Case–control study
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.
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.
Study methodology
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.
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 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.
See also
- Nested case-control study
- Epi Info software program
- OpenEpi software program
References
- ↑ "8. Case-control and cross sectional studies".
- ↑ "Sir Richard Doll: A life's research ", BBC News, 22 June, 2004
- ↑ Bob Phillips (May 2001). "Levels of Evidence". Oxford Centre for Evidence-based Medicine. Retrieved 2008-05-16. Unknown parameter
|coauthors=ignored (help) - ↑ 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
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.)
External links
- A list of computer programs for genetic analysis including case-control genetic association analysis
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