Detecting Signals of Disproportionate Reporting from Singapore’s Spontaneous Adverse Event Reporting System: An Application of the Sequential Probability Ratio Test View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-08

AUTHORS

Cheng Leng Chan, Sowmya Rudrappa, Pei San Ang, Shu Chuen Li, Stephen J. W. Evans

ABSTRACT

INTRODUCTION: The ability to detect safety concerns from spontaneous adverse drug reaction reports in a timely and efficient manner remains important in public health. OBJECTIVE: This paper explores the behaviour of the Sequential Probability Ratio Test (SPRT) and ability to detect signals of disproportionate reporting (SDRs) in the Singapore context. METHODS: We used SPRT with a combination of two hypothesised relative risks (hRRs) of 2 and 4.1 to detect signals of both common and rare adverse events in our small database. We compared SPRT with other methods in terms of number of signals detected and whether labelled adverse drug reactions were detected or the reaction terms were considered serious. The other methods used were reporting odds ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN) and Gamma Poisson Shrinker (GPS). RESULTS: The SPRT produced 2187 signals in common with all methods, 268 unique signals, and 70 signals in common with at least one other method, and did not produce signals in 178 cases where two other methods detected them, and there were 403 signals unique to one of the other methods. In terms of sensitivity, ROR performed better than other methods, but the SPRT method found more new signals. The performances of the methods were similar for negative predictive value and specificity. CONCLUSIONS: Using a combination of hRRs for SPRT could be a useful screening tool for regulatory agencies, and more detailed investigation of the medical utility of the system is merited. More... »

PAGES

703-713

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40264-017-0531-4

DOI

http://dx.doi.org/10.1007/s40264-017-0531-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085087580

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28455793


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