Bias,%20Confounding%20and%20the%20Role%20of%20Chance

1
Bias, Confounding and the Role of Chance

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

2
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

4
BIAS

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

5
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

6
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

7
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

8
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

9
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

10
Information Bias (cont.)

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

11
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

12
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
13
Misclassification Bias (cont.)
True Classification
Cases Controls Total
Exposed 100 50 150
Nonexposed 50 50 100
150 100 250
OR ad/bc 2.0 RR a/(ab)/c/(cd) 1.3
Differential misclassification - Underestimate
exposure for 10 cases, deflate rates
Cases Controls Total
Exposed 90 50 140
Nonexposed 60 50 110
150 100 250
OR ad/bc 1.5 RR a/(ab)/c/(cd) 1.2
14
Misclassification Bias (cont.)
True Classification
Cases Controls Total
Exposed 100 50 150
Nonexposed 50 50 100
150 100 250
OR ad/bc 2.0 RR a/(ab)/c/(cd) 1.3
Differential misclassification - Underestimate
exposure for 10 controls, inflate rates
Cases Controls Total
Exposed 100 40 140
Nonexposed 50 60 110
150 100 250
OR ad/bc 3.0 RR a/(ab)/c/(cd) 1.6
15
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
Cases Controls Total
Exposed 100 60 160
Nonexposed 50 40 90
150 100 250
OR ad/bc 1.3 RR a/(ab)/c/(cd) 1.1
16
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

17
Misclassification Bias (cont.)
True Classification
Cases Controls Total
Exposed 100 50 150
Nonexposed 50 50 100
150 100 250
OR ad/bc 2.0 RR a/(ab)/c/(cd) 1.3
Nondifferential misclassification - Overestimate
exposure in 10 cases, 10 controls bias towards
null
Cases Controls Total
Exposed 110 60 170
Nonexposed 40 40 80
150 100 250
OR ad/bc 1.8 RR a/(ab)/c/(cd) 1.3
18
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

19
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