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

Bias

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

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

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:

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.

Selection bias can occur in several ways: 1.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) 2.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) 3.Refusal, non-response, or agreement to participate that is related to the exposure and disease (Self-selection bias) 4.Using the general population as a comparison group for an occupational cohort study ("Healthy worker" effect) 5.Differential referral or diagnosis of subjects

MGH 100 Hospital Cases 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? Selection Bias in a Case-Control Study 200 Controls: Door-to-door survey of neighborhood around the hospital during work day.

200 Controls: Door-to-door survey of neighborhood around the hospital during work day. Problems: 1.SE status of people living around the hospital may generally be different from that of the population that produced the cases. 2.The door-to-door method of selecting controls may tend to select people of lower (or higher) SE status.

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. Selection bias can occur in a case-control study if controls are more (or less) likely to be selected if they have the exposure.

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

Control Selection Bias The “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.

Entire Population Cancer Cases Normal Low SES (<median) 1501,000,000 High SES (>median) 501,000,000 2,000,000 women > age 20 in MA, & about 200 cases of cervical cancer per year. If low SES were associated with cervical cancer with OR=3.0, MA would look like this. SampleCancer Cases Normal Low SES75 High SES25 Cases are referred to MGH from all over, so their SES distribution is same as the state’s, i.e. 3 to 1. OR = (75/25) = 2.0 (120/80) (Biased) But, controls selected from area around MGH may have lower SES than MA

Referred Cases 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? Could mothers of controls be more or less likely to be diabetic than the cases (regardless of any association between diabetes and microsomia)? Referral mechanism of controls might be very different from that of the cases with microsomia. How would you select controls for this 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. Self- Selection Bias in a Case-Control Study

OR=2.25 OR= (80%) 120 (60%) 120 (60%) 180 (60%) Exp. YNYN YNYN Dis. Y N Dis. Y N Self-Selection Bias True Best solution is to work toward high participation (>80%) in all groups.

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

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

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

General Population Rubber Workers vs. Mortality Rates? The general population is often used in occupational studies of mortality, since data is readily available, and they are mostly unexposed. 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). 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).

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 Differential Retention (Loss to Follow Up) in Prospective Cohort Studies

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

Without Losses TENormal OC+209,980 OC-109,990 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. There is 40% loss to follow up overall, but a greater tendency to loose OC users with TE results in a de facto selection. Final Sample TENormal OC+85,980 OC-85,990 RR = (8/5988) = 1.0 (8/5998) If OC users with TE are more likely to be lost than non-OC-users with TE… (Biased)

Observation Bias (Information Bias) Systematic errors due to incorrect categorization. DiseasedNot Diseased Exposed Not Exposed The Correct Classification

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). Errors Misclassification Bias Differential Misclassification (non-random): If information is better in one group than another, the association maybe over- or underestimated. Subjects are misclassified with respect to their risk factor status or their outcome, i.e., errors in classification. Errors =

Non-Differential Misclassification 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. When errors in exposure or outcome status occur with approximately equal frequency in groups being compared. Errors =

Random errors in classification of risk factors or outcome (i.e., error rate about the same in all groups). Non-Differential Misclassification 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. 1.MDs don’t list all relevant diagnoses. 2.Coders assign incorrect codes (they aren’t MDs). Errors occur in 25-30% of records.

Random errors in classification of risk factors or outcome (i.e., error rate about the same in all groups). Non-Differential Misclassification Tends to minimize differences, generally causing an underestimate of effect. Effect: CAD Controls Diabetes4010 No diabetes6090 OR= 40x90 = x60 CAD Controls Diabetes20 5 No diabetes8095 OR= 20x95 = x80 True Relationship With Nondifferential Misclassification 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.

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

DiseasedNot Diseased Exposed Not Exposed Nondifferential Misclassification of Exposure #1

DiseasedNot Diseased Exposed Not Exposed Nondifferential Misclassification of Exposure #2

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.

Differential Misclassification 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. When there are more frequent errors in exposure or outcome classification in one of the groups. Errors

Recall Bias 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. (If the groups have the same % of errors based on faulty memory, that’s non-differential misclassification.) (Differential)

Systematic difference in soliciting, recording, or interpreting information. 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. (Differential)

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

Misclassification of Outcome Can Also Introduce Bias … but it usually has much less of an impact than misclassification of exposure, because: 1.Most of the problems with misclassification occur with respect to exposure status, not outcome. 2.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. 3.Most outcomes are relatively uncommon. 4.Misclassification of outcome will generally bias toward the null, so if an association is demonstrated, if anything the true effect might be slightly greater.

Any concerns? 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. 1. No 2. Differential misclassification 3. Interviewer bias 4. Recall bias 5. Selection bias

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

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). Diagnosis of AAA Known risk factors:  Age  Male gender  Smoking  Hypertension

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

Was there selection bias? AAA Other 60 1,242 1,302 Black White ‘Other’: variety of readily apparent conditions. 1. Yes 2. No

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? South Africa, 1986 AAA Other 60 1,242 1,302 Black White Was there selection bias? ‘Other’: variety of readily apparent conditions.

A possibility of misclassification? “AAA in blacks are more often due to infectious causes.” BlacksWhites Atherosclerotic34%99% Inflammatory or Infectious 47%0.5% Uncertain etiology 19%0. 0% “All black patients were screened for TB … and for syphilis.” 1. No 2. Yes, random. 3. Yes, differential.

More Details About the Study WhiteBlack Male:Female2:11:1 Mean age Admitted for Uncontrolled HBP0%17% Smoking76%48% (Known risk factors…)  Age  Male gender  Smoking  Hypertension

Environmental tobacco smoke and tobacco related mortality in a prospective study of Californians, James E. Enstrom, Geoffrey C. Kabat. BMJ 2003;326: ,094 adults enrolled in an ACS cancer study in 1959 were followed until For “never smokers married to ever smokers” compared with “never smokers married to never smokers”: RR in Males RR in Females Heart disease 0.94 ( ) 1.01 ( ) Lung cancer 0.75 ( ) 0.99 ( ) Chr. Pulm. Dis ( ) 1.13 ( ) 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.

“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.” Environmental tobacco smoke and tobacco related mortality in a prospective study of Californians, James E. Enstrom, Geoffrey C. Kabat. BMJ 2003;326:1057

Any potential selection bias in the ETS study? 1. I don’t think so. 2. Yes, there was a potential for it.

Any potential information bias in the ETS study? 1. I don’t think so. 2. Non-differential misclassification. 3. Differential misclassification. 4. Interviewer bias. 5. Recall bias.

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.

Conclusion: Acetaminophen & NSAIDS increase risk of renal failure, but not aspirin. OR95% CI Acetaminophen > Aspirin > NSAIDs > Could any biases have influenced the conclusion? Case-Control Study: Analgesic Use & Renal Failure

Could interviewer bias have affected results? 1. Highly unlikely. 2. Definitely a possibility.

Could recall bias have affected results? 1. Highly unlikely. 2. Definitely a possibility.

Reverse Causation Example: Chronic diabetes is a common cause of renal failure. Suppose diabetics more frequently have conditions that require analgesics. Diabetes Renal Failure Vascular Disease InfectionsSurgery Analgesics In this case, it may appear that analgesic use that is greater than in “controls” is associated with a greater risk of renal failure.

 Select subjects by similar mechanism.  Blind interviewers.  Get subjects with equal tendency to remember.  Use clear, homogeneous definitions of disease & exposure.  Get accurate data collected in a similar way.  Confirm data; error trapping during data entry.  Use procedures to minimize loss to follow-up. Once it’s in the study, you can’t fix it. Avoiding Bias

A bias that occurs in observational studies of drug effects. Allocation is not randomized and drug selection may be influenced by pre-existing disease. Confounding By Indication Example: Physicians might advise their patients with renal failure not to take aspirin.

253 women (108 African American, 145 white) were surveyed within the first month of discharge from the hospital for a [PCTA, CABG, or MI]. 234 (99 African American, 135 white) completed the 6-month follow-up. RESULTS: The rate of referral to outpatient phase 2 cardiac rehabilitation was significantly lower for African-American women compared with white women, 12 (12%) vs. 33 (24%) (P=.03). Only 35 (15%) of women in the study reported enrollment in phase 2 cardiac rehabilitation programs, with fewer African-American women reporting enrollment compared with white women, 9 (9%) versus 26 (19%) (P=.03). Controlling for age, education, angina class, and co-morbidities, women with annual incomes $20,000 (P=.01). Although borderline significant, African- American women were 55% less likely to be referred (P=.059) and 58% less likely to enroll (P=.059) than white women. JK Allen, et al.: Disparities in Women’s Referral to and Enrollment in Outpatient Cardiac Rehabilitation J. Gen. Intern. Med 2004;19:

Methods: “…women were identified at the time of hospitalization for a coronary event. They were interviewed by telephone within the first 4 weeks following their hospital discharge to collect baseline socio-demographic and clinical data. They were interviewed again 6 months later by telephone to obtain information on referral to and enrollment in cardiac rehabilitation programs, and information on psychosocial and behavioral factors that may be associated with rehabilitation utilization. Interviews were conducted by three trained research assistants…. “ “The 6-month interview assessed the receipt of a referral from self-report of the patient, including the patient’s recall of having received a verbal or written referral by a health professional at any time since being hospitalized. For those who reported receiving a referral, the reinforcing factors of the patient’s perception of the strength of the health professional’s and family/significant others’ encouragement to participate in cardiac rehabilitation was measured using a scale of 1 (little or no encouragement) to 10 (strongly encouraged). Enabling factors such as the accessibility, availability, and acceptability of cardiac rehabilitation services were assessed.”

DiseasedNot Diseased Exposed Not Exposed Differential Misclassification of Outcome

DiseasedNot Diseased Exposed Not Exposed Nondifferential Misclassification of Outcome