1. Bias
Thanks to T. Grein

2. The truth is out there …..
Agent Mulder

3. Errors in epidemiological studies
Two broad types of error
Random error
Systematic error (or: Bias)
Definition of Bias:
Any systematic error in an epidemiological study that results in an incorrect estimate of the association between exposure and risk of disease

4. Errors in epidemiological studies
Random error (chance)
Systematic error (bias)
Study size
Source: Rothman, 2002

5. Should I believe my measurement?
Grocery store A Legionella
OR = 11,6
True association
causal
non-causal
Chance?
Confounding?
Bias?

6. Categories of bias
Selection bias
Information bias
Confounding

7. Selection bias
Errors in the process of identifying the study population
Non-random selection of subjects related to their
case/control status
exposure status

8. Selection bias in case-control studies

9. Selection bias
OR=6
How representative are hospitalised trauma patients of the population which gave rise to the cases?

10. Selection bias
OR= OR=36
Hospital admissions with severe injuries are more likely to be heavy drinkers than the source population

11. Selection bias Case detection influenced by exposure status
Overestimation of “a”  overestimation of OR
OC use  breakthrough bleeding  increased screening for uterine cancer

12. Selection bias
Exposed cases have a different chance of admission than controls
Overestimation of “a”  overestimation of OR
Professor “Pulmo”, head of respiratory department, world authority on asbestos exposure

13. Selection bias Only survivors of a highly fatal disease enter study
Underestimation of “a”  underestimation of OR
Age is risk factor for death

14. Selection bias (non- response)
Cases of myocardial infarction who are smokers are less likely to take part in study
Underestimation of “d”  underestimation of OR
NB no bias if % non-response same in smokers and non smokers

15. Selection bias in cohort studies

16. Healthy worker effect
Source: Rothman, 2002

17. Healthy worker effect
Source: Rothman, 2002

18. Non-response bias lung cancer yes no Smoker 90 910 1000
Non-smoker

19. 10% of smokers dare to respond
Non-response bias
lung cancer
yes no
10% of smokers dare to respond
Smoker
Non-smoker
No bias !

20. 50% of cases that smoked lost to follow up
Non-response bias
lung cancer
yes no
Smoker
Non-smoker
50% of cases that smoked lost to follow up

21. Loss to follow-up
Bias due to differences 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)

22. Categories of bias
Selection bias
Information bias

23. Information bias
Systematic error in the measurement of information on exposure or outcome
Differences in accuracy
of exposure data between cases and controls
of outcome data between different exposure groups
 Study subjects are classified in the wrong category

24. Misclassification
Measurement error leads to assigning wrong exposure or outcome category
Non-differential
Unrelated to exposure and outcome status
Weakens measure of association
Differential
Related to exposure and outcome status
Measure of association distorted in any direction

25. Differential bias Reporting bias Observer bias Recall bias
Interviewer bias

26. Recall bias (differential)
Cases remember exposure differently than controls
Overestimation of “a”  overestimation of OR
Mothers of children with malformations will remember past exposures better than mothers with healthy children

27. Interviewer bias (differential)
Investigator asks cases and controls differently about exposure
Overestimation of “a”  overestimation of OR
Investigator may probe listeriosis cases about consumption of soft cheese

28. Non-differential misclassification
Misclassification does not depend on values of other variables
Exposure classification unrelated to disease status, or
Disease classification unrelated to exposure status
Consequence
Weakening of measure of association (“bias towards the null”)

29. Non-differential misclassification
Cohort study: Alcohol  laryngeal cancer

30. Non-differential misclassification
Cohort study: Alcohol  laryngeal cancer

32. 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

33. 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

34. 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)

35. Minimising selection bias
Precise case and exposure definitions
Clear definition of study population
Controls representative of source population
Classification of exposed and non-exposed without knowing disease status (retrospective cohort)
Aim for high response and follow up
(check on non-responders, loss to follow up)

36. Minimising information bias
Standardise measurement instruments
(closed, precise, clear questions, field tested)
Standardise interviews (training)
Administer instruments equally to cases and controls (exposed/unexposed)
Use multiple controls

38. 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?