Post on 27-Mar-2015
Bias
Update: S. BracebridgeSources: T. Grein, M. Valenciano, A. Bosman
EPIET Introductory Course, 2011Lazareto, Menorca, Spain
Objective of this session
• Define bias
• Present types of bias
• How bias influences estimates
• Identify methods to prevent bias
Epidemiologic Study
An attempt to obtain an epidemiologic measure
• An estimate of the truth
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
Main sources of bias
1. Selection bias
2. Information bias
3. [Confounding]
Should I believe the estimated effect?
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
1. Selection bias
• Due to errors in study population selection
• Two main reasons:
- Selection of study subjects
- Factors affecting study participation
Selection bias
• At inclusion in the study
• Preferential selection of subjects
related to their
- Exposure status (case control)
- Disease status (cohort)
Types of selection bias
• Sampling bias
• Ascertainment bias - surveillance- referral, admission- diagnostic
• Participation bias- self-selection (volunteerism)- non-response, refusal- survival
Design Issues
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
• Cases and controls from same population
- Selection independent of exposure
• 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
• 3 main types:
- Reporting bias
• Recall bias
• Prevarication
- Observer 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
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
• 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
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
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
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
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
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
Questionnaire
• Favour closed, precise questions
• Seek information on hypothesis through
different questions
• Field test and refine
• Standardise interviewers’ technique
through training with questionnaire
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?
Objective of this session
• Define bias
• Present types of bias
• How bias influences estimates
• Identify methods to prevent bias
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