Case-Control Studies

1
Case-Control Studies
2
Feature of Case-control Studies

• Directionality
• Outcome to exposure
• 2. 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

3
Two Characteristics of Cases

• 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)

4

• 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.

5
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.

6
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)

7
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.

8

• 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).

9
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.

10

• 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.

11

• 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.

12
Five issues in Matching

• 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.

13
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

14
Five issues in Matching

• 3. 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.

15
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

16
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

17
Examples of Problems

• Dolls 1952 study of smoking and lung cancer. The
  problem was that the control population ( lung
  disease) was biased in relation to the exposure.
• McMahons 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.

18
Some Important Discoveries

• 1950s
• Cigarette smoking and lung cancer
• 1970s
• Diethyl stilbestrol and vaginal adenocarcinoma
• Post-menopausal estrogens and endometrial cancer

19

• 1980 s
• Aspirin and Reyes sydrome
• Tampon use and toxic shocks syndrome
• L-tryptopham and eosinophilia-myalgia syndrome
• AIDS and sexual practices
• 1990s
• Vaccine effectiveness
• Diet and cancer

20
Basic Analysis

• For one control
• Data is expressed in a four-fold table, and
  an odds ratio is calculated (relative risks have
  no meaning here-why?)

OR ad/bc
21
Paired Analysis
22
Paired Analysis
23
More points about case-control analysis

• The odds ratio is a good estimate of the relative
  risk when the disease is rare (prevalence lt20)
• Can be extended to Ngt1 controls
• Statistical testing is by simple chi-square
  (unmatched analysis) or by McNemars chi square
  (matched-pairs analysis)
• Can be extended to multiple strata (
  Mantel-Haenzel chi-square)

24
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