EBP Glossary

Evidence-Based Practice Glossary

Absolute risk

The observed or calculated probability of an event in the population under study.

Absolute risk difference
The difference in the risk for disease or death between an exposed population and an unexposed population.

Absolute risk reduction (ARR)
The difference in the absolute risk (rates of adverse events) between control group (CER) and treated group (EER): ARR = CER – EER.

Adjustment
A summarizing procedure for a statistical measure in which the effects of differences in composition of the populations being compared have been minimized by statistical methods.

Association
Statistical dependence between two or more events, characteristics, or other variables. An association may be fortuitous or may be produced by various other circumstances; the presence of an association does not necessarily imply a causal relationship.

Bias (Syn: systematic error)
Deviation of results or inferences from the truth, or processes leading to such deviation. See also Referral Bias, Selection Bias

Blind(ed) study (Syn: masked study)
A study in which observer(s) and/or subjects are kept ignorant of the group to which the subjects are assigned, as in an experimental study, or of the population from which the subjects come, as in a non-experimental or observational study. Where both observer and subjects are kept ignorant, the study is termed a double-blind study. If the statistical analysis is also done in ignorance of the group to which subjects belong, the study is sometimes described as triple blind. The purpose of “blinding” is to eliminate sources of bias.

Case-series
Report of a number of cases of disease.

Case-control study
Retrospective comparison of exposures of persons with disease with those of persons without the disease (See Retrospective study).

Case-series
A report on a series of patients with an outcome of interest. No control group is involved.

CER
Control Event Rate: see Event Rate.

Causality
The relating of causes to the effects they produce. Most of epidemiology concerns causality and several types of causes can be distinguished. It must be emphasized, however, that epidemiological evidence by itself is insufficient to establish causality, although it can provide powerful circumstantial evidence.

Clinical Practice Guideline
A systematically developed statement designed to assist practitioners and patients in making decisions about appropriate health care for specific clinical circumstances.

Co-interventions
Interventions other than the treatment under study that are applied differently to the treatment and control groups. Co-intervention is a serious problem when double blinding is absent or when the use of very effective non-study treatments is permitted.

Cohort study
A cohort study is a study in which researchers compare two groups over a period of time. At the start of the study, one of the groups has a particular condition or receives a particular treatment, and the other does not. At the end of a certain amount of time, researchers compare the two groups to see how they did.

Comparison group
Any group to which the index group is compared. Usually synonymous with control group.

Co-morbidity
Coexistence of a disease or diseases in a study participant in addition to the index condition that is the subject of study.

Confidence interval (CI)
The range of numerical values in which we can be confident (to a computed probability, such as 90 or 95%) that the population value being estimated will be found. Confidence intervals indicate the strength of evidence; where confidence intervals are wide, they indicate less precise estimates of effect.

Confounding variable, Confounder
A variable that can cause or prevent the outcome of interest, is not an intermediate variable, and is associated with the factor under investigation. A confounding variable may be due chance or bias. Unless it is possible to adjust for confounding variables, their effects cannot be distinguished from those of factor(s) being studied.

Control group
The control group of a study is a group that receives a treatment other than the one being studied (for instance, a placebo pill that looks identical to the medication being studied but that has no active ingredients). Control groups are necessary to be able to compare the results of the treatment being studied to available alternatives.

Cost-Benefit Analysis
Converts effects into the same monetary terms as the costs and compares them.

Cost-Effectiveness Analysis
Converts effects into health terms and describes the costs for some additional health gain (e.g. cost per additional MI prevented).

Cost-Utility Analysis
Converts effects into personal preferences (or utilities) and describes how much it costs for some additional quality gain (e.g. cost per additional quality-adjusted life-year, or QUALY).

Cross-Sectional Study
The observation of a defined population at a single point in time or time interval. Exposure and outcome are determined simultaneously.

Decision Analysis

The application of explicit, quantitative methods to analyze decisions under conditions of uncertainty.

Diagnosis
Diagnosis problems are questions about the degree to which a particular test is reliable and clinically useful, generally asked in order to decide whether a patient would benefit enough from the test, on average, to justify having it done. Most diagnosis articles compare the results of the diagnostic test being studied to the results of another standard test that is regarded as being definitive – a ‘gold standard’ test.

Determinant
Any definable factor which effects a change in a health condition or other characteristic.

Dose-response relationship
A relationship in which change in amount, intensity, or duration of exposure is associated with a change-either an increase or decrease-in risk of a specified outcome.

Double Blind
A double blind study is one in which neither the patients nor the health care personnel involved in treatment know whether a particular patient is receiving the treatment being studied or is part of the control group.

EER
Experimental Event Rate: see Event Rate.

Effectiveness
A measure of the benefit resulting from an intervention for a given health problem under usual conditions of clinical care for a particular group; this form of evaluation considers both the efficacy of an intervention and its acceptance by those to whom it is offered, answering the question, “Does the practice do more good than harm to people to whom it is offered?” See Intention to treat.

Efficacy
A measure of the benefit resulting from an intervention for a given health problem under the ideal conditions of an investigation; it answers the question, “Does the practice do more good than harm to people who fully comply with the recommendations?”

Event Rate
The proportion of patients in a group in whom an the event is observed. Thus, if out of 100 patients, the event is observed in 27, the event rate is 0.27. Control Event Rate (CER) and Experiemental Event Rate (EER) are used to refer to this in control and experimental groups of patients respectively.

Exclusion Criteria
Conditions which preclude entrance of candidates into an investigation even if they meet the inclusion criteria.

Follow-up
Observation over a period of time of an individual, group, or initially defined population whose relevant characteristics have been assessed in order to observe changes in health status or health-related variables.

Gold standard

A method, procedure, or measurement that is widely accepted as being the best available.

Harm
Harm problems are questions about the relationship between a disease and a possible cause. For example, does a diet rich in saturated fats increase the risk of heart disease, and if so, by how much?

Incidence
The number of new cases of illness commencing, or of persons falling ill, during a specified time period in a given population. See also Prevalence.

Intention to treat analysis
A method for data analysis in a randomized clinical trial in which individual outcomes are analyzed according to the group to which they have been randomized, even if they never received the treatment they were assigned. By simulating practical experience it provides a better measure of effectiveness. (versus efficacy)

Interviewer bias
Systematic error due to interviewer’s subconscious or conscious gathering of selective data.

Lead-time bias

If prognosis study patients are not all enrolled at similar, well-defined points in the course of their disease, differences in outcome over time may merely reflect differences in duration of illness.

Likelihood ratio
The likelihood that a given test result would be expected in a patient with the target disorder compared to the likelihood that the same result would be expected in a patient without that disorder.

N-of-1 Trials
The patient undergoes pairs of treatment periods organized so that one period involves the use of the experimental treatment and one period involves the use of an alternate or placebo therapy. The patients and physician are blinded, if possible, and outcomes are monitored. Treatment periods are replicated until the clinician and patient are convinced that the treatments are definitely different or definitely not different.

Negative Predictive Value (-PV)
The proportion of people with a negative test who are free of disease.

Number Needed to Treat (NNT)
The number of patients who must be exposed to an intervention before the clinical outcome of interest occurred; for example, the number of patients needed to treat to prevent one adverse outcome. It is the inverse of the ARR: NNT = 1/ARR.

Odds

A ratio of nonevents to events. If the event rate for a disease is 0.1 (10 per cent), its nonevent rate is 0.9 and therefore its odds are 9:1. Note that this is not the same expression as the inverse of event rate.

Odds Ratio (Syn: cross-product ratio, relative odds)
A measure of the degree of association; for example, the odds of exposure among the cases compared with the odds of exposure among the controls.

Overview
A systematic review and summary of the medical literature.

Placebo Effect
This effect is one in which patients who believe that they are receiving an effective treatment for their condition tend to show substantial improvement, even if they are actually only receiving a dummy treatment such as a sugar pill. This effect is surprisingly strong, and is the main reason for the use of the ‘double blind’ experimental design.

Positive Predictive Value (+PV)
The proportion of people with a positive test who have disease.

Precision
The range in which the best estimates of a true value approximate the true value. See Confidence interval.

Predictive value
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., does have the disease), or that a person with a negative test truly does not have the disease. The predictive value of a screening test is determined by the sensitivity and specificity of the test, and by the prevalence of the condition for which the test is used.

Prevalence
The proportion of persons with a particular disease within a given population at a given time.

Prognosis
The possible outcomes of a disease or condition and the likelihood that each one will occur. Prognosis problems are questions about a patient’s future health, life span, and quality of life in the event that s/he chooses a particular treatment option.

Prognostic factor
Demographic, disease-specific, or co-morbid characteristics associated strongly enough with a condition’s outcomes to predict accurately the eventual development of those outcomes. Compare with risk factors. Neither prognostic nor risk factors necessarily imply a cause and effect relationship.

Prospective study
Study design where one or more groups (cohorts) of individuals who have not yet had the outcome event in question are monitored for the number of such events which occur over time.

Randomized controlled trial

Study design where treatments, interventions, or enrollment into different study groups are assigned by random allocation rather than by conscious decisions of clinicians or patients. If the sample size is large enough, this study design avoids problems of bias and confounding variables by assuring that both known and unknown determinants of outcome are evenly distributed between treatment and control groups. These groups are followed up for the variables / outcomes of interest.

Recall bias
Systematic error due to the differences in accuracy or completeness of recall to memory of past events or experiences.

Referral filter bias
The sequence of referrals that may lead patients from primary to tertiary centers raises the proportion of more severe or unusual cases, thus increasing the likelihood of adverse or unfavorable outcomes.

Relative risk (RR)
The ratio of the probability of developing, in a specified period of time, an outcome among those receiving the treatment of interest or exposed to a risk factor, compared with the probability of developing the outcome if the risk factor or intervention is not present.

Relative risk reduction (RRR)
The percent reduction in events in the treated group event rate (EER) compared to the control group event rate (CER): RRR = (CER – EER) / CER * 100

Reproducibility (Repeatability, Reliability)
The results of a test or measure are identical or closely similar each time it is conducted.

Retrospective study
Study design in which cases where individuals who had an outcome event in question are collected and analyzed after the outcomes have occurred (See also Case-control study).

Risk factor
Patient characteristics or factors associated with an increased probability of developing a condition or disease in the first place. Compare with prognostic factors. Neither risk nor prognostic factors necessarily imply a cause and effect relationship. The ratio of risk in the treated group (EER) to the risk in the control group (CER): RR = EER/CER. RR is used in randomized trials and cohort studies.

Selection Bias
A bias in assignment or a confounding variable that arises from study design rather than by chance. These can occur when the study and control groups are chosen so that they differ from each other by one or more factors that may affect the outcome of the study.

Sensitivity (of a diagnostic test)
The proportion of truly diseased persons, as measured by the gold standard, who are identified as diseased by the test under study.

SnNout
When a sign/test has a high sensitivity, a negative result rules out the diagnosis; e.g. the sensitivity of a history of ankle swelling for diagnosing ascites is 92%, therefore is a person does not have a history of ankle swelling, it is highly unlikely that the person has ascites.

SpPin
When a sign/test has a high specificity, a Positive result rules in the diagnosis; e.g. the specificity of fluid wave for diagnosing ascites is 92%. Therefore, if a person has a fluid wave, it is highly likely that the person has ascites.

Specificity (of a diagnostic test)
The proportion of truly non-diseased persons, as measured by the gold standard, who are also identified by the diagnostic test under study.

Stratification
Division into groups. Stratification may also refer to a process to control for differences in confounding variables, by making separate estimates for groups of individuals who have the same values for the confounding variable.

Strength of Inference
The likelihood that an observed difference between groups within a study represents a real difference rather than mere chance or the influence of confounding factors, based on both p values and confidence intervals. Strength of inference is weakened by various forms of bias and by small sample sizes.

Survival curve
A graph of the number of events occurring over time or the chance of being free of these events over time. The events must be discrete and the time at which they occur must be precisely known. In most clinical situations, the chance of an outcome changes with time. In most survival curves the earlier follow-up periods usually include results from more patients than the later periods and are therefore more precise.

Therapy
Therapy problems are questions about what treatment, if any, to give a patient, and what the outcomes of different treatment options might be.

Validity
The extent to which a variable or intervention measures what it is supposed to measure or accomplishes what it is supposed to accomplish. The internal validity of a study refers to the integrity of the experimental design. The external validity of a study refers to the appropriateness by which its results can be applied to non-study patients or populations.

Information gathered from the University of Alberta John W. Scott Health Sciences Library and the University of Toronto Gerstein Sciences Information Centre.