Assessing bias in case-control studies. Proper selection of cases and controls.

Case-control studies are retrospective investigations in which a diseased group (cases) and a disease-free group (controls) are compared with the aim of uncovering risk factors that differ between the groups. Many times, cause-effect relationships between the risk factor and disease are inferred from the results. Many important risk factors have been identified through the use of a casecontrol design, including the relationship of smoking to lung cancer, the use of tampons and toxic shock syndrome, and the link between vaginal cancer and maternal use of diethylstilbestrol. Case-control studies can be executed quickly and at a relatively low cost, even when the disease of interest is rare. Such advantages have made the case-control design popular, resulting in a progressive increase in its use. The validity of a casecontrol study, however, is dependent on representative selection of both the case and control groups. Overrepresentation or underrepresentation of either of these groups in the study sample results in a systematic error referred to as bias. Bias can cause an inaccurate assessment of the relationship between the risk factor and the disease. Although all studies can be affected by bias, casecontrol studies are particularly susceptible because of the retrospective nature of the data and the resulting lack of control the investigator has over many items of interest. Case-control studies done in a clinical setting are even further prone to bias because the factors that bring patients to the clinical setting are often related to the disease or risk factor of interest. Investigators who embark on case-control studies must maintain a constant awareness of sources of potential bias that could result in invalid conclusions from the study data. This can be a difficult task because of the numerous and insidious ways that bias can exist. The purpose of this paper is to illustrate how bias can result from improper selection of study patients. Specific types of bias illustrated are those particularly applicable to clinically based research.