Case-Control Studies
(retrospective studies)
Sue Lindsay, Ph.D., MSW, MPH
Division of Epidemiology and Biostatistics
Institute for Public Health
San Diego State University
Case Control Study Design
select
select
Source
Population
Cases:
Controls:
With Disease
No Disease
Exposed to
risk factor
Not exposed
to risk factor
Exposed to
risk factor
Not exposed
to risk factor
Case-Control Study Design
• The hallmark of the case-control study
design is that it begins with cases and
compares them with non-cases (controls).
ASSESS
EXPOSURE
SOMETIME
IN
THE PAST
SELECT CASES
AND CONTROLS
Start here
Design Considerations
• These investigations are initially oriented to disease status
• The objective is to compare the odds of exposure among
persons with the disease to the odds of exposure among
persons without the disease
• You need a well-defined source population
• How well can you identify individuals with the disease?
Case identification should be as complete as possible
within the source population.
• The sample of cases should be representative of all cases.
Design Considerations
• The sample of controls should be representative of the
general population.
• Can you accurately detect exposures to your risk factor?
• When possible verify exposures by multiple methods:
interview, medical record review, blood test etc.
• How should you select your cases? How should you select
your controls?
• Selection of cases or controls should not be influenced by
prior exposure to the risk factor.
Steps in Conducting
Case-Control Studies
1. Select source population
2. Identify and select cases
3. Identify and select controls
•
Match cases to controls
•
Group matching or individual matching?
4. Measure exposure in cases and controls
5. Compare odds of exposure in diseased to odds
of exposure in non-diseased persons.
Sources of Case Selection
• Population-based case-control studies
• Surveillance systems
• Patients identified by:
•
•
•
•
Physician practices
Clinics
Registries
Hospitals
• Hospital-based case-control studies
• Cases admitted to a hospital or hospitals
Issues in Case Selection
• Are the cases selected representative of all cases
in the community?
• Are there institutional or hospital differences
which may affect the study?
• Are there physician practice differences that may
affect the study?
• Should you use incident cases or prevalent
cases?
Incident or Prevalent Cases?
Prevalent Cases
• More cases available
• May over-represent survivors
• Risk factors may be associated with survivorship
Incident Cases
• Must be able to identify new cases
• Survivorship/risk bias less of a problem
• Early deaths will still be excluded
Characteristics of Controls
• Should be from source population
• Should be representative of general population, or at
least the source population
• Should be comparable to cases except on risk factor
• Random selection when possible
• Selected independently of exposure
• Should be from same sampling time frame
• Should be “at risk” for being categorized as a case
Sources of Population-Based Controls
• Random sample of total population
• Random sample from source population
• Neighborhood controls (random households)
• Primary care clinics, private practice offices
• Other diseases – registries
• Friends
Hospital-Based Controls
• Captive population
• Poorly defined reference population
• Not comparable to general community
• Possibly older, sicker, risk factor differences
• Use a sample of all other patients admitted?
• Select specific diagnoses for control group?
• What diseases to include and exclude in the
control group?
Selection Bias
Disease

No Disease

Not Exposed
Exposed
Selection bias stems from an absence of comparability between the two
groups being studied (cases and controls).
Misclassification Bias
Disease

No Disease

Not Exposed

Exposed
Incorrect determination of exposure or outcome or both.
Non-differential misclassification bias
Differential misclassification bias
Diagnostic suspicion bias particularly challenging
Case-Control 2 X 2 Table
First Select
Exposed (+)
Not Exposed
(-)
Cases
Controls
a
b
c
d
a+c
b+d
Then Classify Exposure
Case-Control Analysis
• In case-control studies we cannot calculate
risk or incidence: therefore we cannot
calculate relative risk as we can in cohort
studies
• Instead, calculate the Odds Ratio (OR).
Based on the concept of relative odds of
disease
Case-Control Analysis
Odds of case exposure
Odds of control exposure
Proportion cases exposed
Proportion controls exposed
Proportion cases not exposed
Proportion controls not exposed
The Case-Control 2 X 2 Table
Cases
Controls
Exposed (+)
a
b
Not Exposed (-)
c
d
Proportions Exposed
a/a+c
b/(b+d)
Proportions Not Exposed
c/a+c
d/(b+d)
The Odds of Case Exposure
The Odds of Control Exposure
Exposed (+)
Not Exposed
(-)
Odds of case exposure:
Cases
Controls
a
b
c
d
a/(a+c)
c/(a+c)
=
Odds of control exposure: b/(b+d) =
d/(b+d)
a
c
b
d
The Odds Ratio in a Case Control Study
Odds of case exposure
Odds
=
Ratio
Odds of control exposure
a/c
OR =
=
b/d
a
c
ad
b
d
bc
= cross-product ratio
Case-Control Study of CHD and Smoking
CHD Cases
Controls
Smoking (+)
112
176
No Smoking (-)
88
224
OR = (112 x 224)
(88 x 176)
=
1.62
The odds that a patient with CHD was exposed to smoking is 1.62 times greater
than a patient without CHD.
Interpretation of Odds Ratio Estimates
• If OR = 1: Risk in Exposed = Risk in Non-exposed
(No Association)
• If OR > 1: Risk in Exposed > Risk in Non-exposed
(Positive Association)
• If OR < 1: Risk in Exposed < Risk in Non-exposed
(Protective Association)
Another way to look at the Odds Ratio
Cases
Controls
Exposed (+)
a
b
Not
Exposed (-)
c
d
The OR can be viewed as the ratio
of the product of the 2 cells that
support the hypothesis, cells a and d,
(diseased people exposed and
non-diseased people unexposed)
to the product of the 2 Cells
that negate the null hypothesis of association,
cells b and c, (exposed non-diseased
people and unexposed diseased people)
Case-Control Odds Ratio:
An Estimation of Relative Risk
• Case- control Odds Ratios can be used to estimate Relative
Risk if the following conditions are met:
• The controls are representative of the general population
• The cases are representative of all cases
• The frequency of the disease in the population is small
Exposed
Not Exposed
RR= a/(a+b)
c/(c+d)
Cases
Controls
a
c
b
d
If a is small in relation to b
If c is small in relation to d
a/b
=
c/d
ad
=
bc
A Rare Disease
Exposed (+)
Not Exposed
(-)
Cases
Controls
45
4955
5000
29
4971
5000
Relative Risk = (45/5000)/(29/5000) = 1.55
Odds Ratio
= (45 x 4971)/(29 x 4955) = 1.56
A Common Disease
Cases
Exposed (+)
Not Exposed
(-)
Controls
4500
500
5000
2900
2100
5000
Relative Risk = (4500/5000)/(2900/5000) = 1.55
Odds Ratio
= (4500 x 2100)/(2900 x 500) = 6.52
Problems with Selections of Controls:
An Example Using Coffee and
Pancreatic Cancer
• MacMahan, 1981, case-control study of pancreatic cancer
• Cases drawn from 11 Boston and Rhode Island hospitals histologically confirmed pancreatic cancer
• Controls selected from same hospitals, admitted by the
same physician as each case
• The association between coffee drinking and pancreatic
cancer was not the main hypothesis of the study
Odds Ratio in Men
Men
Coffee drinking
No coffee
Pancreatic Cancer
Controls
207
275
9
32
OR = (207 x 32)/(275 x 9) = 2.68
Odds Ratio in Women
Women
Coffee drinking
No coffee
Pancreatic Cancer
Controls
140
280
11
56
OR = (140 x 56)/(280 x 11) = 2.55
Biased Control Selection
• Controls were patients hospitalized at the same time
by the same physician who hospitalized the cases
• Easier to obtain physician cooperation and control
participation
• Most admitting physicians were gastroenterlogists
• Gastroenterologists were more likely to admit control
patients with other GI disorders
• Patients with serious GI disorders were less likely to
consume coffee
Odds Ratio in Women
Women
Coffee drinking
No coffee
Pancreatic Cancer
Controls
140
280
11
56
1. The percent of controls reporting coffee drinking was less than expected
2. The percent of controls reporting no coffee drinking was greater than expected
3. Controls were not representative of the general population
Recall Issues
Can subjects remember exposure accurately?
• Recall Limitations
• Subject has incorrect information, forgets, does
not have knowledge
• Recall Bias
• Selective recall by cases
• Differential recall between cases and controls
Matching in Case-Control Studies
• Purpose: To control for confounding
• Confounder:
•
A known risk factor for your disease of interest
•
Also associated with your risk factor
•
Distorts the association between your risk factor and disease
• Matching: Selects controls so that they are similar to the cases
on confounding variables: age, sex, ses, etc.
• Increases statistical precision of estimates allowing smaller
sample size
Types of Matching in Case-Control Studies
• Group Matching
• Match by frequency or
proportion of a selected
characteristic
• Individual Matching
• Pair-wise matching, each
case is paired with a
similar control
Examples of Types of Matching
• Group Matching
• 25% of cases married,
controls selected to be
25% married
• Individual Matching
• Case is a 45 year old
Caucasian woman, control
is selected who is also a
45 year old Caucasian
woman
Problems With Matching in CaseControl Studies
• Practical
• Attempting to match on too many characteristics
• Time consuming
• Cases who are not successfully matched must be discarded
from the analysis
• Analytical
• When controls are matched to cases on a given characteristic,
that characteristic cannot be studied as an independent risk
factor for the disease
• Do not match on a characteristic you are interested in
studying!!
Practical Problems with Matching
• Match on age, sex, race,
marital status, number of
children, zip code
• Can you find a control who
is a 35 year old Caucasian
male, married, 4 children in
zip code 92123?
General Guidelines for Matching in
Case-Control Studies
• Only match on variables that are known risk
factors for your disease of interest.
• Do not match on variables whose relationship
with the disease needs to be studied
• Beware of unplanned matching and overmatching
Oral Contraceptives and Cancer:
An Example of Unplanned Matching
Cancer
Best-Friend
Controls
Contraceptive use
a
b
No contraceptive use
c
d
The % of controls
reporting OC use
Is likely to be greater
than expected
Best friends share lifestyle characteristics with cases which will affect
any association that is observed
Analysis of a Case-Control Study with
Pair-wise Matching
Control
Exposed
Control
Not Exposed
Case exposed
W
X
Case not exposed
Y
Z
W and Z are concordant pairs, X and Y are discordant pairs
OR = X
Y
Example of a Case-Control Study with
Pair-wise Matching
• Antunes, 1979, case-control study of endometrial cancer
• Baltimore hospitals: 1973-1977
• Research Question: Is there an association between
estrogen use and endometrial cancer?
• Selected cases with Stage 1 tumors
• Pair-wise matched with controls by hospital, race, and age
Estrogen Use and Endometrial Cancer
Control
Used Estrogen
Control
No estrogen
Case: used estrogen
17
76
Case: no estrogen
10
111
OR
= 76 = 7.6
10
Use of Multiple Controls
• Controls of the same type
• Controls of different types
• Multiple controls per case will increase the statistical
power of your study
• Up to case-control ratios of approximately 1:4
When to Use Multiple Controls of
Different Types?
• A single control group may be biased in some way
• A hospitalized control group is non-representative of the
community
• Neighborhood or best-friend controls are overmatched
• Can learn more about the disease process
Multiple Controls of Different Types:
Prenatal Radiation and Brain Tumors in Children
Cases
Children with
brain tumors
Normal Controls
Cancer Controls
Children
with no cancer
Children with
other types of
cancer
Radiation and Brain Tumors
Is there recall bias?
25
% Radiation Exposure
• Prenatal radiation is a
risk factor specifically
for brain tumors (not
all cancers)
20
15
10
5
0
Brain
Tumors
Other Normal
Cancer Controls