Assessing the extent and impact of the masking effect of disproportionality analyses on two spontaneous reporting systems databases

Background: Masking is a statistical issue by which signals are hidden by the presence of other medicines in the database. In the absence algorithm, the impact of the masking effect has not been fully investigated. Objective: Our study is aimed at assessing the extent and the impact of the masking effect on two large spontaneous reporting databases. Study design: Cross sectional study using a set of terms of importance for public health in two spontaneous reporting databases. Setting: The analyses were performed on EudraVigilance (EV) and the Pfizer spontaneous reporting database (PfDB). Main outcome measure: Using the masking ratio, we have identified and removed the products inducing the highest masking effect. Results: Studying a total of almost 50000 drug-event combinations masking had an impact on approximately 60% of drug-event combinations were masked by another product with a masking ratio >1 in EV and 84% in PfDB. The prevalence of important masking was quite rare (0.003% of the DECs) and mainly affected events rarely reported in EV. The products involved in the highest masking effects are products known to induce the reaction. The removal of the masking effect of the highest masking product has revealed 974 signals of disproportionate reporting in EV including true signals. The study shows that the original ranking provided by the quantitative methods included in our study is marginally affected by the removal of the masking product. Conclusion: Our study suggests that significant masking is rare in large spontaneous databases and mostly affects events rarely reported in EV.