Outlier removal to uncover patterns in adverse drug reaction surveillance - a simple unmasking strategy
2013 (English)In: Pharmacoepidemiology and Drug Safety, ISSN 1053-8569, E-ISSN 1099-1557, Vol. 22, no 10, 1119-1129 p.Article in journal (Refereed) Published
PurposeThis study aimed to develop an algorithm for uncovering associations masked by extreme reporting rates, characterize the occurrence of masking by influential outliers in two spontaneous reporting databases and evaluate the impact of outlier removal on disproportionality analysis. MethodsWe propose an algorithm that identifies influential outliers and carries out parallel analysis after their omission. It considers masking of drugs as well as of adverse drug reactions (ADRs), uses a direct measure of the masking effect and makes no assumptions regarding the number of outliers per drug or ADR. The occurrence of masking is characterized in the WHO Global Individual Case Safety Report database, VigiBase and a regional collection of reports from Shanghai, China. ResultsFor WHO-ART critical terms such as myocardial infarction, rhabdomyolysis and hypoglycaemia outlier removal led to a 25-50% increase in the number of Statistics of Disproportionate Reporting (SDR) and gains in time to detection of 1-2years, while keeping the rate of spurious SDRs from the parallel analysis at 1%. Twenty-three per cent of VigiBase and 18% of Shanghai SRS reports listed an influential outlier. Twenty-seven per cent of the ADRs and 5% of the drugs in VigiBase, and 2% of the ADRs and 3% of the drugs in Shanghai SRS were involved in an outlier. The overall increase in the number of SDRs for both datasets was 3%. ConclusionMasking by outliers has substantial impact on specific ADRs including, in VigiBase, rhabdomyolysis, myocardial infarction and hypoglycaemia. It is a local phenomenon involving a fair number of reports but yielding a limited number of additional SDRs.
Place, publisher, year, edition, pages
2013. Vol. 22, no 10, 1119-1129 p.
adverse drug reactions, disproportionality analysis, outliers, masking, statistical shrinkage, competition bias, pharmacoepidemiology
Mathematics Medical and Health Sciences
IdentifiersURN: urn:nbn:se:uu:diva-210227DOI: 10.1002/pds.3474ISI: 000325146100013OAI: oai:DiVA.org:uu-210227DiVA: diva2:661975
De två (2) första författarna delar förstaförfattarskapet.2013-11-052013-11-042013-11-05Bibliographically approved