Bias. Sam Bracebridge

Transcription

1 Bias Sam Bracebridge

2 Bias Errors in epidemiological measurements Based on the slides of Sam Bracebridge

3 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

4 Epidemiologic Study What do epidemiologists do? Measure effects Attempt to define a cause - an estimate of the truth Implement public health measure

5 Estimated effect: the truth? Mayonnaise RR = 4.3 Salmonella True association? Bias? Chance? Confounding?

6 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

7 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

8 Errors in epidemiological studies

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

10 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 don t

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

12 Selection bias in case-control studies

13 Selection of controls Estimate association of alcohol intake and cirrhosis Cases liver cirrhosis Controls A trauma ward Heavy alcohol use OR = 6 Light/no alcohol use How representative are hospitalised trauma patients of the population which gave rise to the cases?

14 Selection of controls Cases liver cirrhosis Controls A trauma ward Controls B non-trauma Heavy alcohol use Light/no alcohol use OR = 6 OR = 36 Higher proportion of controls drinking alcohol in trauma ward than non-trauma ward a b c d

15 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

16 Oral contraceptive and uterine cancer You are aware OC use can cause breakthrough bleeding Takes oral contraceptives Cases uterine cancer a Controls b Does not take oral contraceptives c d OC use breakthrough bleeding increased chance of testing & detecting uterine cancer a b c d Overestimation of a overestimation of OR Diagnostic bias

17 Asbestos and lung cancer Prof. Pulmo, head specialist respiratory referral unit, has 145 publications on asbestos/lung cancer C o n t a c t w i t h a s b e s t o s N o c o n t a c t w i t h a s b e s t o s C a s e s a d m i t t e d a n d d i a g n o s e d w i t h l u n g c a n c e r a c C o n t r o l s f r o m s u r g i c a l w a r d s b d a b c d Lung cancer cases exposed to asbestos not representative of lung cancer cases Overestimation of a overestimation of OR Admission bias

18 Selection bias in cohort studies

19 Healthy worker effect Association between occupational exposure X and disease Y Exposed workers General population Deaths 50 7,000 Person-time in years Mortality (cases/year) 1, , RR=0.7

20 Healthy worker effect General population Exposed workers Workers Nonworkers Total Deaths 50 4,500 2,500 7,000 Persontime Mortality (cases/yr) 1,000 90,000 10, ,

21 Prospective cohort study- Year 1 lung cancer yes no Smoker Non-smoker RR = =

22 Loss to follow up Year 2 lung cancer yes no Smoker Non-smoker RR = = % of cases that smoked lost to follow up

23 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

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

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

26 Information bias Arises if the information about or from study subjects is erroneous

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

28 Recall bias Cases remember exposure differently than controls e.g. risk of malformation Mothers of Took tobacco, alcohol, drugs Children w ith m alform ation a Controls b Did not take c d Mothers of children with malformations remember past exposures better than mothers with healthy children Overestimation of a overestimation of OR

29 Interviewer bias Investigator asks cases and controls differently about exposure e.g: soft cheese and listeriosis Cases of listeriosis Controls Eats soft cheese a b Does not eat soft cheese c d Investigator may probe listeriosis cases about consumption of soft cheese (knows hypothesis) Overestimation of a overestimation of OR

30 Misclassification Measurement error leads to assigning wrong exposure or outcome category Exposure Outcome

31 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

32 True Classification Exposed Nonexposed Misclassification Cases Controls Total OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3 Differential misclassification Cases Controls Total Exposed Nonexposed OR = ad/bc = 3.0; RR = a/(a+b)/c/(c+d) = 1.6

33 Misclassification True Classification Cases Controls Exposed Nonexposed OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3 Total Differential misclassification Cases Controls Total Exposed Nonexposed OR = ad/bc = 1.5; RR = a/(a+b)/c/(c+d) = 1.2

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

35 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

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

37 The epidemiologist s 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?