Analytic epidemiologic studies examine the association between two or more variables. Predictor variable and independent variable are terms for an exposure to an agent of interest in a human population. Outcome variable and dependent variable are terms for a health event seen in that population. Outcomes can also include a number of nonhealth results, such as use of services, social changes, and employment changes. A principal objective of epidemiology is to understand whether exposure to a specific agent is associated with disease occurrence or other health outcomes. That is most straightforwardly accomplished in experimental studies in which the investigator controls the exposure and the association between exposure and outcome can be measured directly. In the case of TBI followup studies, however, human experiments that directly examine the association between TBI and health outcomes are neither ethically nor practically feasible; instead, the association has to be measured in observational studies, and causality has to be inferred. Although they are commonly used synonymously by the general public, the terms association and causation have distinct meanings (Alpert and Goldberg, 2007).
There are several possible reasons for associations in observational studies: random error (chance); systematic error (bias); confounding; effect–cause; and cause–effect. Spurious associations, that is, the finding of an association that does not truly exist, can be due to random error or chance, systematic error or bias, or a combination of them. Random error or chance is a statistical variation in a measurement taken from a sample of a population that can lead to the appearance of an association when none is present or the failure to find an association when one is present. Systematic error or bias is the result of errors in how the study was designed or conducted. Systematic error can cause an observed value to deviate from its true value and can falsely strengthen or weaken an association or generate a spurious association. Selection bias occurs when there has been systematic error in recruiting a study population, which is different from the target population of the study, with the result that the findings cannot be generalized to the target population. Information bias results from a flaw in how data on exposure or outcome factors are collected.
Other reasons for finding associations that are incorrect are confounding and effect–cause relationships. Confounding occurs when a third variable, termed a confounding variable (or confounder), is associated with both the exposure and the outcome and mistakenly leads to the conclusion that the exposure is associated with the outcome. Effect–cause relationships occur when the outcome precedes the exposure; for example, a study might suggest that a particular psychiatric outcome was associated with a TBI when the psychiatric condition actually preceded the TBI and increased the risk of a TBI. In a true association, the exposure precedes the outcome and the association is free of random error, bias, and confounding (or the chance of them has been minimized); finding these types of associations is the goal of epidemiologic studies.
In epidemiologic studies, the strength of an association between exposure and outcome is generally estimated by using prevalence ratios, relative risks (RRs), odds ratios (ORs), correlation coefficients, or hazard ratios depending on the type of epidemiologic study performed. To conclude that an association exists, it is necessary for the exposure to be followed by the outcome more (or less in the case of a protective exposure) frequently than it would be expected to by chance alone. The strength of an association is typically expressed as a ratio of