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Bias –distortion of the truth

Systematic error- Bias

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Bias in epidemiological studies•Systematic (non-random) error that results in an incorrect estimate of the association between exposure and risk of disease.• Can occur in all stages of a study• Not affected by study sample size•Difficult to adjust for afterwards, but can be reduced by adequate study design.•Can never be totally avoided, but we must be aware of it and interpret our results accordingly

•To what extent does our outcome measure correspond to the“true value”?

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Consequences of bias

We make the wrong conclusion about the relation between exposure and outcome in our study

• Conclude there is no association while in reality there is one

• Conclude there is an association while in reality there is none

• Overestimate the strength of the association

• Underestimate the strength of the association

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Bias in all stages of research1. Research topic2. Choice of population3. Participation in a study4. Inequality in study populations – cases-

controls / exposed and non-exposed5. Assessment of exposure factor/disease6. Measurement of follow-up7. Analysis and interpretation of the data8. Publication9. Interpretation, judgment and action by

readers and listeners

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Two main classifications of biasSelection bias : occurs when comparisons aremade between groups of patients that are not comparable in determinants for the outcome other than the one(s) under study.

Unequal Groups

Measurement / information bias: occurs when the validity of measurement of Exposure and/or Disease is dissimilar among groups of patients

not comparing the same exposure / outcome parameters

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Selection biasSystematic error that results from:

- the way subjects are selected into a study

Consequence: there is a difference between subjectswho are selected or participant, and those who are not.

Problem: we may make an incorrect conclusion aboutthe association between an exposure and an outcome.

Selection Bias1. Are the characteristics of the study population selected for the study different than those who were not selected?

2. If yes, in what way / in which factor do theydiffer?

3. Is this factor likely to affect the outcome being studied?

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Measurement/ Information bias

xposed

Systematic error that results from:• the way information is obtained on one or morevariables (exposure, disease) that aremeasured in the study.

• Differences in validityof exposure data between cases and controlsof outcome data between exposed and une

Can occur in all types of studies

Information bias1. Is there any possibility that the collection of

information has been influenced by :1. Observer ?2. Respondent?3. Instrument?

2. Is there any possibility that the assessment in the different groups has not been done equally similar, appropriate, accurate and complete?

Why are we prone to bias?Possible pitfalls in observational research

• Selectivity - selective nature of perception including of selectivity of choice of topics

• Expectations -we only see what we expect to see

• Expert seeing - We only can see if we have the skills to see it(e.g.microscope)

• The observer effect - Observing is already an intervention in itself – the act of observation can sometimes affect what we observe

We only see what we expect to seeOur ability to learn and to see is profoundly affected by:

1. Information to which we have previously been exposed

2. References and pathways we have built in our brain system

3. Emotional links_ emotionally charged events are remembered better

4. Pleasant emotions are usually remembered better than unpleasant ones

SelectivityHuman mind has to filter information from outside

Perception is selective – we take what we feel is important

Risk of neglecting /overlooking important factors

The moment we label a person or a situation, we put on blinders to all contradictory evidence

Direction of biasThe precise magnitude of bias can never really be quantified, however, the direction of bias can often be determined.

• The target parameter can be overestimated (association measure (OR/RR) further away from 1)

• The target parameter can be underestimated (association measure towards 1).

Examples of selection bias (1)Healthy Worker Effect (HWE): The overall mortality experience of an employed population is typically better than that of the general population (in Western countries at least). Use of blood donors as controls is a kind of HWE. Blood donors are self-selected on the basis of better life styles.Non-respondent bias: Non-respondents to a survey often differ from respondents. This difference may be related to the subject of interestVolunteer bias:Those who do participate in a study might be different than those who do not participate. This difference is related to the subject of interestAttrition bias: Number of individuals lost to follow up may bedifferent between exposed/unexposed / intervention/ control

Non response bias in case controlstudy• Case control study to study the effect of passive

smoking on the risk of heart attack• Controls who were current smokers were less

likely toparticipated in the study

• Smoking exposure in control group is likely to be an underestimate of the true proportion of smokers in the population

• Odds of smoking in control group is lower than ‘true’ odds

• Overestimate of the strength of the association

Selection bias in case-control studiesThe major problem in case-control studies is choice of controls.

How representative are hospitalised trauma patients of the population which gave rise to the cases? Is OR under- or overestimated?

Cases liver cirrhosis

Controls A trauma ward

80 40

20 60

Heavy alcohol use

OR=6

Light/no alcohol use

Avoid selection bias – controls should arisefrom same population as casesCases

liver cirrhosisControls A

trauma wardControls B non-trauma

Heavy alcohol use

Light/no alcohol use

80 40 10

20 60 90

OR=6

OR=36

Hospital based case control studies more prone to selection bias than population based case-control studies

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Attrition bias in cohort studies

Systematic error that results from:- the way we lose subjects during a study (i.e., this is called loss to follow-up or LTF)

Consequence: there is a difference between subjects who are selected or participant, and those who are not at the end of the study for analysis

Problem: we may make an incorrect conclusion about the association between an exposure and an outcome may lead to a over/ underestimation of effect

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Attrition bias -during follow up-

Problem:Crossover or loss-to-follow up cause bias whenrelated to exposure

The exposure & non-exposure groups that were comparable at the start of the study are no longer comparable

Measurement/ information biasCan be caused by:

• The observer (interviewer bias)

• The measurement instrument (diagnostic bias)

•The way the participant remembers and reports events in that happened the past (recall bias)

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Observer bias -Interviewer biasInvestigator gathers or interprets information in a different way for

cases and controls or for exposed and uneposed

Cases of listeriosis Controls

Eats soft cheese a b

Does not eat soft cheese c d

Investigator may probe listeriosis cases about consumption of soft cheese

• Overestimation of “a” overestimation of OR

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Diagnostic biasWhen the exposure under study leads to intensified diagnostic procedures and increased chance of identifying the disease

Exposure and non-exposure groups not comparable anymore

Example: effect of benign breast disease on breast cancer. Women with benign disease undergo more extensive diagnostic procedure and are more likely to have cancer detected.

Bias : overestimation of effect

Can we trust what wethink we remember?

Recall bias

•The way the participant remembers events in

that happened the past

Recall bias: what and when do we remember?

Children with malformation

Controls

Took tobacco, alcohol, drugs

a b

Did not take c d

Occurs when: Cases remember exposure differently than controls

Mothers of

Mothers of children with malformations will remember past exposures better than mothers with healthy children. But maybe, they are ashamed and will underreport their tobacco, alcohol and drug use

Overestimation or underestimation of “a” overestimation/underestimation of OR

Recall bias can be minimized by:

• Interviewing cases and controls in a standardway

• Making use of independent sources for measuring exposure (or at least validate a sample)

Minimizing recall bias

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Measurement/ information biasCan lead to misclassification biasStudy subjects are classified in the wrong category (disease /not diseased; exposed / non-exposed)Non-differential misclassification• Misclassification is equally divided among comparison

groups• Generally dilutes the exposure effect (toward to null

effect)

Differential misclassification• It is worse when the proportions of subjects misclassified

differ between the study groups• Such a differential between groups may mask an association

or cause one when there is none.• Effect is unpredictable

Exercise: alcohol consumption and colon rectal cancerYou have conducted a case control study to examine the association between high alcohol consumption and colon rectal cancer.

In pairs:List and discuss types of biases that may occur when conducting a case control study to study this particular question.

Exercise: Potential biasProne to selection bias: Selection of controls can introducebiasexplain• If controls do not represent the population from which the

casescome from

Prone to information bias: Recall biasexplain• non-differential recall bias – difficult to remember for both

cases and controls how much alcohol one has consumed in past

• differential recall bias – cases may remember or report theiralcohol consumption more or less accurate than controls

Exercise: diabetes and strokeYou have conducted a cohort study looking at the association between adult onset diabetes and risk of death from stroke, during 10 years of follow-up.

In pairs:List and discuss types of biases that may occur when conducting a cohort study to study this particular question.

Titel

Exercise: potential biasesProne to: Information bias: outcome assessmentExplain• If the person determining stroke/no stroke had prior

knowledge of diabetic history they may be more or less likely to classify the outcome as stroke

Prone to: Selection bias: attrition bias

Explain• If in a prospective cohort study loss to follow up was

different between those with and without diabetes the incidence rates of death from stroke is difficult to interpret

Titel

Study type and BiasA study that suffers from bias lacks internal validity.In case-control studies, recall bias (knowledge of disease status influences the determination of exposure status) and selection bias (knowledge of exposure status influences the identification of diseased and non-diseased study subjects) are most important.

In cohort studies, bias due to loss to follow-up (attrition)would be the greatest danger.The potential for misclassification is present in all types of epidemiologic studies and in all stages of the study

Minimising selection biasIn case control studies:• Clear definition of study population• Explicit case and control definitions• Cases and controls from same population

In cohort studies:• Selection of exposed and non-exposed without knowing disease status (retrospective cohort)

• Tracking procedures for loss to follow-up

Minimising information biasInterviewer/observer bias• Blinding of observer/interview for exposure/disease• Development of a protocol for collection, measurement and

interpretation of information• Standardize questionnaires, calibrated diagnostic tools• Using trained and experienced interviewer

Attrition bias• Minimise lost to follow up

Recall bias• Blind participants to study hypothesis• Objectively collect exposure data (e.g. work / medical

records)

Titel

Bias in randomised controlled trialsGold-standard: randomised, placebo-controlled, double- blinded study

Least biased when:

• Exposure randomly allocated to subjects - minimises selection bias

• Masking of exposurestatus in subjects andstudy staff - minimises information bias

Selection bias

Performance bias

Attrition bias

Detection bias

Reference population

Informed consent sought

Assignment by randomization

Participants Non-participants

Follow for outcome

Outcomeknown

Outcome unknown

InterventionYES

InterventionNO

Follow for outcome

Outcome known

Outcome unknown

Study population

Remember the consequences of bias

We make the wrong conclusion about the relation between exposure and outcome in our study

• Conclude there is no association while in reality thereis one

• Conclude there is an association while in reality there is none

• Overestimate the strength of the association• Underestimate the strength of the association

How to deal with bias – think ahead1. Design stage - minimize or avoid bias.

• Avoid selection bias by including/excluding eligible subjects, by• Choice of source population• Choice of the comparison group

2. Analysis stage - determine presence or direction of possible bias and also account for confounding in analysis.

3. Publication stage - Potential biases typically described in "Discussion" section. Provide judgment and possible consequences of bias on results