Significance of data mining in routine signal detection: analysis based on the safety signals identified by the FDA

Fukazawa, Chisato; Hinomura, Yasushi; Kaneko, Masayuki; Narukawa, Mamoru

doi:10.1002/pds.4672

Cited by 57Pharmacovigilance and Adverse Drug ReactionsData-Driven Disease SurveillancePharmaceutical industry and healthcare

Significance of data mining in routine signal detection: analysis based on the safety signals identified by the FDA

Fukazawa, Chisato, Hinomura, Yasushi, Kaneko, Masayuki, Narukawa, Mamoru·2018·Pharmacoepidemiology and Drug Safety

DOI 10.1002/pds.4672

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Abstract

Purpose: Data mining has been introduced as one of the most useful methods for signal detection by spontaneous reports, but data mining is not always effective in detecting all safety issues. To investigate appropriate situations in which data mining is effective in routine signal detection activities, we analyzed the characteristics of signals that the US Food and Drug Administration (FDA) identified from the FDA Adverse Event Reporting System (FAERS). Methods: Among the signals that the FDA identified from the FAERS between 2008 1Q and 2014 4Q, we selected 233 signals to evaluate in this study. We conducted a disproportionality analysis and classified these signals into two groups according to the presence or absence of statistical significance in the reporting odds ratio (ROR). Then, we compared the two groups based on the characteristics of the suspected drugs and adverse events (AEs). Results: Safety signals were most frequently identified for new drugs that had been on the market for less than 5 years, but some signals were still identified for old drugs (≥20 years), and most of them were statistically significant. The proportion of the signals for “serious” events was significantly higher in the group of nonsignals by ROR (Fisher's exact test, P = 0.032). Conclusions: Data mining was shown to be effective in the following situations: (1) early detection of safety issues for newly marketed drugs, (2) continuous monitoring of safety issues for old drugs, and (3) signal detection of nonserious AEs, to which little attention is usually given.

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FAERS is one of the largest publicly available repositories of unsolicited reports, gathering more than 15 million reports worldwide since 2004.
“FAERS is one of the largest publicly available repositories of unsolicited reports, gathering more than 15 million reports worldwide since 2004; it was already used to early identify and characterize AEs for recently marketed drugs as well as for continuous monitoring of safety issues for old drugs (5).”
From The Changing Face of Drug-induced Adrenal Insufficiency in the Food and Drug Administration Adverse Event Reporting System (2022) · cites this paper as [5]

The environment and disease: association or causation?
Hill, A. B. (2015) · Journal of the Royal Society of Medicine · doi:10.1177/0141076814562718 · Cited by 7,302 · see citing works
The reporting odds ratio and its advantages over the proportional reporting ratio
Rothman, K. J., Lanes, S., & Sacks, S. T. (2004) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1001 · Cited by 1,112 · see citing works
Combining electronic healthcare databases in Europe to allow for large‐scale drug safety monitoring: the EU‐ADR Project
Coloma, P. M., Schuemie, M. J., Trifirò, G., Gini, R., Herings, R. M. C., Hippisley‐Cox, J., Mazzaglia, G., Giaquinto, C., Corrao, G., Pedersen, L. H., et al. (2010) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.2053 · Cited by 259 · see citing works
Principles of Signal Detection in Pharmacovigilance
Meyboom, R. H. B., Egberts, T. C. G., Edwards, I. R., Hekster, Y. A., Koning, F. H. P. D., & Gribnau, F. W. J. (1997) · Drug Safety · doi:10.2165/00002018-199716060-00002 · Cited by 241 · see citing works
Data Mining Techniques in Pharmacovigilance: Analysis of the Publicly Accessible FDA Adverse Event Reporting System (AERS)
Poluzzi, E., Raschi, E., Piccinni, C. P., & De, F. (2012) · InTech eBooks · doi:10.5772/50095 · Cited by 227 · see citing works
Attitudinal survey of adverse drug reaction reporting by medical practitioners in the United Kingdom [see comments]
Belton, K. J., Lewis, S. C., Payne, S., Rawlins, M., & Wood, S. (1995) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.1995.tb04440.x · Cited by 187 · see citing works
Comparison of Statistical Signal Detection Methods Within and Across Spontaneous Reporting Databases
Candore, G., Juhlin, K., Manlik, K., Thakrar, B. T., Quarcoo, N., Seabroke, S., Wisniewski, A. F. Z., & Slattery, J. (2015) · Drug Safety · doi:10.1007/s40264-015-0289-5 · Cited by 149 · see citing works
Evaluation of FDA safety‐related drug label changes in 2010
Lester, J., Neyarapally, G. A., Lipowski, E. E., Graham, C. F., Hall, M., & Pan, G. J. D. (2013) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.3395 · Cited by 113 · see citing works
Comparative Performance of Two Quantitative Safety Signalling Methods
Almenoff, J. S., LaCroix, K., Yuen, N., Fram, D. M., & DuMouchel, W. (2006) · Drug Safety · doi:10.2165/00002018-200629100-00005 · Cited by 110 · see citing works
Signal Selection and Follow-Up in Pharmacovigilance
Meyboom, R. H. B., Lindquist, M., Egberts, T. C. G., & Edwards, I. R. (2002) · Drug Safety · doi:10.2165/00002018-200225060-00011 · Cited by 105 · see citing works
Monitoring product safety in the postmarketing environment
Sharrar, R. G., & Dieck, G. S. (2013) · Therapeutic Advances in Drug Safety · doi:10.1177/2042098613490780 · Cited by 78 · see citing works
Determinants of signal selection in a spontaneous reporting system for adverse drug reactions
van Puijenbroek, E., van Grootheest, K., Diemont, W. L., Leufkens, H. G. M., & Egberts, T. C. G. (2001) · British Journal of Clinical Pharmacology · doi:10.1046/j.0306-5251.2001.01501.x · Cited by 68 · see citing works
A Description of Signals During the First 18 Months of the EMA Pharmacovigilance Risk Assessment Committee
Pacurariu, A. C., Coloma, P. M., van Haren, A., Genov, G., Sturkenboom, M., & Straus, S. M. J. M. (2014) · Drug Safety · doi:10.1007/s40264-014-0240-1 · Cited by 58 · see citing works
The FDA and New Safety Warnings
Moore, T. J., Singh, S., & Furberg, C. D. (2012) · Archives of Internal Medicine · doi:10.1001/archinternmed.2011.618 · Cited by 43 · see citing works
Impact Analysis of Signals Detected from Spontaneous Adverse Drug Reaction Reporting Data
WALLER, P. C., Heeley, E. L., & Moseley, J. (2005) · Drug Safety · doi:10.2165/00002018-200528100-00002 · Cited by 43 · see citing works
Surveillance of Adverse Events After Seasonal Influenza Vaccination in Pregnant Women and Their Infants in the Vaccine Adverse Event Reporting System, July 2010–May 2016
Moro, P. L., Baumblatt, J. A. G., Lewis, P., Cragan, J. D., Tepper, N. K., & Cano, M. V. (2016) · Drug Safety · doi:10.1007/s40264-016-0482-1 · Cited by 42 · see citing works
Signal of Gastrointestinal Congenital Malformations with Antipsychotics After Minimising Competition Bias: A Disproportionality Analysis Using Data from Vigibase®
Montastruc, F., Salvo, F., Arnaud, M., Bégaud, B., & Pariente, A. (2016) · Drug Safety · doi:10.1007/s40264-016-0413-1 · Cited by 41 · see citing works
Use of Triage Strategies in the WHO Signal-Detection Process
Lindquist, M. (2007) · Drug Safety · doi:10.2165/00002018-200730070-00014 · Cited by 40 · see citing works
Lack of essential information in spontaneous reports of adverse drug reactions in Catalonia—a restraint to the potentiality for signal detection
Plessis, L., Gómez-Lumbreras, A., García, N., Cereza, G., & Figueras, A. (2017) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-017-2223-5 · Cited by 33 · see citing works
Pediatric drug safety signal detection: a new drug–event reference set for performance testing of data-mining methods and systems
Osokogu, O. U., Fregonese, F., Ferrajolo, C., Verhamme, K., de Bie, S., Jong, G. ’., Catapano, M., Weibel, D., Kaguelidou, F., Bramer, W. M., et al. (2015) · Drug Safety · doi:10.1007/s40264-015-0265-0 · Cited by 31 · see citing works
Drug Safety Monitoring in Children: Performance of Signal Detection Algorithms and Impact of Age Stratification
Osokogu, O. U., Dodd, C. N., Pacurariu, A. C., Kaguelidou, F., Weibel, D. M., & Sturkenboom, M. (2016) · Drug Safety · doi:10.1007/s40264-016-0433-x · Cited by 18 · see citing works
Exploring the Potential Routine Use of Electronic Healthcare Record Data to Strengthen Early Signal Assessment in UK Medicines Regulation: Proof-of-Concept Study
Donegan, K. L., Owen, R., Bird, H., Burch, B., Smith, A., & Tregunno, P. (2018) · Drug Safety · doi:10.1007/s40264-018-0675-x · Cited by 8 · see citing works

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Cutroneo, P. M., Sartori, D., Tuccori, M., Crisafulli, S., Battini, V., Carnovale, C., Rafaniello, C., Capuano, A., Poluzzi, E., Moretti, U., et al. (2024) · Frontiers in Drug Safety and Regulation · doi:10.3389/fdsfr.2023.1323057 · Cited by 224 · see citing works
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Raschi, E., Mazzarella, A., Antonazzo, I. C., Bendinelli, N., Forcesi, E., Tuccori, M., Moretti, U., Poluzzi, E., & De Ponti, F. (2019) · Targeted Oncology · doi:10.1007/s11523-019-00632-w · Cited by 101 · see citing works
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Bailey, S., Singh, A., Azadian, R., Huber, P., & Blum, M. (2010) · Drug Safety · doi:10.2165/11319000-000000000-00000 · Cited by 25 · see citing works

Authors: Fukazawa, C., Hinomura, Y., Kaneko, M., & Narukawa, M.
Affiliations: Japan Pharmaceutical Information Center (JP)
Kitasato University (JP)
Corresponding author: Fukazawa, Chisato
ORCID: Fukazawa, Chisato
Kaneko, Masayuki
Narukawa, Mamoru
Venue: Pharmacoepidemiology and Drug Safety
Publisher: Wiley
Published: 2018-10-15
Volume: 27
Issue: 12
Pages: 1402-1408
ISSN: 1053-8569 (ISSN-L)
1099-1557
DOI: 10.1002/pds.4672
OpenAlex: W2896939971
Cited by: 57
References: 22
Citations by year: 2026: 8 · 2025: 4 · 2024: 11 · 2023: 13 · 2022: 8 · 2021: 9 · 2020: 1 · 2019: 3
FWCI: 2.72
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Pharmacoepidemiology and Drug Safety (publishedVersion)
PubMed (publishedVersion)
Funders: Foundation for Anesthesia Education and Research
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmaceutical industry and healthcare
Keywords: data mining, safety signal detection, FDA Adverse Event Reporting System, spontaneous adverse event reporting, disproportionality analysis, reporting odds ratio, pharmacovigilance, newly marketed drugs, serious adverse events
MeSH terms: Data Interpretation, Statistical, Humans, United States, United States Food and Drug Administration, Odds Ratio, Databases, Factual, Adverse Drug Reaction Reporting Systems, Pharmacoepidemiology, Data Mining, Drug-Related Side Effects and Adverse Reactions

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Significance of data mining in routine signal detection: analysis based on the safety signals identified by the FDA

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