Bias, Confounding and the Role of Chance

1
Bias, Confounding and the Role of Chance

• Principles of Epidemiology
• Lecture 5
• Dona Schneider, PhD, MPH, FACE

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To Show Cause We Use

• Kochs Postulates for Infectious Disease
• Hills Postulates for Chronic Disease and Complex
  Questions
• Strength of Association Tonights entire
  lecture
• Biologic Credibility
• Specificity
• Consistency with Other Associations
• Time Sequence
• Dose-Response Relationship
• Analogy
• Experiment
• Coherence

3
To Show a Valid Statistical Association

• We need to assess
• Bias whether systematic error has been built
  into the study design
• Confounding whether an extraneous factor is
  related to both the disease and the exposure
• Role of chance how likely is it that what we
  found is a true finding

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BIAS

• Systematic error built into the study design
• Selection Bias
• Information Bias

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Types of Selection Bias

• Berksonian bias There may be a spurious
  association between diseases or between a
  characteristic and a disease because of the
  different probabilities of admission to a
  hospital for those with the disease, without the
  disease and with the characteristic of interest
• Berkson J. Limitations of the application of
  fourfold table analysis to hospital data.
  Biometrics 1946247-53

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Types of Selection Bias (cont.)

• Response Bias those who agree to be in a study
  may be in some way different from those who
  refuse to participate
• Volunteers may be different from those who are
  enlisted

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Types of Information Bias

• Interviewer Bias an interviewers knowledge may
  influence the structure of questions and the
  manner of presentation, which may influence
  responses
• Recall Bias those with a particular outcome or
  exposure may remember events more clearly or
  amplify their recollections

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Types of Information Bias (cont.)

• Observer Bias observers may have preconceived
  expectations of what they should find in an
  examination
• Loss to follow-up those that are lost to
  follow-up or who withdraw from the study may be
  different from those who are followed for the
  entire study

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Information Bias (cont.)

• Hawthorne effect an effect first documented at
  a Hawthorne manufacturing plant people act
  differently if they know they are being watched
• Surveillance bias the group with the known
  exposure or outcome may be followed more closely
  or longer than the comparison group

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Information Bias (cont.)

• Misclassification bias errors are made in
  classifying either disease or exposure status

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Types of Misclassification Bias

• Differential misclassification Errors in
  measurement are one way only
• Example Measurement bias instrumentation may
  be inaccurate, such as using only one size blood
  pressure cuff to take measurements on both adults
  and children

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Misclassification Bias (cont.)
True Classification
Total
Controls
Cases
Exposed
150
50
100
Nonexposed
100
50
50
250
100
150
OR ad/bc 2.0 RR a/(ab)/c/(cd) 1.3
Differential misclassification - Overestimate
exposure for 10 cases, inflate rates
Total
Controls
Cases
160
50
110
Exposed
90
50
40
Nonexposed
250
100
150
OR ad/bc 2.8 RR a/(ab)/c/(cd) 1.6
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Misclassification Bias (cont.)
True Classification
OR ad/bc 2.0 RR a/(ab)/c/(cd) 1.3
Differential misclassification - Underestimate
exposure for 10 cases, deflate rates
OR ad/bc 1.5 RR a/(ab)/c/(cd) 1.2
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Misclassification Bias (cont.)
True Classification
OR ad/bc 2.0 RR a/(ab)/c/(cd) 1.3
Differential misclassification - Underestimate
exposure for 10 controls, inflate rates
OR ad/bc 3.0 RR a/(ab)/c/(cd) 1.6
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Misclassification Bias (cont.)
True Classification
Total
Controls
Cases
150
50
100
Exposed
100
50
50
Nonexposed
250
150
100
OR ad/bc 2.0 RR a/(ab)/c/(cd) 1.3
Differential misclassification - Overestimate
exposure for 10 controls, deflate rates
OR ad/bc 1.3 RR a/(ab)/c/(cd) 1.1
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Misclassification Bias (cont.)

• Nondifferential (random) misclassification
  errors in assignment of group happens in more
  than one direction
• This will dilute the study findings -
  BIAS TOWARD THE NULL

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Misclassification Bias (cont.)
True Classification
OR ad/bc 2.0 RR a/(ab)/c/(cd) 1.3
Nondifferential misclassification - Overestimate
exposure in 10 cases, 10 controls bias towards
null
OR ad/bc 1.8 RR a/(ab)/c/(cd) 1.3
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Controls for Bias

• Be purposeful in the study design to minimize the
  chance for bias
• Example use more than one control group
• Define, a priori, who is a case or what
  constitutes exposure so that there is no overlap
• Define categories within groups clearly (age
  groups, aggregates of person years)
• Set up strict guidelines for data collection
• Train observers or interviewers to obtain data in
  the same fashion
• It is preferable to use more than one observer or
  interviewer, but not so many that they cannot be
  trained in an identical manner

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Controls for Bias (cont)

• Randomly allocate observers/interviewer data
  collection assignments
• Institute a masking process if appropriate
• Single masked study subjects are unaware of
  whether they are in the experimental or control
  group
• Double masked study the subject and the
  observer are unaware of the subjects group
  allocation
• Triple masked study the subject, observer and
  data analyst are unaware of the subjects group
  allocation
• Build in methods to minimize loss to follow-up