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. Method: 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). Conclusion: 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.