Evidence-based medicine and clinical epidemiology

Case reports and case series

The basic element or unit of observational studies, as described by Grimes and Schulz, is the case report ( ). Case reports and case series are the least methodologically sound of all observational study designs, but this does not mean that they are not valuable contributors to the literature. Case reports often describe rare or new entities in medicine and offer an opportunity to describe characteristics about a disease and allow for postulation of hypotheses of pathophysiology. It was through case reports of unusual infections and disturbed immunity that acquired immunodeficiency syndrome (AIDS) was first described ( ). Case reports can describe infrequent adverse events associated with medications and other treatments. The association of phocomelia with thalidomide, a drug used to treat pregnancy-associated nausea in the 1960s, was first published in the form of two case reports in 1962, resulting in the swift removal of the drug from the market ( ; ). Case reports can also describe the plausibility and early use of novel treatment methods or surgery. However, the scientific audience may use weaker objective landmarks, such as historical controls, when interpreting the meaning of noted observations. Case reports and case series should be considered no more than “the first step toward more sophisticated research” ( ).

Cross-sectional studies

Cross-sectional studies are prevalence studies that examine the relationship between exposure and the outcomes of interest in a defined population at a single point in time. Prevalence is defined as the number of cases in a population at a given time. It is a ratio, or proportion, of affected individuals in relation to a pooled population. Cross-sectional studies cannot determine incidence, or the number of new cases in a population over time. Rather, they are snapshots of a disease in a specific population. With reference to only a designated moment in time, these studies are not able to provide causal evidence. Case reports and cross-sectional studies can highlight possible associations that deserve additional evaluation, but they cannot determine causality.

One advantage of cross-sectional studies is efficiency . Because the study population is examined at one moment in time, conclusions can be generated at the same time as data collection. However, cross-sectional studies are plagued by uncertain causality. Population selection, participation bias, and recall bias are also possible limitations . If a tertiary care center or major referral center is conducting a research study and the study population is taken from patients that present to these facilities, they are unlikely to accurately represent the general population or even a more specific population of patients with a particular disease undergoing therapy within the community. In 1990, Gayle et al. published data regarding the prevalence of human immunodeficiency virus (HIV) among university students, examining more than 17,000 specimens from 19 universities ( ). Thirty students, or 0.2%, had detectable HIV antibodies, which was higher than prior studies within the public. The media sensationalized these data, reporting that more than 25,000 college students across the nation might be infected with HIV. However, patient selection in this study was poor because specimens collected for examination were not random, but rather represented those students who presented to student health whose condition warranted a blood sample. Researchers must be careful that patients selected for cross-sectional studies are representative of the study population desired.

Participation bias arises when selected subjects do not participate, such as in survey studies. If 100,000 surveys are sent out but only 10,000 are completed, the study is likely affected by participation bias. The minority of patients who respond may not be representative of the desired study population. Recall bias becomes an issue when self-reporting, as in survey studies, is a part of study design. Patients often report inaccurate information regarding certain exposures or events. Despite these limitations, well-conducted cross-sectional studies have their place in evidence-based medicine. They are simply prevalence studies, which allow us to determine frequencies of disease or outcomes within particular populations or groups.

Case-control studies

The purpose of a case-control study is to determine whether an exposure is associated with an outcome (e.g., a disease of interest). Study participants are selected on the basis of having or not having the outcome of interest (the case group versus the control group, respectively). Case-control studies are always retrospective because they start with an outcome and then evaluate previous exposures or habits. Fig. 5.1 illustrates the differences in methodologies between case-control studies compared with cohort studies. Participants in the case group need to be carefully defined and should include all cases of new-onset disease drawn from an identifiable population. Controls should be sampled from that same population. The purpose of the control group is to allow for comparison in frequencies of exposures of a case group with the outcome of interest versus the control group without that outcome. In 1971, Herbst et al. published a case-control study of 8 cases and 32 matched controls identifying a strong association between vaginal adenocarcinoma and in utero exposure to diethylstilbestrol (DES) ( ). Although further cohort studies were required to confirm causality, this case-control study allowed for identification of a suspected culprit (exposure) for the development of vaginal adenocarcinoma in young women (outcome of interest).

Case-control studies offer the advantages of being relatively inexpensive, simple to conduct, and efficient . They are retrospective and thus do not require a prolonged period of data collection. They can be used to study multiple exposures as they relate to a particular outcome of interest, and they offer the ability to study rare diseases. The quality of the results from these studies is dependent on meticulous selection of cases, control groups, and data collection among the groups. There are also disadvantages to case-control studies, including the risk of recall and sampling biases. Recall bias is particularly problematic because cases and controls are likely to recount historical exposures differently . Patients or families coping with an illness may recall in great detail all events they believe might be associated with the illness, whereas healthy controls may not remember similar exposures. The potential problematic power of recall bias was highlighted in the case-control studies that suggested a correlation between talcum powder use and epithelial ovarian cancer. Although significant legal claims were made concerning this correlation, no significant relationship was ever documented in a prospective study. In the case-control studies, talcum powder use was a solely subjective measure that could not be tracked by any method other than patient report; thus the potential for recall bias was great ( ). Recall bias may also occur when information on the case group is obtained by chart review but information on the control group is obtained either by interview or mail survey. It may ultimately be impossible to eliminate recall bias.

Sample selection, or sampling bias, arises if the cases selected do not appropriately represent a particular disease or outcome . This is similar to sample selection issues in cross-sectional studies. Sampling bias can also occur within the control group if a representation of the desired general population either underestimates or overestimates exposures. Matching, or selecting control group participants similar in characteristics to the case group, helps to decrease bias in the selection of controls. Matching also helps to decrease possible confounding, which occurs when factors relate both to the measured outcome and measured exposures. As Stephen Gehlbach, a renowned epidemiologist, once wrote, “Confounding is the epidemiologist’s eternal triangle...Are we seeing cause and effect, or is a confounding factor exerting its unappreciated influence?” ( ). Controlling sample selection and confounding factors allows for external validity, or the generalizability of the study to the desired population. Researchers can use statistical techniques such as multivariate analysis and logistic regression to help eliminate confounders.

Because case-control studies are retrospective by design, they are also limited in their statistical analysis. They cannot provide data on incidence, relative risks, or attributable risks between an exposure and a measured outcome. Case-control study results are reported as odds ratios, which represent the odds that an individual affected by the specific disease being studied has been exposed to a particular risk factor (case group) divided by the odds that the control group has been exposed . It is loosely considered a reasonable estimate of relative risk, but it is not a true calculation of relative risk.