bias update: s. bracebridge sources: t. grein, m. valenciano, a. bosman epiet introductory course,...

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Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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Page 1: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Bias

Update: S. BracebridgeSources: T. Grein, M. Valenciano, A. Bosman

EPIET Introductory Course, 2011Lazareto, Menorca, Spain

Page 2: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Objective of this session

• Define bias

• Present types of bias

• How bias influences estimates

• Identify methods to prevent bias

Page 3: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Epidemiologic Study

An attempt to obtain an epidemiologic measure

• An estimate of the truth

Page 4: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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

an incorrect estimate of association between exposure and risk of disease

Page 5: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Main sources of bias

1. Selection bias

2. Information bias

3. [Confounding]

Page 6: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Should I believe the estimated effect?

Mayonnaise Salmonella

RR = 4.3

Bias? Chance?Confounding?

True association?

Page 7: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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

Page 8: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

1. Selection bias

• Due to errors in study population selection

• Two main reasons:

- Selection of study subjects

- Factors affecting study participation

Page 9: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Selection bias

• At inclusion in the study

• Preferential selection of subjects

related to their

- Exposure status (case control)

- Disease status (cohort)

Page 10: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Types of selection bias

• Sampling bias

• Ascertainment bias - surveillance- referral, admission- diagnostic

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

Page 11: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Design Issues

Case-control studies

Page 12: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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

Page 13: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Selection of controls

OR = 6 OR = 36

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

a b

c d

Page 14: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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

Page 15: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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

Page 16: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

• 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

Page 17: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Selection Bias in Cohort Studies

Page 18: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Healthy worker effect

Source: Rothman, 2002

Association between occupational exposure X and disease Y

Page 19: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Healthy worker effect

Source: Rothman, 2002

Page 20: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Prospective cohort study- Year 1

Smoker 90 910 1000

Non-smoker 10 990 1000

lung canceryes no

9 1000

10

1000

90 RR

Page 21: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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

Page 22: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Minimising selection bias

• Clear definition of study population

• Explicit case, control and exposure

definitions

• Cases and controls from same population

- Selection independent of exposure

• Selection of exposed and non-exposed

without knowing disease status

Page 23: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Sources of bias

1. Selection bias

2. Information bias

Page 24: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Information bias

• During data collection

• Differences in measurement

- of exposure data between cases and controls

- of outcome data between exposed and unexposed

Page 25: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Information bias

• 3 main types:

- Reporting bias

• Recall bias

• Prevarication

- Observer bias

• Interviewer bias

- Misclassification

Page 26: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

• 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

Page 27: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Prevarication bias

• Relatives of dead elderly may deny isolation

• Underestimation “a” underestimation of OR

Exposure reported differently in cases than controlse.g. isolation and heat related death

Page 28: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

• 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

Page 29: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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.

=> Weakens 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

Page 30: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Nondifferential misclassification

250100150

1005050Nonexposed

15050100Exposed

TotalControlsCases

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

True Classification

250100150

804040Nonexposed

17060110Exposed

TotalControlsCases

OR = ad/bc = 1.8; RR = a/(a+b)/c/(c+d) = 1.3

Nondifferential misclassification - Overestimate exposure in 10 cases, 10 controls – bias towards null

Page 31: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Differential 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

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

Differential misclassification - Underestimate exposure for 10 controls

Page 32: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Differential 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

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

Differential misclassification - Underestimate exposure for 10 cases

Page 33: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Minimising information bias

• Standardise measurement instruments

- questionnaires + train staff

• Administer instruments equally to

- cases and controls

- exposed / unexposed

• Use multiple sources of information

Page 34: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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

Page 35: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Summary: Controls for Bias

• Direct measurement

- registries

- case records

• Optimise questionnaire

• Minimize loss to follow-up

Page 36: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Questionnaire

• Favour closed, precise questions

• Seek information on hypothesis through

different questions

• Field test and refine

• Standardise interviewers’ technique

through training with questionnaire

Page 37: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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

Page 38: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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?

Page 39: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

Objective of this session

• Define bias

• Present types of bias

• How bias influences estimates

• Identify methods to prevent bias

Page 40: Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

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