3. Introduction
Bias is a fundamental concept in epidemiology
It is defined as ‘deviation of results or inferences from the
truth , or processes leading to such deviation’
- Grimes and Schulz, 2002
It is the result of errors not random but systematic
results invalid
Results mainly from faulty design
4. Scientifically speaking, bias can be explained as:
lack of internal validity or incorrect assessment of the
association between an exposure and an effect in the target
population in which the statistic estimated has an expectation
that does not equal the true value.
Delgado-Rodríguez M, Llorca J. Bias. J Epidemiol Community Health. 2004 Aug 1;58(8):635–41.
5. Etymology
mid 16th century (in the sense ‘oblique line’; also as an adjective meaning
‘oblique’): from French biais, from Provençal, perhaps based on
Greek epikarsios ‘oblique’.
6. Classification of bias
Several classifications of bias exist in literature. Prominent
among them are:
1. Sackett (19 types) and Choi (65 types) – based on stages of
research
2. Maclure and Schneeweiss – causal diagram theory
3. Kleinbaum et al – three main groups (selection, information
and confounding)
4. Steineck and Ahlbom – misclassification, misrepresentation
and analysis deviation
8. WHO classification
Selection bias
Occurs from the manner in which study population is
selected
Most common type of bias in health research
Seen in observational and analytical studies
Ascertainment or information bias
Occurs due to measurement error or misclassification of
subjects according to one or more variables
9. WHO classification
Health research methodology - A Guide for training in Research methods. Second edition. WHO
Bias
Selection bias
Prevalence –
incidence bias
Admission rate
bias
Non-response
bias
Information
bias
Diagnostic bias
Recall bias
10. How to reduce selection bias
The study population should be clearly identified i.e. clear
definition of study population.
The choice of the right comparison/ reference group
(unexposed or controls) is crucial
In a cohort study:
exposed and unexposed groups should be identical but for
the exposure
in a retrospective cohort study, the selection of exposed
and unexposed groups should be done without knowing
the outcome (disease status).
11. In a case-control study:
the control group should reflect the exposure of the
population which gave rise to the cases
controls should be selected independently of the exposure
status
precise case definition and exposure definition should be
used by all investigators.
In a clinical trial:
Randomization and allocation concealment from the
investigator
12. How to reduce information bias
Blinding
Placebo arm in case of RCT’s
13. 1. SELECTION BIAS
Error introduced when the study population does not include
the target population
Causes:
Due to design
Bad definition of eligible population
Lack of accuracy of sampling frame
Uneven diagnostic procedures in target population
Due to implementation
14. 1.1 Inappropriate definition of
eligible population
Occurs when kind of patients gathered does not represent
the cases originated in the population
It is of the following types:
1.1.a – Competing risks bias
When two or more outputs are mutually exclusive, any of
them competes with each other in the same subject. Eg:
death
15. 1.1.b – Healthcare access bias
When patients admitted to an institution does not
represented the cases originated in the community
Popularity bias
Centripetal bias
Referral filter bias
Diagnostic/treatment access bias
1.1.c – Length bias
Cases with disease with longer duration are more easily
included in surveys
16. 1.1.d - Neyman bias
When a series of survivors is selected, if the exposure is
related to prognostic factors, or the exposure itself is a
prognostic determinant , the sample of cases offers a
distorted frequency of exposure.
1.1.e – Spectrum bias
In the assessment of validity of a diagnostic test, this bias is
produced when researchers include only clear or definite
cases, not representing the whole spectrum of disease. Also
applicable for controls
17. 1.1.f – Survivor treatment selection bias
In observational studies, patients who live longer have
more probability to receive a certain treatment
1.1.g – Berkson bias
When probability of hospitalization of cases and controls
differ, and it is also influenced by exposure
1.1.h – Healthy worker effect
Lower mortality observed in employed population
compared to general population
18. 1.1.h – Inclusion bias
When one or more conditions of controls are related with
exposure. Frequency of exposure higher in control group.
Seen commonly in hospital based studies
1.1.i – Exclusion bias
When controls with conditions related to exposure are
excluded where as cases with co-morbidities are included
19. 1.2 lack of accuracy of sampling
frame
1.2.a – Non random sampling bias
Results in non-representative sample
1.2.b – telephone random sampling bias
Excludes some households from the sample . . Coverage
issues
20. In systematic reviews and meta analysis, selection of samples (relevant
studies) is most important
1.2.c – Citation bias
More frequently cited, more easily found
1.2.d – Dissemination bias
Biases in retrieval of information (language, reporting of results)
1.2.e – Post-hoc analysis
Due to subgroup analysis which give misleading results
1.2.f – Publication bias
When published reports do not represent the studies carried out
on that association
21. 1.3 uneven diagnostic procedures
In case control studies, if exposure influences diagnosis of
disease, detection bias occurs
1.3.a – diagnostic suspicion bias
Exposure is taken as a diagnostic criterion
1.3.b – Mimicry bias
When benign conditions mimic clinically to the disease
22. 1.4 during study implementation
1.4.a – Loss to follow up
Attrition/withdrawal is uneven in exposure and outcome
categories study results affected
1.4.b - Missing information bias
Seen mostly in multivariate analysis
Missing data affects study outcome
1.4.c – Non-response bias
This type of bias is due to refusals to participate in a study.
The individuals concerned are likely to be different from
individuals who do participate.
Non-respondents must be compared with respondents with
regard to key exposure and outcome variables in order to
ascertain the relative degree of non-response bias.
23. 2. INFORMATION BIAS
It occurs during data collection.
Three types of information bias are:
Misclassification bias
Ecological fallacy bias
Regression to the mean bias
24. 2.1 Misclassification bias
When sensitivity and/or specificity of the procedure to detect
exposure and/or effect is not perfect, ie exposed/diseased
subjects can be classified as non-exposed/non-diseased
subjects and vice-versa
Two types:
Differential misclassification bias
Non-differential misclassification bias
25. 2.1.a – Detection bias
Seen in studies with follow-up (cohort, clinical trials)
2.1.b – Observer/Interviewer bias
Knowledge of hypothesis, disease status, exposure status or
intervention received can influence data recording
Interviewers can influence errors into a questionnaire or
guide the respondents to a particular answer.
26. 2.1.c – Recall bias
If presence of disease influences the perception of its causes
(rumination bias) or
In a trial, if patients knows what they receive, it may influence
their answers (participant expectation bias)
2.1.d – Reporting bias
Participants can collaborate with researchers and give answers
in the directions they perceive are of interest (obsequiesness
bias)
Existence of a case triggers family information (family
aggregation bias)
Measures or sensitive questions that embarrass or hurt can be
refused
Reporting of socially undesirable behaviours (underreporting
bias)
27. 2.2 Ecological fallacy
It is produced when analysis realized in an ecological (group
level) analysis are used to make inferences at the individual
level.
Eg: if exposure and disease are measured at group level,
exposure disease relations can be biased from those obtained
at the individual level
28. 2.3 Regression to the mean
It is a phenomenon that a variable that shows an extreme
value on its first assessment will tend to be closer to the
centre of its distribution on a later measurement.
Eg: high cholesterol level measurement
29. 2.4 Other information biases
2.4.a – Hawthorne effect
Increase in outcome under study in participants who are
aware of being observed.
2.4.b – Lead time bias
The added time of illness produced by the diagnosis of a
condition during its latency period.
30. 2.4.c – protopathic bias
When an exposure is influenced by early (subclinical)
stages of disease
Sick quitter bias: People with risky behaviours (eg: alcohol
consumption) quit their habit as a consequence of disease .
. Which will mention them as non-exposed
2.4.d – temporal ambiguity
When it cannot be established that exposure precedes
effect.
Seen in cross-sectional and ecological studies
31. 2.4.e – Will Rogers phenomenon
Improvement in diagnostic tests refines disease staging in
diseases such as cancer
It is seen when survival rates are measured across time
and even among centres with different diagnostic
capabilities
2.4.f – Work-up bias (verification bias)
In the assessment of validity of a diagnostic test, it is
produced when the execution of gold standard is
influenced by the results of the assessed test, ie reference
test is less frequently performed when the test result is
negative
32. 3. Confounding
It occurs when a variable is a risk factor for an effect among
non-exposed persons and is associated with the exposure of
interest in the population from which the effect derives,
without being affected by the exposure or the disease
Confounding can occur in every epidemiological study
Susceptibility bias is a synonym
33. Confounding can be neutralised at the design stage of a
research (for example, by matching or randomisation)
and/or at the analysis, given that the confounders have been
measured properly
3.1 - Confounding by group:
It is produced in an ecological study, when the exposure
prevalence of each community (group) is correlated with the
disease risk in non-exposed of the same community
34. 3.2 - Confounding by indication
This is produced when an intervention (treatment) is indicated by
a perceived high risk, poor prognosis, or simply some symptoms.
Here the confounder is the indication, as it is related to the
intervention and is a risk indicator for the disease
35. 4. Specific biases in trials
4.1 – allocation of intervention bias
Seen in non-randomised trials when sequence of
allocation is known in advance or concealment is
unclear/inadequate
4.2 – compliance bias
In trials requiring adherence to intervention, the degree of
adherence (compliance) influences efficacy assessment of
the intervention
36. 4.3 – Contamination bias
When intervention-like activities find their way into
control group
Seen in community based trials due to relationships
among members and interference by mass media etc
4.4 – Lack of intention to treat analysis
In RCT’s, analysis should be done keeping participants in
the group originally assigned to.
Otherwise, bias results . . .
37. Publication bias
Scientific journals are most likely to accept studies that have
‘positive findings’ than those with ‘negative findings’.
Creates false impression in the literature and may cause
long-term consequences to the scientific community
To overcome this bias, several journals have been launched
which publish only negative findings.
Eg: Journal of Pharmaceutical Negative Results, Journal of
Negative Results in Biomedicine, Journal of Interesting
Negative Results
49. During
conduct of
trial
Population choice
bias
Gender bias
Age bias
Pregnancy bias
Special intervention
bias
Informed consent /
literary bias
Intervention choice
bias
Too early bias
Too late bias
Learning curve bias
Complexity bias
Outcome choice trial
Measurement bias
Time term bias
53. During
uptake of
RCT
Relation to author
bias
Rivalry bias
I owe him bias
Personal habit bias
Morals and values
bias
Clinical practice
bias
Institution bias
Territory bias
Tradition biasDo something bias
Printed word bias
Prestigious journal
bias
Peer review bias
54. During uptake of
RCT
Prominent author bias
Trial design bias
Complimentary medicine bias
Flashy title bias
Careless reading bias
55. During uptake
of RCT
Prominent author bias
Esteemed author
Esteemed professor
Friendship
Trial design bias
Complimentary medicine
bias
Flashy title bias
Careless reading bias
56. During uptake
of RCT
Prominent author bias
Trial design bias
Favoured design
bias
Large trial bias
Small trial bias
Multicentre trial
bias
Complimentary medicine
bias
Flashy title bias
Careless reading bias
59. BIAS in qualitative research
In qualitative research, bias affects validity and reliability of
findings and thus the results
Categories of biases seen in qualitative research are:
Moderator bias
Biased questions
Biased answers
Biased sampling
Biased reporting
60. Moderator bias
The moderator’s facial expressions, body language, tone,
manner of dress, and style of language may introduce bias.
Similarly, the moderator’s age, social status, race, and gender
can produce bias.
Some conditions unavoidable, still some can be controlled
61. Biased questions
Leading question bias
Misunderstood question bias
Unanswerable question bias
Question order bias. Always ask:
general questions before specific questions
unaided before aided questions
positive questions before negative questions
behavior questions before attitude questions
63. Biased sampling
Due to errors in sampling.
Professional respondents should be avoided
Biased reporting
Experiences, beliefs, feelings, wishes, attitudes, culture, views,
state of mind, reference, error, and personality can bias
analysis and reporting.
65. Scenario 1
Investigators recruited both cases and controls from a
defined catchment area in the general population. This is
often difficult to do in the absence of a comprehensive
registry. Suppose investigators had recruited all cases of oral
cancer from a comprehensive national registry, between
August 1, 2013 to July 31, 2014. What bias might be
intoduced if controls were obtained from the catchment
area?
Selection Bias
66. Scenario 2
Suppose you are designing a case-control study on
association between smoking and oral cancer. Which of the
following methods of acquiring cases and controls is most
practical to reduce bias?
Scenario 1: Cases from AIMS (tertiary hospital) and controls
from xyz dental clinic
Scenario 2: Cases from AIMS and controls from admission
office of AIMS with diagnosis of any other cancer other than
oral cancer
Scenario 3: Cases from AIMS and controls from Panangad
satellite clinic
67. Scenario 3
What potential bias could have been introduced if you found
out that those who interviewed cases took 30 minutes longer
on average than those who interviewed controls?
Selection bias
Information bias
Volunteer bias
Loss to follow up bias
68. Scenario 4
A study reported a significant association between long-term
use of smoking and oral cancer compared to no smoking and
oral cancer. The duration of exposure varied among both
cases and controls. What bias could arise when trying to
measure exposures that happened over different time
periods?
Misclassification bias
Measurement bias
Recall bias
69. Scenario 5
What effect would you observe if the interviewers were
aware of the disease status of the study subjects?
It would benefit the validity of results since interviewer
would understand more precisely of the disease and collect
better data for cases
The results would likely not change
It could damage the results by introducing interviewer bias
70. SUMMARY
Bias is any trend or deviation in truth or data collection,
analysis, interpretation and publication which can cause
false conclusions
Occurs either intentionally or unintentionally
Due to consequences of bias, it is unethical to conduct and
publish a biased research even unintentionally
71. Confounding effect cannot be completely avoided.
Every researcher should therefore be aware of all potential
sources of bias and undertake all possible actions to reduce
and minimize the deviation from the truth.
If deviation is still present, authors should confess it in their
articles by declaring the known limitations of their work.
72. References
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