Bias Sam Bracebridge. By the end of the lecture fellows will be able to Define bias Identify different types of bias Explain how bias affects risk estimates.

Bias

Sam Bracebridge

By the end of the lecture fellows will be able to

• Define bias

• Identify different types of bias

• Explain how bias affects risk estimates

• Critique study designs for bias

• Develop strategies to minimise bias

Epidemiologic Study

What do epidemiologists do?

• Measure effects

• Attempt to define a cause

- an estimate of the truth

• Implement public health measure

Estimated effect: the truth?

Mayonnaise Salmonella

RR = 4.3

Bias? Chance?Confounding?

True association?

Warning!

• Chance and confounding can be

evaluated quantitatively

• Bias is much more difficult to evaluate

- Minimise by design and conduct of study

- Increased sample size will not eliminate

bias

Definition of bias

Any systematic error in the design or conduct of an epidemiological study resulting in a conclusion which is different from the truth

Errors in epidemiological studies

Error

Study size

Source: Rothman, 2002

Systematic error (bias)

Random error (chance)

Main sources of bias

1. Selection bias

2. Information bias

Selection bias

Two main reasons:

- Selection of study subjects

- Factors affecting study participation

association between exposure and disease differs between those who participate and those who don’t

Types of selection bias

• Sampling bias

• Ascertainment bias - referral, admission- Diagnostic/surveillance

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

Selection bias in case-control studies

Selection of controls

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

OR = 6

Estimate association of alcohol intake and cirrhosis

Selection of controls

OR = 6 OR = 36

Higher proportion of controls drinking alcohol in trauma ward than non-trauma ward

a b

c d

Some worked examples

• Work in pairs

• In 2 minutes:

- Identify the reason for bias

- How will it effect your study estimate?

- Discuss strategies to minimise the bias

Oral contraceptive and uterine cancer

• OC use breakthrough bleeding increased chance of testing & detecting uterine cancer

You are aware OC use can cause breakthrough bleeding

• Overestimation of “a” overestimation of OR• Diagnostic bias

a b

c d

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

Asbestos and lung cancer

• Overestimation of “a” overestimation of OR• Admission bias

a b

c d

Prof. “Pulmo”, head specialist respiratory referral unit, has 145 publications on asbestos/lung cancer

Selection bias in cohort studies

Healthy worker effect

Source: Rothman, 2002

Association between occupational exposure X and disease Y

Healthy worker effect

Source: Rothman, 2002

Prospective cohort study- Year 1

Smoker 90 910 1000

Non-smoker 10 990 1000

lung canceryes no

9 1000

10

1000

90 RR

Loss to follow up – Year 2

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

Minimising selection bias

• Clear definition of study population

• Explicit case, control and exposure definitions

• CC: Cases and controls from same population- Same possibility of exposure

• Cohort: selection of exposed and non-exposed

without knowing disease status

Sources of bias

1. Selection bias

2. Information bias

Information bias

• During data collection

• Differences in measurement

- of exposure data between cases and controls

- of outcome data between exposed and unexposed

Information bias

Arises if the information about or from study subjects is erroneous

Information bias

• 3 main types:

- Recall bias

- Interviewer bias

- Misclassification

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

Recall bias

Cases remember exposure differently than controls

e.g. risk of malformation

• Overestimation of “a” overestimation of OR

• Investigator may probe listeriosis cases about consumption of soft cheese (knows hypothesis)

Interviewer bias

Investigator asks cases and controls differently about exposure

e.g: soft cheese and listeriosis

Cases oflisteriosis Controls

Eats soft cheese a b

Does not eatsoft cheese c d

• Overestimation of “a” overestimation of OR

Misclassification

Measurement error leads to assigning wrong exposure or outcome category

Exposure Outcome

Misclassification

• Systematic error

• Missclassification of exposure DIFFERS between cases and controls

• Missclassification of outcome DIFFERS between exposed & nonexposed

=> Measure of association distorted in any direction

Misclassification

250100150

1005050Nonexposed

15050100Exposed

TotalControlsCases

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification

250100150

1106050Nonexposed

14040100Exposed

TotalControlsCases

Differential misclassification

OR = ad/bc = 3.0; RR = a/(a+b)/c/(c+d) = 1.6

Misclassification

250100150

1005050Nonexposed

15050100Exposed

TotalControlsCases

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification

250100150

1105060Nonexposed

1405090Exposed

TotalControlsCases

Differential misclassification

OR = ad/bc = 1.5; RR = a/(a+b)/c/(c+d) = 1.2

Minimising information bias

• Standardise measurement instruments

- questionnaires + train staff

• Administer instruments equally to

- cases and controls

- exposed / unexposed

• Use multiple sources of information

Summary: Controls for Bias

• Choose study design to minimize the chance for bias

• Clear case and exposure definitions

- Define clear categories within groups (eg age groups)

• Set up strict guidelines for data collection- Train interviewers

Summary: Controls for Bias

• Direct measurement

- registries

- case records

• Optimise questionnaire

• Minimize loss to follow-up

The epidemiologist’s role

1. Reduce error in your study design

2. Interpret studies with open eyes:

• Be aware of sources of study error

• Question whether they have been

addressed

Bias: the take home message

• Should be prevented !!!!

- At PROTOCOL stage

- Difficult to correct for bias at analysis stage

• If bias is present: Incorrect measure of true association

Should be taken into account in interpretation of results

•Magnitude = overestimation? underestimation?

Questions?

Rothman KJ; Epidemiology: an introduction.

Oxford University Press 2002, 94-101

Hennekens CH, Buring JE; Epidemiology in

Medicine. Lippincott-Raven Publishers 1987, 272-

285

References