ArticleStatistics in medicine2020
Propensity score-adjusted three-component mixture model for drug-drug interaction data mining in FDA Adverse Event Reporting System.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Possible clinical drug interactions between ticagrelor and concomitant medications: for adverse drug event monitoring in real-world.Cardiovascular drugs and therapy · 2026Article
- Sex-stratified disproportionality signals of adverse events with allopurinol and febuxostat: a retrospective pharmacovigilance analysis of FAERS and EudraVigilance.Clinical rheumatology · 2026Article
- Exploring Risks of Adverse Drug Events From Single Drug to Three-Drug Combination.Clinical and translational science · 2026Article
- Evaluation of Dexmedetomidine-Associated Bradycardia and Related Drug-Drug Interactions Using Electronic Health Record (EHR) and miRNA Target Analysis.Current issues in molecular biology · 2025Article
- A Dose-Aware Model for Revealing Dose-Risk Relationship of Drug-Drug Interaction.CPT: pharmacometrics & systems pharmacology · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- A trajectory-informed model for detecting drug-drug-host interaction from real-world data.Journal of biomedical informatics · 2025Article
- A theoretical model for detecting drug interaction with awareness of timing of exposure.Scientific reports · 2025Article
- Caveats of Covariate Adjustment in Disproportionality Analysis for Best Practices.Drug safety · 2025Article
- Analysis of adverse events with ruxolitinib using real-world datasets and drug-interaction networks.SAGE open medicine · 2025Article
- Article
- Multimorbidity in Atherosclerotic Cardiovascular Disease and Its Associations With Adverse Cardiovascular Events and Healthcare Costs: A Real-World Evidence Study.Journal of health economics and outcomes research · 2024Article
- Evaluation of neuroleptic malignant syndrome induced by antipsychotic drugs using spontaneous reporting system.Heliyon · 2023Article
- A pharmacovigilance study of pharmacokinetic drug interactions using a translational informatics discovery approach.British journal of clinical pharmacology · 2022Review
- Analysis of drug-induced hand-foot syndrome using a spontaneous reporting system database.Therapeutic advances in drug safety · 2022Article
- New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection.BMC medical research methodology · 2021Article
- Analysis of chemotherapy-induced peripheral neuropathy using the Japanese Adverse Drug Event Report database.Scientific reports · 2021Article
- Analysis of Drug-Induced Gastrointestinal Obstruction and Perforation Using the Japanese Adverse Drug Event Report Database.Frontiers in pharmacology · 2021Article
- Analysis of immune-related adverse events caused by immune checkpoint inhibitors using the Japanese Adverse Drug Event Report database.Pharmacoepidemiology and drug safety · 2020Article
- Propensity score-adjusted three-component mixture model for drug-drug interaction data mining in FDA Adverse Event Reporting System.Statistics in medicine · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
With increasing trend of polypharmacy, drug-drug interaction (DDI)-induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score-adjusted three-component mixture model (PS-3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug-drug-ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS-3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS-3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS-3CMM prioritizes DDI signals differently. PS-3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS-3CMM is a new method that is complement to the existing DDI signal detection methods.
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