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Case-Control Studies
Feature of Case-control Studies
1. Directionality Outcome to exposure2. Timing Retrospective for exposure, but case-
ascertainment can be either retrospective or concurrent
3. Sampling Almost always on outcome, with
matching of controls to cases
Two Characteristics of Cases
1. Representativeness:
Ideally, cases are a random sample of all cases of interest in the source population (e.g. from vital data, registry data). More commonly they are a selection of available cases from a medical care facility. (e.g. from hospitals, clinics)
2. Method of Selection Selection may be from incidence
or prevalence case:
Incident cases are those derived
from ongoing-ascertainment of
cases over time.
Prevalent cases are derived from a
cross-sectional survey.
Characteristics of Controls
Who is the best control? What
universe should controls come from?
If cases are a random sample of cases
in the population. Then controls should
be a random sample of all non-cases
in the population sampled at the same
time.
Three Qualities Needed in Controls
Comparability is more important than
representativeness in the selection of controls
The control should be at risk of the disease
The control should resemble the case in all
respects except for the presence of disease
(and any as yet undiscovered risk factors for
disease)
Comparability vs. Representativeness
Usually, cases in a case-control
study are not a random sample of all
cases in the population. And if so, the
controls must be selected in the same
way (and with the same biases) as the
cases.
If follows from the above, that a
pool of potential controls must be
defined. This is a universe of people
from whom controls may be selected
(study base).
Study Base
Imagining the study base is a useful
exercise before deciding on control
selection.
The study base is composed of a
population at risk of exposure over
a period of risk of exposure.
Cases emerge within a study base. Controls should emerge from the same study base, except that they are not cases.
For example, if cases are selected exclusively from hospitalized patients, controls must also be selected from hospitalized patients.
If cases must have gone through a certain ascertainment process (e.g. screening), controls must have also.
If cases must have reached a certain age before they can become cases, so must controls.
If the exposure of interest is cumulative over time, the controls and cases must each have the same opportunity to be exposed to that exposure.
Five issues in Matching
1. Control selection is usually through matching.
Matching variables (e.g. age), and matching criteria (e.g. within the same 5 year age group) must be set up in advance.
Five issues in Matching 2. Controls can be individually matched (most
common) or Frequency matched.
Individual matching: search for one (or more) controls who have the required matching criteria, paired (triplet) matching is when there is one (two) control (s) individually matched to each cases.
Frequency matching: select a population of controls such that the overall characteristics of the case, e.g. if 15% cases are under age 20, 15% of the controls are also
Five issues in Matching3. Avoid over-matching, match only on factors
KNOWN to be cause of the disease.
4. Obtain POWER by matching MORE THAN
ONE CONTROL per case. In general, N of
controls should be ≤ 4, because there is no
further gain of power above that.
5. Obtain Generalizability by matching by
matching more than one type of control.
Advantages and Disadvantages of C-C Studies
Advantages:
1. Only realistic study design for uncovering etiology in rare diseases
2. Important in understanding new diseases
3. Commonly used in outbreaks investigation
4. Useful if inducing period is long
5. Relatively inexpensive
Disadvantages
1. Susceptible to bias if not carefully designed
2. Especially susceptible to exposure
misclassification
3. Especially susceptible to recall bias
4. Restricted to single outcome
5. Incidence rates not usually calculate
6. Cannot assess effects of matching
variables
Examples of Problems
Doll’s 1952 study of smoking and lung cancer. The problem was that the control population ( lung disease) was biased in relation to the exposure.
McMahon’s 1981 study of coffee and pancreatic cancer. Problem was that some of the controls may have been biased in relation to the exposure, because diseases related to coffee were excluded from the control series.
Some Important Discoveries
1950‘s Cigarette smoking and lung cancer
1970’s Diethyl stilbestrol and vaginal
adenocarcinoma Post-menopausal estrogens and
endometrial cancer
1980 ’s Aspirin and Reyes sydrome Tampon use and toxic shocks syndrome L-tryptopham and eosinophilia-myalgia
syndrome AIDS and sexual practices
1990’s Vaccine effectiveness Diet and cancer
Basic AnalysisFor one control
Data is expressed in a four-fold table, and an odds ratio is calculated (relative risks have no meaning here-why?)
Case Controls
Exposed a b
Unexposed c d
OR= ad/bc
Paired Analysis
Case
Exposed Unexposed
Exposed Both Mixed
Controls
Unexposed Mixed Neither
Paired Analysis
For one control
Case
Exposed Unexposed
Exposed r sControls
Unexposed t uMcNemar chi2=(t+s)2/(t-s)
More points about case-control analysis
The odds ratio is a good estimate of the relative risk when the disease is rare (prevalence <20%)
Can be extended to N>1 controls Statistical testing is by simple chi-square
(unmatched analysis) or by McNemar’s chi square (matched-pairs analysis)
Can be extended to multiple strata ( Mantel-Haenzel chi-square)
Theoretical Foundation
“Case-control studies should be viewed
as efficient sampling schemes of the
disease experience of the underlying
open or closed cohorts”
“The exposure odds ratio derived from
case-control studies equals the disease
odds ratio derived from cohort studies”
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