Disproportionality Analysis for Pharmacovigilance Signal Detection in Small Databases or Subsets: Recommendations for Limiting False-Positive Associations.

Introduction: Uncovering safety signals through the collection and assessment of individual case reports remains a core pharmacovigilance activity. Despite the widespread use of disproportionality analysis in signal detection, recommendations are lacking on the minimum size of databases or subsets of databases required to yield robust results.

Objective: This study aims to investigate the relationship between database size and robustness of disproportionality analysis, with regards to limiting spurious associations.

Reported adverse drug reactions in women and men: Aggregated evidence from globally collected individual case reports during half a century.

Background: Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Reports on differences in reporting patterns between women and men exist nationally. The goal of the present study was to assess the global evidence on spontaneous post-marketing ADR reporting differences between reports for women and men.

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.

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.

Characteristics, Quality and Contribution to Signal Detection of Spontaneous Reports of Adverse Drug Reactions Via the WEB-RADR Mobile Application: A Descriptive Cross-Sectional Study.

Introduction: Spontaneous reporting of suspected adverse drug reactions is key for efficient post-marketing safety surveillance. To increase usability and accessibility of reporting tools, the Web-Recognising Adverse Drug Reactions (WEB-RADR) consortium developed a smartphone application (app) based on a simplified reporting form.

Objective: The objective of this study was to evaluate the characteristics, quality and contribution to signals of reports submitted via the WEB-RADR app.

Does patient reporting lead to earlier detection of drug safety signals? A retrospective comparison of time to reporting between patients and healthcare professionals in a global database.

Aims: To explore if there is a difference between patients and healthcare professionals (HCPs) in time to reporting drug-adverse drug reaction (ADR) associations that led to drug safety signals.

Methods: This was a retrospective comparison of time to reporting selected drug-ADR associations which led to drug safety signals between patients and HCPs. ADR reports were selected from the World Health Organization Global database of individual case safety reports, VigiBase.

vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use.

Purpose: vigiRank is a data-driven predictive model for emerging safety signals. In addition to disproportionate reporting patterns, it also accounts for the completeness, recency, and geographic spread of individual case reporting, as well as the availability of case narratives. Previous retrospective analysis suggested that vigiRank performed better than disproportionality analysis alone.

Current Safety Concerns with Human Papillomavirus Vaccine: A Cluster Analysis of Reports in VigiBase.

Introduction: A number of safety signals-complex regional pain syndrome (CRPS), postural orthostatic tachycardia syndrome (POTS), and chronic fatigue syndrome (CFS)-have emerged with human papillomavirus (HPV) vaccines, which share a similar pattern of symptomatology. Previous signal evaluations and epidemiological studies have largely relied on traditional methodologies and signals have been considered individually.

Objective: The aim of this study was to explore global reporting patterns for HPV vaccine for subgroups of reports with similar adverse event (AE) profiles.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.

Objective: Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models.

Quantitative benefit-risk assessment of methylprednisolone in multiple sclerosis relapses.

Background: High-dose short-term methylprednisolone is the recommended treatment in the management of multiple sclerosis relapses, although it has been suggested that lower doses may be equally effective. Also, glucocorticoids are associated with multiple and often dose-dependent adverse effects. This quantitative benefit-risk assessment compares high- and low-dose methylprednisolone (at least 2000 mg and less than 1000 mg, respectively, during at most 31 days) and a no treatment alternative, with the aim of determining which regimen, if any, is preferable in multiple sclerosis relapses.

Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank.

Background: Detection of unknown risks with marketed medicines is key to securing the optimal care of individual patients and to reducing the societal burden from adverse drug reactions. Large collections of individual case reports remain the primary source of information and require effective analytics to guide clinical assessors towards likely drug safety signals. Disproportionality analysis is based solely on aggregate numbers of reports and naively disregards report quality and content.

Zoo or savannah? Choice of training ground for evidence-based pharmacovigilance.

Pharmacovigilance seeks to detect and describe adverse drug reactions early. Ideally, we would like to see objective evidence that a chosen signal detection approach can be expected to be effective. The development and evaluation of evidence-based methods require benchmarks for signal detection performance, and recent years have seen unprecedented efforts to build such reference sets.

Computing limits on medicine risks based on collections of individual case reports.

Background: Quantifying a medicine's risks for adverse effects is crucial in assessing its value as a therapeutic agent. Rare adverse effects are often not detected until after the medicine is marketed and used in large and heterogeneous patient populations, and risk quantification is even more difficult. While individual case reports of suspected harm from medicines are instrumental in the detection of previously unknown adverse effects, they are currently not used for risk quantification.

The development and evaluation of triage algorithms for early discovery of adverse drug interactions.

Background: Around 20 % of all adverse drug reactions (ADRs) are due to drug interactions. Some of these will only be detected in the postmarketing setting. Effective screening in large collections of individual case safety reports (ICSRs) requires automated triages to identify signals of adverse drug interactions.

Quantitative benefit-risk assessment using only qualitative information on utilities.

Background: Utilities of pertinent clinical outcomes are crucial variables for assessing the benefits and risks of drugs, but numerical data on utilities may be unreliable or altogether missing. We propose a method to incorporate qualitative information into a probabilistic decision analysis framework for quantitative benefit-risk assessment.

Objective: To investigate whether conclusive results can be obtained when the only source of discriminating information on utilities is widely agreed upon qualitative relations, for example, ''sepsis is worse than transient headache'' or ''alleviation of disease is better without than with complications.

Dose variations associated with formulations of NSAID prescriptions for children: a descriptive analysis of electronic health records in the UK.

Background: NSAIDs, particularly ibuprofen, are commonly prescribed for children but there is limited published research on real-life prescribed doses for this class of drugs.

Objective: The aim of the study was to investigate if variations in NSAID doses prescribed to children can be explained by patient age, indication, dosage form, type of NSAID or year of prescription.

Study Design: Recorded daily doses for drugs within the 'Anti-rheumatics, non-steroidal plain' anatomical classification were studied.

Reporting patterns indicative of adverse drug interactions: a systematic evaluation in VigiBase.

Background: Adverse drug interaction surveillance in collections of Individual Case Safety Reports (ICSRs) remains underdeveloped. Most efforts to date have focused on disproportionality analysis, but the empirical support for its value is based on isolated examples. Additionally, too little attention has been given to the potential value of the detailed content of ICSRs for improved adverse drug interaction surveillance.

The lasso--a novel method for predictive covariate model building in nonlinear mixed effects models.

Covariate models for population pharmacokinetics and pharmacodynamics are often built with a stepwise covariate modelling procedure (SCM). When analysing a small dataset this method may produce a covariate model that suffers from selection bias and poor predictive performance. The lasso is a method suggested to remedy these problems.