1. Bias Tunis, 30th October 2014 Dr Sybille Rehmet
Sources: T. Grein, M. Valenciano, A. Bosman
Dr Sybille Rehmet

2. By the end of the lecture, you 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

3. If an association is observed, the first question asked must always be ...
“Is it real?”

4. Should I believe the estimated effect?
Gastroenteritis Mayonnaise
RR = 4.3
True association?
causal?
non-causal?
Chance?
Confounding?
Bias?

5. 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

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

7. Main sources of bias Selection bias
Preferential selection or participation of subjects into the study according to exposure or outcome status
Information bias
Systematic error in the measurement of information on exposure or outcome

8. Selection bias
association between exposure and disease differs between those who participate and those who don’t
Selection of study subjects
Allocation to study groups
Factors affecting study participation

9. Selection of controls Association of alcohol intake and cirrhosis,
hospital-based case control study
OR = 6
How representative are hospitalised trauma patients of the population which gave rise to the cases?

10. Selection of controls
OR = OR = 36
Higher proportion of controls drinking alcohol in trauma ward than in non-trauma ward

11. e.g. association between haemorrhagic fever and stay in hospital
Survival bias
Only survivors of a highly lethal disease enter study
e.g. association between haemorrhagic fever and stay in hospital
This bias occurs when survivors of a highly lethal disease are more likely to enter a study than other cases.
Exposure to hospital leads to death
Underestimation of “a”  underestimation of OR

12. Diagnostic bias Oral contraceptives and uterine cancer
OC use  can cause breakthrough bleeding  increased chance of testing & detecting uterine cancer
Overestimation of “a”  overestimation of OR

13. Non-response bias Cohort study on lung cancer and smoking
Scenario 1: all exposed and unexposed participate in the study
Lung cancer
No lung cancer
Total
Rate/1000 RR
smoker 90
910
1000 9
non-smoker 10
990
reference

14. Non-response bias
Scenario 2: Non-response among exposed (only 10% respond)
Lung cancer
No lung cancer
Total
Rate/1000 RR
smoker 9 91
100
non-smoker 10
990
1000 1
reference

15. Non-response bias
Scenario 3: Non-response among exposed cases (only 10% respond)
Lung cancer
No lung cancer
Total
Rate/1000 RR
smoker 9
910
919 10 1
non-smoker
990
1000
reference

16. Loss to follow-up
Difference in completeness of follow-up between comparison groups, esp. important in prospective cohort studies
Study of risk of severe disease in migrants
Migrants might be more likely to return to their place of origin when having a severe disease
 lost to follow-up  lower disease rate among exposed (=migrant)

17. Not everything that looks like selection bias is really bias…
Look around in the room – just by eyeballing, is there anything that might be a bias??
We count 9 men and 33 women – gender-bias???
How do we know?
Is this distribution representative of the underlying population, or do we have selection bias??
What IS the underlying population?
General population? Medical doctors? Public health doctors? Epidemiologists?
…you have to thoroughly assess!

18. Information bias
Systematic error in the measurement or classification of participants in a study due to inaccurate information on exposure or outcome
Can be related to reporting or the observer

19. Information bias When? How? Consequences? During data collection
Differences in accuracy
of exposure data between cases and controls
of outcome data between exposed and unexposed
Consequences?
Missclassification – study subjects are classified in the wrong category

20. Information bias: misclassification
Measurement error leads to assigning wrong exposure or outcome category
Non-differential
Random error
Missclassifcation of exposure SIMILAR between cases and controls
Missclassification of outcome SIMILAR between exposed & nonexposed
=> Weakens measure of association
Differential
Systematic error
Missclassification of exposure DIFFERS between cases and controls
Missclassification of outcome DIFFERS between exposed & nonexposed
=> Measure of association distorted in any direction

21. Non-differential misclassification
Cohort study: Alcohol  laryngeal cancer
No misclassification
exposure
Total population
cases
Incidence/ RR
drinkers 50 5
Non-drinkers 10
50% of drinkers misclassified
exposure
Total population
cases
Incidence/ RR
drinkers 25 50
1.7
Non-drinkers 30

22. Cases remember exposure differently than controls
Recall bias
Cases remember exposure differently than controls
Overestimation of “a”  overestimation of OR
Mothers of children with fetal malformations remember past exposures better than mothers of healthy children

23. Cases report exposure differently than controls
Prevarication bias
Cases report exposure differently than controls
e.g. isolation and heat related death
Relatives of dead elderly may deny isolation
Underestimation “a”  underestimation of OR

24. Interviewer bias
Investigator asks cases and controls differently about exposure
e.g: soft cheese and listeriosis
Cases of
Controls
listeriosis
Overestimation of “a”  overestimation of OR
Eats soft cheese a b
Does not eat c d
soft cheese
Investigator may probe listeriosis cases about consumption of soft cheese

25. Example – your evaluation form
One group fills in the evaluation every day as requested
One group fills in the evaluation form as a whole on the weekend because they did not have time before
Will this influence the evaluation results for Monday?
And if yes, why?
What type of bias might occur?

26. How to prevent or minimize bias?

27. be aware of the potential of bias, Be creative in foreseeing potential biases, and be critical with your own study design!

28. Start at the beginning…
Consider carefully your choice of study design and your protocol to minimize the chance for bias
Clearly define study population, cases and exposures
Choose the right comparison group
Case Control studies: Cases and controls from same population to have the same possibility of exposure
Cohort studies: selection of exposed and non-exposed without knowing disease status
Randomization in intervention studies

29. …then keep going … Set up strict guidelines for data collection
Standardized procedures, questionnaires, training for interviewers etc
Administer instruments equally to cases and controls/ exposed and unexposed
Use multiple sources of information
Use direct measurements, registries, case records etc

30. …and if you could not completely avoid it … address it!!!!
Discuss your potential bias in your study protocol
Estimate the direction and magnitude of the distortion
Take it into account when you interpret your results
Discuss it in your report and publication

32. Thank you!
Sybille Rehmet

33. Some more examples
What potential bias could have been introduced if you found out that those who interviewed cases took 30 minutes longer on average than those who interviewed controls?
Selection bias
Information bias
Volunteer bias
Loss to follow-up

34. Healthy worker effect
Source: Rothman, 2002

35. Healthy worker effect
Source: Rothman, 2002

36. Differential misclassification
OR = ad/bc = 3.0

37. What effect (if any) would you expect if the interviewers were aware of the disease status of the study subjects?
It would benefit the validity of the results since the interviewers would understand more precisely how the exposure is related to the disease and collect better data for the cases.
The results would likely not change.
It could damage the validity of the results by introducing interviewer bias.

38. Volunteer or self-selection bias
You want to determine the prevalence of HIV infection. You ask for volunteers for testing. You find no HIV. Is it correct to conclude that there is no HIV in this location?
Volunteer or self-selection bias

39. Selection bias in case-control studies
Risk factors for menstrual toxic shock syndrome: results of a multistate case-control study. Reingold AL et al. Rev Infect Dis Jan-Feb;11 Suppl 1:S35-41,32
For assessment of current risk factors for developing toxic shock syndrome (TSS) during menstruation, a case-control study was performed.
Cases with onset between 1 January 1986 and 30 June 1987 were ascertained in six study areas with active surveillance for TSS.
Age-matched controls were selected from among each patient's friends and women with the same telephone area code.
Of 118 eligible patients, 108 were enrolled, as were 185 "friend controls" and 187 telephone area code-matched controls

40. Selection bias in case-control studies
Risk factors for menstrual toxic shock syndrome: results of a multistate case-control study
Results for tampon use as a risk factor:
OR when both control groups were combined = 29
OR when friend controls were used = 19
OR when neighborhood controls were used = 48
Why did use of friend controls produce a lower OR?
Friend controls were more likely to have used tampons than were neighborhood controls (71% vs. 60%)
Reingold AL et al. Rev Infect Dis Jan-Feb;11 Suppl 1:S35-41

41. 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

42. 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

43. 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)