ArticleDrug safety2016
A Method for the Minimization of Competition Bias in Signal Detection from Spontaneous Reporting Databases.
Article in Drug safety, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 35 citations in OpenAlex.
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- Signals of Adverse Reactions to Herbal Medicines: Evidence and Document Analysis Based on a Scoping Review.Drug safety · 2025Article
- Exploring the complexities of disproportionality analysis in pharmacovigilance: reflections on the READUS-PV guideline and a call to action.Frontiers in pharmacology · 2025Article
- Influence of differential source patterns in the detection of signals of disproportionate reporting for PARP inhibitors.Frontiers in drug safety and regulation · 2024Article
- Impact of Covid-19 Vaccination on Spontaneous Pharmacovigilance Reporting in France.Drug safety · 2023Article
- Conducting and interpreting disproportionality analyses derived from spontaneous reporting systems.Frontiers in drug safety and regulation · 2023Review
- Identifying new drugs associated with pulmonary arterial hypertension: A WHO pharmacovigilance database disproportionality analysis.British journal of clinical pharmacology · 2022Article
- High-Dimensional Propensity Score-Adjusted Case-Crossover for Discovering Adverse Drug Reactions from Computerized Administrative Healthcare Databases.Drug safety · 2022Article
- New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection.BMC medical research methodology · 2021Article
- Thromboembolic Events with Cyclin-Dependent Kinase 4/6 Inhibitors in the FDA Adverse Event Reporting System.Cancers · 2021Article
- SGLT-2 inhibitors and atrial fibrillation in the Food and Drug Administration adverse event reporting system.Cardiovascular diabetology · 2021Article
- Cyclin-dependent kinase 4/6 inhibitors and interstitial lung disease in the FDA adverse event reporting system: a pharmacovigilance assessment.Breast cancer research and treatment · 2021Article
- Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System.Frontiers in pharmacology · 2021Article
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- A stratification method based on clustering for the minimization of data masking effect in signal detection.BMC medical informatics and decision making · 2020Article
- A mathematical framework to quantify the masking effect associated with the confidence intervals of measures of disproportionality.Therapeutic advances in drug safety · 2017Article
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Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe two methods for minimizing competition bias in signal of disproportionate reporting (SDR) detection--masking factor (MF) and masking ratio (MR)--have focused on the strength of disproportionality for identifying competitors and have been tested using competitors at the drug level.
objectivesThe aim of this study was to develop a method that relies on identifying competitors by considering the proportion of reports of adverse events (AEs) that mention the drug class at an adequate level of drug grouping to increase sensitivity (Se) for SDR unmasking, and its comparison with MF and MR.
methodsReports in the French spontaneous reporting database between 2000 and 2005 were selected. Five AEs were considered: myocardial infarction, pancreatitis, aplastic anemia, convulsions, and gastrointestinal bleeding; related reports were retrieved using standardized Medical Dictionary for Regulatory Activities (MedDRA(®)) queries. Potential competitors of AEs were identified using the developed method, i.e. Competition Index (ComIn), as well as MF and MR. All three methods were tested according to Anatomical Therapeutic Chemical (ATC) classification levels 2-5. For each AE, SDR detection was performed, first in the complete database, and second after removing reports mentioning competitors; SDRs only detected after the removal were unmasked. All unmasked SDRs were validated using the Summary of Product Characteristics, and constituted the reference dataset used for computing the performance for SDR unmasking (area under the curve [AUC], Se).
resultsPerformance of the ComIn was highest when considering competitors at ATC level 3 (AUC: 62 %; Se: 52 %); similar results were obtained with MF and MR.
conclusionThe ComIn could greatly minimize the competition bias in SDR detection. Further study using a larger dataset is needed.
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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.