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Bias

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

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

  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 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

  5. Main sources of bias • Selection bias • Information bias • [Confounding]

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

  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 Estimate association of alcohol intake and cirrhosis How representative are hospitalised trauma patients of the population which gave rise to the cases? OR = 6

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

  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. 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

  16. 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

  17. Selection Bias in Cohort Studies

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

  19. Healthy worker effect Source: Rothman, 2002

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

  21. 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

  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 • Selection bias • 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. Overestimation of “a”  overestimation of OR 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

  27. 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

  28. Overestimation of “a”  overestimation of OR 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

  29. 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

  30. Nondifferential misclassification

  31. Differential misclassification

  32. 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 epidemiologist’s role • Reduce error in your study design • 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. References 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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