US Food and Drug Administration. Best Practices for FDA Staff in the Post-marketing Safety Surveillance of Human Drug and Biological Products. US FDA. 2024. https://www.fda.gov/media/130216/download. Accessed 23 Jan 2024.

US Food and Drug Administration. US FDA Adverse Events Reporting System (FAERS) Public Dashboard. US FDA. 2024. https://fis.fda.gov/sense/app/95239e26-e0be-42d9-a960-9a5f7f1c25ee/sheet/7a47a261-d58b-4203-a8aa-6d3021737452/state/analysis. Accessed 27 Nov 2024.

Duggirala HJ, Tonning JM, Smith E, et al. Use of data mining at the Food and Drug Administration. J Am Med Inform Assoc. 2016;23(2):428–34.

Article  PubMed  Google Scholar 

Moore TJ, Furberg CD, Mattison DR, Cohen MR. Completeness of serious adverse drug event reports received by the US food and drug administration in 2014. Pharmacoepidemiol Drug Saf. 2016;25(6):713–8.

Article  PubMed  Google Scholar 

Stephenson WP, Hauben M. Data mining for signals in spontaneous reporting databases: proceed with caution. Pharmacoepidemiol Drug Saf. 2007;16(4):359–65.

Article  PubMed  Google Scholar 

Lester J, Neyarapally GA, Lipowski E, Graham CF, Hall M, Dal Pan G. Evaluation of FDA safety-related drug label changes in 2010. Pharmacoepidemiol Drug Saf. 2013;22(3):302–5.

Article  PubMed  Google Scholar 

Croteau D, Pinnow E, Wu E, Muñoz M, Bulatao I, Dal Pan G. Sources of evidence triggering and supporting safety-related labeling changes: a 10-year longitudinal assessment of 22 new molecular entities approved in 2008 by the US Food and Drug Administration. Drug Saf. 2022;45(2):169–80.

Article  PubMed  Google Scholar 

Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys. 2002;74:47–97.

Article  Google Scholar 

Ball R, Botsis T. Can network analysis improve pattern recognition among adverse events following immunization reported to VAERS? Clin Pharmacol Ther. 2011;90(2):271–8.

Article  PubMed  CAS  Google Scholar 

Botsis T, Ball R. Network analysis of possible anaphylaxis cases reported to the US vaccine adverse event reporting system after H1N1 influenza vaccine. Stud Health Technol Inform. 2011;169:564–8.

PubMed  Google Scholar 

Botsis T, Scott J, Goud R, Toman P, Sutherland A, Ball R. Novel algorithms for improved pattern recognition using the US FDA adverse event network analyzer. Stud Health Technol Inform. 2014;205:1178–82.

PubMed  Google Scholar 

Zhao, W. et al. Discovering drug-drug associations in the FDA adverse event reporting system database with data mining approaches. In: IEEE. 2021. p. 1197–1204.

Nazir A, Ichinomiya T, Miyamura N, Sekiya Y, Kinosada Y. Identification of suicide-related events through network analysis of adverse event reports. Drug Saf. 2014;37(8):609–16.

Article  PubMed  Google Scholar 

Le H, Hong H, Ge W, et al. A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database. Exp Biol Med (Maywood). 2023;248(21):1944–51.

PubMed  CAS  Google Scholar 

Fusaroli M, et al. Unveiling the burden of drug-induced impulsivity: a network analysis of the FDA adverse event reporting system. Drug Saf. 2024;47:1275–92.

Article  PubMed  PubMed Central  Google Scholar 

Pétervári M, Benczik B, Balogh OM, Petrovich B, Ágg B, Ferdinandy P. Network analysis for signal detection in spontaneous adverse event reporting database: application of network weighting normalization to characterize cardiovascular drug safety. Drug Saf. 2022;45(11):1423–38.

Article  PubMed  PubMed Central  Google Scholar 

Scott J, Botsis T, Ball R. Simulating adverse event spontaneous reporting systems as preferential attachment networks: application to the Vaccine Adverse Event Reporting System. Appl Clin Inform. 2014;5(1):206–18.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Dutta A. MedDRA—Terminologies & Coding. Medical dictionary for regulatory activities. 2020. https://meddra.org/sites/default/files/page/documents_insert/meddra_-_terminologies_coding.pdf. Accessed 27 Nov 2024.

US Food and Drug Administration. Postmarketing reporting of adverse experiences. US FDA. 2025 https://www.ecfr.gov/current/title-21/chapter-I/subchapter-F/part-600/subpart-D/section-600.80. Accessed 23 Mar 2025.

Spiker J, Kreimeyer K, Dang O, et al. Information visualization platform for postmarket surveillance decision support. Drug Saf. 2020;43(9):905–15.

Article  PubMed  Google Scholar 

Kreimeyer K, Spiker J, Dang O, De S, Ball R, Botsis T. Deduplicating the FDA adverse event reporting system with a novel application of network-based grouping. J Biomed Inf. 2025;165:104824.

Article  Google Scholar 

Muñoz MA, Dal Pan GJ. The impact of litigation-associated reports on signal identification in the US FDA’s adverse event reporting system. Drug Saf. 2019;42(10):1199–201.

Article  PubMed  Google Scholar 

Coco TJ, Klasner AE. Drug-induced rhabdomyolysis. Curr Opin Pediatr. 2004;16(2):206–10.

Article  PubMed  Google Scholar 

Muñoz MA, Tonning JM, Brinker AD, Delaney JAC, Gatti JC, Avigan M. Data mining of the US FDA’s adverse events reporting system database to evaluate drug-drug interactions associated with statin-induced rhabdomyolysis. Pharm Med. 2016;30:327–37.

Article  Google Scholar 

Furberg CD, Pitt B. Withdrawal of cerivastatin from the world market. Curr Control Trials Cardiovasc Med. 2001;2(5):205–7.

PubMed  PubMed Central  Google Scholar 

Clauset A, Shalizi CR, Newman MEJ. Power-law distributions in empirical data. Siam Rev. 2009;51:661–703.

Article  Google Scholar 

Broido AD, Clauset A. Scale-free networks are rare. Nat Commun. 2019;10(1):1017.

Article  PubMed  PubMed Central  Google Scholar 

Mitzenmacher M. A brief history of generative models for power law and lognormal distributions. Internet Math. 2003;1:226–51.

Article  Google Scholar 

Gillespie CS. Fitting heavy tailed distributions: the poweRlaw package. J Stat Softw. 2015;6:1–16.

Google Scholar 

Fruchterman TMJ, Reingold EM. Graph drawing by force-directed placement. Softw Pract Exper. 1991;21:1129–64.

Article  Google Scholar 

Ooms J, James D, DebRoy S, Wickham H, Horner J. RMySQL: Database Interface and 'MySQL' Driver for R. R package version 0.10.28. 2024. https://CRAN.R-project.org/package=RMySQL. Accessed 13 Dec 2024.

Wickham H, et al. Welcome to the tidyverse. J Open Sour Softw. 2019;4:1–6.

Google Scholar 

Dowle M, Srinivasan A. data.table: Extension of `data.frame`. R package version 1.14.8. 2023. https://CRAN.R-project.org/package=data.table. Accessed 13 Dec 2024.

Bache S, Wickham H. magrittr: A Forward-Pipe Operator for R. R package version 2.0.3. 2022. https://CRAN.R-project.org/package=magrittr. Accessed 13 Dec 2024.

Wickham H, Seidel D. scales: Scale Functions for Visualization. R package version 1.2.1. 2022. https://CRAN.R-project.org/package=scales. Accessed 13 Dec 2024.

Pedersen T. patchwork: The Composer of Plots. R package version 1.2.0. 2024. https://CRAN.R-project.org/package=patchwork. Accessed 13 Dec 2024.

Csárdi G, Nepusz T. igraph: Network Analysis and Visualization in R. R package version 1.5.1. 2024. https://CRAN.R-project.org/package=igraph. Accessed 13 Dec 2024.

R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2024. https://www.R-project.org/. Accessed 13 Dec 2024.

Tieu C, Breder CD. A critical evaluation of safety signal analysis using algorithmic standardised MedDRA queries. Drug Saf. 2018;41:1375–85.

Article  PubMed  Google Scholar 

Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393:440–2.

Article  PubMed  CAS  Google Scholar