Detecting Signals of Disproportionate Reporting from Singapore’s Spontaneous Adverse Event Reporting System: An Application of the Sequential Probability Ratio Test
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The ability to detect safety concerns from spontaneous adverse drug reaction reports in a timely and efficient manner remains important in public health.
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.
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).
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.
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.
- 2.Waller P. An introduction to pharmacovigilance. UK: Wiley-Blackwell; 2010.Google Scholar
- 3.Koh Y LA, Tan L, Ang PS, Tan SH, Toh D, Chan CL. Pharmacovigilance in Singapore—harnessing IT and genomics to detect safety signals. Regul Aff J-Pharma. 2012;(Oct issue):13–5. https://pink.pharmamedtechbi.com/PS116976/Pharmacovigilance-in-Singapore--harnessing-IT-and-genomics-to-detect-safety-signals. Accessed 15 Mar 2017.
- 11.Szarfman A, Machado SG, O’Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA’s Spontaneous Reports Database. Drug Saf. 2002;25(6):381–92. doi:10.2165/00002018-200225060-00001.CrossRefPubMedGoogle Scholar
- 15.Wald A. Sequential analysis. New York: Wiley; 1947.Google Scholar
- 16.Evans S. Sequential probability ratio tests applied to public health problems. Control Clin Trials. 2003;24:67S.Google Scholar
- 17.Talbot J, Aronson JK. Stephens’ detection and evaluation of adverse drug reactions: principles and practice. 6th ed. Oxford: Wiley-Blackwell; 2012.Google Scholar
- 23.Micromedex® 2.0 (electronic version). Truven Health Analytics, Greenwood Village, Colorado, USA. http://www.micromedexsolutions.com/. Accessed 19 Sept 2015.
- 24.Safety reporting requirements for human drug and biological products; Federal Register. Department of Health and Human Services. US Food and Drug Administration. 14 March 2003. http://www.fda.gov/OHRMS/DOCKETS/98fr/03-5204.pdf. Accessed 19 Sept 2015.
- 25.Important Medical Event Terms list (based on MedDRA version 18.1). EudraVigilance Expert Working Group. https://eudravigilance.ema.europa.eu/human/textforIME.asp. Accessed 19 Sept 2015.
- 26.R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2014. http://www.R-project.org. Accessed 23 Sept 2016.
- 27.Ahmed I, Poncet A. Package ‘PhViD’. Version:1.0.6. 2013. https://cran.r-project.org/web/packages/PhViD/PhViD.pdf Accessed 19 Sept 2015.