Classification and common biases encountered in health research

1. Dr. R. VENKITACHALAM

2. Contents
 Introduction
 Classification of bias
 Bias in clinical trials
 Bias in qualitative research
 Case scenarios
 Summary and conclusion
 References

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

7. Maclure and Schneeweiss episcope of bias

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

38. BIAS in clinical trials

39. Selection bias
Ascertainment
bias

40. Other
biases
in RCT’s
Planning
phase
During
conduct
During
Reporting
During
dissemination
of results
During
uptake
of results

41. During planning
phase
Choice of question
bias
Regulation bias
Wrong design bias

42. During
planning
phase
Choice of question bias
Hidden agenda bias
Self-fulfilling prophecy
bias
Cost and convenience bias
Funding availability biasRegulation bias
Wrong design bias

43. During
planning
phase
Choice of question bias
Regulation bias
IRB/Bureaucracy bias
Complicated informed
consent
Wrong design bias

44. During
planning
phase
Choice of question
bias
Hidden agenda bias
Self-fulfilling prophecy
bias
Cost and convenience
bias
Funding availability bias
Regulation bias
IRB/Bureaucracy bias
Complicated informed
consent
Wrong design bias

45. During conduct of
trial
Population choice
bias
Intervention choice
bias
Outcome choice
bias

46. During conduct
of trial
Population choice
bias
Gender bias
Age bias
Pregnancy bias
Special circumstances bias
Informed consent / literary
bias
Intervention
choice bias
Outcome choice
bias

47. During
conduct of
trial
Population choice bias
Intervention choice bias
Too early bias
Too late bias
Learning curve bias
Complexity bias
Outcome choice trial

48. During
conduct of
trial
Population choice bias
Intervention choice bias
Outcome choice trial Measurement bias
Time term bias

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

50. During reporting of
trial
Withdrawal bias
Selective reporting
bias
Fraud bias

51. During
reporting of
RCT
Withdrawal
bias
Selective reporting bias
Social desirability bias
Data dredging bias
Interesting data bias
Fraud
bias

52. During
dissemination
of results
Publication bias
Language bias
Country of
publication bias
Time lag 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

57. During
uptake of
RCT
Prominent author
bias
Esteemed
author
Esteemed
professor
Friendship
Trial design bias
Favoured
design bias
Large trial bias
Small trial bias
Multicentre
trial bias
Complimentary
medicine bias
Flashy title bias
I am an
epidemiologist
bias
Careless reading
bias

58. BIAS in qualitative research

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

62. Biased answers
 Consistency bias
 Dominant respondent bias
 Error bias
 Hostility bias
 Moderator acceptance bias
 Mood bias
 Overstatement bias
 Reference bias
 Sensitivity bias
 Social acceptance bias
 Sponsor bias

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.

64. Case scenarios

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
 1. 8.4 Introduction to sources of bias in clinical trials [Internet]. [cited 2014
Dec 22]. Available from:
http://handbook.cochrane.org/chapter_8/8_4_introduction_to_sources_of_bias_i
n_clinical_trials.htm
 2. 9781405132664_4_003.indd - 9781405132664_4_003.pdf [Internet].
[cited 2014 Dec 22]. Available from:
http://www.blackwellpublishing.com/content/BPL_Images/Content_store/Sample_
chapter/9781405132664/9781405132664_4_003.pdf
 3. Delgado-Rodríguez M, Llorca J. Bias. J Epidemiol Community Health. 2004
Aug 1;58(8):635–41.
 4. bias.aspx [Internet]. [cited 2014 Dec 22]. Available from:
http://www.ashpfoundation.org/mainmenucategories/researchresourcecenter/fost
eringyounginvestigators/ajhpresearchfundamentalsseries/bias.aspx
 5. Sackett DL. Bias in analytic research. J Chronic Dis. 1979;32(1-2):51–63.

73.  6. Kopec JA, Esdaile JM. Bias in case-control studies. A review. J
Epidemiol Community Health. 1990 Sep;44(3):179–86.
 7. FEM - Preventing bias [Internet]. [cited 2014 Dec 22]. Available
from: https://wiki.ecdc.europa.eu/fem/w/fem/preventing-bias.aspx
 8. Pannucci CJ, Wilkins EG. Identifying and Avoiding Bias in Research.
Plast Reconstr Surg. 2010 Aug;126(2):619–25.
 9. Qualitative Research Bias - How to Recognize It [Internet]. [cited
2014 Dec 22]. Available from:
http://www.focusgrouptips.com/qualitative-research.html
 10. Types of bias.pdf [Internet]. [cited 2014 Dec 22]. Available from:
http://www.medicalbiostatistics.com/Types%20of%20bias.pdf