Case-control

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Case-control is a type of epidemiological study
design. Case-control studies are used to identify factors that may
contribute to a medical condition by comparing subjects who have that condition
(the 'cases') with patients who do not have the condition but are otherwise
similar (the 'controls').[1]

Case-control studies are a relatively inexpensive and frequently-used type of
epidemiological study that can be carried out by small teams or individual
researchers in single facilities in a way that more structured experimental
studies often cannot be. They have pointed the way to a number of important
discoveries and advances, but their retrospective,
non-randomized nature limits the conclusions that can be drawn from them.

The great triumph of the case-control study was the demonstration of
the link between tobacco smoking and lung
cancer, by Sir Richard Doll and others
after him. Doll was able to show a statistically significant association between
the two in a large case control study.[2]
Opponents, usually backed by the tobacco industry,
argued (correctly) for many years that this type of study cannot prove
causation, but the eventual results of cohort studies confirmed
the causal link which the case-control studies suggested, and it is now
accepted that tobacco smoking is the
cause of about 87% of all lung cancer mortality in the
US.

Contents

1 Case-control
studies

1.1 Study
methodology

1.2 Comparison with
cross-sectional studies

2 Problems with case-control
studies

3 See also

4 References

5 Further reading

6 External links

[edit]
Case-control studies

For establishing cause-and-effect relationships, e.g., between types of
sexual behavior developing cervical cancer, no study design is more highly
regarded than the randomized experiment. For medical
interventions, the 'gold standard' is the double
blind randomized
controlled trial, a specific type of experiment. While such trials may be
ideal for testing the efficacy of (what are hoped to be) beneficial
interventions, such as surgeries or drug treatments, there are many instances in
which trials would be impossible, impractical, and/or unethical. For example, it
would generally be seen as unethical to randomly assign research subjects to be
exposed to toxic substances in order to evaluate the substances' effects.

Studying infrequent events such as death from cancer using randomized
clinical trials or other controlled prospective studies requires that large
populations be tracked for lengthy periods to observe disease development. In
the case of lung cancer this could involve 20 to 40 years, potentially longer
than the careers of many epidemiologists. In addition, these studies, which
generally rely on government funding, are unlikely to be supported because of
the low likelihood that the population will develop the disease. Case-control
studies use patients who already have a disease or other condition and look back
to see if there are characteristics of these patients that differ from those who
don’t have the disease.

The case-control study provides a cheaper and quicker study of risk factors;
if the evidence found is convincing enough, then resources can be allocated to
more "credible" and comprehensive studies.

One major disadvantage of case-control studies is that they do not
give any indication of the absolute risk of the factor in question. For
instance, a case-control study may tell you that a certain behavior may
be associated with a tenfold increased risk of death as compared with the
control group. Although this sounds alarming, it would not tell you that the
actual risk of death would change from one in ten million to one in one million,
which is quite a bit less alarming. For that information, data from outside the
case-control study must be consulted.

[edit]
Study methodology

[edit]
Comparison with cross-sectional studies

Cross-sectional
studies involve data collected at a single point in time, often using survey
research methods. In epidemiology, cross-sectional studies often involve
secondary analysis of data collected for another purpose. Major sources of such
data are often large institutions like the Census
Bureau or the Centers for
Disease Control in the United States. Such studies can cover study groups as
large as the entire population of the United States, but others are small and
geographically limited.

Cross-sectional studies can contain individual-level data (one record
per individual, for example, in national health surveys). Others, however, might
only convey group-level information; that is, no individual records are
available to the researcher. Recent census data is not provided on individuals -
in the UK individual census data is released only after a century. Instead data
are aggregated at the group level. For example, by zip code, urban zone, or even
by states/provinces or country.

For example, although cross-sectional
studies confirm that people who consume large amounts of alcohol also show high rates of
many other diseases, they cannot provide confirmation that the first variable is
a cause and the second variable is its effect. The primary difficulty is that
most cross-sectional data have nothing to say about temporal order: which came
first, the drinking or the depression? An important secondary difficulty is that
cross-sectional studies often fail to 'control for' confounding factors, third
variables that affect or even determine the relationship between the putative
cause and effect.

Another complication facing epidemiologists conducting secondary analysis of
cross-sectional data is that often data are only available on an aggregate or
"ecological" basis. For example, statistics on infant mortality and low birth
weight might not be available on a level below the city or county. Inferences
about individuals cannot reliably be made from ecological data; such inferences
run afoul of the ecological fallacy.
For example, it might be true that there is no correlation between infant
mortality and family income at the city level, while still being true that there
is a strong relationship between infant mortality and family income at the
individual level. All aggregate statistics are subject to compositional effects,
so that what matters is not only the individual-level relationship between
income and infant mortality, but also the proportions of low, middle, and high
income individuals in each city.

Because case-control studies are based on individual-level data, they do not
exhibit the problems associated with aggregated cross-sectional data.

In the case-control study, the association is determined for each
individual case-control pair, then aggregated. This provides a more
specific analysis of the possible associations, and potentially determines more
accurately which possible causes are directly related to the effect being
studied, and which are merely related by a common cause.

One benefit of cross-sectional studies is that they are considered to be
"hypothesis generating", such that clues to exposure/disease relationships can
often be seen in these studies, and then other studies, such as case-control,
cohort studies or even sometimes randomized trials can be implemented to study
this relationship.

[edit]
Problems with case-control studies

They are rated as low quality, grade 3, on a standard scale of medical
evidence.[3]

One problem is that of confounding. The nature of
case-control studies is such that it is difficult, often impossible, to separate
the chooser from the choice. For example, studies of road accident victims found
that those wearing seat belts were 80% less likely to suffer serious injury or
death in a collision, but data comparing rates for those collisions involving
two front-seat occupants of a vehicle, one belted and one unbelted, show a
measured efficacy only around half that. Several case-control studies have shown
a link between bicycle helmet use and
reductions in head injury, but long-term trends - including from countries which
have substantially increased helmet use through compulsion - show no such
benefit. Analysis of the studies shows substantial differences between the
'case' and 'control' populations, with much of the measured benefit being due to
fundamental differences between those who choose to wear helmets voluntarily and
those who do not.

More controversially, a significant number of case-control studies identified
a link between combined hormone replacement therapy (HRT) and reductions in
incidence of coronary heart disease (CHD) in women. Credible mechanisms were
advanced as to why this link might be causal, and a consensus arose that HRT was
protective against CHD.[4]
The evidence was sufficiently compelling that a full clinical trial was
initiated - and this indicated that the effect was both far smaller and in
the opposite direction - combined HRT showed a small but significant
increase in risk of CHD in the study population. Subsequent analysis has
shown that the group of women opting for HRT were predominantly from higher
socio-economic groups and therefore had, on average, better diet and exercise
habits. The studies had falsely attributed the benefits of these confounding
factors to the intervention itself.[5]
There have been similar controversies regarding links between vitamins and
cancer; MMR and autism; antibiotics and asthma; cannabis and psychosis. All
these have been identified through small-scale case-control studies but fail to
show any effect in whole population time series or other investigations.

A comparison with the tobacco/cancer link is instructive. Here the
case-control studies pointed the way, but further confirmation was available in
the form of time series showing rates of lung cancer tracking levels of smoking
in whole populations, and in the form of laboratory experiments on animals.

A further problem is that case-control studies depend on correct and honest
reporting of the risk factor, which may be many years in the past or may be seen
as socially (un)desirable. Case-control studies can be biased if the risk factor
inquired about is incorrectly reported. Recent research has shown that a
substantial majority of highly cited case-control studies are subsequently
contradicted or found to be substantially over-ambitious when more rigorous
investigations are conducted.

As a result the following guidelines have been proposed when assessing
case-control evidence [6]:

Do not turn a blind eye to
contradiction. Do not ignore contradictory evidence but try to understand the
reasons behind the contradictions.

Do not be seduced by mechanism. Even where a plausible mechanism exists,
do not assume that we know everything about that mechanism and how it might
interact with other factors.

Suspend belief. Of the researchers defending observational studies,
Pettiti says this: "belief caused them to be unstrenuous in considering
confounding as an explanation for the studies". Do not be seduced by your
desire to prove your case.

Maintain scepticism. Question whether the factor under investigation can
really be that important; consider what other differences might
characterise the case and control groups. Do not extrapolate results beyond
the limits of reasonable certainty (e.g. with grandiose forecasts of "lives
saved"). Specifically, ask whether the the rare
disease assumption was used to overreach.

Case-control studies are a valuable investigative tool, providing rapid
results at low cost, but caution should be exercised unless results are
confirmed by other, more robust evidence.

[edit]
See also

Nested
case-control study

Epi
Info software program

OpenEpi
software program

[edit]
References

^
"8. Case-control and
cross sectional studies".

^
"Sir Richard
Doll: A life's research ", BBC News, 22 June 2004

^
Bob Phillips; Chris Ball, Dave Sackett, Doug Badenoch, Sharon Straus, Brian
Haynes, Martin Dawes (May 2001). "Levels of
Evidence". Oxford Centre for Evidence-based Medicine. Retrieved on
2008-05-16.

^
Estrogen replacement therapy and coronary heart disease; a quantitive
assessment of the epidemiological evidence Stampfer M, Colditz G. Int Jour
Epid 2004;33:445-53

^
The hormone replacement - coronary heart disease conundrum: is this the
death of observational epidemiology? Lawlor DA, Smith GD & Ebrahim S,
International Journal of Epidemiology, 2004;33:464-467

^ Hormone replacement therapy and coronary heart disease: four
lessons. Petitti D, International Journal of Epidemiology, 2004;33:461-463

[edit]
Further reading

Stolley, Paul D.; Schlesselman, James J. (1982).
Case-control studies: design, conduct, analysis. Oxford [Oxfordshire]:
Oxford University Press. ISBN
0-19-502933-X.  (Still a very useful book, and a
great place to start, but now a bit out of date.)

[edit]
External links

A list of computer
programs for genetic analysis including case-control genetic association
analysis

v • d • e

Biomedical
research: Study designs /
Design of
experiments

Overview

Clinical
trial · Clinical
trial protocol · Clinical
trial management ·
Academic
clinical trials · Study
design

Controlled
study
(EBM
I to II-1; A to B)

Randomized
controlled trial (Blind
experiment, Open-label
trial)

Observational
study
(EBM
II-2 to II-3; B to C)

Cross-sectional
study vs. Longitudinal
study

Cohort study
(Retrospective
cohort study, Prospective
cohort study)

Case-control
study (Nested
case-control study)
Case series · Case study/Case
report

Epidemiology/
methods

Occurrence:
Incidence
(Cumulative
incidence) · Prevalence (Point
prevalence, Period
prevalence)

Association:
Relative
risk · Odds ratio · Hazard
ratio · Attributable
risk
other: Virulence · Infectivity · Mortality
rate · Morbidity · Case
fatality · Sensitivity
and specificity

Trial/test types

In
vitro/In vivo · Animal
testing · First-in-man
study · Multicenter
trial · Seeding
trial · Vaccine
trial

Analysis
of clinical trials

Risk-benefit
analysis

Category •
Glossary •
List
of topics

Retrieved from "http://en.wikipedia.org/wiki/Case-control"

Categories: Epidemiology
| Experimental
design

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This page was last modified on 29 December 2008, at 19:19.

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