Dr. R. VENKITACHALAM

Contents

Introduction

Classification of bias

Bias in clinical trials

Bias in qualitative research

Case scenarios

Summary and conclusion

References

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

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.

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’.

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 theory3. Kleinbaum et al – three main groups (selection, information

and confounding)4. Steineck and Ahlbom – misclassification, misrepresentation

and analysis deviation

Maclure and Schneeweiss episcope of bias

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

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

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).

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

How to reduce information bias

Blinding Placebo arm in case of RCT’s

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 frameUneven diagnostic procedures in target population

Due to implementation

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

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 biasDiagnostic/treatment access bias

1.1.c – Length bias Cases with disease with longer duration are more easily

included in surveys

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

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

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

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

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

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

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.

2. INFORMATION BIAS

It occurs during data collection.

Three types of information bias are: Misclassification bias Ecological fallacy bias Regression to the mean bias

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

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.

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 (obsequiesnessbias)

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)

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

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

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.

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

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

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

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

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

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

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 . . .

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

BIAS in clinical trials

Selection biasAscertainment

bias

Other biases

in RCT’s

Planning phase

During conduct

During

Reporting

During dissemination

of results

During uptake

of results

During planning phase

Choice of question bias

Regulation bias

Wrong design bias

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

During planning

phase

Choice of question bias

Regulation bias

IRB/Bureaucracy bias

Complicated informed consent

Wrong design bias

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

During conduct of trial

Population choice bias

Intervention choice bias

Outcome choice bias

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

During conduct of

trial

Population choice bias

Intervention choice bias

Too early bias

Too late bias

Learning curve bias

Complexity bias

Outcome choice trial

During conduct of

trial

Population choice bias

Intervention choice bias

Outcome choice trial Measurement bias

Time term bias

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 trialMeasurement bias

Time term bias

During reporting of trial

Withdrawal bias

Selective reporting bias

Fraud bias

During reporting of

RCT

Withdrawal

bias

Selective reporting bias

Social desirability bias

Data dredging bias

Interesting data bias

Fraud

bias

During dissemination

of results

Publication bias

Language biasCountry of

publication bias

Time lag bias

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

During uptake of RCT

Prominent author bias

Trial design bias

Complimentary medicine bias

Flashy title bias

Careless reading bias

During uptake of RCT

Prominent author bias

Esteemed author

Esteemed professor

FriendshipTrial design bias

Complimentary medicine bias

Flashy title bias

Careless reading bias

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

During uptake of

RCT

Prominent author bias

Esteemed author

Esteemed professor

Friendship

Trial design bias

Favoureddesign bias

Large trial bias

Small trial bias

Multicentretrial bias

Complimentary medicine bias

Flashy title bias

I am an epidemiologist

bias

Careless reading bias

BIAS in qualitative research

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

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

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

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

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.

Case scenarios

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

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 Panangadsatellite clinic

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

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

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

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

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.

References

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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/fosteringyounginvestigators/ajhpresearchfundamentalsseries/bias.aspx

5. Sackett DL. Bias in analytic research. J Chronic Dis. 1979;32(1-2):51–63.

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