Glossary

Absolute risk difference is the risk in one group compared to (minus) the risk in another group over a specified period of time. For example, if the absolute risk of myocardial infarction over 5 years was 15% for the comparator and 10% for the intervention, then the absolute risk difference was 5% (15% - 10%) over 5 years. See here for further discussion.

Refers to the process that prevents patients, clinicians, and researchers from predicting which intervention group the patient will be assigned. This is different from blinding; allocation concealment refers to patients/clinicians/outcome assessors/etc. being unaware of group allocation prior to randomization, whereas blinding refers to remaining unaware of group allocation after randomization. Allocation concealment is a necessary condition for blinding. It is always feasible to implement.

Systematic deviation of an estimate from the truth (either an overestimation or underestimation) caused by a study design or conduct feature. See the Catalog of Bias for specific biases, explanations, and examples.

An outcome which consists of multiple component endpoints. For example, a cardiovascular composite may include stroke, myocardial infarction, and death.

See here for a discussion of confidence intervals.

See here for discussion regarding confounders.

Occurs when participants receive treatment intended for the other study group (a phenomenon known as contamination). For example, a participant assigned to the placebo group may end up taking active treatment. This bias results in underestimating the difference between groups.

Double-blinding does not have a standardized definition and, consequently, further examinations are needed to ascertain exactly who was blinded (Lang TA et al).

A trial strategy to identify populations where the intervention will show the greatest effect. There is no singular method. One method is to enroll subjects and put them all on active treatment, then randomize only those who responded to treatment to either continue active treatment or switch to placebo (withdrawal trial). Another method is to include risk factors for the outcome of interest in the study as inclusion criteria (enrichment criteria) (e.g. recent diabetes trials assessing cardiovascular outcomes have selectively enrolled patients with established atherosclerotic cardiovascular disease (ASCVD) or multiple additional ASCVD risk factors to be included).

Refers to the extent to which the trial results are applicable beyond the patients included in the study. Also known as generalizability.

The fixed-effects model assumes that all trials estimate the same underlying “true” effect, and thus that any differences between trials are due to chance.

Refers to the extent to which the trial results are applicable beyond the patients included in the study. Also known as external validity.

Hazard ratios are a relative measure of effect. Hazards refer to average instantaneous incidence rate at every point during the trial. This differentiates it from other measures, such as relative risk, which rely only on cumulative event rates. See here for a more detailed discussion.

Refers to variability between studies in a systematic review. It can refer to clinical differences, methodological differences, or variable results between studies. Heterogeneity occurs on a continuum and, in the case of heterogeneity amongst results, can be expressed numerically via measures of statistical heterogeneity. See here for a further discussion of statistical heterogeneity.

Participant outcomes are analyzed according to their assigned treatment group, irrespective of treatment received. A common "modified ITT" approach used in pharmacotherapy trials considers only participants who received at least one dose of the study drug (thereby excluding participants who were randomized but did not receive any study intervention).

The extent to which the study results are attributable to the intervention and not to bias. If internal validity is high, there is high confidence that the results are due to the effects of treatment (with low internal validity entailing low confidence).

A method of evaluating patients who have dropped out partway through a trial when performing an intention-to-treat analysis. It treats the patients as if they were still in the trial and their outcome status remained the same as when they were last observed. For example, a patient who reported a pain score of 7/10 at day 3 and dropped out prior to the 1-week follow-up would be analyzed as having 7/10 pain at the end of 1 week (despite no outcome data being recorded past day 3).

Loss to follow-up may occur when participants stop coming to study follow-up visits, do not answer follow-up phone calls, and cannot otherwise be assessed for study outcomes. This leads to missing data from the time they became "lost". Underlying reasons may include leaving the trial without informing investigators, moving to a new location, debilitation due to illness, or death.

A meta-analysis is a quantitative combination of the data obtained in a systematic review.

The minimum difference in a value that would be of importance to a patient. There are various methods of calculating a minimally important difference.

In superiority analyses, this is the hypothesis that there is no difference in the outcome of interest between the intervention group and the comparator group. In non-inferiority analyses, this is the hypothesis that there is a difference in the outcomes of interest between the treatment group and the control group.

Odds ratios are the ratio of odds (events divided by non-events) in the intervention group to the odds in the comparator group. For example, if the odds of an event in the treatment group is 0.2 and the odds in the comparator group is 0.1, then the OR is 2 (0.2/0.1). See here for a more detailed discussion.

See here for a p-value discussion.

This type of analysis examines patients only if they sufficiently adhered to the treatment group in which they were assigned.

An acronym for "patient, intervention, comparator, and outcome". These are the four basic elements of a study. For instance, a study may examine an elderly population (P) to understand the effects of statin therapy (I) compared to placebo (C) in terms of cardiovascular events (O). Sometimes extended to PICO(T) to include the time at which outcomes were assessed, or (D)PICO to incorporate the study design.

An inert intervention, such as a sugar pill, that does not have a physiological mechanism of influencing any of the outcomes of interest. Typically given to the comparator group in an effort to blind participants and clinicians.

A single value given as an estimate of the effect. For example, results may be listed as a relative risk of 0.5 (95% CI 0.4-0.6). In this case 0.5 is the point estimate, and 0.4-0.6 is the 95% confidence interval.

This is the most accessible healthcare setting where generalist services are provided. For example, a family medicine clinic.

A primary outcome is an outcome from which trial design choices are based (e.g. sample size calculations). Primary outcomes are not necessarily the most important outcomes.

A measure of time to disease progression or death. This outcome is frequently used in cancer trials where disease progression is typically defined as an increase in radiographic tumor mass above a certain threshold.

Refers to a systematic tendency for results to be published based upon the direction or statistical significance of the results. This results in bias when aggregating evidence if methods are more likely to include published literature than unpublished literature.

The random-effects model does not assume that all trials estimate the exact same underlying effect (e.g. different populations may vary in their response to intervention).

Randomized controlled trials are those in which participants are randomly allocated to two or more groups which are given different treatments.

Calculates the effect of an intervention via a fractional comparison with the comparator group (i.e. intervention group measure ÷ comparator group measure). Used for binary outcomes. Relative risk, odds ratio, or hazards ratio are all expressions of relative effect. For example, if the risk of developing neuropathy was 1% in the treatment group and 2% in the comparator group, then the relative risk is 0.5 (1 ÷ 2). See the Absolute Risk Differences and Relative Measures of Effect discussion here for more information.

Relative risk (or risk ratio) is the risk in one group relative to (divided by) risk in another group. For example, if 10% in the treatment group and 20% in the placebo group have the outcome of interest, the relative risk in the treatment group is 0.5 (10% ÷ 20%; half) the risk in the placebo group. See here for a more detailed discussion.

The difference between two relative risks (RRs). If the intervention has a RR of 70% and the comparator a risk of 100%, then the relative risk reduction is 30% (100% - 70%).

A pre-randomization trial phase where all patients are assigned to active treatment, placebo, or no treatment (observation only). A run-in phase may be implemented for several reasons, including to restrict randomization only to patients who can adhere to study follow-up or treatment, or to exclude patients who cannot tolerate the intervention. Run-in periods by design select a certain subgroup of patients for enrolment, which introduces selection bias (i.e. potential issues with generalizability), which may be important in some cases. Note that this selection bias occurs prior to randomization, and therefore does not introduce differences between randomized groups (i.e. allocation bias).

Healthcare services provided via specialists in settings less advanced than tertiary care. For example, an outpatient cardiology clinic.

A secondary outcome is any outcome that is not a primary outcome (i.e. secondary outcomes are not the focal point of design choices like sample size). Secondary outcomes may be more clinically important than the primary outcome.

This type of analysis explores to what degree the results are dependent upon certain decisions and assumptions. It can be thought of as a "stress test" of study assumptions. For example, a meta-analysis including trials performed across a date range of 1960 to 2020 may perform a sensitivity analysis to explore if the estimated effect size differed across decades.

The process by which allocation of participants to groups is conducted. Computer generation and coin tosses are examples of methods of random sequence generation.

Standardized definition encompassing any adverse event that:
(1) Results in death or is life-threatening;
(2) Requires or prolongs hospitalization;
(3) Results in persistent, significant, or permanent disability or incapacity;
(4) Causes congenital malformation;
(5) Per the clinician's judgement led to an important medical event.

A tendency for smaller published studies to demonstrate a larger effect size than larger published studies. One possible cause is publication bias. However, other possible causes include systematic differences between smaller and larger studies (e.g. stricter enrolment criteria, adherence and/or follow-up in smaller studies, more pragmatic design in larger studies).

Transformation of continuous data that consists of dividing the difference in means between two groups by the standard deviation of the variable. In clinical research, this is often used to summarize and/or pool continuous outcomes that are measured in several ways. For example, a meta-analysis of antidepressants may need to use the SMD if trials used different scales (e.g. Beck Depression Inventory, Hamilton Depression Rating Scale) to report change in depression symptoms. See here for further discussion on SMD interpretation.

A multistage approach to randomization in which participants are initially allocated to strata based on certain defined commonalities (e.g. stratified according to LDL levels). After stratification these participants are then randomized within their respective stratum.

A superiority trial tests for whether an intervention has a greater effect than a comparator with respect to the primary outcome. This is contrasts with non-inferiority trials.

These markers or outcomes act as proxies for clinical outcomes under the assumption that the proxy is sufficiently predictive of the clinical outcome. For example, LDL cholesterol lowering may be used as a surrogate marker for lowering the risk of cardiovascular events. Surrogate markers are typically used because they are more convenient to measure.

A review that systematically identifies all potentially relevant studies on a research question. The aggregate of studies is then evaluated with respect to factors such as risk of bias of individual studies or heterogeneity among results. The qualitative combination of results is a systematic review.

Care provided in a specialized institutional centre. For example, neurosurgery or severe burn treatment.