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Bias. EPIET Introductory Course, 2011 Lazareto, Menorca, Spain. Update: S. Bracebridge Sources: T. Grein , M. Valenciano, A. Bosman. Objective of this session. Define bias Present types of bias How bias influences estimates Identify methods to prevent bias. Epidemiologic Study.

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Bias

EPIET Introductory Course, 2011Lazareto, Menorca, Spain

Update: S. Bracebridge

Sources: T. Grein, M. Valenciano, A. Bosman


Objective of this session
Objective of this session

  • Define bias

  • Present types of bias

  • How bias influences estimates

  • Identify methods to prevent bias


Epidemiologic study
Epidemiologic Study

An attempt to obtain an epidemiologic measure

  • An estimate of the truth


Definition of bias
Definition of bias

Anysystematic errorin the design or conduct of an epidemiological study resulting in a conclusion which is different from the truth

  • anincorrect estimateof association between exposure and risk of disease


Main sources of bias
Main sources of bias

  • Selection bias

  • Information bias

  • [Confounding]


Should i believe the estimated effect

True association?

Bias?

Chance?

Confounding?

Should I believe the estimated effect?

Mayonnaise Salmonella

RR = 4.3


Warning
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
1. Selection bias

  • Due to errors in study population selection

  • Two main reasons:

    • Selection of study subjects

    • Factors affecting study participation


Selection bias
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
Types of selection bias

  • Sampling bias

  • Ascertainment bias

    • surveillance

    • referral, admission

    • diagnostic

  • Participation bias

    • self-selection (volunteerism)

    • non-response, refusal

    • survival


Design issues

Design Issues

Case-control studies


Selection of controls
Selection of controls

Estimate association of alcohol intake and cirrhosis

How representative are hospitalised trauma patients of the population which gave rise to the cases?

OR = 6


Selection of controls1

a

b

d

c

Selection of controls

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

OR = 6 OR = 36


Some worked examples
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

a

b

d

c

  • Overestimation of “a”  overestimation of OR

  • Diagnostic bias

Oral contraceptive and uterine cancer

You are aware OC use can cause breakthrough bleeding

  • OC use  breakthrough bleeding  increased chance of testing & detecting uterine cancer


Asbestos and lung cancer

a

b

d

c

  • Overestimation of “a”  overestimation of OR

  • Admission bias

Asbestos and lung cancer

Prof. “Pulmo”, head specialist respiratory referral unit, has 145 publications on asbestos/lung cancer

  • Lung cancer cases exposed to asbestos not representative of lung cancer cases


Selection bias in cohort studies

Selection Bias in Cohort Studies


Healthy worker effect
Healthy worker effect

Association between occupational exposure X and disease Y

Source: Rothman, 2002


Healthy worker effect1
Healthy worker effect

Source: Rothman, 2002


Prospective cohort study year 1
Prospective cohort study- Year 1

lung cancer

yes no

Smoker 90 910 1000

Non-smoker 10 990 1000


Loss to follow up year 2
Loss to follow up – Year 2

lung cancer

yes no

Smoker 45 910 955

Non-smoker 10 990 1000

50% of cases that smokedlost to follow up


Minimising selection bias
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
Sources of bias

  • Selection bias

  • Information bias


I nformation 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
Information bias

  • 3 main types:

    • Reporting bias

      • Recall bias

      • Prevarication

    • Observer bias

      • Interviewer bias

    • Misclassification


Recall bias

Recall bias

Cases remember exposure differently than controls

e.g. risk of malformation

  • Mothers of children with malformations remember past exposures better than mothers with healthy children


Prevarication bias
Prevarication bias

Exposure reported differently in cases than controls

e.g. isolation and heat related death

  • Relatives of dead elderly may deny isolation

  • Underestimation “a”  underestimation of OR


Interviewer bias

Interviewer bias

Investigator asks cases and controls differently about exposure

e.g: soft cheese and listeriosis

  • Investigator may probe listeriosis cases about consumption of soft cheese (knows hypothesis)

Cases of

Controls

listeriosis

Eats soft cheese

a

b

Does not eat

c

d

soft cheese


Misclassification

Measurement error leads to assigning wrong exposure or outcome category

Misclassification

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





Minimising information bias
Minimising information bias outcome category

  • Standardise measurement instruments

    • questionnaires + train staff

  • Administer instruments equally to

    • cases and controls

    • exposed / unexposed

  • Use multiple sources of information


Summary controls for bias
Summary: Controls for Bias outcome category

  • 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 bias1
Summary: Controls for Bias outcome category

  • Direct measurement

    • registries

    • case records

  • Optimise questionnaire

  • Minimize loss to follow-up


Questionnaire
Questionnaire outcome category

  • 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
The epidemiologist’s role outcome category

  • Reduce error in your study design

  • Interpret studies with open eyes:

    • Be aware of sources of study error

    • Question whether they have been addressed


Bias the take home message
Bias: the take home message outcome category

  • 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 session1
Objective of this session outcome category

Define bias

Present types of bias

How bias influences estimates

Identify methods to prevent bias


References
References outcome category

Rothman KJ; Epidemiology: an introduction. Oxford University Press 2002, 94-101

Hennekens CH, Buring JE; Epidemiology in Medicine. Lippincott-Raven Publishers 1987, 272-285


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