ArticleDrug safety2019
A Comparison Study of Algorithms to Detect Drug-Adverse Event Associations: Frequentist, Bayesian, and Machine-Learning Approaches.
Article in Drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Multimodal investigation of the association between bevacizumab and pulmonary arterial hypertension.ERJ open research · 2026Article
- Optimising Pharmacovigilance Efficiency with MLIT (Machine Learning for Intelligent Triage): A Tool for Statistical Safety Alerts.Drug safety · 2026Article
- Drug-Induced Raynaud's Phenomenon and Underlying Mechanism: A Disproportionality Analysis From the World Health Organization Pharmacovigilance Database.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- Medicines prescribed in pregnancy: Protocol for a signal detection study using routinely collected data in England.PloS one · 2026Article
- Reporting of the harms from randomized controlled trials for psoriasis: a cross-sectional meta-epidemiological study.Frontiers in medicine · 2026Article
- Adverse events associated with Lutetium-177-PSMA-617 (PluvictoBMC cancer · 2025Article
- Osimertinib-related myotoxicity: a disproportionality analysis of the FDA adverse event reporting system.BMC cancer · 2025Article
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- Development of a Drug Safety Signal Detection Reference Set Using Japanese Safety Information.Therapeutic innovation & regulatory science · 2025Article
- Towards interpretable drug interaction predictionPeerJ. Computer science · 2025Article
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- Screening for clinically relevant drug-drug interactions between direct oral anticoagulants and antineoplastic agents: a pharmacovigilance approach.Journal of thrombosis and thrombolysis · 2023Article
- Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems.Frontiers in drug safety and regulation · 2023Review
- Evaluation of four machine learning models for signal detection.Therapeutic advances in drug safety · 2023Article
- Identifying new drugs associated with pulmonary arterial hypertension: A WHO pharmacovigilance database disproportionality analysis.British journal of clinical pharmacology · 2022Article
- Association between NMDAR antagonists, drug abuse and dependence: A disproportionality analysis from the WHO pharmacovigilance database.British journal of clinical pharmacology · 2022Article
- Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system.Scientific reports · 2022Article
- Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports.CPT: pharmacometrics & systems pharmacology · 2022Review
- Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIt is important to monitor the safety profile of drugs, and mining for strong associations between drugs and adverse events is an effective and inexpensive method of post-marketing safety surveillance.
objectiveThe objective of our work was to compare the accuracy of both common and innovative methods of data mining for pharmacovigilance purposes.
methodsWe used the reference standard provided by the Observational Medical Outcomes Partnership, which contains 398 drug-adverse event pairs (165 positive controls, 233 negative controls). Ten methods and algorithms were applied to the US FDA Adverse Event Reporting System data to investigate the 398 pairs. The ten methods include popular methods in the pharmacovigilance literature, newly developed pharmacovigilance methods as at 2018, and popular methods in the genome-wide association study literature. We compared their performance using the receiver operating characteristic (ROC) plot, area under the curve (AUC), and Youden's index.
resultsThe Bayesian confidence propagation neural network had the highest AUC overall. Monte Carlo expectation maximization, a method developed in 2018, had the second highest AUC and the highest Youden's index, and performed very well in terms of high specificity. The regression-adjusted gamma Poisson shrinkage model performed best under high-sensitivity requirements.
conclusionOur results will be useful to help choose a method for a given desired level of specificity. Methods popular in the genome-wide association study literature did not perform well because of the sparsity of data and will need modification before their properties can be used in the drug-adverse event association problem.
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Registered trials
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.