Bias

1. There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. There are things we don't know we don't know. Donald Rumsfeld

2. Bias

3. Precise, but biased Also biased Bias A systematic error (caused by the investigator or thesubjects)that causes an incorrect (over- or under-) estimate of an association. True Effect Relative Risk 0 1.0 10 Protective effect No Difference Increased risk

4. Suppose a study was conducted multiple times in an identical way. True value Null Random Error And Bias Precise & Accurate Random Error Biased

5. Errors Affecting Validity Chance (Random Error; Sampling Error) Bias (Systematic Errors [inaccuracies]) • Selection bias • Loss to follow-up bias • Information bias • Nondifferential (e.g. simple misclassification) • Differential Biases (e.g., recall bias, interviewer bias) Confounding (Imbalance in Other Factors) Consider:

6. 0.3 1.0 2 3 Selection Bias Occurs when selection, enrollment, or continued participation in a study is somehow dependent on the likelihood of having the exposure of interest or the outcome of interest. Selection bias can cause an overestimate or underestimate of the association.

7. Selection bias can occur in several ways: • Selection of a comparison group ("controls") that is not representative of the population that produced the cases in a case-control study. (Control selection bias) • Differential loss to follow up in a cohort study, such that the likelihood of being lost to follow up is related to outcome status and exposure status. (Loss to follow-up bias) • Refusal, non-response, or agreement to participate that is related to the exposure and disease (Self-selection bias) • Using the general population as a comparison group for an occupational cohort study ("Healthy worker" effect) • Differential referral or diagnosis of subjects

8. Selection Bias in a Case-Control Study Selection bias can occur in a case-control study if controls are more (or less) likely to be selected if they have the exposure. Do women of low SES have higher risk of cervical cancer? MGH 100 Hospital Cases 200 Controls: Door-to-door survey of neighborhood around the hospital during work day.

9. 200 Controls: Door-to-door survey of neighborhood around the hospital during work day. • Problems: • SE status of people living around the hospital may generally be different from that of the population that produced the cases. • The door-to-door method of selecting controls may tend to select people of lower (or higher) SE status.

10. Selection bias can occur in a case-control study if controls are more (or less) likely to be selected if they have the exposure. Selection bias is not caused by differences in other potential risk factors (confounding). It is caused by selecting controls who are more (or less) likely to have the exposure of interest.

11. Selection Bias in a Case-Control Study Dis. Dis. Y N Y N Y N Y N Exp. Exp. Control Selection Bias True OR=3.0 OR=2.0

12. Control Selection BiasThe “Would” Criterion • Are the controls a representative sample of the population that produced the cases? • If a control had developed cervical cancer, would she have been included in the case group? (“Would” criterion) You should try to fulfill the “would” criterion: if a control patient had had the disease being studied, is it likely that they would have ended up in the case group? If the answer is “not necessarily,” then there is likely to be a problem with selection bias.

13. 2,000,000 women > age 20 in MA, & about 200 cases of cervical cancer per year. Cases are referred to MGH from all over, so their SES distribution is same as the state’s, i.e. 3 to 1. But, controls selected from area around MGH may have lower SES than MA. 120 80 If low SES were associated with cervical cancer with OR=3.0, MA would look like this. OR = (75/25) = 2.0 (120/80) (Biased)

14. Are mothers of children with hemifacial microsomia more often diabetic? Cases are referred, but what if controls are selected from the general pediatrics ward at MGH? Referred Cases Referral mechanism of controls might be very different from that of the cases with microsomia. Could mothers of controls be more or less likely to be diabetic than the cases (regardless of any association between diabetes and microsomia)? How would you select controls for this study?

15. Self- Selection Bias in a Case-Control Study Selection bias can be introduced into case-control studies with low response or participation rates if the likelihood of responding or participating is related to both the exposure and outcome. Example: A case-control study explored an association between family history of heart disease (exposure) and the presence of heart disease in subjects. Volunteers are recruited from an HMO. Subjects with heart disease may be more likely to participate if they have a family history of disease.

16. Self-Selection Bias in a Case-Control Study Dis. Dis. Y N Y N Y N Y N Exp. Exp. Self-Selection Bias True OR=2.25 OR=3.0 Best solution is to work toward high participation (>80%) in all groups.

17. Selection Bias in a Retrospective Cohort Study In a retrospective cohort study selection bias occurs if selection of exposed & non-exposed subjects is somehow related to the outcome. What will be the result if the investigators are more likely to select an exposed person if they have the outcome of interest?

18. Selection Bias in a Retrospective Cohort Study Example: Investigating occupational exposure (an organic solvent) occurring 15-20 yrs. ago in a factory. Exposed & unexposed subjects are enrolled based on employment records, but some records were lost. Suppose there was a greater likelihood of retaining records of those who were exposed & got disease.

19. Selection Bias in a Retrospective Cohort Study Differential “referral” or diagnosis of subjects Dis. Dis. Y N Y N Y N Y N Exp. Exp. 20% of employee health records were lost or discarded, except in “solvent” workers who reported illness (1% loss). True RR=2.0 RR=2.42 Workers in the exposed group were more likely to be included if they had the outcome of interest.

20. Rubber Workers General Population The “Healthy Worker” Effect Can be considered a form of selection bias because the general population controls have a higher probability of getting the outcome (death). The general population is often used in occupational studies of mortality, since data is readily available, and they are mostly unexposed. vs. Mortality Rates? The main disadvantage is bias by the “healthy worker effect.” The employed work force (mostly healthy) generally has lower rates of mortality and disease than the general population (with healthy & ill people).

21. 0.3 1.0 2 3 Differential Retention (Loss to Follow Up) in Prospective Cohort Studies Enrollment into a prospective cohort study will not be biased by the outcome, because the outcome has not occurred at enrollment. However, prospective cohort studies can have selection bias if the exposure groups have differential retention of subjects with the outcomes of interest. This can cause either an over- or under- estimate of association

22. Selection Bias in a Prospective Cohort Study More ‘events’ lost in one exposure group Dis. Dis. Y N Y N Y N Y N Exp. Exp. Loss to Follow Up Bias True OR=2.0 RR=1.0

23. Differential loss to follow up in a prospective cohort study on oral contraceptives (OC) & thromboembolism (TE). If OC were associated with TE with RR=2.0 (TRUTH), the 2x2 for all of MA would look like this. If OC users with TE are more likely to be lost than non-OC-users with TE… There is 40% loss to follow up overall, but a greater tendency to loose OC users with TE results in a de facto selection. (Biased) RR = (8/5988) = 1.0 (8/5998)

24. Observation Bias (Information Bias) Systematic errors due to incorrect categorization. The Correct Classification

25. Errors Errors Errors Errors Misclassification Bias Subjects are misclassified with respect to their risk factor status or their outcome, i.e., errors in classification. Differential Misclassification (non-random):If information is better in one group than another, the association maybe over- or underestimated. Non-differential Misclassification (random):If errors are about the same in both groups, it tends to minimize any true difference between the groups (bias toward the null). =

26. Errors Errors Non-Differential Misclassification When errors in exposure or outcome status occur with approximately equal frequency in groups being compared. • Difficulty remembering exposures (equal in both groups) • Example: Case-control study of heart disease and past activity: difficulty remembering your specific exercise frequency, duration, intensity over many years • Recording and coding errors in records and databases. • Example: ICD-9 codes in hospital discharge summaries. • Using surrogate measures of exposure: • Example: Using prescriptions for anti-hypertensive medications as an indication of treatment • Non-specific or broad definitions of exposure or outcome. • Example:“Do you smoke?” to define exposure to tobacco smoke. =

27. Non-Differential Misclassification Random errors in classification of risk factors or outcome (i.e., error rate about the same in all groups). • Example: • When patients are discharged, the MD dictates a summary which is transcribed. Diagnoses and procedures noted on the summary are encoded (ICD-9 codes) and sent to the MA Health Data Consortium. • MDs don’t list all relevant diagnoses. • Coders assign incorrect codes (they aren’t MDs). • Errors occur in 25-30% of records.

28. Non-Differential Misclassification Random errors in classification of risk factors or outcome (i.e., error rate about the same in all groups). Effect: Tends to minimize differences, generally causing an underestimate of effect. Example: A case-control study comparing CAD cases & controls for history of diabetes. Only half of the diabetics are correctly recorded as such in cases and controls. With Nondifferential Misclassification True Relationship CAD Controls Diabetes 40 10 No diabetes 60 90 OR= 40x90 = 6.0 10x60 CAD Controls Diabetes 20 5 No diabetes 80 95 OR= 20x95 = 4.75 5x80

29. “Null” means no difference 0.3 0.5 1.0 2 3 Relative Risk Non-Differential Misclassification When there are random errors in classification of risk or outcome, i.e. errors occur with equal frequency in both groups. Effect: With a dichotomous exposure, it minimizes differences & causes an underestimate of effect, i.e. “bias toward the null.”

30. Nondifferential Misclassification of Exposure #1

31. Nondifferential Misclassification of Exposure #2

32. Validation to Identify Random Misclassification in a Prospective Cohort Study Obesity & heart disease in women (questionnaires): • Guessing at weight? “Self-reported weights were validated in a subsample of 184 NHS participants living in the Boston, MA area and were highly correlated with actual measured weights (r = 0.96).” Cho E, Manson JE, et al.: A Prospective Study of Obesity and Risk of Coronary Heart Disease Among Diabetic Women. Diabetes Care 25:1142–1148, 2002.

33. Errors Errors Differential Misclassification When there are more frequent errors in exposure or outcome classification in one of the groups. • Differences in accurately remembering exposures (unequal) • Example: Mothers of children with birth defects will remember the drugs they took during pregnancy better than mothers of normal children (maternal recall bias). • Interviewer or recorder bias. • Example: Interview has subconscious belief about the hypothesis. • More accurate information in one of the groups. • Example: Case-control study with cases from one facility and controls from another with differences in record keeping.

34. (Differential) Recall Bias (If the groups have the same % of errors based on faulty memory, that’s non-differential misclassification.) People with disease may remember exposures differently (more or less accurately) than those without disease. • To Minimize: • Use a control group that has a different disease (unrelated to the disease under study). • Use questionnaires that are constructed to maximize accuracy and completeness. Ask specific questions. More accuracy means fewer differences. • For socially sensitive questions, such as alcohol and drug use or sexual behaviors, use a self-administered questionnaire instead of an interviewer. • If possible, assess past exposures from biomarkers or from pre-existing records.

35. (Differential) Interviewer Bias(& Recorder Bias in Chart Reviews) Minimized by: • Blinding the interviewers if possible. • Using standardized questionnaires consisting of closed-end, easy to understand questions with appropriate response options. • Training all interviewers to adhere to the question and answer format strictly, with the same degree of questioning for both cases and controls. • Obtaining data or verifying data by examining pre-existing records (e.g., medical records or employment records) or assessing biomarkers. Systematic difference in soliciting, recording, or interpreting information.

36. Errors Errors Errors Errors 0.3 1.0 2 3 Effects of Bias Non-Differential Misclassification 0.3 1.0 2 3 Bias to Null Selection bias Interviewer bias Recall Bias Differential Misclassification These are differential and can bias toward or away from null.

37. Misclassification of Outcome Can Also Introduce Bias … but it usually has much less of an impact than misclassification of exposure, because: Most of the problems with misclassification occur with respect to exposure status, not outcome. There are a number of mechanisms by which misclassification of exposure can be introduced, but most outcomes are more definitive and there are few mechanisms that introduce errors in outcome. Most outcomes are relatively uncommon. Misclassification of outcome will generally bias toward the null, so if an association is demonstrated, if anything the true effect might be slightly greater.

38. A study is conducted to see if serum cholesterol screening reduces the rate of heart attacks. 1,500 members of an HMO are offered the opportunity to participate in the screening program, & 600 volunteer to be screened. Their rates of MI are compared to those of randomly selected members who were not invited to be screened. After 3 years of follow-up rates of MI are found to be significantly less in the screened group. Any concerns? • No • Differential misclassification • Interviewer bias • Recall bias • Selection bias

39. Abdominal Aortic Aneurysm (AAA) Background Information on Abdominal Aortic Aneurysms

40. Diagnosis of AAA • Usually asymptomatic (surgery if > 5 cm.) • Discovered during routine abdominal exam by palpation, or • Seen on x-ray or ultrasound of abdomen (done for other reasons). • Known risk factors: • Age • Male gender • Smoking • Hypertension

41. AAA Other Black 60 1,242 1,302 White 260 620 880 Costa & Robbs: Br. J. Surg. 1986Abdominal Aneurysms…. A vascular surgery (referral) service in So. Africa reviewed records of elective peripheral vascular surgery. ‘Other’: a variety of readily apparent conditions. 320 1,862 Conclusion: AAA uncommon in Blacks and more often due to infections. OR = 0.12 (0.09 – 0.15)

42. AAA Other Black 60 1,242 1,302 White 260 620 880 Was there selection bias? ‘Other’: variety of readily apparent conditions. • Yes • No

43. AAA Other Black 60 1,242 1,302 White 260 620 880 South Africa, 1986 If a black had had a AAA, would he/she have been as likely to have been identified as a case? Is a subject’s likelihood of being included as a case somehow related to the exposure of interest? Was there selection bias? ‘Other’: variety of readily apparent conditions.

44. “All black patients were screened for TB … and for syphilis.” A possibility of misclassification? BlacksWhites Atherosclerotic 34% 99% Inflammatory or Infectious 47% 0.5% Uncertain etiology 19% 0. 0% • No • Yes, random. • Yes, differential. “AAA in blacks are more often due to infectious causes.”

45. More Details About the Study • (Known risk factors…) • Age • Male gender • Smoking • Hypertension

46. Environmental tobacco smoke and tobacco related mortality in a prospective study of Californians, 1960-98. James E. Enstrom, Geoffrey C. Kabat. BMJ 2003;326:1057 118,094 adults enrolled in an ACS cancer study in 1959 were followed until 1998. For “never smokers married to ever smokers” compared with “never smokers married to never smokers”: RR in MalesRR in Females Heart disease 0.94 (0.85 - 1.05) 1.01 (0.94 - 1.08) Lung cancer 0.75 (0.42 - 1.35) 0.99 (0.72 - 1.37) Chr. Pulm. Dis. 1.27 (0.78 - 2.08) 1.13 (0.80 - 1.58) Conclusions: The results do not support a causal relation between environmental tobacco smoke and tobacco related mortality, although they do not rule out a small effect.

47. Environmental tobacco smoke and tobacco related mortality in a prospective study of Californians, 1960-98. James E. Enstrom, Geoffrey C. Kabat. BMJ 2003;326:1057 “The independent variable … was exposure to environmental tobacco smoke based on smoking status of the spouse in 1959, 1965, and 1972.” “Never smokers married to a current smoker were subdivided into categories according to the smoking status of their spouse: 1-9, 10-19, 20, 21-39, 40 cigarettes consumed per day for men and women, with the addition of pipe or cigar usage for women. Former smokers were considered as an additional category.”

48. Any potential selection bias in the ETS study? • I don’t think so. • Yes, there was a potential for it.

49. Any potential information bias in the ETS study? • I don’t think so. • Non-differential misclassification. • Differential misclassification. • Interviewer bias. • Recall bias.

50. Are Analgesic Drugs Associated with Increased Risk of Renal Failure? Case-Control study in Maryland, Virginia, West Virginia, & D.C. • Cases found with renal dialysis registry. • Controls: random digit dial. Data: Estimated lifetime analgesic use based on phone interview.