SynthesisPharmacoepidemiology and drug safety2023
Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review.
Synthesis in Pharmacoepidemiology and drug safety, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review.Pharmacoepidemiology and drug safety · 2023Pooled it
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Active surveillance of drug safety in healthcare data: Sequential monitoring of bacterial and serious urinary tract infection risk in sodium-glucose cotransporter 2 inhibitor users.British journal of clinical 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
- Metabolic Disturbances Associated with Beta-Blocker Therapy: New Insights from a Large-Scale Pharmacovigilance Study.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Calcitonin gene-related peptide antagonists in Raynaud's phenomenon: a disproportionality study based on real data and drug-gene network analysis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Early Identification of Cardiovascular Adverse Events Associated With Rofecoxib Using Real-World Data From the UK: A Nested Case-Control and Case-Crossover Study.Pharmacoepidemiology and drug safety · 2026Article
- Performance of the Self-Controlled Case Series for Drug Safety Signal Detection: A Multi-Database Study.Pharmacoepidemiology and drug safety · 2026Article
- Sequential analysis for post-marketing drug safety surveillance using routinely collected electronic healthcare data: a scoping review.Therapeutic advances in drug safety · 2026Article
- Serotonin syndrome risk with concomitant opioid and serotonergic antidepressant use: a multinational pharmacovigilance study.Frontiers in pharmacology · 2026Article
- Pharmacovigilance analysis of central nervous system adverse events associated with sevoflurane and drug interactions: a disproportionality study based on the FDA adverse event reporting system (FAERS) database.Frontiers in pharmacology · 2026Article
- Pharmacovigilance in Cell and Gene Therapy: Evolving Challenges in Risk Management and Long-Term Follow-Up.Drug safety · 2026Review
- Performance of the Self-Controlled Case Series With Active Comparators for Drug Safety Signal Detection Using the Clinical Practice Research Datalink (CPRD).Pharmacoepidemiology and drug safety · 2025Article
- Performance of the Self-Controlled Case Series With Active Comparators for Drug Safety Signal Detection Using Merative MarketScan Research Databases.Pharmacoepidemiology and drug safety · 2025Article
- Performance of the Self-Controlled Case Series With Active Comparators for Drug Safety Signal Detection Using the French Administrative Healthcare Database (SNDS).Pharmacoepidemiology and drug safety · 2025Article
- Recommendations to Enable Broader Use of Real-World Evidence to Inform Decision-Making Throughout Pharmacovigilance Signal Management.Pharmacoepidemiology and drug safety · 2025Review
- Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool.International journal of clinical pharmacy · 2025Review
- Comparative Pharmacovigilance Analysis of Approved and Repurposed Antivirals for COVID-19: Insights from EudraVigilance Data.Biomedicines · 2025Article
- High Throughput Pharmacovigilance Screening for Roflumilast Adverse Effects in Real-World Settings: A Sequence Symmetry Analysis.Basic & clinical pharmacology & toxicology · 2025Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSignal detection is a crucial step in the discovery of post-marketing adverse drug reactions. There is a growing interest in using routinely collected data to complement established spontaneous report analyses. This work aims to systematically review the methods for drug safety signal detection using routinely collected healthcare data and their performance, both in general and for specific types of drugs and outcomes.
methodsWe conducted a systematic review following the PRISMA guidelines, and registered a protocol in PROSPERO. MEDLINE, EMBASE, PubMed, Web of Science, Scopus, and the Cochrane Library were searched until July 13, 2021.
resultsThe review included 101 articles, among which there were 39 methodological works, 25 performance assessment papers, and 24 observational studies. Methods included adaptations from those used with spontaneous reports, traditional epidemiological designs, methods specific to signal detection with real-world data. More recently, implementations of machine learning have been studied in the literature. Twenty-five studies evaluated method performances, 16 of them using the area under the curve (AUC) for a range of positive and negative controls as their main measure. Despite the likelihood that performance measurement could vary by drug-event pair, only 10 studies reported performance stratified by drugs and outcomes, in a heterogeneous manner. The replicability of the performance assessment results was limited due to lack of transparency in reporting and the lack of a gold standard reference set.
conclusionsA variety of methods have been described in the literature for signal detection with routinely collected data. No method showed superior performance in all papers and across all drugs and outcomes, performance assessment and reporting were heterogeneous. However, there is limited evidence that self-controlled designs, high dimensional propensity scores, and machine learning can achieve higher performances than other methods.
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Registered trials
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