• Corpus ID: 53417309

Primer on Disproportionality Analysis

@inproceedings{2018PrimerOD,
  title={Primer on Disproportionality Analysis},
  author={},
  year={2018}
}
  • Published 2018
  • Psychology
This primer explains meaning, calculation and uses of various methods for assessing disproportionality in pharmacovigilance data by observed-expected ratios. Disproportionality can stimulate further research whether an adverse event (AE) should be considered an adverse drug reaction (ADR). Disproportionality analysis is thus only suited for hypothesis generation, not for hypothesis testing. Note the cave-at documents from OpenVigil, FDA and WHO before drawing any conclusions from the ratios… 

Figures from this paper

Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity
TLDR
This study suggests that specific members of the ACEI antihypertensive class (quinapril and trandolapril) have a significantly higher cluster of pulmonary ADEs.
Frequency and Associated Costs of Anaphylaxis- and Hypersensitivity-Related Adverse Events for Intravenous Iron Products in the USA: An Analysis Using the US Food and Drug Administration Adverse Event Reporting System
TLDR
Reporting rates of hypersensitivity and anaphylaxis with intravenous iron preparations were highest with ferumoxytol and lowest with ferric carboxymaltose in the US FAERS database.
Adverse Events Associated with Intranasal Sprays: An Analysis of the Food and Drug Administration Database and Literature Review
TLDR
The clinical significance of this reporting tool from the FDA for these classes of medications remains unvalidated when compared against the existing scientific literature.
Nonmedical Use of Xtampza® ER and Other Oxycodone Medications in Adults Evaluated for Substance Abuse Treatment: Real-World Data from the Addiction Severity Index-Multimedia Version (ASI-MV®)
TLDR
Xtampza ER had significantly lower rates of NMU than other oxy codone ER products and oxycodone IR products, as well as significantly higher rates of non-oral NMU more than oxycod oneIR products, in a population of individuals seeking substance abuse treatment.
Deep Learning for Adverse Event Detection
TLDR
Evaluation results on three large real-world event datasets show that DeepSAVE outperforms existing detection methods as well as comparison deep learning 14 auto encoders and demonstrates the viability of the proposed architecture for detecting adverse events from search query logs.
Deep Learning for Adverse Event Detection From Web Search
TLDR
Evaluation results on three large real-world event datasets show that DeepSAVE outperforms existing detection methods as well as comparison deep learning auto encoders and demonstrates the viability of the proposed architecture for detecting adverse events from search query logs.
Real world adverse events of interspinous spacers using Manufacturer and User Facility Device Experience data
TLDR
A disproportionality analysis was conducted to determine whether a statistically significant signal exists in the three interspinous spacers and the reported adverse events using the Manufacturer and User Facility Device Experience (MAUDE) database maintained by the US Food and Drug Administration.

References

SHOWING 1-10 OF 10 REFERENCES
Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports
The process of generating ‘signals’ of possible unrecognized hazards from spontaneous adverse drug reaction reporting data has been likened to looking for a needle in a haystack. However, statistical
The upper bound to the Relative Reporting Ratio—a measure of the impact of the violation of hidden assumptions underlying some disproportionality methods used in signal detection
TLDR
The tools for allowing signal detection experts to assess the consequence of the violation of this assumption of the Relative Reporting Ratio on their specific SRD are provided.
Bayesian Methods in Pharmacovigilance
TLDR
This work describes statistical methods for post-approval data analysis that attempt to detect drug safety problems as quickly as possible and Bayesian approaches are especially useful because of the high dimensionality of the data.
A statistical methodology for drug–drug interaction surveillance
TLDR
A shrinkage observed‐to‐expected ratio is implemented and evaluated for exploratory analysis of suspected drug–drug interaction in ICSR data, based on comparison with an additive risk model.
Mining multi-item drug adverse effect associations in spontaneous reporting systems
TLDR
It is demonstrated that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using the method proposed, suggesting that the method is a valid approach for the initial identification of multi- item ADEs.
Use of Screening Algorithms and Computer Systems to Efficiently Signal Higher-Than-Expected Combinations of Drugs and Events in the US FDA’s Spontaneous Reports Database
TLDR
The operating characteristics of data mining in detecting early safety signals, exemplified by studying a drug recently well characterised by large clinical trials confirms the experience that the signals generated by data mining have high enough specificity to deserve further investigation.
Mining Association Rules between Sets of Items in Large Databases
TLDR
This paper proposes a new interactive approach to prune and filter discovered rules and proposes the Rule Schema formalism extending the specification language proposed by Liu et al. for user expectations to improve the integration of user knowledge in the post processing task.
Medical Applications of Artificial Intelligence
  • A. Agah
  • Computer Science, Medicine
  • 2013
TLDR
The book captures the breadth and depth of the medical applications of artificial intelligence, exploring new developments and persistent challenges.
The reporting odds ratio versus the proportional reporting ratio: ‘deuce’
OpenVigil caveat document
  • 2011