2. Readings
4th Edition
Chapter 14, pp. 227-245
Chapter 15, pp. 247-256 (not
responsible for section on Interaction)
5th Edition
Chapters 14, pp. 243-260
Chapter 15, pp. 262-270 (not
responsible for section on Interaction)
3. Lecture Objectives
Understand how the presence of bias or confounding or
interaction can influence a measure of association
Name the types of biases that occur in epidemiologic
studies
List the different reasons for erroneous classification of
disease and exposure status
Know the approaches available to handle confounding
Know the guidelines for assessing causality
Given a set of results identify the presence of interaction
4. Goal of Epidemiologic Studies
The goal of epidemiologic studies is to test
hypothesis of association between an exposure and
outcome
If there IS an association, the exposure is called a
risk factor
A risk factors can be
A predictor (marker or proxy)
Living in an apartment building
A causal factor
Component of paint on the walls of apartment
building
5. Goal of Epidemiologic Studies
It is important to measure exposures and
outcomes as well as possible
What limits our ability to derive inferences
from epidemiologic studies?
Bias
Confounding
Interaction
6. Association
We conduct epi studies to
estimate a measure of
association
The presence of an association
(RR or OR > 1) is NOT an
indication that the exposure is
the cause of the disease
6
7. Causality
Before we can make a
statement on causality
we need to consider:
Study design
Can results be
explained by errors
in the design, data
collection, or
analyses phases of
the study?
What is currently
known about this
association in the
scientific literature
8. How Epidemiologic
Studies Fit In
Often begin with clinical
observations
Examine routinely available data
to identify statistical associations
Carry out new studies to demonstrate
specific associations and derive causal
inferences
9. Types of study design
Results of a randomized
trial are less likely to be
explained by errors than
those from a cohort or
case-control study
Not always possible,
however, to do a
randomized trial or even a
cohort study
Strongest evidence will be
from study design that
minimizes most errors9
10. Ecological Studies
Unit of analysis is population or group, rather than
individual
Example
Level of flouride in water supply and dental caries by
city
Study of dietary consumption of fiber and heart
disease by country
Useful to give us idea of what is happening at a
population level, but cannot make conclusions
regarding individuals
11. Ecologic studies
Easy and cheap (if data is available)
Big problem is that we may ascribe to
members of a group characteristics
that they do not possess – ecologic
fallacy
Useful to develop hypothesis but
never to address causality
12. If We Find An Association…
If an association is observed, we must ask:
Is it “REAL?”
13. If We Find an Association
Is it by chance?
To minimize this, we make sure we have a large
enough population
Is it because of bias?
Bias is a systematic error in the design, conduct, or
analysis of a study that results in a mistaken
estimate of an exposure’s effect on the risk of disease
After we evaluate if an association is by chance or
because of bias, we can be more comfortable
concluding that it is real
14. Bias
An error in
Study design
Data collection
Data analysis
Measures of association that may be incorrect
estimates of the true association
Wrong conclusions about the
exposure-disease association
15. Selection Bias
Is a method of selected participants that distorts
the exposure-outcome relationship from that
present in the target population
Example: Select volunteers as exposed group and
non-volunteers as non-exposed group in a study of
screening effectiveness
Volunteers could be more health conscious than non-
volunteers, thus resulting in less disease
Volunteers could also be at higher risk, such as
having a family history of illness, thus resulting in
more disease
16. Controlling Selection Bias
Define criteria of selection of disease and non-
diseased participants independent of exposures
in a case-control study
Define criteria of selection of exposed and non-
exposed participants independent of disease
outcomes in a cohort study
Use randomized clinical trials
17. Information Bias
Occurs when information is collected differently
between two groups, leading to an error in the
conclusion of the association
Examples
Interviewer knows the status of subjects before
the interview and probes cases and controls
differently about their exposures
Subjects may recall past exposures better or in
more detail if he or she has the disease
18. Types of information bias
Recall bias: People with a health condition
would be more likely to remember an exposure
Interviewer bias: Interviewer who is aware of
case (or exposure) status may let expectations
influence how vigorously s/he probes for
information
Surveillance bias: Occurs when one group is
followed more closely than another group
19. Controlling Information Bias
Have a standardized protocol for data collection
Make sure sources and methods for data
collection are similar for all study groups
Make sure interviewers are NOT aware of
exposure/disease status
Determine strategy to evaluate information
bias
20. Bias and Confounding
Bias is a systematic error in a study and cannot
be fixed
Confounding may lead to errors in the
conclusion of the study, but, when confouding
variables are known, the effect may be fixed
21. What is Confounding?
Confounding occurs when
An apparent association between a presumed
exposure and an outcome is in fact explained by
a THIRD variable not in the causal pathway
This THIRD variable is associated with BOTH the
exposure and the outcome
24. Example of Confounding
(from Chapter 15)
Study of 100 cases and 100 controls in an
unmatched case-control study
30% of cases and 18% of the controls were
exposed
Measure of association = Odds ratio = 1.95
Could age be a confounder?
26. Example of Confounding
If age is a confounder, then
Age must be a risk factor for the disease
AND
Age must be associated with the exposure
AND
Age must NOT be in the causal pathway
27. Example of Confounding
Distribution of Cases and Controls by Age
Age Cases Controls
< 40 years 50 80
> 40 years 50 20
Total 100 100
Cases were older Age meets criterion 1 that is
that age is a risk factor for the disease
28. Example of Confounding
Older subjects were exposed more Age meets
criterion 2 that age is associated with exposure
Relationship of Exposure to Age
Age Total Exposed Not
Exposed
Percent
Exposed
< 40 years 130 13 117 10%
> 40 years 70 35 35 50%
30. How to Address Confounding
In design
Matching
In analysis
Stratification
Adjustment
31. Confounding
In order to evaluate for
confounding in the analysis:
The investigator must decide to
measure the potential confounders
during the design stage
Deciding what potential
confounders to measure is based
on previous research
32. Interaction
• Be familiar with the concept as reviewed in the next few slides
• You will not be evaluated on this concept on examinations
• You may be given data for the Project that requires you to
evaluate for interaction
33. What is Interaction?
Interaction involves two risk factors
If the effect of one risk factor is the same
within strata defined by the other risk factor,
then there is no interaction
When the effect of one risk factor is different
within strata defined by the other, then there is
interaction
Also known as effect modification
34. Is there an association?
If so, is it due to confounding?
Is there an association equally strong in strata
formed on the basis of a third variable
Is there Interaction?
NO YES
Interaction
Present
Interaction
Not Present
35. Risks of Liver Cancer for
Persons Exposed to Aflatoxin or
Chronic Hepatitis B Infection
Aflatoxin
Negative
Aflatoxin
Positive
Hepatitis B Negative 1.0 3.4
Hepatitis B Positive 7.3 59.4
• Hepatitis infection increases risk to 7.3
• Aflatoxin exposure only increases risk to 3.4
• If BOTH, your risk is 59.4 which is more than the combination of
the two effects (either adding them or multiplying them)
36. Confounding versus Interaction
Confounding is a nuisance
It is a distortion of exposure groups
We generally wish to tease out confounding
effects
Effect modification is of interest
If the effect of the exposure is different
between two groups, then it is of interest to
report this information rather than teasing it
out
37. Review
If the study is free of bias and has been
adjusted for confounders
And is of an adequate sample size
THEN
We can evaluate whether the exposure is a
CAUSAL factor of the disease
38. Evidence for a Causal Relationship
“Postulates for Causation” were suggested by
Henle-Koch (1880s)
In order to establish a causal relationship between
a parasite and disease:
1. The organism is ALWAYS found with the disease
2. The organism is NOT found with any other disease
3. The cultured organism causes disease in healthy
animal
4. The organism can be re-isolated from the
experimentally infected animal
Not perfect, but useful for infectious diseases
39. Criteria for Causal Association
“Statistical methods cannot establish proof of a
causal relationship in an association. The
causal significance is a matter of judgment
which goes beyond any statement of statistical
probability. To judge or evaluate the causal
significance of the association between the
attribute or agent and the disease, or effect
upon health, a number of criteria must be
utilized, no one of which is an all-sufficient
basis for judgment.” (1964 Surgeon General’s
Report on Smoking and Health)
40. Criteria for Causal Association
Sir Bradford Hill, 1965
Strength
Consistency
Specificity
Temporality
Biological gradient
Plausibility
Coherence
Experiment
Analogy
41. Guidelines for Causal Association
Gordis
1. Temporal relationship
2. Strength of the association
3. Dose-response relationship
4. Replication of the findings
5. Biologic plausibility
6. Consideration of alternative explanations
7. Cessation of exposure
8. Consistency with other knowledge
9. Specificity of the association
42. See book for more details and
examples of each of these
guidelines
43. Criteria for Causality
(1990 modification to guidelines)
Major criteria (in descending order of priority)
Temporal relationship
Biologic plausibility
Consistency
Alternative explanations (confounding)
Other considerations
Dose-response relationship
Strength of the association
Cessation effects
44. Use of Guidelines
There is a great deal of judgment used in
determining causality
Also, there is always going to be new evidence
that accumulates to support or dispute our
current understanding