ArticleDrug design, development and therapy2015
Effect of database profile variation on drug safety assessment: an analysis of spontaneous adverse event reports of Japanese cases.
Article in Drug design, development and therapy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it, 93 citations in OpenAlex.
- Artificial intelligence and computational methods in the Asia-Pacific pharmacovigilance landscape: a systematic review.International journal of public health · 2026Pooled it
- The Safety Profile of Ticagrelor among Older Adults (Age ≥75 years): A Pharmacovigilance Analysis.Cardiology · 2026Article
- Disproportionality Analysis and Timing of Drug-Associated Guillain-Barré Syndrome Onset Based on the Japanese Adverse Drug Event Report Database.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Real-world incidence of nausea and vomiting induced by antineoplastic agents: insights and future directions from the Japanese adverse drug event report database.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Orphan drug propranolol for infantile hemangioma: ten-year real-world safety data from the FAERS database.Orphanet journal of rare diseases · 2026Article
- Potential heterogeneity of epcoritamab associated adverse events in demographics and dosing regimens.Frontiers in medicine · 2026Article
- Evaluation of a Community Pharmacist-Led Intervention Program for Early Detection of Gastrointestinal Adverse Events of Dipeptidyl Peptidase-4 Inhibitors: A Multicenter, Non-Randomized Comparative Study.Pharmacy (Basel, Switzerland) · 2025Article
- Evaluation of Adverse Events Associated with the Sulfamethoxazole/Trimethoprim Combination Drug.Journal of clinical medicine · 2025Article
- Postmarketing safety evaluation of pemetrexed using FAERS and JADER databases.Scientific reports · 2025Article
- Safety profile of faricimab: a multi-source pharmacovigilance analysis using FAERS and JADER.BMC pharmacology & toxicology · 2025Article
- Development of a Drug Safety Signal Detection Reference Set Using Japanese Safety Information.Therapeutic innovation & regulatory science · 2025Article
- Analysis of postmarketing neuropsychiatric adverse events of avapritinib based on the FDA adverse event reporting system.Scientific reports · 2025Article
- A multidimensional assessment of adverse events associated with paliperidone palmitate: a real-world pharmacovigilance study using the FAERS and JADER databases.BMC psychiatry · 2025Article
- Exploratory disproportionality analysis of potentially drug-induced eosinophilic pneumonia using United States Food and Drug Administration adverse event reporting system.Scientific reports · 2025Article
- Article
- Disproportionality analysis of taste disorders using the FDA adverse event reporting system and Japanese adverse drug event report databases.Frontiers in pharmacology · 2025Article
- Integrated pharmacovigilance of fentanyl: multinational analysis reveals novel safety signals and demographic-specific risk profiles.Frontiers in pharmacology · 2025Article
- Safety assessment of Edaravone: A real-world adverse event analysis based on the FAERS Database.PloS one · 2025Article
- Safety assessment of obinutuzumab: Real-world adverse event analysis based on the FAERS and JADER databases from 2013 to 2025.PloS one · 2025Article
- Unveiling the hidden risk of caspofungin: insights from three adverse event reporting systems and network pharmacology integration.Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of a statistical approach to analyze cumulative adverse event (AE) reports has been encouraged by regulatory authorities. However, data variations affect statistical analyses (eg, signal detection). Further, differences in regulations, social issues, and health care systems can cause variations in AE data. The present study examined similarities and differences between two publicly available databases, ie, the Japanese Adverse Drug Event Report (JADER) database and the US Food and Drug Administration Adverse Event Reporting System (FAERS), and how they affect signal detection.
methodsTwo AE data sources from 2010 were examined, ie, JADER cases (JP) and Japanese cases extracted from the FAERS (FAERS-JP). Three methods for signals of disproportionate reporting, ie, the reporting odds ratio, Bayesian confidence propagation neural network, and Gamma Poisson Shrinker (GPS), were used on drug-event combinations for three substances frequently recorded in both systems.
resultsThe two databases showed similar elements of AE reports, but no option was provided for a shareable case identifier. The average number of AEs per case was 1.6±1.3 (maximum 37) in the JP and 3.3±3.5 (maximum 62) in the FAERS-JP. Between 5% and 57% of all AEs were signaled by three quantitative methods for etanercept, infliximab, and paroxetine. Signals identified by GPS for the JP and FAERS-JP, as referenced by Japanese labeling, showed higher positive sensitivity than was expected.
conclusionThe FAERS-JP was different from the JADER. Signals derived from both datasets identified different results, but shared certain signals. Discrepancies in type of AEs, drugs reported, and average number of AEs per case were potential contributing factors. This study will help those concerned with pharmacovigilance better understand the use and pitfalls of using spontaneous AE data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.