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Effect of database profile variation on drug safety assessment: an analysis of spontaneous adverse event reports of Japanese cases

Authors Nomura K, Takahashi K, Hinomura Y, Kawaguchi G, Matsushita Y, Marui H, Anzai T, Hashiguchi M, Mochizuki M

Received 1 February 2015

Accepted for publication 30 March 2015

Published 12 June 2015 Volume 2015:9 Pages 3031—3041

DOI https://doi.org/10.2147/DDDT.S81998

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Shu-Feng Zhou


Kaori Nomura,1 Kunihiko Takahashi,2 Yasushi Hinomura,3 Genta Kawaguchi,4 Yasuyuki Matsushita,5 Hiroko Marui,6 Tatsuhiko Anzai,7 Masayuki Hashiguchi,8 Mayumi Mochizuki8

1Division of Molecular Epidemiology, Jikei University School of Medicine, Tokyo, 2Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, 3Japan Pharmaceutical Information Center, 4Global Pharmacovigilance, Kissei Pharmaceutical Co Ltd, Tokyo, 5Medical Affairs Department, Daiichi Sankyo Co Ltd, 6Drug Safety Division, Chugai Pharmaceutical Co Ltd, 7Data Science Center, EPS Corporation, 8Faculty of Pharmacy, Keio University, Tokyo, Japan

Background: The use of a statistical approach to analyze cumulative adverse event (AE) reports has been encouraged by regulatory authorities. However, data variations affect statistical analyses (eg, signal detection). Further, differences in regulations, social issues, and health care systems can cause variations in AE data. The present study examined similarities and differences between two publicly available databases, ie, the Japanese Adverse Drug Event Report (JADER) database and the US Food and Drug Administration Adverse Event Reporting System (FAERS), and how they affect signal detection.
Methods: Two AE data sources from 2010 were examined, ie, JADER cases (JP) and Japanese cases extracted from the FAERS (FAERS-JP). Three methods for signals of disproportionate reporting, ie, the reporting odds ratio, Bayesian confidence propagation neural network, and Gamma Poisson Shrinker (GPS), were used on drug-event combinations for three substances frequently recorded in both systems.
Results: The two databases showed similar elements of AE reports, but no option was provided for a shareable case identifier. The average number of AEs per case was 1.6±1.3 (maximum 37) in the JP and 3.3±3.5 (maximum 62) in the FAERS-JP. Between 5% and 57% of all AEs were signaled by three quantitative methods for etanercept, infliximab, and paroxetine. Signals identified by GPS for the JP and FAERS-JP, as referenced by Japanese labeling, showed higher positive sensitivity than was expected.
Conclusion:
The FAERS-JP was different from the JADER. Signals derived from both datasets identified different results, but shared certain signals. Discrepancies in type of AEs, drugs reported, and average number of AEs per case were potential contributing factors. This study will help those concerned with pharmacovigilance better understand the use and pitfalls of using spontaneous AE data.

Keywords: drug safety, spontaneous reports system, Japan, reporting disproportionality
 

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