Designs for Health-Related Studies Section
A proper study design means that the approach and methods will yield results that are as valid and as precise as possible. It also means that the study design is appropriate for the current scientific thinking on the topic. Each of the study designs listed below can provide useful information when applied in the appropriate situation and with the proper methods. The designs are listed in increasing order of their ability to demonstrate causality, with the stronger designs being those in which the researcher controls the administration of the treatment. However, the study design alone does not ensure that the results are valid and precise, and generalizable. Epidemiologists must develop a keen ability to recognize the strengths and limitations of any study.
- Case Study
- A study of one diseased individual. Typically, an uncommon disease or set of symptoms. The study design would not require a comparison.
- Case Series
- A study of multiple occurrences of unusual cases that have similar characteristics. Investigators can calculate the frequency of symptoms or characteristics among the cases. Results may generate causal hypotheses. Neither a case study nor a case series would include a comparison group.
- Ecological Study
- A study in which at least one variable, either exposure or the outcome, is measured at the group (not individual) level. Examples of group-level measures include…
- the incidence rate of cancer among a specific population,
- the mean level of blood pressure of patients seen at a clinic,
- the average sunlight exposure at a specific geographic location on the earth, or
- a preventive service included in a health insurance plan.
The occurrence of disease is compared between groups that have different levels of exposure, which affords this study design to have at least one comparison group.
- Cross-Sectional Study
- A study with individual-level variables that measures exposure and disease at one point in time. A snapshot of the study population. This study design provides weak evidence of a causal association between exposure and outcome because we may not be certain that the exposure preceded the disease. A patient survey is an example of a cross-sectional study.
- Case-Control Study
- A study that identifies individuals who develop the disease (cases) and individuals without the disease (controls), and then determines the previous exposure for each case and control. The case group is composed only of individuals known to have the disease or outcome; the control group is drawn from a comparable population who do NOT have the disease or outcome. We then compare the odds of exposure between cases and controls. The measure of association for a case-control study is typically an odds ratio. A case-control study is stronger than a cross-sectional study in establishing individual-level causality because we are more certain that exposure preceded the disease outcome.
- Cohort Study
- A study that begins with persons who do not have the disease but with a known level of exposure to the putative risk factor. The known level is often no exposure. Thus, the study sample is drawn only from individuals at risk of developing the disease or outcome. Individuals are followed through time until some of them develop the disease. We then compare the rate of the outcome for the exposed group to the rate of the outcome for the non-exposed group. The measure of association is a relative risk, attributable risk, or depicted with survival analysis. Incidence density rates can be calculated. A cohort study takes more time, money, and subjects than does a case-control study, but will also provide stronger evidence of individual-level causation because we are measuring incidence rates of the disease. Longitudinal surveys may be considered a cohort study.
Some epidemiologic studies are interventional, intended to test methods to reduce the incidence or severity of the disease.
- Community-based epidemiologic studies
- Instead of randomizing individuals, communities may be randomly selected to receive treatment. For instance, in the 1950s, certain communities were randomly selected to have fluoride added to the drinking water; in other communities, no fluoride was added. The incidence of dental caries between the fluoridated communities and the non-fluoridated communities were then compared.
- Clinical (experimental) study
- In this type of study, the researcher controls the exposure that individuals receive. A prime example is a clinical trial, in which patients may be randomized to receive a specific treatment. Measurements are made on the individual, but these studies do not typically measure the effect on study participants that might come from a group-level exposure.
To distinguish between observational and experimental designs, ask whether the investigator (or the study) is controlling the primary exposure. For instance, suppose you volunteer for a study in which a specified diet is prescribed and provided. You are randomly selected to receive one specific dietary plan, while another person is randomly selected to receive a different diet. You are to eat the prescribed food and keep a food diary. This would be an experimental study. A limitation to experimental studies is that participants tend to be more homogeneous, for example, they may be highly-educated and live in urban areas. This can limit the generalizability of the results if education or residence is related to the outcome.
If the study did not prescribe which diet you ate but let you choose what you ate, it would be an observational study. Observational studies can not control for all the things that people do or happen to them, so there is the possibility of uncontrolled confounding. However, these studies tend to enroll people from broader backgrounds, possibly strengthening the generalizability of their results.
Internal Validity for Health-Related Studies Section
Once a study is conducted, the investigators must assess the internal validity of the results. A study is considered valid only when the three following alternative explanations have been eliminated.
- Random Error
Unlike bias and confounding, which are problems that the investigator needs to eliminate, effect modification is a natural phenomenon of scientific interest that the investigator aims to describe and understand. Effect modification will also be covered in this unit.