ArticleDrug safety2015
Comparison of statistical signal detection methods within and across spontaneous reporting databases.
Article in Drug safety, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.
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Who cites it
88 citing papers in PubMed.
- Seizure-related phenomena associated with propofol: insights from the FDA Adverse Event Reporting System (FAERS).Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Quarterly persistence as a signal prioritization framework in FAERS: an analysis of the 2025 public-use releases.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Adverse events associated with drug-related thiamine deficiency: A pharmacovigilance study.JPEN. Journal of parenteral and enteral nutrition · 2026Article
- Drug-related pulmonary fibrosis: A real-world pharmacovigilance study of the FDA Adverse Event Reporting System.Medicine · 2026Article
- Pharmacovigilance assessment of drug-induced hypokalemia: from systemic risk screening to multi-dataset bioinformatics analysis focused on cisplatin.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Evaluating COVID-19 Vaccine Masking and Unmasking Methods in Two National Pharmacovigilance Databases.Drug safety · 2026Article
- Hypophosphatemia as a Potential Class Effect of Histone Deacetylase Inhibitors: Evidence from Disproportionality Analysis and Mendelian Randomization Analysis of Drug Targets.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Signal Detection and Machine Learning-Based Prediction of Cytokine Release Syndrome in B-Cell Maturation Antigen-Targeting Immunotherapies Using FAERS Data.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Charting and Sidestepping the Pitfalls of Disproportionality Analysis.Drug safety · 2026Review
- Two decades of prescription opioid-related cardiovascular safety signals: a FAERS-based pharmacovigilance study using machine learning.Frontiers in drug safety and regulation · 2026Article
- Post-marketing safety profile of meningococcal group B vaccines: a real-world disproportionality analysis of the VAERS database, 2015Q1-2025Q3.Frontiers in pharmacology · 2026Article
- Safety profile of entrectinib in NSCLC: Multi-source pharmacovigilance analysis using FAERS and JADER.PloS one · 2026Article
- Adverse events following measles, mumps, rubella and varicella virus vaccine live (PROQUADVirology journal · 2025Article
- Pharmacovigilance assessment of adverse events following yellow fever vaccination: Disproportionality analysis of VAERS reports, 2015-2025.Human vaccines & immunotherapeutics · 2025Article
- Adverse events following 9-valent human papillomavirus vaccine (GARDASIL® 9) reported to the Vaccine Adverse Event Reporting System (VAERS), 2015-2024.Human vaccines & immunotherapeutics · 2025Article
- MDDC: An R and Python package for adverse event identification in pharmacovigilance data.Scientific reports · 2025Article
- Pharmacovigilance analysis of drug-induced hypofibrinogenemia using the FDA Adverse Event Reporting System.International journal of clinical pharmacy · 2025Article
- Disproportionality Analysis and Causal Inference in Drug Safety.Pharmaceutical medicine · 2025Review
- Development of a Drug Safety Signal Detection Reference Set Using Japanese Safety Information.Therapeutic innovation & regulatory science · 2025Article
- A pharmacovigilance analysis of abrocitinib-related skin adverse events based on the FDA Adverse Event Reporting System (FAERS).Archives of dermatological research · 2025Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMost 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.
objectiveThe 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.
methods220 products were chosen and a reference set of ADRs was compiled. Within four company, one national and two international databases, 15 SDAs based on five disproportionality methods were tested. Signals of disproportionate reporting (SDRs) were calculated at monthly intervals and classified by comparison with the reference set. These results were summarised as sensitivity and precision for each algorithm in each database.
resultsDifferent algorithms performed differently between databases but no method dominated all others. Performance was strongly dependent on the thresholds used to define a statistical signal. However, the different disproportionality statistics did not influence the achievable performance. The relative performance of two algorithms was similar in different databases. Over the lifetime of a product there is a reduction in precision for any method.
conclusionsIn designing signal detection systems, careful consideration should be given to the criteria that are used to define an SDR. The choice of disproportionality statistic does not appreciably affect the achievable range of signal detection performance and so this can primarily be based on ease of implementation, interpretation and minimisation of computing resources. The changes in sensitivity and precision obtainable by replacing one algorithm with another are predictable. However, the absolute performance of a method is specific to the database and is best assessed directly on that database. New methods may be required to gain appreciable improvements.
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