Literature DB >> 28455793

Detecting Signals of Disproportionate Reporting from Singapore's Spontaneous Adverse Event Reporting System: An Application of the Sequential Probability Ratio Test.

Cheng Leng Chan1,2, Sowmya Rudrappa3,4, Pei San Ang3, Shu Chuen Li5, Stephen J W Evans6.   

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.

Mesh:

Year:  2017        PMID: 28455793     DOI: 10.1007/s40264-017-0531-4

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  18 in total

1.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports.

Authors:  S J Evans; P C Waller; S Davis
Journal:  Pharmacoepidemiol Drug Saf       Date:  2001 Oct-Nov       Impact factor: 2.890

2.  Use of measures of disproportionality in pharmacovigilance: three Dutch examples.

Authors:  Antoine C G Egberts; Ronald H B Meyboom; Eugène P van Puijenbroek
Journal:  Drug Saf       Date:  2002       Impact factor: 5.606

3.  Evaluation of statistical association measures for the automatic signal generation in pharmacovigilance.

Authors:  Emmanuel Roux; Frantz Thiessard; Annie Fourrier; Bernard Bégaud; Pascale Tubert-Bitter
Journal:  IEEE Trans Inf Technol Biomed       Date:  2005-12

Review 4.  Quantitative signal detection using spontaneous ADR reporting.

Authors:  A Bate; S J W Evans
Journal:  Pharmacoepidemiol Drug Saf       Date:  2009-06       Impact factor: 2.890

5.  Early detection of pharmacovigilance signals with automated methods based on false discovery rates: a comparative study.

Authors:  Ismaïl Ahmed; Frantz Thiessard; Ghada Miremont-Salamé; Françoise Haramburu; Carmen Kreft-Jais; Bernard Bégaud; Pascale Tubert-Bitter
Journal:  Drug Saf       Date:  2012-06-01       Impact factor: 5.606

6.  Statistical and graphical approaches for disproportionality analysis of spontaneously-reported adverse events in pharmacovigilance.

Authors:  Richard C Zink; Qin Huang; Lu-Yong Zhang; Wen-Jun Bao
Journal:  Chin J Nat Med       Date:  2013-05

7.  A Bayesian neural network method for adverse drug reaction signal generation.

Authors:  A Bate; M Lindquist; I R Edwards; S Olsson; R Orre; A Lansner; R M De Freitas
Journal:  Eur J Clin Pharmacol       Date:  1998-06       Impact factor: 2.953

8.  Comparison of statistical signal detection methods within and across spontaneous reporting databases.

Authors:  Gianmario Candore; Kristina Juhlin; Katrin Manlik; Bharat Thakrar; Naashika Quarcoo; Suzie Seabroke; Antoni Wisniewski; Jim Slattery
Journal:  Drug Saf       Date:  2015-06       Impact factor: 5.606

9.  Data mining spontaneous adverse drug event reports for safety signals in Singapore - a comparison of three different disproportionality measures.

Authors:  Pei San Ang; Zhaojin Chen; Cheng Leng Chan; Bee Choo Tai
Journal:  Expert Opin Drug Saf       Date:  2016-04-07       Impact factor: 4.250

10.  Risk-adjusted sequential probability ratio tests: applications to Bristol, Shipman and adult cardiac surgery.

Authors:  David Spiegelhalter; Olivia Grigg; Robin Kinsman; Tom Treasure
Journal:  Int J Qual Health Care       Date:  2003-02       Impact factor: 2.038

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  2 in total

1.  Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.

Authors:  Hae Reong Kim; MinDong Sung; Ji Ae Park; Kyeongseob Jeong; Ho Heon Kim; Suehyun Lee; Yu Rang Park
Journal:  Medicine (Baltimore)       Date:  2022-06-24       Impact factor: 1.817

2.  An algorithm to detect unexpected increases in frequency of reports of adverse events in EudraVigilance.

Authors:  Luis C Pinheiro; Gianmario Candore; Cosimo Zaccaria; Jim Slattery; Peter Arlett
Journal:  Pharmacoepidemiol Drug Saf       Date:  2017-11-16       Impact factor: 2.890

  2 in total

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