection bias can occur for a
number of reasons

20. TYPES OF SELECTION BIAS
• Sampling Bias
• Allocation Bias

21. SAMPLING BIAS
• Describes the scenario in which some individuals within a
target population are more likely to be selected for
inclusion than others.
• For example, if participants are asked to volunteer for
a study, it is likely that those who volunteer will not be
representative of the general population, threatening
the generalizability of the study results. Volunteers
tend to be more health conscious than the general
population

24. CONFOUND
to confuse and very much surprise
someone, so that they are unable to
explain or deal with a situation:
• AVOID CONFOUNDING VARIABLE
IN REASEARCH - BEFOR IT START
• AS CONFOUNDING OBSCURES
THE ‘REAL’ EFFECT OF AN
EXPOSURE ON OUTCOME

25. CONFOUNDING
• Confounding provides an alternative explanation for an association
between an exposure (X) and an outcome.
• It occurs when an observed association is in fact distorted because the exposure
is also correlated with another risk factor (Y).
• This risk factor Y is also associated with the outcome, but independently of the
exposure under investigation, X. As a consequence, the estimated association is
not that same as the true effect of exposure X on the outcome.

31. • An unequal distribution of the additional risk factor, Y, between the study groups
will result in confounding. The observed association may be due totally, or in part,
to the effects of differences between the study groups rather than the exposure
under investigation.
• A potential confounder is any factor that might have an effect on the risk of
disease under study. This may include factors with a direct causal link to the
disease, as well as factors that are proxy measures for other unknown causes,
such as age and socioeconomic status

32. • In order for a variable to be considered as a
confounder:
1. The variable must be independently
associated with the outcome (i.e. be a risk
factor).
2. The variable must also be associated with
the exposure under study in the source
population.
3. The variable should not lie on the causal
pathway between exposure and disease.

35. TASK
• A study was done to compare the lung capacity of coal miners to the lung capacity of
farm workers. The researcher studied 200 workers of each type. Other factors that
might affect lung capacity are smoking habits and exercise habits. The smoking habits
of the two worker types are similar, but the coal miners generally exercise less than
the farm workers.
• Which of the following is a confounding variable in this study?
1. Exercise
2. Lung capacity
3. Smoking or not
4. Occupation