Types of bias

TYPES OF BIAS
Presented by:
Dr. Diplina Barman, 1st year Post Graduate Trainee
Department of Public Health Dentistry
1

CONTENTS:
 INTRODUCTION
 TERMINOLOGIES
 DEFINITION OF BIAS
 CLASSIFICATION OF BIAS
 DESCRIPTION OF BIAS
 FACTORS AFFECTING BIAS
 METHODS TO AVOID/MINIMIZE BIAS
 SUMMARY
 CONCLUSION
 BIBLIOGRAPHY
2

EPIDEMIOLOGY
EPI = AMONG DEMOS = PEOPLE LOGOS = STUDY
 "Epidemiology is the study of the distribution and determinants of
health-related states or events in specified populations and the
application of this study to the control of health problems."
(Last, 1988)
-WORLD HEALTH ORGANIZATION
3
Parks textbook of preventive and social medicine, K Park, 24th edition.

P R I N C I P L E S :
1) EXACT OBSERVATION
2) CORRECT INTERPRETATION
3) RATIONAL EXPLANATION
4) SCIENTIFIC CONSTRUCTION
4
Park’s textbook of preventive and social medicine, K Park, 24th edition.

INTRODUCTION
 STUDY DESIGNS:
EPIDEMIOLOGICAL
OBSERVATIONAL
COHORT
CROSS-SECTIONAL
CASE-CONTROL
ECOLOGICAL
EXPERIMENTAL
FIELD TRIALS
RANDOMIZED
CONTROL TRIALS
COMMUNITY TRIALS
DESCRIPTIVE ANALYTICAL
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Park’s textbook of preventive and social medicine, K Park, 24th edition.

TERMINOLOGIES:
 INCIDENCE – The number of NEW cases occurring in a defined population
during a specified period of time.
No. of new cases of specific disease during a given time period x 1000
Population at risk during that period
PREVALENCE = INCIDENCE X MEAN DURATION
 PREVALENCE – The total number of all individuals (both old and new) who
have an attribute or disease at a particular time (or during a particular
period) by the population at risk of having the attribute or disease at this
point in time or midway through the period.
No. of cases (both new and old) of specific disease
during a given time period x 1000
Population at risk during that period
6

EXTERNAL VALIDITY:
External validity is the process of generalization, and refers to
whether results obtained from a small sample group can be
extended to make predictions about the entire population.
INTERNAL VALIDITY:
Internal validity refers to how well an experiment is done, especially
whether it avoids confounding (more than one possible independent
variable [cause] acting at the same time).
7

Relative Risk = Incidence among Non exposed
Incidence among exposed
a
(b+d)(a+c)
b
/=
8
Odds Ratio (Cross-product ratio)
It is the measure of the strength of the association between risk factors and
Outcome
It is closely related to Relative risk
Exposed
Not exposed
Yes
a
b
No
b
d
Diseases
Odds ratio = ad / bc

 Two broad types of error
 Random : Can be reduced toward zero by
increasing sample size
 Systematic: Would not be reduced by increasing
sample size. Bias is a form of systematic error
9

Bias
 A process at any state of inference tending to produce results
that depart systematically from the true values (Fletcher et al,
1988)
 Systematic error in design or conduct of a study (Szklo et al,
2000)
 Any trend in the collection, analysis, interpretation, publication
or review of data that can lead to conclusions that are
systematically different from the truth (Last, 2001)
10

By Sackett and Choi:
 Reading up on the field
 Specification and selection of the study sample
 Execution of the experimental manoeuvre
 Measurement of exposures/outcomes
 Data analysis
 Results interpretation
 Publication
11
CLASSIFICATION OF BIAS
Jul 31, 1993 - Dl SackettBias in analytic research. J Chron Dis, 32 (1979), ... Bck Choi, Al
NoseworthyClassification, direction, and prevention of bias in epidemiologic research. J Occup Med, 34
(1992) ... 1038-1041. 8. Ch Hennekens, Je Buring, Sl Mayrent (Eds.), Epidemiology in medicine, Little, Brown,
Boston (1987). 9.

By Kleinbaum et al:
 Selection bias
 Information bias
 Confounding
By the direction of the change in parameter:
 Positive bias– observed effect is higher than the true value
 Negative bias– observed effect is lower than the true value
12
(1992) ... 1038-1041. 8. Ch Hennekens, Je Buring, Sl Mayrent (Eds.), Epidemiology in medicine, Little, Brown,
Boston (1987). 9.

BIAS IN CASE CONTROL STUDIES
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 Berksonian bias
 Detection bias
 Diagnostic suspicion bias
 Exclusion bias
 Exposure suspicion bias
 Neyman bias
 Relative control bias
 Rumination bias
 Spectrum bias
 Unmasking detection signal bias
Jul 31, 1993 - Dl SackettBias in analytic research. J Chron Dis, 32 (1979), ... Bck Choi,
Al NoseworthyClassification, direction, and prevention of bias in epidemiologic
research. J Occup Med, 34 (1992) ... 1038-1041. 8. Ch Hennekens, Je Buring, Sl
Mayrent (Eds.), Epidemiology in medicine, Little, Brown, Boston (1987). 9.

Case-selection Bias in Case-Control studies:
 CASE SURVIVAL BIAS
 CASE REFERRAL BIAS
 CASE ELIGIBILITY ASSESSMENT BIAS
 CASE NON-PARTICIPATION BIAS
 CASE ASCERTAINMENT BIAS
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Control-selection Bias in Case-Control studies:
• CONTROL SOURCE BIAS
• CONTROL SAMPLING FRAME BIAS
• EXPOSURE-RELATED CONTROL ILLNESS BIAS
• EXPOSURE-RELATED CONTROL ILLNESS BIAS
• CONTROL SURVIVAL, REFERRAL AND DIAGNOSIS BIAS
• CONTROL NON-PARTICIPATION BIAS
Introduction to Epidemiology, Ray M Merril and Thomas C. Timmreck, 4th edition.

BIAS IN COHORT STUDIES:
 Withdrawal bias
 Misclassification bias
 Confounding
 Healthy worker effect
 Survivor treatment selection bias
 Will Rogers phenomenon
15

BIAS IN CROSS SECTIONAL STUDIES
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 Obsequissness bias
 Underreporting bias
 Recall bias
 Observer bias
 Non response bias
 Healthcare access bias
 Neyman bias

BIAS IN OBSERVATIONAL STUDIES
 Centripetal bias
 Diagnostic /treatment access bias
 Family aggregation bias
 Healthy volunteer effect
 Non response bias
 Referral filter bias
 Protopathic bias
 Telephone random sampling bias
 Underreporting bias
17
(1992) ... 1038-1041. 8. Ch Hennekens, Je Buring, Sl Mayrent (Eds.), Epidemiology in medicine, Little,
Brown, Boston (1987). 9.

BIASES IN QUESTIONNAIRES
 Questionnaire - principal instrument for collecting data in surveys.
 Bias is a pervasive problem in the design of questionnaires.
TYPES:
 The way a question is designed
 The way the questionnaire as a whole is designed
 How the questionnaire is administered.
18

SELECTION BIAS
 An error due to systematic difference between probabilities of being included in the
study sample according to relevant study characteristic – the exposure and outcome of
interest.
 Introduced at any stage of a research study:
 Inappropriate definition of the eligible population
 Lack of accuracy of sampling frame
 Uneven diagnostic procedures in the target population
 Implementation.
19
SELECTION BIAS

1) INAPPROPRIATE DEFINITION OF
ELIGIBLE POPULATION
 When the study population does not represent the target population
COMPETING RISKS:
 When two or more outputs are mutually exclusive, any of them competes with each
other in the same subject.
 e.g. Early death by AIDS can produce a decrease in liver failure mortality in parenteral
drug users. Estimating the probability of death by a specific cause of death if any other
risk of death is removed (net probability of death)
20
SELECTION BIAS

HEALTHCARE ACCESS BIAS
 When the patients admitted to an institution do not represent the cases originated in the
community.
 Popularity bias: if admission in an institution is determined by the interest of health
personnel on certain kind of cases.
 Centripetal bias: if patients are attracted by the prestige of certain clinicians.
 Referral filter bias: If healthcare is organised in increasing levels of complexity
(primary, secondary, and tertiary care) and ‘‘difficult’’ cases are referred to tertiary care.
 Diagnostic/treatment access bias: If patients by cultural, geographical, or economic
reasons show a differential degree of access to an institution.
21
SELECTION BIAS

LENGTH-BIAS SAMPLING:
 Individuals who develop a rapidly progressive disease are more likely to die than the
majority of individuals with disease & are unlikely to be found in a population that
presents for screening.
 Cases with diseases of long duration are more easily included in surveys & may not
represent the cases originated in the target population.
 Cases have a better prognosis, so effectiveness of screening in terms of survival is
overstated.
e.g. Faster-growing tumors have shorter asymptomatic phase and are detected easily&
have poorer prognosis.
Slower-growing tumors are over-represented in screening tests. This means screening
tests are associated with improved survival, even if they have no actual effect on
prognosis.
22
SELECTION BIAS

NEYMAN BIAS (INCIDENCE-PREVALENCE
BIAS, SELECTIVE SURVIVAL BIAS):
o In both cross sectional and case-control studies
 When a gap in time occurs between exposure and selection of study participants.
 In studies of diseases that are quickly fatal, transient, or subclinical.
 Creates a case group not representative of cases in the community.
 Example : If cases with coronary artery disease die rapidly, persons available for
study are not the more severe cases. The association between serum cholesterol
(high vs low) and coronary artery disease will be underestimated.
23
SELECTION BIAS

SPECTRUM BIAS
 Performance of a diagnostic test changes between different subgroups owing to
changes in the patient case-mix or spectrum of disease.
 Spectrum effect- inherent variation in test performance among population subgroups.
 Causes:
Case mix of patients with disease
Case mix of patients without disease
 e.g.In a study investigating the ability of MR imaging to detect cirrhosis, if only
advanced clinical cases are included the sensitivity will be overestimated.
24
SELECTION BIAS

SURVIVOR TREATMENT SELECTION BIAS
 Patients who live longer have more probability to receive a certain treatment
 Retrospective analysis can yield a positive association between treatment and survival.
 e.g. Observational cohort of patients with the AIDS.
At start - none of whom are using placebovir.
At 1 year - begin receiving treatment.
 If we compare survival in treated and untreated patients, the use of placebovir will
obviously correlate strongly with longer survival
25
SELECTION BIAS

HEALTHY WORKER EFFECT
 Lower mortality/ morbidity rates observed in subjects with occupational exposure
compared to the general population.
 Any excess risk associated with an occupation will tend to be underestimated by a
comparison with the general population, leading to decrease in relative risk (RR) for
occupational exposure and disease.
 Two components
Healthy worker hire effect: The selection of healthier workers at hire, either due to
self-selection or employer selection
26
SELECTION BIAS

Healthy worker survivor effect:
 Less healthy workers are more likely than healthy co-workers to leave high-exposure
jobs, either by ending employment or being transferred out.
 This may reduce the impact of exposure in given patient & lead to the false conclusion
that the higher-exposure jobs are safe.
 e.g.
 Incidence rate among exposed workers is 1/100
 General population 1.4 / 100
 Among workers in the general population 1/100
 Non-workers in the general population 5/100
27
SELECTION BIAS

BERKSONIAN BIAS:
 Berkson in 1946 for case control studies
 When the probability of hospitalisation of cases and controls differ.
 Relative frequency of disease in a group of patients who are hospitalised is inherently
biased when compared to the population served by the hospital.
 Attributable to the way in which the probabilities of hospitalisation combine in
patients with more than one disease.
28
SELECTION BIAS

 In a case-control study of smoking and chronic obstructive pulmonary disease (COPD),
controls were selected from the same hospital with other lung diseases (e.g. tuberculosis,
lung cancer).
EXCLUSION BIAS
 Controls with conditions related to the exposure are excluded, whereas cases with these
diseases as co-morbidities are kept in the study
 In association between reserpine and breast cancer; controls with cardiovascular disease
were excluded but this criterion was not applied to cases.
29
SELECTION BIAS

INCLUSION BIAS:
 In hospital based case-control studies when one or more conditions of controls are
related with the exposure.
 Frequency of exposure is higher than expected in the reference group, producing a
toward the null bias.
 e.g. Cases of lung cancer and the controls of other lung diseases.
RELATIVE CONTROL BIAS:
 Correlation in exposure status between cases and their relative controls yield biased
estimates of the association between exposure and outcome.
30
SELECTION BIAS

LANGUAGE BIAS:
 In systematic reviews and meta-analysis it has been common to exclude reports in
other languages than English.
 There was a trend to publish in English compared with German when the results
achieved statistical significance
 It was found that language bias has little effect on summary effect estimates.
31
SELECTION BIAS

2)LACK OF ACCURACY OF SAMPLING FRAME
NON-RANDOM SAMPLING BIAS:
 It can yield a non-representative sample in which a parameter estimate differs from
the existing at the target population.
Telephone random sampling bias:
 It excludes some households from the sample, thus producing a coverage bias.
 This can be mainly seen in less developed countries than in developed ones.
32
SELECTION BIAS

CITATION BIAS:
 Articles more frequently cited are more easily found and included in systematic
reviews and meta-analysis.
 Citation is closely related to the impact factor of the publishing journal & also to
statistical significance.
DISSEMINATION BIAS:
 The biases associated to the whole publication process, from biases in the retrieval of
information (including language bias) to the way the results are reported.
33
SELECTION BIAS

PUBLICATION BIAS:
 Regarding an association that is produced when the published reports do not
represent the studies carried out on that association.
 Most important influencing factors - statistical significance,sample size, funding,
prestige, type of design, and study quality.
34
SELECTION BIAS

3)UNEVEN DIAGNOSTIC PROCEDURES IN THE
TARGET POPULATION
DIAGNOSTIC SUSPICION BIAS:
 A knowledge of the subject’s prior exposure may influence both the intensity and the
outcome of the diagnostic process.
 Exposure can be taken as another diagnostic criterion & trigger the search for the
disease
 e.g. Benign anal lesions increases the diagnosis
of anal cancer.
35
SELECTION BIAS

UNMASKING/DETECTION SIGNAL BIAS
 An innocent exposure may become suspect if, rather than causing a disease, it causes a
sign or symptom which precipitates a search for the disease.
 May lead to increased estimates of relative odds.
 Estrogens might cause the search for endometrial cancer by causing symptomless
patients to bleed rather than the cancer.
 Restriction of cases and controls to only those patients who have undergone identical
detection maneuvers.
 Lead to ‘over-matching’ in case-control studies.
36
SELECTION BIAS

4)DURING STUDY IMPLEMENTATION
WITHDRAWAL BIAS:
 When losses/withdrawals are uneven in both the exposure and outcome categories
 When subjects who are lost to follow-up differ from those who remain in the study
until an event occurs or the study is terminated.
 The probability of the outcome of interest may differ in subjects lost to follow-up
versus in those who remain in the study.
 Study with CT to determine the incidence of lung cancer in a exposed (smoker)
population & nonexposed (nonsmoker) population
37
SELECTION BIAS

NON-RESPONSE BIAS
 Occurs when some subjects do not to respond to particular questions and non-
respondents (or late-comers) may exhibit exposures or outcomes which differ from
those of respondents (or early comers)
 “Item non-response" subjects often fail to “respond to a particular question”
 “Unit non-response" subjects may fail to respond to all questions.
 In a mailed questionnaire study of the smoking habits ,85% of non-smokers, but only
67% of cigarette smokers, returned the questionnaire.
38

Healthy volunteer effect :
 When the participants are healthier than the general population.
 Important when screening test is evaluated in the general population, producing an
away from the null bias; thus the benefit of the intervention is spuriously increased.
39
SELECTION BIAS

INFORMATION BIAS
 Results from differences in the methods in which
information is collected about or from study subjects.
 3 main types:
 Misclassification bias
 Ecological fallacy
 Regression to the mean
40
INFORMATION BIAS

MISCLASSIFICATION BIAS
 When sensitivity and/or specificity of the procedure to detect exposure and/or effect is
not perfect, i.e. exposed/diseased subjects can be misclassified as nonexposed/ non-
diseased and vice versa.
 Misclassification results in an incorrect estimation of the association between
exposure and outcome
 The size and direction of misclassification depends on the type of misclassification of
exposure or outcome.
41
INFORMATION BIAS
Introduction to Epidemiology, Ray M Merril and Thomas C. Timmreck, 4th edition.

Differential
misclassification
Non-differential
misclassification
Systematic error Random error
Misclassification of exposure
DIFFERS between cases
and controls
Misclassification of exposure
is SIMILAR between cases
and controls
Misclassification of outcome
DIFFERS between exposed
and unexposed
Misclassification of outcome
is SIMILAR between exposed
and unexposed
Measure of association may
be exaggerated or
underestimated
Weakness of the measure of
association ("bias towards
the null) 42
INFORMATION BIAS

BIAS PRODUCING MISCLASSIFICATION:
 OBSERVER BIAS
 RECALL BIAS
 REPORTING BIAS
43

OBSERVER/INTERVIEWER BIAS
 The knowledge of the hypothesis,the disease status, or the exposure status (including
the intervention received) can influence data recording.
 Error into a questionnaire while administering the interview or helping the
respondents in different ways by putting emphases in different questions
 In a case-control study of food borne outbreak of listeriosis, investigators may probe
listeriosis cases about consumption of a suspected food item more than controls.
44
INFORMATION BIAS

WISH BIAS:
 Failure to look for contrary evidence & too easy acceptance of incomplete proof may
lead to conclusions that happen to coincide with what the investigator wanted.
ROSENTHAL EFFECT:
 The phenomenon whereby higher expectations lead to an increase in performance.
45
INFORMATION BIAS

RECALL BIAS
 When exposure information is differentially misclassified for subjects with and
without disease
 Can be particularly problematic in studies where subjects are interviewed to collect
information, as might occur in case-control and retrospective cohort studies.
 In mothers whose recent pregnancies had ended in fetal death or malformation (cases)
and group of mothers whose pregnancies ended normally (controls).
46
INFORMATION BIAS

Rumination bias: if the presence of disease influences the perception of its
causes.
Exposure suspicion bias:
 In case control or cross sectional study.
 Produced when information about the supposed cause is collected after occurrence
of disease.
 Knowledge about the subject’s disease status may influence the intensity of a
search for exposure to the putative cause.
 Studies of thyroid cancer among children, depending upon the intensity of the
search for prior irradiation, markedly different rates of exposure were reported.
47
INFORMATION BIAS

Participant expectation bias:
 In a trial if the patient knows what they receive may influence their answers.
 More common in case-control studies
 Can occur in cohort studies & trials without participants’ blinding.
48
INFORMATION BIAS

REPORTING BIAS
Obsequiousness bias:
 Participants can ‘‘collaborate’’ with researchers and give answers in the direction they
perceive are of interest.
Family aggregation bias:
 Flow of information about exposures and diseases is stimulated by a family member
who develops the disease.
 Person who develops a disease is more likely to know that a parent has a history of the
disease than his or her unaffected siblings
49
INFORMATION BIAS

 Individuals with rheumatoid arthritis and their unaffected siblings whether their
parents had arthritis.
Unacceptable disease/exposure:
 Measures or sensitive questions that embarrass or hurt can be refused
50
INFORMATION BIAS

Underreporting bias:
 Common with socially undesirable behaviours, such as alcohol consumption.
Mode for mean bias:
 Occurs when frequency-quantity questionnaires are used to assess consumption of
alcohol and foods
 Subjects tend to report modal rather than average behaviour
 Hence with data skewed towards zero, the average intakes are underestimated
51
INFORMATION BIAS

ECOLOGICAL FALLACY
 When results obtained in an ecological (group level) analysis are used to make
inferences at the individual level.
 Ecological fallacy can be produced by within group (individual level) biases, such as
confounding, selection bias, or misclassification, and by confounding by group or
effect modification by group.
 Effect modification by group on an additive scale is produced when the rate difference
for the exposure effect changes across communities.
52
INFORMATION BIAS
www,braitanica.com/topic/ecological-fallacy.

REGRESSION TO THE MEAN
 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
 Relevant when the efficacy of a treatment to reduce high levels of a variable
(e.g.cholesterol) is assessed, when researchers are interested in the relation between the
initial value of a variable and the change in that measurement over time.
 Prevention : existence of an appropriate reference group and a selection based on more
than one measurement.
54

HAWTHORNE EFFECT
 Described in the 1920s in the Hawthorne plant of the western electric company
(Chicago, IL).
 An increase in productivity or other outcome under study—in participants who are
aware of being observed.
55
INFORMATION BIAS

LEAD TIME BIAS:
 Length of time between the detection of a disease (new, experimental criteria) and its
usual clinical presentation and diagnosis (traditional criteria).
 Relevant in the evaluation of the efficacy of screening, in which the cases detected in
the screened group has a longer duration of disease than those diagnosed in the non-
screened one.
56

PROTOPATHIC BIAS:
 When a exposure is influenced by early (subclinical) stages of disease.
e.g. Preclinical pancreatic cancer can produce diabetes mellitus, and thus an association
between diabetes and cancer can occur
Sick quitter bias:
 People with risky behaviours (such as heavy alcohol consumption) quit their habit as a
consequence of disease.
 Studies analysing current behaviour as a risk factor will labelled them as non-exposed,
thus underestimating the true association.
57
INFORMATION BIAS

WILL ROGERS PHENOMENON
 Named in honour of the philosopher Will Rogers by Feinstein et al.
 Improvement in diagnostic tests refines disease staging in diseases such as cancer.
 Produces a stage migration from early to more advances stages and an apparent higher
survival.
 Relevant when comparing cancer survival rates across time or even among centres
with different diagnostic capabilities e.g. tertiary compared with primary care hospitals
58
INFORMATION BIAS

CONFOUNDING
 When a variable is a risk factor for an effect among non-
exposed persons and is associated both with the exposure &
disease.
 Susceptibility bias : when people who are particularly
susceptible to development of a outcome are also prone to
be exposed.
 Confounding by group: Produced in an ecological study,
when the exposure prevalence of each community is
correlated with the disease risk in non-exposed of the same
community.
60

Confounding by indication:
 Produced when an intervention is indicated by a perceived high risk, poor prognosis,
or some symptoms.
 Confounder is the indication, as it is related to the intervention and is a risk indicator
for the disease.
 e.g.In the study of the association between cimetidine and gastric cancer, the
indication peptic ulcer is considered the potential confounder.
61

BIASES DURING THE COURSE OF AN RCT
POPULATION CHOICE BIAS:
 If the sample is overly restricted by not including women (gender bias) or people over
(or under) a specific age group (age bias)
62
DURING THE COURSE

 Pregnancy bias (excluding pregnant women) may be necessary for reasons of safety to
the fetus.
 When trials are restricted to, or exclude, people in special circumstances (special
circumstances bias)
 Population choice may be restricted when potential participants are approached
(recruitment bias).
63
DURING THE COURSE

 Informed consent bias, literacy bias, language bias: Eligible patients may be kept out
of a trial because they do not understand the consent form
 Severity of illness bias - Patients with a mild form of an illness may not respond in the
same way as those with a more severe form.
INTERVENTION CHOICE BIAS:
 The nature of the intervention chosen can have a major effect on the results obtained.
 The stage at which an intervention is studied can be very important.
64
DURING THE COURSE

Complexity bias- when a trial is used to study complex interventions, with a number of
components, or where outcomes depends on multiple contingencies outside of the
control of the investigator.
COMPARISON CHOICE (CONTROL GROUP) BIAS:
 If an intervention is compared to a poorly chosen control group, it can erroneously
appear to be more (or less) effective.
 In case of placebo control, the results will only tell us whether the intervention has a
specific effect or not.
 Not imply that the experimental intervention has a different or better effect than existing
alternatives.
65
DURING THE COURSE

OUTCOME CHOICE BIAS:
 Measurement bias- evaluate outcomes that are easy to measure, rather than the
outcomes that are relevant.
 Time term bias- short-term outcomes are measured rather than the important long-
term outcomes.
COMPLIANCE BIAS:
 In trials requiring adherence to intervention, the degree of adherence (compliance)
influences efficacy assessment of the intervention.
 e.g.when high risk patients quit exercise programmes.
66
DURING THE COURSE

CONTAMINATION BIAS:
 When intervention-like activities find their way into the control group.
 Distort the estimate of the intervention effect toward the null hypothesis.
 More frequently in community intervention trials because of the relationships among
members of different communities and interference by mass media, health
professionals, etc.
67
DURING THE COURSE

BIASES DURING THE REPORTING OF A TRIAL:
WITHDRAWAL BIAS:
SELECTIVE REPORTING BIAS:
 Describing those outcomes with positive results, or which favour the studied
intervention.
 This is not always consciously done, may even unconsciously be attracted more to
certain outcomes than others.
68
DURING THE REPORTING

 Optimism bias: in which the items hoped for, are more likely to be reported.
 Data dredging bias: investigators can report the outcomes they wish to stress, and
not mention the less desirable outcomes.
 Interesting data bias: in which the authors report the data that they find most
interesting.
69
DURING THE REPORTING

DURING DISSEMINATION OF RCT
TIME LAG BIAS:
 Occurs when the speed of publication depends on the direction and strength of the
trial results.
 Trials with ‘negative’ results take twice as long to be published as ‘positive’ trials.
70
DURING DISSEMINATION

BIASES DURING THE UPTAKE PHASE
RIVALRY BIAS: Underrating the strengths or exaggerating the weaknesses of studies
published by a rival
I OWE HIM ONE BIAS: Favouring flawed results from a study by someone who did the
same for the reader
PERSONAL HABIT BIAS: Occurs when readers overrate or underrate a study depending
on their own habits
71
DURING UPTAKE

MORAL BIAS: In which readers overrate or underrate a study depending on how much
it agrees or disagrees with their moral views
CLINICAL PRACTICE BIAS: When readers judge a study according to whether it
supports or challenges their current or past clinical practice
72
DURING UPTAKE

TERRITORY BIAS: when readers overrate studies that support their own speciality or
profession.
TRADITION BIAS: when a reader rates a study depending on whether it supports or
challenges traditional procedures.
DO SOMETHING BIAS:
 Overrating a study that suggests that intervention is effective, when there is no
alternative effective intervention available.
 Common among clinicians and patients
73
DURING UPTAKE

TECHNOLOGY BIAS: Relates to judging a study according to the reader’s attraction or
aversion for technology in health care.
RESOURCE ALLOCATION BIAS: when readers have a strong preference for one type
of resource allocation
PRINTED WORD BIAS: Occurs when a study is overrated because of undue
confidence in published data.
74
DURING UPTAKE

Peer review bias: When readers have an unwarranted belief in the ability of peer review
to guarantee the validity of a study.
PROMINENT AUTHOR BIAS: Occurs when the results of studies published by
prominent authors are overrated.
Professional background bias: Physicians underrating research done by nurses or vice
versa; basic scientists underrating research done by clinicians or vice versa; PhDs
underrating studies published by MDs and vice versa.
75
DURING UPTAKE

Esteemed author/ friendship bias:
When the reader overrates results obtained by a close friend or mentor.
TRIAL DESIGN BIAS:
Favoured design bias occurs when a study that uses a design supported, publicly or
privately, by the reader
Large trial bias: In which the results of large trials are overrated
Multicentre trial bias: When the results of multicentre collaborative trials are overrated
76
DURING UPTAKE

Small trial bias: When the results of trials with small
sample size are underrated
Flashy title bias: When the results of studies with
attractive titles are overrated (particularly by patients
/journalists) or underrated (particularly by academics)
Substituted question bias: When a reader
substitutes a question for the question that the study
is designed to answer and regards the results of the
study as invalid if they do not answer the substituted
question.
77
DURING UPTAKE

Reader attitude biases include
Belligerence bias: Which results in underrating
studies systematically just for the sake of being
difficult
Empiricism bias: Overrating or underrating a
study because it challenges the clinical experience
of the reader.
78
DURING UPTAKE

TYPES OF BIAS IN QUESTION DESIGN
A)Problems with wording:
1.Ambiguous question:
 Respondents answer a different question than was intended.
 Example: Is your work made more difficult because you are expecting a baby?
79
QUESTION DESIGN

2.Complex question: Complex and lengthy questions should be avoided.
3. Double-barrelled question:
 Difficult for the respondent to know which part of the question to answer and for the
investigator to know which part is actually answered.
 e.g.Do you agree that AIDS can be transmitted by shaking hands with a person with
AIDS or through other means of physical contact?
80
QUESTION DESIGN

5. Technical jargon:
 Technical terms may not be understood by the general public
 Example: What was your age at menarche?
6.Uncommon word:Uncommon and difficult words should be avoided in questionnaires.
7. Vague word: Vague words in questions encourage vague answers.
81
QUESTION DESIGN

B) missing or inadequate data for intended
purpose
1.Belief vs behavior (hypothetical or personalized question):
 About a belief can yield quite different answers than questions that ask
the respondent about his or her behaviours .
2. Starting time
 Failure to identify a common starting time for exposure or illness.
 e.g.: In the last 12 months, have you had an accident causing head
injury?
82
QUESTION DESIGN

3. Data degradation:
 Accurate, continuous data at source instead of degraded data.
 Example: What is your birth date?
What is your age in years?
4.Insensitive measure:
 When outcome measures make it impossible to detect clinically significant changes or
differences
 Example: How important is health to you?
(Unimportant) 1 - 2 - 3 (Important)
(Unimportant) 1-2-3-4-5-6 -7-8-9-10 (Important)
83
QUESTION DESIGN

C) Faulty scale
1.Forced choice (insufficient category):
 Few categories can force respondents to choose imprecisely among limited options.
e.g.: Do you agree? Yes [ ] No [ ]
Do you agree? Yes [ ] No [ ] Don’t Know [ ]
2. Missing interval :in response choices can cause confusion.
84
QUESTION DESIGN

3.Overlapping interval: in response choices can cause confusion.
Example: How many cigarettes do you smoke per day?
[ ] None [ ] 5 or less [ ] 5-25 [ ] 25 or more
4.Scale format:
 An even or an odd number of categories in the scale may produce different results
 Example: Do you agree?
(Agree) 1 – 2 – 3 (Disagree)
(Agree) 1 – 2 – 3 – 4 (Disagree)
85
QUESTION DESIGN

D)Leading questions
1.Framing:
e.g.: Which operation would you prefer?
An operation that has a 5% mortality.
An operation in which 90% of the patients will
survive
2.Leading question:
 Different wording of the same question can lead to different answer.
 e.g.Do you do physical exercise, such as cycling?
86
QUESTION DESIGN

3.Mind-set: can affect his perception of questions and can affect answers.
E) INTRUSIVENESS:
1.Reporting:
 Respondent may selectively suppress information, such as past history of sexually
transmitted disease.
87
QUESTION DESIGN

2. Sensitive question:
 Questions, such as personal or household incomes, sexual orientation may elicit
inaccurate answers.
F) Inconsistency:
1.Case definition:
 Different criteria may change over time or across regions
 Example: How many bladder cancer cases do you see in a year?
 How many histologically confirmed bladder cancer cases do you see in a year?
88
QUESTION DESIGN

2.Change of scale: If the measurement scale for a quantity changes in different surveys,
the results may not be comparable
3.Change of wording: If the precise wording of a question changes in different surveys,
the results may not be comparable.
4.Diagnostic vogue: The same illness may receive different diagnostic labels at different
points in space or time .
89
QUESTION DESIGN

TYPES OF BIAS IN QUESTIONNAIRE
DESIGN
A)FORMATTING PROBLEM:
1.Horizontal response format:
 Can cause confusion among the respondents because of poor spacing & result in the
wrong answers being checked or circled.
 The vertical response format has been suggested as better for listing response options.
90
QUESTIONNAIRE DESIGN

2.Juxtaposed scale :
 Asks respondents to give multiple responses to one item, may elicit different responses
than separate scales.
 Can force respondents to think and compare the importance and satisfaction for each
item because they are side by side.
 Cause confusion among respondents who are less educated.
91

3.Left alignment and right alignment:
 Placing the response choices to the right side of list of possible responses will result
in fewer errors on the part of interviewers.
 Placing the response choices to the left of the possible responses makes it easier for
the respondent to circle or check them
e.g.
excellent . . .[ ]
[ ] excellent
92

B) Questionnaire too long
1.No-saying and yes-saying:
 Tend to answer no to all questions or to answer yes to all questions.
 Use both positive and negative statements about the same issue in a list of items to
break the pattern.
2.Open question:
 Can result in data with differential quality
 More appropriate , particularly in surveys of knowledge and attitudes, and can yield
useful information
93

3.Response fatigue:
 Too long can induce fatigue & result in uniform and inaccurate answers.
 Personal interviews-50 to 90 minutes
 Telephone interviews-30 to 60 minutes
 Self administered questionnaires-10 to 20 minutes
4.Skipping question:
 Lead to the loss of important information because of logical errors in the flow of
questions.
94

RECAP
 Epidemiological Studies
 Definition of Bias
 Classification of Bias
 Selection Bias
 Information Bias
 Bias in RCT
 Bias in Questionnaire studies..
96

BIAS IN ADMINISTRATION OF QUESTIONNAIRE
A)Interviewer :
1.Interviewer: Interviewer’s subconscious or even conscious gathering of selective data
can result in inter-interviewer or intra-interviewer errors.
2.Nonblinding: When an interviewer is not blind to the study hypotheses, he or she may
consciously gather selective data
97ADMINISTRATION OF QUESTIONNAIRE

B) Respondent’s subconscious reaction
1.End aversion (central tendency):
 Avoid ends of scales in their answers.
 Tend to try to be conservative and wish to be in the middle.
2.Positive satisfaction (positive skew):
 Tend to give positive answers when answering questions on satisfaction
98
ADMINISTRATION OF QUESTIONNAIRE

C)Respondent’s conscious reaction
Social desirability bias:
 Tendency of research subjects to give socially desirable responses instead of choosing
responses that are reflective of their true feelings.
 Form of over-reporting good behaviour or under-reporting bad behaviour.
99

Unacceptable exposure:
 Socially unacceptable exposures (e.g., smoking, drug abuse) tend to be underreported.
 Ask whether the person had ever engaged in the behavior in the past before
asking about current practices.
Unacceptability:
 Measurements which hurt, embarrass or invade privacy may be systematically refused
or evaded.
 Avoid measurements or consider using incentives to increase participation rate.
100

D) Respondent’s learning:
1.Learning:
 Completing a questionnaire can be a learning experience for respondent about
the hypotheses and expected answers in a study.
 Randomize the order of questions for different respondents.
2.Hypothesis guessing:
Respondents may systematically alter questionnaire responses when, during the
process of answering the questionnaire, they think they know the study
hypothesis.
101

 E) Respondent’s inaccurate recall:
 Primacy bias: In mailed surveys, respondents tend to choose the first few response
options on the list.
 Recency bias: In telephone or personal interview surveys, more likely to respond in
favour of the later categories.
 Minimized by reducing the number of categories and by randomizing the order of
categories.
102

Proxy respondent :
 For diseased cases or surviving cases whose ability to recall details is defective,
information from proxies may result in differential data accuracy.
Telescope:
 Respondents usually recall an event in the distant past as happening more recently.
 Bounded recall procedure - respondents are interviewed at the beginning and end of
the time period referenced in a survey questionnaire.
Recall: Because of differences in accuracy or completeness of recall prior to major events
or experiences
103

MINIMISING NON-RESPONSE BIAS:
Can be prevented by achieving high response rates
(≥80%) by :
 Offering incentives to participate in the study
 Making it easy to contribute e.g. by using questionnaires that are not too long
 Sending reminders e.g. a first reminder by post at 1 week and a second reminder at 2
weeks after the initial questionnaire.
104

MINIMISING INFORMATION BIAS
 Using standard measurement instruments
 Collecting information similarly from the groups that are compared e.g.cases/ controls,
exposed/ unexposed
 Use multiple sources of information
 Questionnaires: test the same hypothesis using different questions
Direct measurements
Registries (e.g. cancer registries etc)
Case records (e.g. from GPs, hospital notes)
105

MINIMISING INTERVIEWER/ OBSERVER BIAS
 'Blinding' of investigator to the study participant's outcome/ exposure status
 Collecting information about exposure prior to definitive diagnosis / knowledge of
outcome
 Using a small number of interviewers to prevent too much variation between
observers.
 Training interviewers to ask questions the same way
106

MINIMISING RECALL BIAS
 Interval between the event/ illness of interest & recall period - as short as possible, thus
reducing non-differential recall bias
 Framing questions to aid accurate recall, so that inaccurate recall is limited among
controls as well as among cases, thus reducing differential recall bias
 Taking a different control group that will not be subject to the same incomplete recall
i.e. using as controls individuals with a disease considered to have a similar impact on
recall to the one being studied
 Using information from medical records/ other independent sources recorded before
the diagnosis/ disease outcome was known rather than information from questionnaires
collected after the outcome
107Jul 31, 1993 - Dl SackettBias in analytic research. J Chron Dis, 32 (1979), ... Bck Choi, Al
(1992) ... 1038-1041. 8. Ch Hennekens, Je Buring, Sl Mayrent (Eds.), Epidemiology in medicine, Little,
Brown, Boston (1987). 9.

MINIMISING WITHDRAWAL BIAS
 Intention to- treat analysis: All the study participants are included in the analyses as
part of the groups to which they were randomized, regardless of whether they completed
the study or not.
 Worst-case scenario or sensitivity analysis: Assigning the worst possible outcomes to
the missing patients or time points in the group that shows the best results & best possible
outcomes to the missing patients or time points in the group with the worst results.
108

MINIMISING CONFOUNDING
When planning the study design by:
 Restricting the study to homogenous group
 Matching
 Randomisation
109

During analysis of the study by:
1) Stratification:
 Variable suspected of being confounding factor can be held constant
 Study population is divided into strata in accordance with categories of suspected
confounder
 Association between dependent & independent variable is measured separately in
each stratum & compared with association found in data as whole.
 Provides estimates of strength of true association when confounding variable is
controlled.
110
Jul 31, 1993 - Dl SackettBias in analytic research. J Chron Dis, 32 (1979), ... Bck Choi, Al NoseworthyClassification, direction, and prevention of
bias in epidemiologic research. J Occup Med, 34 (1992) ... 1038-1041. 8. Ch Hennekens, Je Buring, Sl Mayrent (Eds.), Epidemiology in medicine,

2) Confounder can be neutralised by statistical techniques, such as standardization,
multiple linear or logistic regression, Mantel-Haenzel procedure.
 Computes an adjusted measure of strength of association.
111

Standardization
A set of techniques used to remove as far as possible the effects of differences
in age or other confounding variables when comparing two or more
populations.
The method uses weighted averaging of rates specific for age, sex, or some
other potentially confounding variable(s), according to some specified
distribution of these variables
(Last)
112

SUMMARY:
113
EPIDEMIOLOGICAL
STUDIES
BIAS IN
INDIVIDUAL
STUDIES
DESCRIPTION OF
BIAS
Minimizing Bias

CONCLUSION
 Bias is a ubiquitous and insidious problem in research study design and execution
 While no study design is exempt from bias, some are more prone to particular
types
 When a statistical association appears in research,knowledge about different biases
can help a reader decide whether the association is bogus, indirect, or real.
114

References:
1. Fletcher RH et al.Clinical Epidemiology : The Essentials – 3rd ed.
2. Beaglehole R et al. Basic Epidemiology, WHO
3. Last JM. Dictionary in Epidemiology – 3rd ed.
4. Maxcy-Rosenau-Last. Public Health & Preventive Medicine – 14th ed.
5. Norell SE. Workbook of Epidemiology
6. Park K. Park’s textbook of preventive and social medicine – 24th ed.
7. Introduction to epidemiology, 4th edition, Ray M Merrill.
8. Jul 31, 1993 - Dl Sackett Bias in analytic research. J Chron Dis, 32 (1979), ... Bck Choi, Al Noseworthy
Classification, direction, and prevention of bias in epidemiologic research. J Occup Med, 34 (1992) ... 1038-1041.
115

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