1. Bias Update: S. Bracebridge Sources: T. Grein, M. Valenciano, A. Bosman EPIET Introductory Course, 2011 Lazareto, Menorca, Spain

2. Objective of this session Define bias Present types of bias How bias influences estimates Identify methods to prevent bias

3. Epidemiologic Study An attempt to obtain an epidemiologic measure An estimate of the truth

4. 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

5. Main sources of bias 1.Selection bias 2.Information bias 3.[Confounding]

6. Should I believe the estimated effect? MayonnaiseSalmonella RR = 4.3 Bias? Chance? Confounding? True association?

7. 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

8. 1. Selection bias Due to errors in study population selection Two main reasons: -Selection of study subjects -Factors affecting study participation

9. Selection bias At inclusion in the study Preferential selection of subjects related to their -Exposure status (case control) -Disease status (cohort)

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

11. Design Issues Case-control studies

12. 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

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

14. 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

15. 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 ab c d

16. Lung cancer cases exposed to asbestos not representative of lung cancer cases Asbestos and lung cancer Overestimation of a overestimation of OR Admission bias ab c d Prof. Pulmo, head specialist respiratory referral unit, has 145 publications on asbestos/lung cancer

17. Selection Bias in Cohort Studies

18. Healthy worker effect Source: Rothman, 2002 Association between occupational exposure X and disease Y

19. Healthy worker effect Source: Rothman, 2002

20. Prospective cohort study- Year 1 Smoker 90 910 1000 Non-smoker 10 990 1000 lung cancer yes no

21. Loss to follow up – Year 2 Smoker 45 910 955 Non-smoker 10 990 1000 lung cancer yes no 50% of cases that smoked lost to follow up

22. 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

23. Sources of bias 1.Selection bias 2.Information bias

24. Information bias During data collection Differences in measurement -of exposure data between cases and controls -of outcome data between exposed and unexposed

25. Information bias 3 main types: -Reporting bias Recall bias Prevarication -Observer bias Interviewer bias -Misclassification

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

27. Prevarication bias Relatives of dead elderly may deny isolation Underestimation a underestimation of OR Exposure reported differently in cases t han controls e.g. isolation and heat related death

28. 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 of listeriosis Controls Eats soft cheeseab Does not eat soft cheese cd Overestimation of a overestimation of OR

29. 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

30. Nondifferential misclassification

31. Differential misclassification

33. Minimising information bias Standardise measurement instruments -questionnaires + train staff Administer instruments equally to - cases and controls - exposed / unexposed Use multiple sources of information

34. 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

35. Summary: Controls for Bias Direct measurement -registries -case records Optimise questionnaire Minimize loss to follow-up

36. Questionnaire Favour closed, precise questions Seek information on hypothesis through different questions Field test and refine Standardise interviewers technique through training with questionnaire

37. The epidemiologists 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

38. 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?

39. Objective of this session Define bias Present types of bias How bias influences estimates Identify methods to prevent bias

40. 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