Investigating Overlap in Signals from EVDAS, FAERS, and VigiBase.

Introduction: The Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and VigiBase are two established databases for safety monitoring of medicinal products, recently complemented with the EudraVigilance Data Analysis System (EVDAS).

Objective: Signals of disproportionate reporting (SDRs) can characterize the reporting profile of a drug, accounting for the distribution of all drugs and all events in the database. This study aims to quantify the redundancy among the three databases when characterized by two disproportionality-based analyses (DPA).

Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR.

Unlabelled: INTRODUCTION AND OBJECTIVE: Social media has been suggested as a source for safety information, supplementing existing safety surveillance data sources. This article summarises the activities undertaken, and the associated challenges, to create a benchmark reference dataset that can be used to evaluate the performance of automated methods and systems for adverse event recognition.

Methods: A retrospective analysis of public English-language Twitter posts (Tweets) was performed.

Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR.

Over a period of 3 years, the European Union's Innovative Medicines Initiative WEB-RADR project has explored the value of social media (i.e., information exchanged through the internet, typically via online social networks) for identifying adverse events as well as for safety signal detection.

Recommendations on the Use of Mobile Applications for the Collection and Communication of Pharmaceutical Product Safety Information: Lessons from IMI WEB-RADR.

Over a period of 3 years, the European Union's Innovative Medicines Initiative WEB-RADR (Recognising Adverse Drug Reactions; https://web-radr.eu/ ) project explored the value of two digital tools for pharmacovigilance (PV): mobile applications (apps) for reporting the adverse effects of drugs and social media data for its contribution to safety signalling. The ultimate intent of WEB-RADR was to provide policy, technical and ethical recommendations on how to develop and implement such digital tools to enhance patient safety.

Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project.

Introduction And Objective: Social media has been proposed as a possibly useful data source for pharmacovigilance signal detection. This study primarily aimed to evaluate the performance of established statistical signal detection algorithms in Twitter/Facebook for a broad range of drugs and adverse events.

Methods: Performance was assessed using a reference set by Harpaz et al.

Good Signal Detection Practices: Evidence from IMI PROTECT.

Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g.

Performance of Stratified and Subgrouped Disproportionality Analyses in Spontaneous Databases.

Introduction: Disproportionality analyses are used in many organisations to identify adverse drug reactions (ADRs) from spontaneous report data. Reporting patterns vary over time, with patient demographics, and between different geographical regions, and therefore subgroup analyses or adjustment by stratification may be beneficial.

Objective: The objective of this study was to evaluate the performance of subgroup and stratified disproportionality analyses for a number of key covariates within spontaneous report databases of differing sizes and characteristics.

An Empirical Approach to Explore the Relationship Between Measures of Disproportionate Reporting and Relative Risks from Analytical Studies.

Introduction: Although it seems reasonable to suppose that a drug that increases the risk of an adverse event might tend to show increased disproportionality statistics in spontaneous reporting databases, that relationship is not clear. Therefore, an empirical approach was taken to investigate the relationship between proportional reporting ratios (PRRs) and relative risk (RR) estimates from formal studies in a set of known adverse drug reactions (ADRs).

Methods: Drug-event pairs that were the subject of pharmacovigilance-driven European regulatory actions from 2007 to 2010 were selected.

Comparison of statistical signal detection methods within and across spontaneous reporting databases.

Background: Most pharmacovigilance departments maintain a system to identify adverse drug reactions (ADRs) through analysis of spontaneous reports. The signal detection algorithms (SDAs) and the nature of the reporting databases vary between operators and it is unclear whether any algorithm can be expected to provide good performance in a wide range of environments.

Objective: The objective of this study was to compare the performance of commonly used algorithms across spontaneous reporting databases operated by pharmaceutical companies and national and international pharmacovigilance organisations.