Bias Sam Bracebridge. By the end of the lecture fellows will be able to Define bias Identify...
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Transcript of Bias Sam Bracebridge. By the end of the lecture fellows will be able to Define bias Identify...
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