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Definitions and explanations of research terms used by NIHR Evidence.


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.

Adverse event

An undesired harmful effect that occurs during or after the use of a drug or other intervention, but is not necessarily caused by it.


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.


Bias is any underlying factor which may consistently distort the results of a research study. Examples include selection bias (systematic differences in the groups of people who are being compared); recall bias (differences in accuracy in people’s recollections of past events or experiences that are being investigated); and publication bias (the possibility that positive results may be more likely to be published than negative results).


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-control study

A case-control study 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.


A health professional (such as a doctor, dentist, nurse, pharmacist or physiotherapist) whose purpose is to provide and/or manage care to patients.

Cluster randomised controlled trial

In a cluster randomised controlled trial, people are randomly allocated to receive or not receive the intervention 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.


An alternative – or control – that researchers use to compare with the test or treatment that is the subject of the study. For example, it may be an alternative test or drug for a condition, or it may be a placebo (‘dummy’ treatment) or simply no treatment at all.

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.

Confounding factor (confounder)

A null can distort the true relationship between two (or more) characteristics. When it is not taken into account, false conclusions can be drawn about associations. An example is to conclude that if people who carry a lighter are more likely to develop lung cancer, it is because carrying a lighter causes lung cancer. In fact, smoking is a null here. People who carry a lighter are more likely to be smokers and smokers are more likely to develop lung cancer.

Control group

A control group (of cells, individuals or centres, for example) serves as a basis of comparison in a study. In the control group, the treatment or intervention being tested is not received (although participants in this group may receive usual care).

Cost-effectiveness analysis

An economic analysis that compares the relative costs and health outcomes of different interventions or treatments.

Cross sectional study

This is a study that describes characteristics of a population. It is ‘cross sectional’ because data is collected at one point in time and the relationships between characteristics are considered. Importantly, because this study doesn’t look at time trends, it can’t establish what causes what.


Diagnostic study

A diagnostic study tests a new diagnostic method to see if it is as good as the ‘gold standard’ method of diagnosing a disease. The diagnostic method may be used when people are suspected of having a disease because of signs and symptoms, or to try and detect a disease before any symptoms have developed (a screening method).


Effect size

The measure of the strength of the effect of a particular treatment or intervention.


Epidemiology is the study of factors that affect the health and illness of populations.

Evidence synthesis

A study which combines the results of a number of studies into a single set of findings.


Longitudinal study

A longitudinal study is one that studies a group of people over time.



This is a mathematical technique that combines the results of individual studies to arrive at one overall measure of the effect of a treatment.


Illness or harm.




Natural experiment

A study in which people naturally receive or do not receive an intervention (for example, the introduction of new transport infrastructure in one area but not another). Researchers can then measure differences in health outcomes in each group.

Non-randomised study

In this type of study, participants are not randomly allocated to receiving (or not receiving) an intervention.


Observational study

In an observational study, researchers have no control over exposures and instead observe what happens to groups of people.

Odds ratio

An odds ratio is one of several ways to summarise the association between an exposure and an outcome, such as a disease. (Another commonly used approach is to calculate relative risks.)

Odds ratios compare the odds of the outcome in an group receiving a treatment or intervention with the odds of the same outcome in a group that is not. Odds tell us how likely it is that an event will occur compared to the likelihood that the event will not happen. Odds of 1:3 that an event occurs, e.g. that a horse wins in a race, means the horse will win once and lose three times (over four races). Odds ratios are a way of comparing events across groups who are exposed to a particular treatment and those who aren’t.



A ‘dummy’ treatment that resembles a medical treatment but is intended to have no physical effect on participants. New treatments are often compared against placebo to get reliable evidence about effectiveness.


Prevalence describes how common a particular characteristic (for example, a disease) is in a specific group of people or population at a particular time. Prevalence is usually assessed using a cross sectional study.

Prospective observational study

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

Prospective study

A prospective study asks a specific study question (usually about how a particular exposure – such as a behaviour, characteristic or intervention – affects an outcome), recruits appropriate participants and looks at the exposures and outcomes of interest in these people over the following months or years.


Qualitative research

Qualitative research uses individual in-depth interviews, focus groups or questionnaires to collect, analyse and interpret data on what people do and say. It reports on the meanings, concepts, definitions, characteristics, metaphors, symbols and descriptions of things. It is more subjective than quantitative research and is often exploratory and open-ended. The interviews and focus groups involve relatively small numbers of people.

Quality-adjusted life year (QALY)

This is a measure of health, used in economic evaluations, that includes both the quality and quantity of life lived. One null is equal to one year of life in perfect health.

Quantitative research

Quantitative research uses statistical methods to count and measure outcomes from a study. The outcomes are usually objective and predetermined. A large number of participants are usually involved to ensure that the results are statistically significant.


Randomised controlled trial (RCT)

This is a study where people are randomly allocated to receive (or not receive) a particular intervention (this could be two different treatments or one treatment and a placebo). This is the best type of study design to determine whether a treatment is effective.

Relative risk (RR)

Relative risk compares a risk in two different groups of people. All sorts of groups are compared to others in medical research to see if belonging to a particular group increases or decreases the risk of developing certain diseases. This measure of risk is often expressed as a percentage increase or decrease, for example ‘a 20% increase in risk’ of treatment A compared to treatment B. If the relative risk is 300%, it may also be expressed as ‘a three-fold increase’.

Retrospective study

A retrospective study relies on data on exposures and/or outcomes that have already been collected (through medical records or as part of another study). Data used in this way may not be as reliable as data collected prospectively as it relies on the accuracy of records made at the time and on people’s recall of events in the past, which can be inaccurate (referred to as recall bias).


Secondary analysis

A secondary analysis is when researchers revisit data that was collected for a different reason and analyse it again to answer a new research question. This type of analysis is sometimes prone to errors.


This is one of a set of measures used to show the accuracy of a diagnostic test (see also specificity). Sensitivity is the proportion of people with a disease who are correctly identified as having that disease by the diagnostic test. For example, if a test has a sensitivity of 90%, this means that it correctly identified 90% of the people with the disease, but missed 10% (these people were ‘false negatives’ on the test).


This is one of a set of measures used to assess the accuracy of a diagnostic test (see also sensitivity). Specificity is the proportion of people without a disease who are correctly identified as not having that disease by the diagnostic test. For example, if a test has a specificity of 95%, this means that it correctly identified 95% of the people who did not have the disease, but that 5% of people without the disease were incorrectly diagnosed as having the disease (these people were ‘false positives’ on the test).

Statistical significance

If the results of a test have statistical significance, it means that they are not likely to have occurred by chance alone. In such cases, we can be more confident that we are observing a ‘true’ result.

Systematic review

This is a synthesis of the medical research on a particular subject. It uses thorough methods to search for and include all or as much as possible of the research on the topic. Only relevant studies, usually of a certain minimum quality, are included.