1. Bias, Confounding and the Role of Chance
Principles of Epidemiology
Lecture 5
Dona Schneider, PhD, MPH, FACE

2. To Show Cause We Use Koch’s Postulates for Infectious Disease
Hill’s Postulates for Chronic Disease and Complex Questions
Strength of Association – Tonight’s 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 1946;2:47-53

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 interviewer’s 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
Cases
Controls
Total
Exposed
100 50
150
Nonexposed 50 50
100
150
100
250
OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3
Differential misclassification - Overestimate exposure for 10 cases, inflate rates
Cases
Controls
Total
Exposed
110 50
160
Nonexposed 40 50 90
150
100
250
OR = ad/bc = 2.8; RR = a/(a+b)/c/(c+d) = 1.6

13. Misclassification Bias (cont.)
True Classification
Cases
Controls
Total
Exposed
100 50
150
Nonexposed
250
OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3
Differential misclassification - Underestimate exposure for 10 cases, deflate rates
Cases
Controls
Total
Exposed 90 50
140
Nonexposed 60
110
150
100
250
OR = ad/bc = 1.5; RR = a/(a+b)/c/(c+d) = 1.2

14. Misclassification Bias (cont.)
True Classification
Cases
Controls
Total
Exposed
100 50
150
Nonexposed
250
OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3
Differential misclassification - Underestimate exposure for 10 controls, inflate rates
Cases
Controls
Total
Exposed
100 40
140
Nonexposed 50 60
110
150
250
OR = ad/bc = 3.0; RR = a/(a+b)/c/(c+d) = 1.6

15. 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/(a+b)/c/(c+d) = 1.3
Differential misclassification - Overestimate exposure for 10 controls, deflate rates
Cases
Controls
Total
Exposed
100 60
160
Nonexposed 50 40 90
150
250
OR = ad/bc = 1.3; RR = a/(a+b)/c/(c+d) = 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
250
OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3
Nondifferential misclassification - Overestimate exposure in 10 cases, 10 controls – bias towards null
Cases
Controls
Total
Exposed
110 60
170
Nonexposed 40 80
150
100
250
OR = ad/bc = 1.8; RR = a/(a+b)/c/(c+d) = 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 subject’s group allocation
Triple masked study – the subject, observer and data analyst are unaware of the subject’s group allocation
Build in methods to minimize loss to follow-up