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A case–control study (also known as case–referent study) is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. Case–control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have the condition with patients who do not have the condition but are otherwise similar. They require fewer resources but provide less evidence for causal inference than a randomized controlled trial. A case–control study is often used to produce an odds ratio. Some statistical methods make it possible to use a case–control study to also estimate relative risk, risk differences, and other quantities.
The case–control study is frequently contrasted with cohort study, wherein exposed and unexposed subjects are observed until they develop an outcome of interest.
Controls can carry the same disease as the experimental group, but of another grade/severity, therefore being different from the outcome of interest. However, because the difference between the cases and the controls will be smaller, this results in a lower power to detect an exposure effect.
As with any epidemiological study, greater numbers in the study will increase the power of the study. Numbers of cases and controls do not have to be equal. In many situations, it is much easier to recruit controls than to find cases. Increasing the number of controls above the number of cases, up to a ratio of about 4 to 1, may be a cost-effective way to improve the study.
A retrospective study, on the other hand, looks backwards and examines exposures to suspected risk or protection factors in relation to an outcome that is established at the start of the study. Many valuable case–control studies, such as Lane and Claypon's 1926 investigation of risk factors for breast cancer, were retrospective investigations. Most sources of error due to confounding and bias are more common in retrospective studies than in prospective studies. For this reason, retrospective investigations are often criticised. If the outcome of interest is uncommon, however, the size of prospective investigation required to estimate relative risk is often too large to be feasible. In retrospective studies the odds ratio provides an estimate of relative risk. One should take special care to avoid sources of bias and confounding in retrospective studies.
Compared to prospective Cohort study they tend to be less costly and shorter in duration. In several situations, they have greater statistical power than cohort studies, which must often wait for a 'sufficient' number of disease events to accrue.
Case–control studies are observational in nature and thus do not provide the same level of evidence as randomized controlled trials. The results may be confounded by other factors, to the extent of giving the opposite answer to better studies. A meta-analysis of what was considered 30 high-quality studies concluded that use of a product halved a risk, when in fact the risk was, if anything, increased. It may also be more difficult to establish the timeline of exposure to disease outcome in the setting of a case–control study than within a prospective cohort study design where the exposure is ascertained prior to following the subjects over time in order to ascertain their outcome status. The most important drawback in case–control studies relates to the difficulty of obtaining reliable information about an individual's exposure status over time. Case–control studies are therefore placed low in the hierarchy of evidence.
When the logistic regression model is used to model the case–control data and the odds ratio is of interest, both the prospective and retrospective likelihood methods will lead to identical maximum likelihood estimations for covariate, except for the intercept. The usual methods of estimating more interpretable parameters than odds ratios—such as risk ratios, levels, and differences—is biased if applied to case–control data, but special statistical procedures provide easy to use consistent estimators.
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