Transcript Document 7694418

Bias, Confounding and
the Role of Chance
Principles of Epidemiology
Lecture 5
Dona Schneider, PhD, MPH, FACE
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
Epidemiology (Schneider)
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
Epidemiology (Schneider)
BIAS
Systematic error built into the
study design
Selection Bias
Information Bias
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
Epidemiology (Schneider)
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
Epidemiology (Schneider)
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
Epidemiology (Schneider)
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
Epidemiology (Schneider)
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
Epidemiology (Schneider)
Information Bias (cont.)

Misclassification bias – errors are made
in classifying either disease or exposure
status
Epidemiology (Schneider)
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
Epidemiology (Schneider)
Misclassification Bias (cont.)
True Classification
Exposed
Nonexposed
Cases
Controls
Total
100
50
150
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
Exposed
Nonexposed
Cases
Controls
Total
110
40
150
50
50
100
160
90
250
OR = ad/bc = 2.8; RR = a/(a+b)/c/(c+d) = 1.6
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 - 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/(a+b)/c/(c+d) = 1.2
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 - 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/(a+b)/c/(c+d) = 1.6
Misclassification Bias (cont.)
True Classification
Exposed
Nonexposed
Cases
100
50
150
Controls
50
50
100
Total
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
100
250
OR = ad/bc = 1.3; RR = a/(a+b)/c/(c+d) = 1.1
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
Epidemiology (Schneider)
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
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/(a+b)/c/(c+d) = 1.3
Controls for Bias

Be purposeful in the study design to minimize the
chance for bias


Define, a priori, who is a case or what constitutes
exposure so that there is no overlap


Example: use more than one control group
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
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