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Bias Sam Bracebridge. By the end of the lecture fellows will be able to Define bias Identify different types of bias Explain how bias affects risk estimates.

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Presentation on theme: "Bias Sam Bracebridge. By the end of the lecture fellows will be able to Define bias Identify different types of bias Explain how bias affects risk estimates."— Presentation transcript:

1 Bias Sam Bracebridge

2 By the end of the lecture fellows 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 Epidemiologic Study What do epidemiologists do? Measure effects Attempt to define a cause -an estimate of the truth Implement public health measure

4 Estimated effect: the truth? MayonnaiseSalmonella RR = 4.3 Bias? Chance? Confounding? True association?

5 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

6 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

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

8 Main sources of bias 1.Selection bias 2.Information bias

9 Selection bias Two main reasons: -Selection of study subjects -Factors affecting study participation association between exposure and disease differs between those who participate and those who dont

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

11 Selection bias in 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 CC: Cases and controls from same population -Same possibility of exposure Cohort: 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 Arises if the information about or from study subjects is erroneous

26 Information bias 3 main types: -Recall bias -Interviewer bias -Misclassification

27 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

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 ExposureOutcome

30 Misclassification Systematic error Missclassification of exposure DIFFERS between cases and controls Missclassification of outcome DIFFERS between exposed & nonexposed => Measure of association distorted in any direction

31 Misclassification OR = ad/bc = 3.0; RR = a/(a+b)/c/(c+d) = 1.6

32 Misclassification OR = ad/bc = 1.5; RR = a/(a+b)/c/(c+d) = 1.2

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 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

37 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?

38 Questions?

39 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


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