This document discusses sources of bias and error in epidemiological studies. It defines random and systematic errors, and describes the main types of each. Random errors are due to chance and include sampling variability. Systematic or bias errors are due to flaws in study design, implementation or analysis. The key types of bias discussed are selection bias, information bias, and confounding. Selection bias results from non-representative samples. Information bias stems from errors in measuring or recording exposures and outcomes. Confounding occurs when a third variable influences the exposure-outcome relationship. The document also provides examples and ways to reduce biases, such as increasing sample size and using statistical controls.

1. Unit 4.2 :Bias and
Errors in
Epidemiological
Studies
Binita soti
Roll no. 09
MN 1st year, MNC

2. Introduction
• The term error is defined as a false or mistaken result
obtained in a study or experiment.
• It is difficult to make the study free from any type of errors
and inferences.

3. Error Cont.
 Study are never made perfectly valid, therefore the aim is to
maximize fact and minimize error so that the research work
would represent to the population they refer.
 Incorrect inference can be controlled either in the design and
implementation phase or during the analysis.

4. Two types of Error
1. Random error
2. Systematic error

5. Random Error
• Random error is the by chance error which make observed
values differ from the true value.
• May occur through sampling variability or random fluctuation
of event of interest.

6. Random error Example 1.
 E.g. if the true prevalence of leishmania antibodies in
population is 10 percent, a well designed sample of 100
individuals from the population might give results of 10
individual positive for the leishmaniais antibodies.
 However, the sample blood would contain some nearby
number like 7,8,11, 12. occasionally chance would produce a
substantially different number, like 3,4 or 18, 19. is random
error.

7. Example 2.
 Out of a sample of 100 people, 3 consecutive sample drawn
randomly may contain:
o 0% diseased people
o 10% diseased people
o 14% diseased people
 This is called random error where the error is due to chance.
The only way to reduce it is to increase the size of sample.
Elimination of error is not possible

8. Types of Random Errors
 Type I Error – alpha error
 Type II Error – beta error

9. Type I and Type II Error

10. How to reduce random error?
 To increase the representative sample size
 Valid scientific sample selection criteria increase the
precision of the estimate.
o The smaller the sample, the less the likely the findings reflect
the experience of the total population.
o The wider confidence interval generally means lower precision
or reliability.

11. Systemic Error (Bias)
• Any difference between the true value and the observed value
due to all causes other than random fluctuation and sampling
variability.
• Systemic error is an error due to factor that inherent in the
study design, data collection, analysis and interpretation to
yield results or conclusion that depart from the truth.( the
increasing size of sample has no effect on systemic error)

12. Systemic Error Cont.
 Any systemic error in the design, conduct or analysis of a
study that results is a mistake estimate of an exposure’s on the
risk of disease
 Systemic error is the error which often has recognizable source
e.g. a faulty measuring instrument , or pattern

13. Systematic Error cont…
 an example of systemic error is the fact that testing for
antibodies will consistently underestimate the prevalence of
HIV infection because individual who have been infected for
less than six months will not yet have developed antibodies.

15. Types of Bias or systemic error
1. Selection bias
2. Information bias
3. Confounding

16. 1. Selection Bias
 Selection bias is a distortion in the estimate of association
between risk factor and disease that result from how the
subject are selected for the study.
 Based on the result which distorts in the estimate of effect is
called selection bias. This bias concerns with the choice of
groups to be compared and choice of sampling frame.

17. Selection bias cont.
 Selection bias can result in case control studies when the
procedure used to identify disease status varies with exposure
status.
 In follow up studies , efforts to minimize selection bias is to
ensure complete follow up of initial cohort and obtaining as
large response rate as possible.

18. Selection bias cont…
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).
• That is, 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.

19. Types of Selection bias
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 generalisability of the study
results. Volunteers tend to be more health conscious than the
general population.

20. Selection bias cont…
• Allocation bias occurs in controlled trials when there is a systematic
difference between participants in study groups (other than the
intervention being studied). This can be avoided by randomization.
• Loss to follow-up is a particular problem associated with cohort
studies. Bias may be introduced if the individuals lost to follow-up
differ with respect to the exposure and outcome from those persons
who remain in the study. The differential loss of participants from
groups of a randomised control trial is known as attrition bias.

21. Selection bias in case-control
studies
• Selection bias is a particular problem inherent in case-control
studies, where it gives rise to non-comparability between cases
and controls.
• In case-control studies, controls should be drawn from the
same population as the cases, so they are representative of the
population which produced the cases. Controls are used to
provide an estimate of the exposure rate in the population.
• Therefore, selection bias may occur when those individuals
selected as controls are unrepresentative of the population that
produced the cases.

22. Selection bias in cohort studies
• Selection bias can be less of problem in cohort studies
compared with case-control studies, because exposed and
unexposed individuals are enrolled before they develop the
outcome of interest.
• However, selection bias may be introduced when the
completeness of follow-up or case ascertainment differs
between exposure categories. For example, it may be easier to
follow up exposed individuals who all work in the same
factory, than unexposed controls selected from the community
(loss to follow-up bias). This can be minimised by ensuring
that a high level of follow-up is maintained among all study
groups.

23. Selection Bias Cont.
For example , in a cohort study using mailed questionaries' to
evaluate the relation between smoking and myocardial infraction
(MI) to the extent that those who smoke and develop MI are less
or more likely to respond than non- smokers who develop the
disease, a biased estimate of exposure and outcome will be
obtained . Or we can say selection bias occurs when the subject
are not representative of a target population about which
conclusion are to be drawn.

24. 2. Information bias
 It is distortion in the estimate of effect due to measurement
error.
 Major sources of information bias include invalid
measurement , incorrect diagnostic criteria and omission and
inadequacies in previously recorded data.

25. Types of information bias
a) Interview bias = an interviewer’s knowledge may influence
the structure of questions and the manner of presentation
which may influence response.
b) Recall bias = this with a particular outcome or exposure
may remember events more clearly or amplify their
recollections.
c) Observer bias = observers may have preconceived
expectations of what they should find in an examination.

26. Information bias cont…
d) Loss to follow up = those that are lost to follow up or who
withdraw from the study may be different from those who are
followed for the entire study.
e) Reporting bias = occurs when a case emphasizes the
importance of exposure that he/she believes to be important.

27. Information bias cont…
f) Surveillance bias: the group with the known exposure or
outcome may be followed more closely or longer than the
comparison group.
g) Lead time bias: lead time the period between the detection of
a medical condition by screening and when it ordinarily would be
diagnosed because a patient experiences symptoms and seek
medical care.

29. 3. Confounding
 The term ‘confounding’ refers to the effect of an extraneous
variable that entirely or partially explains the apparent
association between the study exposure and the disease.
 Confounding is a distortion in the estimated measure of effect
due to the mixing of the effect of the study factor with the
effect of other risk factor.

30. Criteria for confounders
 It is a risk factors of the study disease
 It is associates with exposure under study
 It is about of interest of current study
 In the absence of exposure it independently able to cause
disease.

31. Control of confounding
 Confounding can be controlled either in research design or
during data analysis phase.
 There are three methods that can be used to control
confounding during the design phase of study :
randomization, restriction and matching.
 If we use restriction or matching to reduce the effect of
confounding , it is no longer possible to study the effects of the
confounding variables.

32. Control of confounding cont…
 In the analytic phase, we can use following to control
confounding variables.
– Startified analysis
– Multivariate analysis

34. Multivariate analysis
• Multivariate analysis (MVA) is a Statistical procedure for
analysis of data involving more than one type of
measurement or observation. It may also mean solving
problems where more than one dependent variable is analyzed
simultaneously with other variables.

35. Common sources of error
Several sources of error are likely to occur in epidemiological
studies .
 Selection bias
 Absence of inadequacy of controls: the lack of control group
adds bias in analytical and experimental studies. If control
group is not relevant to the study group in its characteristics ,
and the observation procedures on both groups are not
identical , bias occurs.

36. Sources of error cont…
 Unwarranted conclusion : if conclusion derived from the
study is far from and not consistent with study design and
collected information , the error results in published research
works. The examples of these types of frequent conclusions
include regarding cost, occurrence of unwanted reactions ,
individual reactions.

37. Sources of error cont.
d) Ignoring the periods of exposure to risk : if someone
studies the disease occurrence without considering duration
exposure , it may results bias.

38. Source of error cont.
 Improper interpretation of associations : one should follow
the steps to establish the casual relationship. Sometimes
presence of other factors may create spurious association . If
those factors are not identified during analysis , bias occurs.

39. Sources of error cont.
 Mixing of non – comparable records : there is a common
error found in studies where comparisons are made without
taking into account the differences within the groups being
compared and differences in changes that takes place over
period of time.
 Error of measurement : both random and systemic errors can
also be components of measurement error. The possible errors
may occur due to :

40. Sources of error (Error of
measurement) cont…
1. Instruments poor calibration or lack of sensitivity.
2. Observer’s variation
o intra- observer variations : semi skilled observers are
often inconsistent in diagnosis of the same specimen
presented to him blindly on different occasions.
o Inter-observer variation : several observers do not always
agree on the diagnosis of same person.

41. Sources of error (Error of
measurement)cont…
o Observer’s lack of skill or experience to give
interpretation of diagnosis.
o Patient’s lack of co-operation.
o Patient’s are not measure in same manner , under the same
condition or atmosphere.

42.  In research , an important point is that the right answer to the
research question can be obtained if the design and
implementation of the study is good . It keeps the extent of
the inferential errors at an acceptable level.

43. References
• Joshi.A.B. (2010). Fundamentals of epidemiology.
• Perk EJ(2012) , An integrated ethical decision making model
for nurses. Nurse ethics, 2012 Jan : 19
• https://www.healthknowledge.org.uk/e-
learning/epidemiology/practitioners/chance-bias-confounding
Accessed Jan 30 2022.