Observational Study for Medical Research

An observational study is an epidemiological research study that doesn't include some intervention or experiment. Subjects are studied under natural living conditions.

Scientists use observational studies to hunt for possible relationships between exposures and outcomes. They're used for several areas of health, and many of the studies you hear about on TV or read about in websites, magazines, and newspapers are observational studies. An 'outcome' is usually a disease or health problem of some sort.

The scientists use information from things like surveys and medical records to see if certain subjects have something or things in common. These things are called 'exposures.' When a sufficient amount of research indicates an exposure increases the risk of the outcome, then the exposure is known as a risk factor. An example of exposure is a risk factor would be eating large amounts of processed meat, which is a risk for developing certain types of cancer. Sometimes exposures can be protective, like eating a diet rich in fruits and vegetables, which appears to reduce the risk of heart disease.

Types

Most observational studies fall into one of three categories, case-control studies, cohort studies, or cross-sectional studies.

Case-control studies start with a group of subjects who have the outcome being studied (the cases) and another group of those who don't have it (the controls). Scientists then look back in time to see if the cases have any exposures in common that the controls do not or vice versa.

Cohort studies take a large number of subjects and group them by exposures, then follow them for some time (often years and decades) to see who develops the outcome they're studying. Again, scientists are looking to see if members of any of the groups have exposures in common. In terms of study design, cohort studies are stronger studies than case-control studies and better at measuring causation of a disease, while being cheaper than the gold standard of study, the random clinical trial.

Scientists may have to wait years for the results unless they use a large ongoing study, such as the National Health and Nutrition Examinations Survey (NHANES). Thousands of people participate every year by answering questions and undergoing physical examinations. Scientists sift through information gathered from NHANES to look for all kinds of connections between foods, dietary supplements, and health. For example, NHANES information was used to determine that folate (a B-complex vitamin) deficiency can lead to birth defects.

Finally, cross-sectional studies don't look forward or backward; they only look at what's going on at one particular time. Scientists can determine how many people have the outcome of interest and attempt to look for exposures, but without a longer time-frame, it's difficult to know for sure. This type of study determines the prevalence of certain variables and is the cheapest to conduct. These studies are typically primary studies that lead to more expensive and longer-term studies such as cohorts or random clinical trials.

Strengths and Weaknesses

Observational studies are extensive, often with thousands of participants, which gives strength to the results, but they usually can't determine any cause. Since subjects live normally, there are usually too many possible exposures that can confound the results. For example in many dietary studies, people who eat large amounts of red meat also tend to smoke, eat less fiber, and exercise less than average. Subjects who eat the least amount of red meat also exercise more, eat more fruits and vegetables than average, and rarely smoke.

Scientists use various statistical techniques to remove potential confounding factors, but sometimes the results are still a little murky. Sometimes the results of observational studies lead to randomized controlled trials (RCT), which are interventional, or experimental, studies and thought to provide the best research evidence. That's because subjects are randomized into treatment and control groups, which reduces the effect of confounding factors. However, these types of studies are expensive.