agent in the cases are then compared with the odds of exposure in controls. An OR greater than 1 indicates that there is a potential association between the exposure and the outcome; the greater the OR, the greater the association. An OR less than 1 indicates that the exposure may protect against the outcome.
Case–control studies are useful for testing hypotheses about relationships between specific exposures and an outcome. They attempt to solve the problem of temporality by considering the order of exposure and outcome. They are especially useful and efficient for studying rare diseases and their associated exposures. Case–control studies have the advantages of ease, speed, and relatively low cost. They are also valuable for their ability to probe multiple exposures or risk factors. However, case–control studies are vulnerable to several types of bias, such as recall bias, in which cases are more likely to report exposures than controls, which can dilute or enhance an association between a health effect and an exposure. Other problems include identifying representative groups of cases, choosing suitable controls, and collecting comparable information on exposures in both cases and controls. The case–control study is often the first approach to testing a hypothesis about factors that might contribute to a specific health effect, especially a rare one.
A nested case–control study draws cases and controls from a previously defined cohort that was assembled for other purposes. Thus, it is said to be nested in a cohort study. Baseline data are collected when the cohort is identified, which to some degree avoids the problem of recall bias when the cases and controls are identified. Members of the cohort identified as having, for example, TBI serve as cases, and a sample of those who are TBI-free serve as controls. Baseline data on exposure in cases and controls are compared, as in a regular case–control study. Nested case–control studies are efficient in terms of time and cost in reconstructing exposure histories of cases and controls. In addition, because the cases and controls come from the same previously established cohort, concerns about selection bias are decreased.
The main distinguishing feature of a cross-sectional study is that exposure and outcome data are collected at the same time. In a cross-sectional study, the strength of an association between an exposure and an outcome is measured as a prevalence ratio, or a prevalence OR. It might compare outcome or symptom rates between groups with and without TBI.
Cross-sectional studies are easier and less expensive to perform than cohort studies and can identify the prevalence of exposures and outcomes in a defined population. They are useful for generating hypotheses, but they are much less useful for determining cause–effect relationships, because collecting exposure and outcome data at the same time makes it impossible to establish which came first. Such studies are also subject to numerous other problems (Monson, 1990). Cross-sectional studies are of limited use for learning about symptom duration and chronicity, latency of onset, and prognosis.
The committee’s next step, after securing the full text of about 1,900 epidemiologic studies, was to determine which studies would be included in its review as primary or secondary