Glossary of terms

Absolute risk

Absolute risk measures the size of a risk in a person or group of people. This could be the risk of developing a disease over a certain period or it could be a measure of the effect of a treatment, for example how much the risk is reduced by treatment in a person or group.
There are different ways of expressing absolute risk. For example, someone with a 1 in 10 risk of developing a certain disease has ‘a 10% risk’ or ‘a 0.1 risk’, depending on whether percentages or decimals are used. Absolute risk does not compare changes in risk between groups, for example risk changes in a treated group compared to risk changes in an untreated group. That is the function of relative risk.

Before and after study

A before and after study measures particular characteristics of a population or group of individuals at the end of an event or intervention and compares them with those characteristics before the event or intervention. The study gauges the effects of the event or intervention.

Blinding

Blinding is not telling someone what treatment a person has received or, in some cases, the outcome of their treatment. This is to avoid them being influenced by this knowledge. The person who is blinded could be either the person being treated or the researcher assessing the effect of the treatment (single blind), or both of these people (double blind).

Case crossover studies

Case crossover studies look at the effects of factors that are thought to increase the risk of a particular outcome in the short term. For example, this type of study might be used to look at the effects of changes in air pollution levels on the short-term risk of asthma attacks. Individuals who have had the outcome of interest are identified and act as their own control.

The presence or absence of the risk factor is assessed for the period immediately before the individual experienced the outcome. This is compared with the presence or absence of the risk factor when the individual did not experience the outcome (control period). If there is a link between the risk factor and the outcome, it would be expected to have been present in the period just before the outcome more often than in the control period.

Case series

A case series is a descriptive study of a group of people, who usually receive the same treatment or who have the same disease. This type of study can describe characteristics or outcomes in a particular group of people, but cannot determine how they compare with people who are treated differently or who do not have the condition.

Case-control study

A case-control study is an epidemiological study  that is often used to identify risk factors for a medical condition. This type of study compares a group of patients who have that condition with a group of patients that do not have it, and looks back in time to see how the characteristics of the two groups differ.

Clinical practice guidelines

Clinical practice guidelines are statements that are developed to help practitioners and patients make decisions about the appropriate healthcare for specific clinical circumstances.

Cluster randomised controlled trial

In a cluster randomised controlled trial, people are randomised in groups (clusters), rather than individually. Examples of clusters that could be used include schools, neighbourhoods or GP surgeries.

Cohort study

This study identifies a group of people and follows them over a period of time to see how their exposures affect their outcomes. This type of study is normally used to look at the effect of suspected risk factors that cannot be controlled experimentally, for example the effect of smoking on lung cancer.

Confidence interval

A confidence interval (CI) expresses the precision of an estimate and is often presented alongside the results of a study (usually the 95% confidence interval). The CI shows the range within which we are confident that the true result from a population will lie 95% of the time. The narrower the interval, the more precise the estimate. There is bound to be some uncertainty in estimates because studies are conducted on samples and not entire populations.

By convention, 95% certainty is considered high enough for researchers to draw conclusions that can be generalised from samples to populations. If we are comparing two groups using relative measures, such as relative risks or odds ratios, and see that the 95% CI includes the value of one in its range, we can say that there is no difference between the groups. This confidence interval tells us that, at least some of the time, the ratio of effects between the groups is one. Similarly, if an absolute measure of effect, such as a difference in means between groups, has a 95% CI that includes zero in its range, we can conclude there is no difference between the groups.

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