ArticleEuropean journal of clinical pharmacology2007
Illusions of objectivity and a recommendation for reporting data mining results.
Article in European journal of clinical pharmacology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS.Drug safety · 2024Article
- Challenges and Opportunities in Accessing and Analysing FAERS Data: A Call Towards a Collaborative Approach.Drug safety · 2023Article
- An industry perspective on the use of machine learning in drug and vaccine safety.Frontiers in drug safety and regulation · 2023Article
- Revisiting the reported signal of acute pancreatitis with rasburicase: an object lesson in pharmacovigilance.Therapeutic advances in drug safety · 2016Article
- Safety of Perflutren Ultrasound Contrast Agents: A Disproportionality Analysis of the US FAERS Database.Drug safety · 2015Article
- Effect of database profile variation on drug safety assessment: an analysis of spontaneous adverse event reports of Japanese cases.Drug design, development and therapy · 2015Article
- Identifying drugs that cause acute thrombocytopenia: an analysis using 3 distinct methods.Blood · 2010Article
- A decade of data mining and still counting.Drug safety · 2010Article
- Prospective data mining of six products in the US FDA Adverse Event Reporting System: disposition of events identified and impact on product safety profiles.Drug safety · 2010Article
- Intelligent risk communication: can it be improved?Drug safety · 2008Article
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
objectiveData mining algorithms (DMAs) are being applied to spontaneous reporting system (SRS) databases in the hope of obtaining timely insights into post-licensure safety data. Some DMAs have been characterized as "objective" screening tools. However, there are numerous available modifiable configuration parameters to choose from, including choice of vendor, that may affect results. Our objective is to compare the data mining results on pre-selected drug-event combinations (DECs) between two commonly used software programs using similar protocols.
methodsTwo DMAs, using three thresholds, were retrospectively applied to the USFDA safety database through Q2 2005 to a set of eight pre-selected DECs.
resultsDifferences between the two vendors were found for the number of cases associated with a signal of disproportionate reporting (SDR), first year of SDRs, and the magnitude of the SDR scores for the selected DECs. These were deemed to be potentially significant for 45.8% (11/24) of the data points.
conclusionThe observed differences between vendors could partially be explained by their differing methods of data cleaning and transformation as well as by the specific features of individual algorithms. The choices of vendors and available data mining configurations maximize the exploratory capacity of data mining, but they also raise questions about the claimed objectivity of data mining results and can make data mining exercises susceptible to confirmation bias given the exploratory nature of data mining in pharmacovigilance. When reporting results, the vendor and all data mining configuration details should be specified.
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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.