bias m.valenciano, 2006 a. bosman, 2005 t. grein, 2001- 2004
TRANSCRIPT
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Bias
M.Valenciano, 2006
A. Bosman, 2005
T. Grein, 2001- 2004
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Every epidemiological study should be viewed as a measurement exercise
Kenneth J. Rothman, 2002
….. in order to understand the truth
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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
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Objective of this session
• Define bias
• Present type of bias and influence in estimates
• Identify methods to prevent bias
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Should I believe my measurement?
Mayonnaise Salmonella
RR = 4.3
Chance?Confounding?Bias?
True associationcausalnon-causal
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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
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Errors in epidemiological studies
Error
Study size
Source: Rothman, 2002
Systematic error (bias)
Random error (chance)
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Categories of bias
• Selection bias
• Information bias
• [Confounding]
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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
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Selection bias
• When?
• How?
• Consequences? frequency of disease (cohort)
frequency of exposure (case control)
different among
- those included in the study - those eligible
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Types of selection bias
• Sampling bias
• Ascertainment bias - surveillance- referral, admission- diagnostic
• Participation bias- self-selection (volunteerism)- non-response, refusal- healthy worker effect, survival
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Selection bias in case-control studies
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Selection bias
How representative are hospitalised trauma patients of the population which gave rise to the cases?
OR = 6
e.g: alcohol and cirrhosis?
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Selection bias
OR = 6 OR = 36
Higher proportion of controls drinking alcohol in trauma ward
than in non-trauma
a b
c d
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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
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• 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
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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
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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
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Selection bias in cohort studies
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SB: Healthy worker effect
Source: Rothman, 2002
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Healthy worker effect
Source: Rothman, 2002
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Non-response bias
Smoker 90 910 1000
Non-smoker 10 990 1000
lung canceryes no
9 1000
10
1000
90 RR
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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 !
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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
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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)
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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
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Categories of bias
• Selection bias
• Information bias
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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
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Information bias
• When?
• How?
• Consequences?
Misclassification:
Study subjects are classified in the wrong category
Cases / controls
Exposed / unexposed
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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
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Two main types of information bias
• Reporting bias- Recall bias- Prevarication
• Observer bias- Interviewer bias- Biased follow-up
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• 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
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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
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• 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
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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
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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”
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Nondifferential misclassification
• Cohort study: Alcohol laryngeal cancer
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Nondifferential misclassification
• Cohort study: Alcohol laryngeal cancer
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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
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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
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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?
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Rothman KJ; Epidemiology: an introduction. Oxford University Press 2002, 94-101Smith (1984)
References
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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
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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
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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)
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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?