Glossary of terms used in Pharmacovigilance (PART-2)

Glossary of terms used in Pharmacovigilance (PART-2)

Causality assessment:

The evaluation of the likelihood that a medicine was the causative agent of an observed adverse reaction. Causality assessment is usually made according established algorithms.

Caveat document:

The formal advisory warning accompanying data release from the WHO Global ICSR Database: it specifies the conditions and reservations applying to interpretations and use of the data.


Software developed by UMC for collection and analysis of data in Cohort Event Monitoring.

Also see Cohort Event Monitoring.

Clinical trial:

A systematic study on pharmaceutical products in human subjects (including patients and other volunteers) in order to discover or verify the effects of and/or identify any adverse reaction to investigational products, and/or to study the absorption, distribution, metabolism and excretion of the products with the objective of ascertaining their efficacy and safety.

Cohort Event Monitoring:

Cohort Event Monitoring (CEM) is a prospective, observational study of events that occur during the use of medicines, for intensified follow-up of selected medicinal products phase. Patients are monitored from the time they begin treatment, and for a defined period of time.

See also Prescription Event Monitoring.


Faithful adherence by the patient to the prescriber’s instructions.

Control group:

The comparison group in drug-trials not being given the studied drug.

Critical terms:

Some of the terms in WHO-ART are marked as ‘Critical Terms’. These terms either refer to or might be indicative of serious disease states, and warrant special attention, because of their possible association with the risk of serious illness which may lead to more decisive action than reports on other terms.

Data mining:

A general term for computerised extraction of potentially interesting patterns from large data sets, often based on statistical algorithms. A related term with essentially the same meaning is ‘pattern discovery’. In pharmacovigilance, the commonest application of data mining is so called disproportionality analysis, for example using the Information component (IC)


The withdrawal of a drug from a patient; the point at which the continuity, reduction or disappearance of adverse effects may be observed.

Disproportionality analysis:

Screening of ICSR databases for reporting rates which are higher than expected. For drug-ADR pairs, common measures of disproportionality are the Proportional Reporting Ratio (PRR), the Reporting Odds Ratio (ROR), The Information Component (IC), and the Empirical Bayes Geometrical Mean (EBGM). There are also disproportionality measures for drug-drug-ADR triplets, such as Omega (Ω).


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