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”