1. Bias M.Valenciano, 2006 A. Bosman, 2005 T. Grein, 2001- 2004

2. Every epidemiological study should be viewed as a measurement exercise Kenneth J. Rothman, 2002 ….. in order to understand the truth

3. 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

4. Objective of this session Define bias Present type of bias and influence in estimates Identify methods to prevent bias

5. Should I believe my measurement? MayonnaiseSalmonella RR = 4.3 Chance? Confounding? Bias? True association causal non-causal

6. 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

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

8. Categories of bias Selection bias Information bias [Confounding]

9. 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

10. Selection bias When? How? Consequences? frequency of disease (cohort) frequency of exposure (case control) different among - those included in the study - those eligible

11. Types of selection bias Sampling bias Ascertainment bias -surveillance -referral, admission -diagnostic Participation bias -self-selection (volunteerism) -non-response, refusal -healthy worker effect, survival

12. Selection bias in case-control studies

13. Selection bias How representative are hospitalised trauma patients of the population which gave rise to the cases? OR = 6 e.g: alcohol and cirrhosis?

14. Selection bias OR = 6 OR = 36 Higher proportion of controls drinking alcohol in trauma ward than in non-trauma ab c d

15. 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 OR ab c d

16. Prof. Pulmo, head respiratory department, 145 publications on asbestos/lung cancer SB: Admission bias Exposed cases different chance of admission than controls e.g: asbestos and lung cancer? Lung cancer cases exposed to asbestos not representative of lung cancer cases Overestimation of a overestimation of OR ab c d

17. 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 cd a

18. 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 test a b c d

19. Selection bias in cohort studies

20. SB: Healthy worker effect Source: Rothman, 2002

21. Healthy worker effect Source: Rothman, 2002

22. Non-response bias Smoker 90 910 1000 Non-smoker 10 990 1000 lung cancer yes no

23. SB: Non-response bias Smoker 9 91 100 Non-smoker 10 990 1000 lung cancer yes no 10% of smokers dare to respond No bias !

24. Non-response bias Smoker 45 910 955 Non-smoker 10 990 1000 lung cancer yes no 50% of cases that smoked lost to follow up

25. 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)

26. 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

27. Categories of bias Selection bias Information bias

28. 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

29. Information bias When? How? Consequences? Misclassification: Study subjects are classified in the wrong category Cases / controls Exposed / unexposed

30. 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

31. Two main types of information bias Reporting bias -Recall bias -Prevarication Observer bias -Interviewer bias -Biased follow-up

32. Mothers of children with malformations remember past exposures better than mothers with healthy children IB: Recall bias Cases remember exposure differently t han controls e.g. risk of malformation Overestimation of a overestimation of OR ab c d

33. IB: Prevarication bias Relatives of dead elderly may deny isolation Underestimation a underestimation of OR b cd a Cases report exposure differently t han controls e.g. isolation and heat related death

34. 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 listeriosis Cases of listeriosis Controls Eats soft cheeseab Does not eat soft cheese cd ab c d Overestimation of a overestimation of OR

35. 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

36. 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

37. Nondifferential misclassification Cohort study: Alcohol laryngeal cancer

38. Nondifferential misclassification Cohort study: Alcohol laryngeal cancer

39. 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

40. 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

41. 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?

42. Rothman KJ; Epidemiology: an introduction. Oxford University Press 2002, 94-101 Smith (1984) References

43. 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

44. 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

45. 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)

46. 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?