For example, we might match the sex of the control to the sex of the case. In a matched study, we enroll controls based upon some characteristic(s) of the case. The other basic type is a matched case-control study.

Unconditional logistic regression (The method is used to simultaneously adjust for multiple confounders a multivariable analysis).

Fisher’s Exact test (This test is used if expected cell size is <5).

Mantel-Hanszel statistic (This test takes into account the possibility that there are different effects for the different strata (e.g., effect modification)).

Analytic methods for non-matched case-control studies include: For example, we may enroll 105 cases and 178 controls. In this study design, the number of controls does not necessarily equal the number of cases. The first is a non-matched case-control study in which we enroll controls without regard to the number, or characteristics of the cases.

There are two basic types of case-control studies, distinguished by the method used to select controls. Controls should be selected without regard to their exposure status (e.g., exposed/non-exposed), but may be sampled proportional to their time at risk (which is called density sampling). For example, cases may be detected from a disease registry but the controls are selected randomly from another data source. Some studies, though, draw the controls from a different data source.

Preferably controls are drawn from the same population as the cases. Controls must be comparable to cases in every way except that they do not have the disease. Furthermore, the investigator selects the number of cases relative to the number of controls.Ī most critical and often controversial component of a case-control study is the selection of the controls. Also, we have not followed persons at risk to monitor the development of disease. For example, we might enoll patients in a hospital, but we don't really know the size of the general popluation that would have come to the hospital. We have selected cases and controls from a population, often an unknown population. The odds ratio is then (A/C)/(B/D), which simplifies, after cross-multiplication, to (A*D)/(B*C). We calculate the odds of exposure among cases (A/C) and the odds of exposure among controls (B/D). The investigator then determines whether cases and controls were exposed or not exposed to the risk factor. Cases are identified first, then controls. With case-control studies, we essentially work down the columns of the 2 × 2 table. While a case-control study design offers less support for a causation hypothesis than the longer and more expensive cohort design, it does provide stronger evidence than a cross-sectional study.īelow is a 2 × 2 table for case-control data: Exposed However, because these studies collect data after the disease has already occurred, they are considered retrospective, which is a limitation. Previous exposure status is subsequently determined for each case and control. Case-control studies begin by enrolling persons based upon their current disease status. The approach for a case-control study is straightforward. Attributable risks may also be calculated.

Case-control studies are useful when epidemiologists investigate an outbreak of a disease because the study design is powerful enough to identify the cause of the outbreak especially when the sample size is small. The estimate is the odds ratio, which is a good estimate of the relative risk especially when the disease is rare. Case-control study designs are used to estimate the relative risk for a disease from a specific risk factor.

Case control study