ArticleDrug safety2005
Data mining in pharmacovigilance: the need for a balanced perspective.
Article in Drug safety, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Psychiatric Safety Signals of GLP-1 Receptor Agonists: A FAERS-Based Pharmacovigilance Study with Explainable Machine Learning.Pharmaceuticals (Basel, Switzerland) · 2026Article
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- New Insights in Computational Methods for Pharmacovigilance:International journal of environmental research and public health · 2019Article
- Real-world Evidence versus Randomized Controlled Trial: Clinical Research Based on Electronic Medical Records.Journal of Korean medical science · 2018Article
- A Survey on Pharmacovigilance Activities in ASEAN and Selected Non-ASEAN Countries, and the Use of Quantitative Signal Detection Algorithms.Drug safety · 2017Article
- Development of Clinical Data Mart of HMG-CoA Reductase Inhibitor for Varied Clinical Research.Endocrinology and metabolism (Seoul, Korea) · 2017Article
- Reducing the noise in signal detection of adverse drug reactions by standardizing the background: a pilot study on analyses of proportional reporting ratios-by-therapeutic area.European journal of clinical pharmacology · 2014Article
- Vaccine-based subgroup analysis in VigiBase: effect on sensitivity in paediatric signal detection.Drug safety · 2012Article
- Article
- Analysis of Relationship between Levofloxacin and Corrected QT Prolongation Using a Clinical Data Warehouse.Healthcare informatics research · 2011Article
- Temporal data mining for adverse events following immunization in nationwide Danish healthcare databases.Drug safety · 2010Article
- Antimicrobials and the risk of torsades de pointes: the contribution from data mining of the US FDA Adverse Event Reporting System.Drug safety · 2010Article
- Exposure to antibacterial agents with QT liability in 14 European countries: trends over an 8-year period.British journal of clinical pharmacology · 2009Review
- Data mining in pharmacovigilance--detecting the unexpected: the role of index of suspicion of the reporter.Drug safety · 2009Article
- Venous thromboembolism in myelodysplastic syndrome patients receiving lenalidomide: results from postmarketing surveillance and data mining techniques.Clinical drug investigation · 2009Article
- Illusions of objectivity and a recommendation for reporting data mining results.European journal of clinical pharmacology · 2007Article
- Gold standards in pharmacovigilance: the use of definitive anecdotal reports of adverse drug reactions as pure gold and high-grade ore.Drug safety · 2007Article
- Article
- Data mining in pharmacovigilance: lessons from phantom ships.European journal of clinical pharmacology · 2006Article
- What counts in data mining?Drug safety · 2006Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data mining is receiving considerable attention as a tool for pharmacovigilance and is generating many perspectives on its uses. This paper presents four concepts that have appeared in various professional venues and represent potential sources of misunderstanding and/or entail extended discussions: (i) data mining algorithms are unvalidated; (ii) data mining algorithms allow data miners to objectively screen spontaneous report data; (iii) mathematically more complex Bayesian algorithms are superior to frequentist algorithms; and (iv) data mining algorithms are not just for hypothesis generation. Key points for a balanced perspective are that: (i) validation exercises have been done but lack a gold standard for comparison and are complicated by numerous nuances and pitfalls in the deployment of data mining algorithms. Their performance is likely to be highly situation dependent; (ii) the subjective nature of data mining is often underappreciated; (iii) simpler data mining models can be supplemented with 'clinical shrinkage', preserving sensitivity; and (iv) applications of data mining beyond hypothesis generation are risky, given the limitations of the data. These extended applications tend to 'creep', not pounce, into the public domain, leading to potential overconfidence in their results. Most importantly, in the enthusiasm generated by the promise of data mining tools, users must keep in mind the limitations of the data and the importance of clinical judgment and context, regardless of statistical arithmetic. In conclusion, we agree that contemporary data mining algorithms are promising additions to the pharmacovigilance toolkit, but the level of verification required should be commensurate with the nature and extent of the claimed applications.
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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.