A conceptual approach to the masking effect of measures of disproportionality.
BACKGROUND
Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other products in the database. Masking is currently not perfectly understood. There is no algorithm to identify the potential masking drugs to remove them for subsequent analyses of disproportionality.


OBJECTIVE
The primary objective of our study is to develop a mathematical framework for assessing the extent and impact of the masking effect of measures of disproportionality.


METHODS
We have developed a masking ratio that quantifies the masking effect of a given product. We have conducted a simulation study to validate our algorithm.


RESULTS
The masking ratio is a measure of the strength of the masking effect whether the analysis is performed at the report or event level, and the manner in which reports are allocated to cells in the contingency table significantly impact the masking mechanisms. The reports containing both the product of interest and the masking product need to be handled appropriately. The proposed algorithm can use simplified masking provided that underlying assumptions (in particular the size of the database) are verified. For any event, the strongest masking effect is associated with the drug with the highest number of records (reports excluding the product of interest).


CONCLUSIONS
Our study provides significant insights with practical implications for real-world pharmacovigilance that are supported by both real and simulated data. The public health impact of masking is still unknown.
European Medicines Agency, London, UK.
Francois
F
Maignen
Manfred
M
Hauben
Eric
E
Hung
Lionel Van
LV
Holle
Jean-Michel
JM
Dogne
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D006801:Humans; D008962:Models, Theoretical; D017891:Pharmacoepidemiology / Q000379:methods; D060735:Pharmacovigilance; D011634:Public Health
algorithm; disproportionality analysis; masking; pharmacoepidemiology