Bias

M.Valenciano, 2006

A. Bosman, 2005

T. Grein, 2001- 2004

Every epidemiological study should be viewed as a measurement exercise

Kenneth J. Rothman, 2002

….. in order to understand the truth

What epidemiologists measure

• Rates, risks

• Effect measures- Rate Ratio- Odds ratio

....... yet these are just estimates of the ´true´ value

- the amount of error cannot be determined

Objective of this session

• Define bias

• Present type of bias and influence in estimates

• Identify methods to prevent bias

Should I believe my measurement?

Mayonnaise Salmonella

RR = 4.3

Chance?Confounding?Bias?

True associationcausalnon-causal

Errors

• Two broad types of error- Random error: reflects amount of variability

• Chance?

- Systematic error (Bias)

Definition of bias:

Any systematic error in an epidemiological study

resulting in an incorrect estimate

of association between exposure and risk of disease

Errors in epidemiological studies

Error

Study size

Source: Rothman, 2002

Systematic error (bias)

Random error (chance)

Categories of bias

• Selection bias

• Information bias

• [Confounding]

Selection bias

Errors in the process of identifying the study population

• When ? - Inclusion in the study

• How ? - Preferential selection of subjects

related to their

Disease status cohort

Exposure status case control

Selection bias

• When?

• How?

• Consequences? frequency of disease (cohort)

frequency of exposure (case control)

different among

- those included in the study - those eligible

Types of selection bias

• Sampling bias

• Ascertainment bias - surveillance- referral, admission- diagnostic

• Participation bias- self-selection (volunteerism)- non-response, refusal- healthy worker effect, survival

Selection bias in case-control studies

Selection bias

How representative are hospitalised trauma patients of the population which gave rise to the cases?

OR = 6

e.g: alcohol and cirrhosis?

Selection bias

OR = 6 OR = 36

Higher proportion of controls drinking alcohol in trauma ward

than in non-trauma

a b

c d

SB: Diagnostic bias

• OC use breakthrough bleeding increased chance of detecting uterine cancer

Diagnostic approach related to knowing exposure status

e.g: OC and uterine cancer?

• Overestimation of “a” overestimation of ORa b

c d

• Prof. “Pulmo”, head respiratory department, 145 publications on asbestos/lung cancer

SB: Admission biasExposed cases different chance of admission

than controlse.g: asbestos and lung cancer?

• Lung cancer cases exposed to asbestos not representative of lung cancer cases

• Overestimation of “a” overestimation of OR

a b

c d

SB: Survival bias

• Contact with risk “hospital” leads to rapid death

Only survivors of a highly lethal disease enter study

e.g. Hospital and haemorrhagic fever?

• Underestimation of “a” underestimation of OR

b

c d

a

SB: Non-response bias

• Controls chosen among women at home: 13000 homes contacted 1060 controls

• Underestimation of “d” underestimation of OR

• Controls mainly housewives with lower chance of testa bc d

Selection bias in cohort studies

SB: Healthy worker effect

Source: Rothman, 2002

Healthy worker effect

Source: Rothman, 2002

Non-response bias

Smoker 90 910 1000

Non-smoker 10 990 1000

lung canceryes no

9 1000

10

1000

90 RR

SB: Non-response bias

Smoker 9 91 100

Non-smoker 10 990 1000

lung canceryes no

9 1000

10

100

9 RR

10% of smokers dare to respond

No bias !

Non-response bias

Smoker 45 910 955

Non-smoker 10 990 1000

lung canceryes no

4.7 1000

10

955

45 RR

50% of cases that smokedlost to follow up

SB: Loss to follow-up

• Difference in completeness of follow-up between comparison groups

• Example- study of disease risk in migrants- migrants more likely to return to place of origin

when having disease

lost to follow-up lower disease rate among exposed (=migrant)

Minimising selection bias

• Clear definition of study population

• Explicit case and control definitions

• Cases and controls from same population- Selection independent of exposure

• Selection of exposed and non-exposed without knowing disease status

Categories of bias

• Selection bias

• Information bias

Information bias

Systematic error in the measurement of information on exposure or outcome

• When?

During data collection

• How?

Differences in accuracy- of exposure data between cases and controls- of outcome data between exposed and unexposed

Information bias

• When?

• How?

• Consequences?

Misclassification:

Study subjects are classified in the wrong category

Cases / controls

Exposed / unexposed

Information bias: misclassification

Measurement error leads to assigning wrong exposure or outcome category

Non-differential

• Random error

• Missclassifcation exposure EQUAL

between cases and controls

• Missclassification outcome EQUAL

between exposed & nonexp.

=> Weakness measure of association

Differential

• Systematic error

• Missclassification exposure DIFFERS

between cases and controls

• Missclassification outcome DIFFERS

between exposed & nonexposed

=> Measure association distorted in any direction

Two main types of information bias

• Reporting bias- Recall bias- Prevarication

• Observer bias- Interviewer bias- Biased follow-up

• Mothers of children with malformations remember past exposures better than mothers with healthy children

IB: Recall bias

Cases remember exposure differently than controls

e.g. risk of malformation

• Overestimation of “a” overestimation of OR

a bc d

IB: Prevarication bias

• Relatives of dead elderly may deny isolation

• Underestimation “a” underestimation of OR

b

c d

a

Cases report exposure differently than controlse.g. isolation and heat related death

• Investigator may probe listeriosis cases about consumption of soft cheese

IB: Interviewer bias

Investigator asks cases and controls differently about exposure

e.g: soft cheese and listeriosisCases oflisteriosis Controls

Eats soft cheese a b

Does not eatsoft cheese c d

a b

c d • Overestimation of “a” overestimation of OR

IB: Biased follow-up

Unexposed less likely diagnosed for disease than exposed

• Cohort study risk factors for mesothelioma

• Difficult histological diagnosis

=> Histologist more likely

to diagnose specimen as mesothelioma

if asbestos exposure kown

Nondifferential misclassification

• Misclassification does not depend on values of other variables

- Exposure classification NOT related to disease status- Disease classification NOT related to exposure status

• Consequence- if there is an association,

weakening of measure of association“bias towards the null”

Nondifferential misclassification

• Cohort study: Alcohol laryngeal cancer

Nondifferential misclassification

• Cohort study: Alcohol laryngeal cancer

Minimising information bias

• Standardise measurement instruments

• Administer instruments equally to- cases and controls - exposed / unexposed

• Use multiple sources of information- questionnaires- direct measurements- registries- case records

• Use multiple controls

Questionnaire (tomorrow)

• Favour closed, precise questions; minimise open-ended questions

• Seek information on hypothesis through different questions

• Disguise questions on hypothesis in range of unrelated questions

• Field test and refine

• Standardise interviewers’ technique through training with questionnaire

Bias

• Should be prevented !!!! - protocol

• If bias- incorrect measure of association

- should be taken into account in the interpretation of the results

• magnitude?• overestimation? underestimation?

Rothman KJ; Epidemiology: an introduction. Oxford University Press 2002, 94-101Smith (1984)

References

Bias in randomised controlled trials

• Gold-standard: randomised, placebo-controlled, double-blinded study

• Least biased- Exposure randomly allocated to subjects -

minimises selection bias- Masking of exposure status in subjects and

study staff - minimises information bias

Bias in prospective cohort studies

• Loss to follow up - The major source of bias in cohort studies- Assume that all do / do not develop outcome?

• Ascertainment and interviewer bias- Some concern: Knowing exposure may influence how

outcome determined

• Non-response, refusals- Little concern: Bias arises only if related to both

exposure and outcome

• Recall bias- No problem: Exposure determined at time of enrolment

Bias in retrospective cohort & case-control studies

• Ascertainment bias, participation bias, interviewer bias- Exposure and disease have already occurred

differential selection / interviewing of compared groups possible

• Recall bias- Cases (or ill) may remember exposures

differently than controls (or healthy)

Question to you:

Suppose a computer error in data entry:- Exposed group classified as unexposed- Unexposed group classified as exposed

• What effect has this error on the result?- Is it bias?

• If so: what type• If not, what type of error?