ArticleDrug safety2009
Drug-versus-drug adverse event rate comparisons: a pilot study based on data from the US FDA Adverse Event Reporting System.
Article in Drug safety, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
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- Amlodipine-Associated Angioedema: An Integrated Pharmacovigilance Assessment Using Disproportionality and Interaction Analysis and Case Reviews.Journal of clinical medicine · 2025Article
- Adverse reactions of immune checkpoint inhibitors combined with Proton pump inhibitors: a pharmacovigilance analysis of drug-drug interactions.BMC cancer · 2024Article
- Analysis on the risk of myasthenia gravis related to immune checkpoint inhibitors based on the US FDA Adverse Event Reporting System.Cancer medicine · 2023Article
- Drug repositioning prediction for psoriasis using the adverse event reporting database.Frontiers in medicine · 2023Article
- Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Clindamycin hydrochloride and clindamycin phosphate: two drugs or one? A retrospective analysis of a spontaneous reporting system.European journal of clinical pharmacology · 2017Article
- Detecting drug-herbal interaction using a spontaneous reporting system database: an example with benzylpenicillin and qingkailing injection.European journal of clinical pharmacology · 2015Article
- Commonality of drug-associated adverse events detected by 4 commonly used data mining algorithms.International journal of medical sciences · 2014Article
- Data mining of the public version of the FDA Adverse Event Reporting System.International journal of medical sciences · 2013Review
- Article
- Enhancing pharmacosurveillance with systematic collection of treatment indication in electronic prescribing: a validation study in Canada.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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA number of published studies compare adverse event rates for drugs on the basis of reports in the US FDA Adverse Event Reporting System (AERS). While the AERS data have the advantage of timely availability and a large capture population, the database is subject to many significant biases, and lacks complete patient information that would allow for correction of those biases. The accuracy of comparative AERS-based data mining has been questioned, but has not been systematically studied.
objectiveTo determine whether AERS could be used as a data source to accurately compare the adverse event rates for pairs of drugs, using pre-defined, stringent criteria to dictate whether a given pair of drugs was considered eligible for such a comparison.
methodsThe Fisher's Exact test was utilized to detect differences in adverse event rates between such pairs of drugs. Concordance was determined between statistically significant AERS-based adverse event rate differences, and adverse event rate differences published in the literature from clinical trials and case-control studies. The conditions for validity included (i) data that are free of 'extreme duplication' in AERS reports; (ii) drugs used in similar patient populations; (iii) drugs used for similar indications; (iv) drugs used with the same spectrum of concomitant medications; and (v) drugs not widely disparate in time on the market.
resultsFor 19 drugs studied, a total of 36 evaluable adverse event rate comparisons were identified. Comparisons were classified as favouring 'drug A', favouring 'drug B' or detecting no difference. Concordance for the resulting 3x3 table (AERS vs literature) gave a kappa statistic of 0.654, indicating moderately good agreement. In only two cases was there absolute discordance, with AERS designating one drug as having a lower rate, while the published study designated the other drug as having a lower rate, with respect to a given adverse event.
conclusionsThis pilot study encourages further research regarding the use of spontaneous report databases such as AERS, under stringently defined conditions, to compare adverse event rates for drugs. While not hypothesis proving, such estimates can be used for purposes such as generating hypotheses for controlled studies, and for designing those studies.
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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.