bias sam bracebridge. by the end of the lecture fellows will be able to define bias identify...
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Bias
Sam Bracebridge
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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
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Epidemiologic Study
What do epidemiologists do?
• Measure effects
• Attempt to define a cause
- an estimate of the truth
• Implement public health measure
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Estimated effect: the truth?
Mayonnaise Salmonella
RR = 4.3
Bias? Chance?Confounding?
True association?
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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
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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
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Errors in epidemiological studies
Error
Study size
Source: Rothman, 2002
Systematic error (bias)
Random error (chance)
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Main sources of bias
1. Selection bias
2. Information bias
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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
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Types of selection bias
• Sampling bias
• Ascertainment bias - referral, admission- Diagnostic/surveillance
• Participation bias- self-selection (volunteerism)- non-response, refusal- survival
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Selection bias in case-control studies
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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
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Selection of controls
OR = 6 OR = 36
Higher proportion of controls drinking alcohol in trauma ward than non-trauma ward
a b
c d
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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
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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
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• 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
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Selection bias in cohort studies
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Healthy worker effect
Source: Rothman, 2002
Association between occupational exposure X and disease Y
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Healthy worker effect
Source: Rothman, 2002
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Prospective cohort study- Year 1
Smoker 90 910 1000
Non-smoker 10 990 1000
lung canceryes no
9 1000
10
1000
90 RR
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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
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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
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Sources of bias
1. Selection bias
2. Information bias
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Information bias
• During data collection
• Differences in measurement
- of exposure data between cases and controls
- of outcome data between exposed and unexposed
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Information bias
Arises if the information about or from study subjects is erroneous
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Information bias
• 3 main types:
- Recall bias
- Interviewer bias
- Misclassification
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• 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
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• 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
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Misclassification
Measurement error leads to assigning wrong exposure or outcome category
Exposure Outcome
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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
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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
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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
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Minimising information bias
• Standardise measurement instruments
- questionnaires + train staff
• Administer instruments equally to
- cases and controls
- exposed / unexposed
• Use multiple sources of information
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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
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Summary: Controls for Bias
• Direct measurement
- registries
- case records
• Optimise questionnaire
• Minimize loss to follow-up
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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
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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?
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Questions?
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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