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Spring 2008. Bias, Confounding, and Effect Modification STAT 6395. Filardo and Ng. Bias. Any systematic error in the design or conduct of a study that results in a mistaken estimate of the association between an exposure and a disease

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spring 2008
Spring 2008

Bias, Confounding,

and Effect Modification

STAT 6395

Filardo and Ng

slide2
Bias

Any systematic error in the design or conduct of a study that results in a mistaken estimate of the association between an exposure and a disease

Bias is often a major problem in observational epidemiologic studies

systematic error bias is different than random error
Systematic error (bias) is different than random error
  • Example: an association between an exposure an a disease in which the true relative risk is 2.0
systematic error bias is different than random error1
Systematic error (bias) is different than random error
  • If the design and conduct of a study are unbiased, and there is no confounding, and we repeat the study an infinite number of times, the mean relative risk will be 2.0, with the individual relative risks from the different studies fluctuating around 2.0
systematic error bias is different than random error2
Systematic error (bias) is different than random error
  • If the design or conduct of the study is biased, and we repeat the study an infinite number of times, the mean relative risk will differ from 2.0 (for example, it may be 1.2), with the individual relative risks from the different studies fluctuating around 1.2
systematic error bias is different than random error3
Systematic error (bias) is different than random error
  • Due to random variation, an association that is far from the truth can be observed in an unbiased study, but it usually won’t be.
systematic error bias is different than random error4
Systematic error (bias) is different than random error
  • Due to random variation, the true association can be observed in a biased study, but it usually won’t be
systematic error bias is different than random error5
Systematic error (bias) is different than random error

Statistical significance does not protect against bias

two major categories of bias
Two major categories of bias
  • Selection bias
  • Information bias
selection bias
Selection bias

Error that results from criteria or procedures used to select study subjects or from factors that influence study participation.

With selection bias, the relation between exposure and disease is different for those who are selected for and participate in the study and those who should be theoretically eligible to participate.

selection bias1
Selection bias

Selection bias can occur as a result of:

  • Incorrect selection criteria for study subjects
  • Differences in characteristics between eligible subjects who agree to participate and eligible subjects who do not participate
information bias
Information bias

Error due to collection of incorrect information about study subjects. Due to this incorrect information, subjects are classified into incorrect exposure or disease categories.

selection bias is a major issue in case control studies
Selection bias is a major issue in case-control studies
  • Source population: the population that gives rise to the cases
selection bias is a major issue in case control studies1
Selection bias is a major issue in case-control studies
  • Cases should be selected such that the distribution of the exposures of interest among the cases selected for the study is the same as it is among all cases that arise in the source population. The cases should be representative of all cases that arise in the source population with respect to the exposures of interest.
selection bias in case control studies cont
Selection bias in case-control studies (cont.)
  • Controls should be selected such that the distribution of the exposures of interest among the controls is the same as it is in the source population. The controls should be representative of the source population with respect to the exposures of interest.
selection bias in case control studies cont1
Selection bias in case-control studies (cont.)
  • Selection bias occurs when either:
    • The cases are not representative of all cases that arise in the source population with respect to the exposures of interest and/or
    • The controls are not representative of the source population with respect to the exposures of interest.
selection bias in case control studies how it works
Selection bias in case-control studies: how it works
  • In the hypothetical data depicted in the following tables, we will assume there is:
          • no information bias,
          • confounding, or
          • random variability

so that all differences are due to differences in selection of cases or controls

hypothetical case control study including all cases and all non cases from source population a
Hypothetical case-control study including all cases and all non-cases from Source Population A

Gold standard OR = 4.5

slide19

Hypothetical case-control study including a 70%unbiased sample of the cases and 0.5% unbiased sample of the controls from Source Population A

Unbiased OR = (350x4,500)/(500x700) = 4.5

This is an unbiased odds ratio because the selection of cases and controls was unrelated to exposure.

slide20

Selection bias in choosing controls in a hypothetical case-control study including a 70% sample of the cases and 0.5% sample of the controls from Source Population A

Biased OR = (350x4,050)/(950x700) = 2.13

Selection of controls was related to exposure

-over selecting exposed controls biases OR downward

slide21
Selection bias in choosing controls in a case-control study due to incorrect criteria for control selection

Example: A hospital-based case-control study of the relation of smoking to a given disease.

slide22
Selection bias in choosing controls in a case-control study due to incorrect criteria for control selection

If the control group includes persons hospitalized for smoking-related diseases (e.g, cardiovascular disease)…

…the control group would likely have a higher proportion of smokers than the source population, and the resultant odds ratio would be biased downward

slide23

Selection bias in choosing controls in a case-control study due to a difference in participation rates between exposed controls and nonexposed controls

  • Example: Case-control study of the relation between housing characteristics and lead poisoning among children 6 years of age or younger who are screened for blood lead levels at the Hill Health Center in New Haven
slide24

Selection bias in choosing controls in a case-control study due to a difference in participation rates between exposed controls and nonexposed controls

  • Cases: all children with a blood lead level of >10 micrograms/dL
  • Controls: a systematic sample of children with a blood lead level of <10 micrograms/dL
housing characteristics and lead poisoning cont
Housing characteristics and lead poisoning (cont.)
  • Incentive for participation: the parents of the children were offered a free lead inspection of their homes
  • Participation rate among cases: 91% (parents were motivated by their child’s elevated blood lead level to have the inspection)
housing characteristics and lead poisoning cont1
Housing characteristics and lead poisoning (cont.)
  • Participation rate among controls: 69% (parents did not have the same motivation to participate)

The condition of the housing of the control parents who refused to participate was better than the condition of the housing of the control parents who did participate

housing characteristics and lead poisoning cont2
Housing characteristics and lead poisoning (cont.)
  • The housing of the controls selected for the study was in poorer condition than the housing of the source population

The odds ratio for the association between measures of dilapidated housing and childhood lead poisoning would be biased downward

housing characteristics and lead poisoning cont3
Housing characteristics and lead poisoning (cont.)
  • Although the criteria for selecting controls were sound, the difference in participation rate between exposed controls and nonexposed controls resulted in a biased odds ratio
slide29

Selection bias in choosing cases in a hypothetical case-control study including a 70% sample of the cases and 0.5% sample of the non-cases from Source Population A

Biased OR = (450x4,500)/(500x600) = 6.75

Selection of cases was related to exposure

-over-selecting exposed cases biases OR upward

slide30

Selection bias in choosing cases in a case-control study

  • Example: Population-based case-control study of pancreatic cancer cancer
  • Hypothesis: vitamin C protects against development of pancreatic cancer

Vitamin C intake assessed by food frequency questionnaire

slide31

Selection bias in choosing cases in a case-control study

  • Median interval between diagnosis and interview: 9 months
  • One-year case fatality rate of pancreatic cancer: 80%

Many cases would die before being interviewed

slide32

Selection bias in choosing cases in a case-control study

Suppose vitamin C intake improves survival from pancreatic cancer

  • Then vitamin C intake among cases selected for the study would be higher than vitamin C intake among all cases
  • Over-selection of exposed cases would bias OR upward
slide33

Compensating Selection Bias

To avoid biased odds ratios, investigators often attempt to equalize selection bias between cases and controls by selecting cases and controls undergoing the same selection processes

slide34

Compensating bias in choosing cases and controls in a hypothetical case-control study including a 70% sample of the cases and 0.5% sample of the non-cases from Source Population A

Unbiased OR = (450x4,286)/(714x600) = 4.5

Equal over-selection (1.5x) of exposed cases and controls

slide35

Hypothetical case-control study including a 70%unbiased sample of the cases and 0.5% unbiased sample of the controls from Source Population A

Unbiased OR = (350x4,500)/(500x700) = 4.5

This is the original table

slide36

Cases and controls undergoing the same selection processes in a case-control study of breast cancer

  • Example: Cases and controls selected from among women attending a breast cancer screening program

These women are likely to have high prevalence of known breast cancer risk factors, (family history of breast cancer, history of benign breast disease, late age at first birth)

slide37

Cases and controls undergoing the same selection processes in a case-control study of breast cancer

  • Example: Cases and controls selected from among women attending a breast cancer screening program

If cases from this population were compared to controls from the general population, an overestimate of the magnitude of some risk factors would probably occur

slide38

Cases and controls undergoing the same selection processes in a case-control study of breast cancer

  • Selecting both cases and controls from the screening program should make the bias the same in both groups, leading to unbiased odds ratios

This is another way of saying that controls should be selected from the source population that gave rise to the cases

minimizing selection bias in case control studies
Minimizing selection bias in case-control studies
  • In the study design stage, carefully consider the criteria for selection of cases and controls, particularly with respect to ensuring internal validity
minimizing selection bias in case control studies1
Minimizing selection bias in case-control studies
  • Choose study procedures aimed at maximizing the participation rate of the subjects selected for the study
slide41

Selection bias in cohort studies using internal comparison groups is unlikely

  • Selection bias would occur if participation were related to both exposure and the subsequent development of disease
  • Because study participants are selected before the development of disease, this is unlikely

The exposed group and nonexposed comparison group were drawn from the same source population and went through the same selection process

slide42

Selection bias in cohort studies using internal comparison groups is unlikely

  • The nurses who participated in the Nurses’ Health Study most likely differed from the nurses who did not, but since the same selection process was used to select the exposed group and the nonexposed internal comparison group, the relative risk estimates should be unbiased.
slide43

Cohort studies using external comparison groups are prone to selection bias

  • Exposed cohort and nonexposed external comparison group are not selected from the same source population

The exposed cohort may be selected such that it is at higher or lower risk for disease than the external comparison group for a reason other than the exposure of interest

slide44

Healthy worker effect

  • A selection bias in occupational cohort studies using a general population external comparison group

Persons selected for employment are usually healthier than and have lower mortality rates than the general population, which includes the sick and disabled.

slide45

Healthy worker effect

  • A selection bias in occupational cohort studies using a general population external comparison group

The healthy worker effect makes any excess disease or mortality associated with an occupational exposure more difficult to detect than it would have been if a valid comparison group had been used, biasing the estimates of relative risk downward

slide46

Losses to follow-up in cohort studies are analogous to selection bias in case-control studies

  • When a subject in a cohort study is lost to follow-up, we do not know whether that subject developed the disease of interest during the remainder of the study’s follow-up period
slide47

Losses to follow-up in cohort studies are analogous to selection bias in case-control studies

  • If the subjects lost to follow-up have a different incidence of the disease of interest than the subjects not lost to follow-up, the estimates of the incidence rate of the disease of interest in the cohort will be biased
slide48

Losses to follow-up in cohort studies are analogous to selection bias in case-control studies

  • However, relative risk estimates will be unbiased if the bias on the incidence rate estimates is the same in the exposed and nonexposed groups.

A biased relative risk estimate will occur only if losses to follow-up are related to both disease and exposure

  • The best defense against bias due to losses to follow-up is to make intense efforts to locate each cohort member, and thus minimize losses
slide49

Losses to follow-up in cohort studies are analogous to selection bias in case-control studies

  • The best defense against bias due to losses to follow-up is to make intense efforts to locate each cohort member, and thus minimize losses
slide50

Hypothetical cohort study with 100% follow-up (to keep the examples simple, we will not use the person-years method, but will use 10-year cumulative incidence)

Gold standard RR = 49.75/11.10 = 4.48

slide51

Hypothetical cohort study with 30% of the cohort lost to follow-up: losses to follow-up independent of exposure and disease

Unbiased RR = 49.75/11.10 = 4.48

slide52

Hypothetical cohort study with 40% of the exposed group and 20% of the nonexposed group lost to follow-up: losses to follow-up related to exposure, but not disease

Unbiased RR = 49.75/11.10 = 4.48

slide53

Hypothetical cohort study with 40% of those who developed disease and 20% of those who did not develop disease lost to follow-up: losses to follow-up related to disease, but not exposure

Unbiased RR = 37.36/8.33 = 4.48

slide54

Hypothetical cohort study: losses to follow-up related to disease and exposure

Biased RR = 37.36/11.10 = 3.37

slide55

Information bias (error due to collection of incorrect information about study subjects) results in misclassification of exposure or disease

  • Nondifferential exposure misclassification: misclassification of exposure unrelated to disease
  • Nondifferential disease misclassification: misclassification of disease unrelated to exposure
  • Differential misclassification: misclassification related to both exposure and disease
slide56

Information bias (error due to collection of incorrect information about study subjects) results in misclassification of exposure or disease

  • Nondifferential misclassification tends to bias an association toward the null hypothesis (no association)
  • Differential misclassification can bias an association either toward or away from the null hypothesis, depending on the specific nature of the misclassification
slide57

Nondifferential exposure misclassification in a cohort study

  • Inclusion of nonexposed subjects in the exposed group and exposed subjects in the nonexposed group will bias the relative risk toward the null if the exposure misclassificiation is unrelated to the future development of disease, which is usually the case

Differential exposure misclassification is not likely in cohort studies

slide58

Hypothetical cohort study with 100% follow-up and 100% accuracy in exposure and disease classification

Gold standard RR = 49.75/11.10 = 4.48

slide59

Hypothetical cohort study with 20% of exposed misclassified as nonexposed and 10% of nonexposed misclassified as exposed, independent of disease: nondifferentialexposure misclassification

Biased RR = 29.33/12.03 = 2.44

slide60

Nondifferential exposure misclassification in a cohort study: dietary assessment example

  • At baseline, study subjects complete a food frequency questionnaire about dietary habits over the past year.

Measurement error due to imperfect recall will result in exposure misclassification –which will occur in both the exposed and nonexposed group

slide61

Hypothetical cohort study with 0.1% of nondiseased misclassified as having developed the disease and 8% of the diseased misclassified as nondiseased, independent of exposure: nondifferentialdisease misclassification

Biased RR = 55.72/20.20 = 2.76

slide62

Hypothetical cohort study with 0.5% of nondiseased in the exposed group misclassified as having developed the disease and 0.04% of the nondiseased in the nonexposed group misclassified as having developed the disease: differentialdisease misclassification

Biased RR = 99.50/15.09 = 6.59

slide63

Disease misclassification in cohort studies

  • Disease misclassification is a particular issue when information on disease is obtained from the members of the cohort themselves (e.g. health questionnaire)

Whenever possible, subject reports about disease should be confirmed by more objective means, such as review of medical records

slide64

Disease misclassification in cohort studies

  • Differential misclassification is a concern if the study members involved in data collection on disease or in disease classification are aware of the exposure status of the subjects
slide65

Hypothetical case-control study with no misclassification of exposure or disease

Gold standard OR = 4.50

slide66

Hypothetical case-control study with 10% of cases misclassified as controls and 5% of controls misclassified as cases, independent of exposure: nondifferential disease misclassification

Biased OR = 3.54

slide67

Nondifferential disease misclassification in case-control study: Alzheimer’s disease

  • Definitive diagnosis can only be made by brain biopsy, which isn’t done.

We therefore must rely for diagnosis on clinical criteria and exclusion of other diseases. The diagnostic criteria are imperfect and will result in misclassification of the disease status

slide68

Nondifferential disease misclassification in case-control study: Alzheimer’s disease

  • Persons with other types of dementia, such as multi-infarct dementia may be included in the case group.
  • Persons with early Alzheimer’s disease may be included in the control group
slide69

Hypothetical case-control study with 10% of exposed controls misclassified as cases and 1% of nonexposed controls misclassified as cases: differential disease misclassification

Biased OR = 5.31

slide70

Differential disease misclassification in case-control study: Alzheimer’s disease

  • Exposure: hypertension

Hypertension is a risk factor for multi-infarct dementia, which could be confused with Alzheimer’s disease

slide71

Exposure misclassification in a case-control study: an important source of both nondifferential and differential misclassification

  • Classifying exposed persons as being nonexposed and nonexposed persons as being exposed will bias the odds ratio toward the null if the exposure misclassification is unrelated to disease status
  • Classifying exposed persons as being nonexposed and nonexposed persons as being exposed can bias the odds ratio in either direction if the exposure misclassification depends on disease status
slide72

Hypothetical case-control study with 20% of the nonexposed misclassified as exposed and 16% of the exposed misclassified as nonexposed, independent of disease: nondifferential exposure misclassification

Biased OR = 1.96

Example: dietary assessment

slide73

Hypothetical case-control study with 20% of the nonexposed cases misclassified as exposed and 5% of the nonexposed controls misclassified as exposed: differential exposure misclassification

Biased OR = 5.16

Example: Recall bias

slide74

Types of information bias that can lead to differential misclassification

  • Recall bias
  • Reporting bias
  • Observer bias
slide75

Recall bias

Systematic error due to differences in accuracy of recall of past exposures or diseases between study groups

  • Example:family history of prostate cancer in a case-control study of prostate cancer
slide76

Recall bias

  • Men diagnosed with prostate cancer are often more aware of their family history than men who have not had prostate cancer

In a case-control study, reporting of family history of prostate cancer could be more complete among cases than among controls, biasing the result away from the null hypothesis

slide77

Reporting bias

Systematic error due to selective revealing or suppression of information about exposure or disease due to attitudes, beliefs, or perceptions

  • Example: married, apparently heterosexual men may not reveal homosexual behavior
slide78

Reporting bias

  • Example: persons who belong to religious groups that proscribe alcohol may lie about alcohol consumption
slide79

Observer bias

Systematic error due to well-intentioned members of the study team subconsciously or consciously collecting data or making decisions about subjects’ exposure or disease status in different ways according to study group. This may occur because the observer has his/her own hypothesis about the relationship between exposure and disease

slide80

Observer bias

  • Interviewer bias: in a case-control study, an interviewer may probe more thoroughly for an exposure in a case than in a control
  • Abstractor bias: in a cohort study, a data abstractor may probe over the medical records of an exposed subject more thoroughly than the medical records of an unexposed subject to identify evidence of disease
slide81

Observer bias

  • Bias on the part of study team members involved in the classification of disease in a cohort study: classification of disease may be influenced by knowledge of the exposure status of the subject
slide82

Reducing bias

  • Ensure that the study design is appropriate for addressing the study hypotheses
  • Carefully define exposure and disease
  • Choose valid measurement methods
  • Train study personnel and standardize procedures
  • Perform quality control on all aspects of data collection and processing
slide83

Reducing bias

Make every effort to maximize participation rates and to minimize losses to follow-up

  • Apply study methods in the same manner and with the same care to all study subjects, irrespective of the group to which they belong
    • Blind interviewers, abstractors, and other study staff involved in data collection or exposure/disease classification to the subjects’ case-control status in case-control studies and exposure status in cohort studies
    • Blind study subjects and data collectors to study hypothesis
slide84

Reducing bias

  • If it is possible to improve the quality of exposure data in a case-control study in the case group or in the control group, but not in both, the investigator should resist the temptation to do so in order to preserve the validity of the comparison of exposures between cases and controls
slide85

Reducing bias

  • If it is possible to improve the quality of disease data in a cohort study in the exposed group or in the nonexposed comparison group, but not in both, the investigator should resist the temptation to do so in order to preserve the validity of the comparison of disease outcome between the exposed and nonexposed
slide86

Detection (surveillance) bias

Error due to persons with an exposure of interest being under closer medical surveillance than persons without the exposure, resulting in a higher probability of detection of the disease of interest in exposed persons than in nonexposed persons

slide87

Detection bias is a threat when:

  • The disease has a high prevalence of asymptomatic cases, and would thus be more likely to be diagnosed in persons under close medical surveillance than in persons not under medical surveillance
  • The exposure of interest leads to frequent medical checkups:
    • A medical therapy
    • A medical condition
    • A harmful exposure
slide88

Detection bias in a case-control study: selection bias in which selection of cases is related to the presence of the exposure

Example: Case-control study of hormone replacement therapy (HRT) use and breast cancer

  • Women who use HRT are likely to have more medical visits than women who do not
  • They may be more likely to have a screening mammography and have subclinical breast cancer detected
slide89

Detection bias in a case-control study: selection bias in which selection of cases is related to the presence of the exposure

Example: Case-control study of hormone replacement therapy (HRT) use and breast cancer

  • HRT would cause breast cancer to be detected, but not to occur

The OR for the relationship between HRT and breast cancer would be biased upward

slide90

Detection bias in a cohort study: information bias in which exposed persons are under closer medical surveillance than nonexposed persons

Example: Cohort study of statin use and prostate cancer

  • Men who take statins have blood drawn periodically to check their serum cholesterol and liver function
  • May be more likely to have a PSA test than men not taking statins
slide91

Detection bias in a cohort study: information bias in which exposed persons are under closer medical surveillance than nonexposed persons

Example: Cohort study of statin use and prostate cancer

  • This would lead to a higher probability of diagnosis of prostate cancer
  • Statin use would cause prostate cancer to be detected, but not to occur

The RR for the relationship between statin use and prostate cancer would be biased upward

slide92

Detection bias: further observations

  • In a cohort study, more likely to occur when disease is ascertained through regular medical channels as opposed to when all study subjects are examined for disease using standardized methods (the same for exposed and nonexposed subjects) by members of the study team.
slide93

Detection bias: further observations

  • When detection bias occurs, the disease tends to be diagnosed in an early subclinical form in exposed persons more often than in nonexposed persons
    • The RR or OR for the association between the exposure and less advanced disease is higher than the relative risk or odds ratio for the association between the exposure and more advanced disease
slide94

Qualitatively assessing how biases in case-control studies work

  • In a case-control study, selection bias, information bias resulting in differential misclassification, or detection bias will lead to a biased distribution of subjects in the 2x2 table that is differential between cases and controls

Assess which cells will be over-represented under various scenarios, as shown in the following slides

slide95

Over-representation of exposed cases

OR = (Ad)/(bc)

OR is biased upward

Detection bias: HRT and breast cancer

slide96

Over-representation of nonexposed cases

OR = (ad)/(bC)

OR is biased downward

Differential exposure misclassification:

Alcohol consumption and automobile accidents

slide97

Over-representation of exposed controls

OR = (ad)/(Bc)

OR is biased downward

Selection (nonparticipation) bias: Poor housing and elevated lead levels

slide98

Over-representation of nonexposed controls

OR = (aD)/(bc)

OR is biased upward

Selection bias: hospital-based case-control study in which investigator goes on a “witch hunt” against exposed controls