Excelsior College PBH 321 Page 1 CASE-CONTROL STU.docx

Excelsior College PBH 321
Page 1
CASE-CONTROL STUD IES
A case-control study is an observational design that involves
studying a population in which cases of disease
are identified and enrolled, and a sample of the population that
produced the cases is identified and enrolled
(controls). Exposures are determined for individuals in both
groups.
Let’s say that we want to test the hypothesis that pesticide
exposure increases the risk of breast cancer.
Consider a hypothetical prospective cohort study of 89,949
women aged 34-59; 1,439 breast cancer cases
were identified over 8 years of follow-up. Blood was drawn on
all 89,949 at beginning of follow-up and
samples were frozen. The exposure was defined as the level of
pesticides (e.g. DDE) in blood, characterized as
high or low. We compare women with high or low exposures to
see if they got breast cancer or not by the end
of follow-up.
Breast Cancer
Yes No Total
DDE

exposure High 360 13,276 13,636
Relative Risk = RR = (360/13,636) / (1,079/76,313) = 1.9
Low 1,079 75,234 76,313
Women with high pesticide levels in the blood have 1.9
times the risk of developing breast cancer after 8 years
than women with low levels
Total 1,439 88,510 89,949
Conducting this study presents a practical problem: quantifying
pesticide levels in the blood is very expensive -
-it's not feasible to analyze all 89,949 blood samples (this
would cost many thousands of dollars).
To be efficient, we could instead analyze blood on all breast
cancer cases (N=1,439) but take only a sample of
the women who did not get breast cancer, say two times as
many cases (N=2,878) (controls). This is a case-
control study! Specifically, because we sampled cases and
controls from within a complete cohort, we refer to
this as a nested case-control study.
Breast Cancer
Cases Controls
DDE

exposure
High 360 432
Low 1,079 2,446
Total 1,439 2,878
Page 2
Timing and Set Up of a Case-Control Study
Cases
When identifying cases, the criteria for the case definition

should lead to accurate classification of disease.
This means the investigator must have efficient and accurate
sources to identify cases, such as existing disease
registries or hospitals.
In our standard 2 x 2 table, the number of cases gives you the
numerators of the rates of disease in exposed
and unexposed groups being compared.
Disease
Yes
(cases)
No
(controls)
Total
Exposure Yes a ? ? Rate of disease in exposed: a/?
No c ? ?
Rate of disease in
unexposed: c/?
Total a+c ? ?
What is missing? The denominators! If this were a cohort
study, you would have the total population (if you

were calculating cumulative incidence) or total person-years (if
you were calculating incidence rates) for both
the exposed and non-exposed groups, which would provide the
complete denominators for the compared
rates.
Controls
The controls provide the information for the denominators in a
case control study. They are only a sample of
the source population that gave rise to the cases - not the total
population (as in a cohort study).
E
E
D
(Cases)
(Controls)
EE
EE
D
E
Identify a group with disease and
a group without disease, then go
back in time to look at their
prior exposure status.E
D
(Cases)

(Controls)
EE
EE
D
*Study starts
Time
Page 3
The purpose of controls is to estimate the exposure distribution
in the source population that produced the
cases. This simply means that controls should be similar enough
to cases that, if they had disease, they would
have been selected as a case. When selecting a control group,
epidemiologists consider the “WOULD
CRITERION”: If a member of the control group actually had the
disease under study WOULD he/she end up as
a case in your study? Answer should be YES.

There are several ways to sample controls from the source
population, each of which has advantages and
disadvantages for an epidemiologic study.
Control type How sampled Pros Cons
General population
controls: selected
from a defined
geographic
population
-Random digit dialing
-Residence lists
-motor vehicle
records
-Investigator usually assured
that they come from the same
base population as the cases
(e.g., residents of NY)
-Time consuming,
-Expensive
-Contact difficult
-Controls may remember
exposure differently than
cases (if they are not sick)
Hospital-based
controls: selected
when cases are
selected from a
hospital population

-Selected from other
units of the same
hospital from which
cases selected.
-Controls may have
disease, but not the
one of interest.
-Have similar selection factors
that led cases to hospital:
example insurance coverage,
distance to the hospital, etc.
-Easily identifiable and
accessible
-Accuracy of exposure recall
comparable to that of cases
since controls are also sick
-Since controls are ill, they
may not accurately represent
the exposure history in the
population that produced the
cases
-Hospital catchment areas
may be different for different
diseases
Special controls:
friends, spouses,
siblings, and
deceased
individuals.

These special
controls are rarely
used but are easily
sampled.
-May control for some
important lifestyle
characteristics which are risk
factors for disease - and are
often shared between family
and friends.
-Cases may have few
appropriate friends, are
widowed, or are only or
adopted children.
-Dead controls are more likely
than living controls to have
smoked and drank
Source
population
Cases Controls
Page 4

When is it desirable to conduct a case-control study?
• When exposure data are expensive or difficult to obtain
- Example: Pesticide study described above
• When disease has long induction and latent period
- Example: Cancer, cardiovascular disease
• When the disease is rare: if we measured a rare disease in a
cohort study, we would have to follow
many individuals to identify just a few cases of disease.
- Example: Studying risk factors for birth defects
• When little is known about the disease
- Example: Early studies of AIDS
• When the underlying population is dynamic
- Ex: Studying breast cancer on Martha’s Vineyard
A case-control study can be considered a more efficient form of
a cohort study. Cases are the same as those
that would be included in a cohort study. Controls provide a
quicker and inexpensive means of obtaining the
exposure experience in the population that gave rise to the
cases.
Analysis of case-control studies
Because controls are a sample of the population that produced
the cases, you often don’t know the size of the
total population, and so cannot calculate cumulative incidence

or incidence rate of disease, or calculate the
measures of association using the methods that we have learned.
Instead, we get a number called an odds.
Definition of odds: The ratio of the probability of an event
occurring to that of it not occurring.
Example: Probability of getting a heads on one coin toss = ½ =
.50. Probability of NOT getting a heads on one
coin toss = ½ = .50. Odds of getting a heads on a coin toss =
.5/.5 = 1:1
This is the 2 x 2 set up for case-control. Notice that we do not
tally totals for rows and columns because we
have only sampled from the population (totals would not reflect
the true population total).
Disease
Yes
(cases)
No
(controls)
Exposure
Yes a b
Exposed person: odds of being case or not:
Odds = a / b
No c d
Unexposed person: odds of being case or not:

Odds = c / d
The measure of association for a case-control study is the odds
ratio (OR): the ratio of odds of disease among
exposed to the odds of disease among not exposed. Just like the
IRR and CIR, the OR is a ratio measure of
association.
Page 5
Odds ratio = odds of an exposed person being a case = a/b =
ad
odds of unexposed person being a case c/d bc
Example: Case control study of spontaneous abortion and prior
induced abortion
Outcome: spontaneous
abortion
Case
(spon.
abortion)
Control

(live birth)
Exposure: prior
induced abortion
Yes 42 247
Exposed person: odds of being case or not:
Odds = 42/247
No 107 825
Unexposed person: odds of being case or not:
Odds = 107/825
Odds ratio = [(a/b) / (c/d)] = [(42/247) / (107/825)] = 1.3
Interpretation: Women with a history of induced abortion had a
30% increased risk of having a spontaneous
abortion compared to women who never had an induced
abortion.
Notice that we say a 30% increased risk here, not a 130%
increased risk. Why? If there is an equal number of
cases and controls exposed, the odds ratio will be 1.0, so we
always consider only the amount above 1.0 when
interpreting an odds ratio. You don’t have to memorize this –
prove it to yourself. Create a 2 x 2 table and plot
‘50’ in cells a and b, and ‘100’ in cells c and d, and calculate
the OR.
Types of bias in case-control studies
1. Information bias is possible, just like in a cohort study. Since

we go back in time to assess exposure in a
case-control study (after exposure and disease have already
occurred), knowledge of disease status may
affect determination of exposure status, and vice versa. The
retrospective nature of a case-control design also
means that the quality of exposure information may not be
ideal.
2. Control selection bias occurs when different criteria used to
define disease or sample controls between
exposed and unexposed individuals.
3. Recall bias may occur if cases are more likely to recall
exposure status because they are ill and are searching
for reasons to explain their illness. As a result, cases may
appear to be more likely to be exposed than controls
when really there is no difference.
We will continue discussing these types of biases in a later
module.
Summary
Advantages of the case control design:
• Efficient for rare diseases and diseases with long induction/
latent period –don’t have to wait for
disease to develop
• Can evaluate many risk factors for the same disease so good
for diseases about which little is known

Disadvantages of the case-control design:
• Inefficient for rare exposures - because the major comparison
it of exposed to unexposed
• Vulnerable to bias because of retrospective nature of study
• May have poor information on exposure because retrospective
• Difficult to infer temporal relationship between exposure and
disease
• Cannot measure incidence rates or cumulative incidence
directly
How do these strengths and weaknesses compare to cohort
studies?
Recap: Cohort versus case-control studies
The best way to keep these two observational designs straight is
to think about what the investigator uses to
classify subjects at the start of the study – exposure or disease.
In a cohort study, whether prospective or retrospective, the
investigator determines an exposed group and an
unexposed group. These individuals are followed for some
length of time (either forward or back in time) to
see if they get disease or not.
In a case-control study, the investigator determines who has
disease (cases) and selects a comparison
population (controls). These individuals are followed back for
some length of time to see if they were exposed
or unexposed.
Case-control Studies

Page 1
COHORT STUDIES
Recall that epidemiologic studies are either observational or
experimental. Epidemiologists most often
employ observational designs, which are generally less costly
and easier to conduct than experimental
designs. Cohort studies are one such observational design.
A cohort study is and observational study in which two or more
groups of people that are free of
disease and that differ according to the extent of exposure (e.g.
exposed and unexposed) are
compared with respect to disease incidence. Cohort studies are
roughly the observational equivalent
of experimental studies, but the researcher cannot randomly
assign the exposure. Instead, the
investigator identifies exposure that occurs “naturally” to
observe the relationship between exposure
and disease. This “natural experiment” starts by identifying a
population that is exposed, and another
group that is not exposed.
Example: Ranch Hand Study
• Exposed group: 1,264 Air Force servicemen who sprayed
agent orange during Vietnam War,

1962-1971
• Unexposed group: 1,264 Air Force servicemen who flew other
missions during Vietnam War
• Outcomes of interest: cancer, posttraumatic stress, adverse
pregnancy outcomes, etc.
• Principle: If Agent Orange is not associated with the
outcomes under study, then the outcome
rates will be the same in both groups (exposed and unexposed)
Timing of Cohort Studies
Cohort studies may be either retrospective or prospective. This
label describes how the study is
conducted. In a prospective cohort study, exposure has
occurred, but disease has not yet occurred,
and participants are followed forward in time either until they
get the outcome of interest or until the
study ends. In the retrospective design (next page), both
exposure and disease have occurred at the
start of the study. The investigator uses records and other
sources of information to determine
exposure status at an earlier time.

E
Prospective
O
O
E
E
O
O
O
O
*Study starts
Time
O and at risk for O
E
Prospective
O
O
O
O

E
EE
O
O
O
O
O
O
O
O
*Study starts
Time
*Study starts
Time
O and at risk for O
Page 2

Choosing Between a Retrospective Versus a Prospective Design
Because retrospective cohort studies rely on information that
has already been collected, they are
often cheaper and faster to conduct. They are also an efficient
means to study diseases with a long
induction (the time between exposure and disease occurrence)
or latent periods (the time between
disease initiation and disease identification), such as cancer.
For example, if we use a prospective
design to study such a disease, we would have to follow
individuals forward in time for many years
after exposure to see if they got the disease. With a
retrospective design, we need only to use the
records and count the time that has passed. However, using
historical data to conduct a study often
means that information about exposure is incomplete (since the
information used was often collected
for purposes other than conducting an epidemiologic study).
Prospective studies are more expensive,

time consuming, and are not efficient for studying diseases with
long latent periods. However, they
may have better exposure and confounder data than
retrospective studies and therefore be less
vulnerable to bias.
Fixed versus Dynamic Cohorts
Recall from Module 1 that in a fixed population, membership is
permanent. In a fixed cohort, that
means all members are followed for the same length of time,
and there is no loss to follow-up.
Exposure may be defined by a specific event, such as eating
contaminated food at a party. In this type
of study, we measure cumulative incidence.
In a dynamic cohort, we may follow individuals for different
lengths of time. Loss to follow-up may be
common, and often in this type of cohort the exposure is a
changeable characteristic (smoking,
occupation). This is where we use the incidence rate as our
measure of association.
Retrospective: When investigator begins
the study, all of the participants who will
develop disease already have it
E
O
O
O
O

E
EE
O
O
O
O
O
O
O
O
*Study starts
Time
O and at risk for O
Page 3
Recall from Module 1, a dynamic population. The x means the
individual got the disease of interest:
Jan 1990 Jan 2000 Jan 2010

Subject 1 ---------------------------x
Subject 2 ---------------------------x
Subject 3 ---------------------------------------------------------------
----
A fixed cohort would look like this:
Jan 1990 Jan 2000 Jan 2010
Subject 1 -------------------------------------------------------------x
Subject 2 -------------------------------------------------------------
Subject 3 -------------------------------------------------------------
Issues in Designing a Cohort Study
1. Selection of the exposed population
The choice of the exposed population depends upon hypothesis
under study and feasibility
considerations.
For rare exposures, you need to assemble special cohorts
(occupational groups, groups with
unusual diets etc.).
Examples of a special cohort study:
• Occupational groups
• Military personnel
• Victims of disaster

If exposure is common, you may want to use a general cohort
that will facilitate accurate and
complete ascertainment of data (doctors, nurses, well-defined
communities).
Example of a general cohort study:
• Residents of a city
• University students
• Members of an HMO
2. Selection of comparison (unexposed) group:
Just as an experimental design, the comparison (unexposed)
group should be as similar as possible to
the exposed group with respect to all other factors except the
exposure. If the exposure has no effect
on disease occurrence, then the rate of disease in the exposed
and comparison groups will be the
same.
Page 4
There are two possible types of comparison groups (unexposed)
in a cohort study, each of which may
be a “best choice” depending on the research question of
interest.

3. Sources of exposure information
• Pre-existing records - inexpensive, data recorded before
disease occurrence but level of detail
may be inadequate; records may be missing, usually don't
contain information on confounders
• Questionnaires, interviews: good for information not
routinely recorded but have potential for
recall bias
• Direct physical exams, tests, environmental monitoring may
be needed to ascertain certain
exposures
4. Sources of outcome information
• Death certificates
• Physician, hospital, health plan records
• Questionnaires
• Medical exams
5. Approaches to follow-up
In any cohort study, the ascertainment of outcome data involves
tracing or following all subjects from
exposure into the future. Resources utilized to conduct follow-
up: town lists, directories, telephone
books; birth, death, marriage records; driver's license lists,
physician and hospital records; relatives,
friends. This is a time consuming process but high losses to
follow-up raise doubts about the validity of
study. The goal is to obtain complete follow-up information on
all subjects regardless of exposure

status.
Analysis of Cohort Studies
Basic analysis of cohort data involves calculation of incidence
of disease among exposed and
unexposed groups.
Depending on available data, you can calculate cumulative
incidence or incidence rates. Whether the
information was collected retrospectively or prospectively does
not affect how the data is analyzed.
Comparison group Example
Internal comparisons:
-Unexposed members of same cohort Ex: Framingham Heart
Study
External comparisons:
-Comparison cohort: a cohort who is not
exposed from another similar population
Ex: Asbestos textile vs. cotton textile workers
-General population: common in occupational
studies; uses pre-existing data from the general
population as the basis for comparison
Ex. A study of asbestos and lung cancer with U.S.
male population as the comparison group

Example: Tuberculosis treatment and breast cancer study
This cohort study followed 1,047 women who were treated with
air collapse therapy and exposed to
numerous fluoroscopic examinations (radiation) and 717 who
received other treatments. A total of
47,036 woman-years of follow-up were accumulated during
which 56 breast cancer cases occurred.
Breast
Cancer
cases
Person-
Years of
follow-up
Exposed 41 28,001 IRexposed = 41/28,011 = 1.5/1,000 person-
years
Unexposed 15 19,025 IRunexposed = 15/19,025 = 0.8/1,000
person-years
Total 56 47,036 Rate ratio = RR = IRexposed/IRunexposed =
1.9
Interpretation: Women exposed to fluoroscopies had 1.9 times
the risk of breast cancer compared to
unexposed women.

Recall from Module 1 the concept of person-time. Those
person-years of follow-up do not necessarily
reflect the same amount of time for each individual. They
reflect the total amount of follow-up time
for all of the individuals in the exposed and unexposed groups –
which could mean many years of
follow-up for one person, but only a few years of follow-up for
another.
Indirect standardization
Another method of analyzing cohort data is called indirect
standardization. Recall from Module 2 that
we used direct standardization in order to make 2 populations
more comparable, by calculating age-
adjusted rates. Indirect standardization is a similar process,
where the general U.S. population again
serves as the “standard” or reference population.
In a cohort study, we can calculate the expected mortality rate
for the study population, using age-
specific mortality rates from the general population. In other
words, we use the general population to
get an idea of how many deaths we expect under average
conditions. These expected values can be
generated for specific age or gender groups. We then compare
these expected values to what we
observe in the study population to get an idea of if
disease/deaths are elevated in the cohort. The
resulting measure is a standardized mortality ratio (SMR).
(SMR also stands for standardized morbidity
ratio, if you are examining disease rather than deaths.) We
won’t spend time in this course working on
how to specifically calculate the SMR, but this is a summary
calculation:

SMR = observed number of disease or deaths / expected number
of disease or deaths
Bias Related to Cohort Studies
Recall from Module 3 that bias is systematic error that gives us
inaccurate estimate of the association
between a risk factor/exposure and the outcome. There are
several biases of concern in cohort
studies: selection bias and information bias.
Page 6
1. Selection bias occurs when characteristics of those
individuals selected for study are systematically
different than those not selected for study. This might occur if
participation is related to exposure and
disease. For example, those who participate in the study are
more likely to have been exposed. Loss-
to-follow-up is another type of selection bias. Here, if the
investigator loses track of individuals in the
cohort for various reasons, the loss-to-follow-up is greater or
lesser for those exposed versus not
exposed. To minimize potential selection bias due to loss-to-
follow-up, the investigator should make
sure to attempt to follow-up both exposed and unexposed
individuals with equal intensity.
2. Another bias observed specifically in occupational cohorts is
called the Healthy Worker Effect.
Individuals who work, because they are healthy enough for

employment, tend to be healthier than the
general population, which includes individuals who do not
work. If an occupational cohort makes
comparisons using the general population as the unexposed
population, this study is subject to
selection bias - because healthier individuals (the workers) are
selected into the exposed population.
This phenomenon tends to minimize the impact of any possible
occupational exposure. Using an
internal comparison group, such as workers with low exposures,
will minimize the Healthy Worker
Effect.
3. Misclassification is a type of information bias that arises
when a subject is incorrectly classified
according to exposure or disease status. This might mean that an
exposed individual is incorrectly
assigned as exposed, and vice versa. It could also reflect an
individual with disease who is incorrectly
classified as not having disease, and vice versa. Recall that in
an experimental study, investigators are
often blinded to participant’s disease status so that knowledge
of disease status does not affect
measurement of exposure. We will discuss more of this type of
bias in Module 5.
Summary
The major advantages of cohort studies are:
• Efficient for rare exposures or for diseases with long
induction and latent period
• Can evaluate multiple effects of an exposure
• If prospective, good information on exposures, less
vulnerable to bias, and clear temporal

relationship between exposure and disease
The major disadvantages of the cohort design include:
• Inefficient for rare outcomes
• If retrospective, poor information on exposure and other key
variables, more vulnerable to bias
• If prospective, expensive and time consuming, inefficient for
diseases with long induction and
latent period
Keep these strengths and weaknesses in mind for comparison
with case-control studies.
Cohort Studies

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