Skip to main content
SpringerLink
Log in

Detection Algorithms for Simple Two-Group Comparisons Using Spontaneous Reporting Systems

  • Review Article
  • Published:
Drug Safety Aims and scope Submit manuscript

Abstract

Medical science has often used adult males as the standard to establish pathological conditions, their transitions, diagnostic methods, and treatment methods. However, it has recently become clear that sex differences exist in how risk factors contribute to the same disease, and these differences also exist in the efficacy of the same drug. Furthermore, the elderly and children have lower metabolic functions than adult males, and the results of clinical trials on adult males cannot be directly applied to these patients. Spontaneous reporting systems have become an important source of information for safety assessment, thereby reflecting drugs’ actual use in specific populations and clinical settings. However, spontaneous reporting systems only register drug-related adverse events (AEs); thus, they cannot accurately capture the total number of patients using these drugs. Therefore, although various algorithms have been developed to exploit disproportionality and search for AE signals, there is no systematic literature on how to detect AE signals specific to the elderly and children or sex-specific signals. This review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Nissen SE, Wolski K. Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. N Engl J Med. 2007;356:2457–71. https://doi.org/10.1056/NEJMoa072761.

    Article  CAS  PubMed  Google Scholar 

  2. Singh S, Loke YK, Furberg CD. Long-term risk of cardiovascular events with rosiglitazone: a meta-analysis. JAMA. 2007;298:1189–95. https://doi.org/10.1001/jama.298.10.1189.

    Article  CAS  PubMed  Google Scholar 

  3. Fujita T. Signal detection of adverse drug reactions. Jpn J Pharmacoepidemiol. 2009;14:27–36. https://doi.org/10.3820/jjpe.14.27.

    Article  Google Scholar 

  4. Noguchi Y, Takaoka M, Hayashi T, Tachi T, Teramachi H. Antiepileptic combination therapy with Stevens-Johnson syndrome and toxic epidermal necrolysis: analysis of a Japanese pharmacovigilance database. Epilepsia. 2020;61:1979–89. https://doi.org/10.1111/epi.16626.

    Article  CAS  PubMed  Google Scholar 

  5. Gastaldon C, Schoretsanitis G, Arzenton E, Raschi E, Papola D, Ostuzzi G, Moretti U, Seifritz E, Kane JM, Trifirò G, Barbui C. Withdrawal syndrome following discontinuation of 28 antidepressants: pharmacovigilance analysis of 31,688 reports from the WHO Spontaneous Reporting Database. Drug Saf. 2022;45:1539–49. https://doi.org/10.1007/s40264-022-01246-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Xia S, Gong H, Zhao Y, Guo L, Wang Y, Ma R, Zhang B, Sarangdhar M, Noguchi Y, Yan M. Tumor lysis syndrome associated with monoclonal antibodies in patients with multiple myeloma: a pharmacovigilance study based on the FAERS database. Clin Pharmacol Ther. 2023. https://doi.org/10.1002/cpt.2920.

    Article  PubMed  Google Scholar 

  7. Noguchi Y, Tachi T, Teramachi H. Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source. Brief Bioinform. 2021;22:bbab347. https://doi.org/10.1093/bib/bbab347.

    Article  PubMed  Google Scholar 

  8. Watanabe H, Matsushita Y, Watanabe A, Maeda T, Nukui K, Ogawa Y, Sawa J, Maeda H. Early detection of important safety information. Jpn J Biomet. 2004;25:37–60. https://doi.org/10.5691/jjb.25.37.

    Article  Google Scholar 

  9. Bate A, Evans SJ. Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiol Drug Saf. 2009;18:427–36. https://doi.org/10.1002/pds.1742.

    Article  CAS  PubMed  Google Scholar 

  10. Huang L, Guo T, Zalkikar JN, Tiwari RC. A review of statistical methods for safety surveillance. Ther Innov Regul Sci. 2014;48:98–108. https://doi.org/10.1177/2168479013514236.

    Article  PubMed  Google Scholar 

  11. Vilar S, Friedman C, Hripcsak G. Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform. 2018;19:863–77. https://doi.org/10.1093/bib/bbx010.

    Article  CAS  PubMed  Google Scholar 

  12. Noguchi Y, Tachi T, Teramachi H. Review of statistical methodologies for detecting drug–drug interactions using spontaneous reporting systems. Front Pharmacol. 2019;10:1319. https://doi.org/10.3389/fphar.2019.01319.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Soldin OP, Mattison DR. Sex differences in pharmacokinetics and pharmacodynamics. Clin Pharmacokinet. 2009;48:143–57. https://doi.org/10.2165/00003088-200948030-00001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Forger NG. Epigenetic mechanisms in sexual differentiation of the brain and behaviour. Philos Trans R Soc Lond B Biol Sci. 2016;371:20150114. https://doi.org/10.1098/rstb.2015.0114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cohen S, Murphy MLM, Prather AA. Ten surprising facts about stressful life events and disease risk. Annu Rev Psychol. 2019;70:577–97. https://doi.org/10.1146/annurev-psych-010418-102857.

    Article  PubMed  Google Scholar 

  16. Noguchi Y, Hayashi Y, Yoshida A, Sugita I, Esaki H, Saito K, Usui K, Kato M, Tachi T, Teramachi H. Search for oral medicine that might exacerbate the prognosis of adverse drug events in elderly patients. Jpn J Drug Inf. 2016;18:277–83. https://doi.org/10.11256/jjdi.18.277.

    Article  Google Scholar 

  17. van Puijenbroek EP, Bate A, Leufkens HGM, Lindquist M, Orre R, Egberts ACG. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf. 2002;11:3–10. https://doi.org/10.1002/pds.668.

    Article  CAS  PubMed  Google Scholar 

  18. https://www.ema.europa.eu/en/documents/other/screening-adverse-reactions-eudravigilance_en.pdf. Accessed June 25, 2023.

  19. Omoto T, Asaka J, Sakai T, Sato F, Goto N, Kudo K. Disproportionality analysis of safety signals for a wide variety of opioid-related adverse events in elderly patients using the Japanese Adverse Drug Event Report (JADER) Database. Biol Pharm Bull. 2021;44:627–34.

    Article  CAS  PubMed  Google Scholar 

  20. Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A, De Freitas RM. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol. 1998;54:315–21. https://doi.org/10.1007/s002280050466.

    Article  CAS  PubMed  Google Scholar 

  21. Lindquist M, Stahl M, Bate A, Edwards IR, Meyboom RHB. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Saf. 2000;23:533–42. https://doi.org/10.2165/00002018-200023060-00004.

    Article  CAS  PubMed  Google Scholar 

  22. Yang K, Li J, Sun Z, Bai C, Zhao L. Effect of age on the risk of immune-related adverse events in patients receiving immune checkpoint inhibitors. Clin Exp Med. 2023. https://doi.org/10.1007/s10238-023-01055-8.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sandberg L, Taavola H, Aoki Y, Chandler R, Norén GN. Risk factor considerations in statistical signal detection: using subgroup disproportionality to uncover risk groups for adverse drug reactions in VigiBase. Drug Saf. 2020;43:999–1009. https://doi.org/10.1007/s40264-020-00957-w.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Wakao R, Lönnstedt IM, Aoki Y, Chandler RE. The use of subgroup disproportionality analyses to explore the sensitivity of a global database of individual case safety reports to known pharmacogenomic risk variants common in Japan. Drug Saf. 2021;44:681–97. https://doi.org/10.1007/s40264-021-01063-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yu Y, Chen J, Li D, Wang L, Wang W, Liu H. Systematic analysis of adverse event reports for sex differences in adverse drug events. Sci Rep. 2016;22(6):24955. https://doi.org/10.1038/srep24955.

    Article  ADS  CAS  Google Scholar 

  26. Kan Y, Nagai J, Uesawa Y. Evaluation of antibiotic-induced taste and smell disorders using the FDA adverse event reporting system database. Sci Rep. 2021;11:9625. https://doi.org/10.1038/s41598-021-88958-2.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  27. Toyoshima M, Noguchi Y, Otsubo M, Tachi T, Teramachi H. Differences in detected safety signals between benzodiazepines and non-benzodiazepine hypnotics: pharmacovigilance study using a spontaneous reporting system. Int J Med Sci. 2021;18:1130–6. https://doi.org/10.7150/ijms.51658.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Nagaoka K, Nagashima T, Asaoka N, Yamamoto H, Toda C, Kayanuma G, Siswanto S, Funahashi Y, Kuroda K, Kaibuchi K, Mori Y, Nagayasu K, Shirakawa H, Kaneko S. Striatal TRPV1 activation by acetaminophen ameliorates dopamine D2 receptor antagonist-induced orofacial dyskinesia. JCI Insight. 2021;6: e145632. https://doi.org/10.1172/jci.insight.145632.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mitsuboshi S, Hamano H, Niimura T, Ozaki AF, Patel PM, Lin TJ, Tanaka Y, Kimura I, Iwata N, Shiromizu S, Chuma M, Koyama T, Yamanishi Y, Kanda Y, Ishizawa K, Zamami Y. Association between immune checkpoint inhibitor-induced myocarditis and concomitant use of thiazide diuretics. Int J Cancer. 2023. https://doi.org/10.1002/ijc.34616.

    Article  PubMed  Google Scholar 

  30. Jin W, Riley RM, Wolfinger RD, White KP, Passador-Gurgel G, Gibson G. The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet. 2001;29:389–95. https://doi.org/10.1038/ng766.

    Article  CAS  PubMed  Google Scholar 

  31. Cui X, Churchill GA. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 2003;4:210. https://doi.org/10.1186/gb-2003-4-4-210.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ebrahimpoor M, Goeman JJ. Inflated false discovery rate due to volcano plots: problem and solutions. Brief Bioinform. 2021;22:bbab053. https://doi.org/10.1093/bib/bbab053.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Matsuda S. Introduction of FDR and comparisons of multiple testing procedures that control it. Jpn J Biometr. 2008;29:125–39. https://doi.org/10.5691/jjb.29.125.

    Article  Google Scholar 

  34. Pham P, Cheng C, Wu E, Kim I, Zhang R, Ma Y, Kortepeter CM, Muñoz MA. Leveraging case narratives to enhance patient age ascertainment from adverse event reports. Pharmaceut Med. 2021;35:307–16. https://doi.org/10.1007/s40290-021-00398-5.

    Article  PubMed  PubMed Central  Google Scholar 

  35. CIOMS Working Group VIII. Practical aspects of signal detection in pharmacovigilance. CIOMS, 2010.

  36. Noguchi Y, Tachi T, Teramachi H. Comparison of signal detection algorithms based on frequency statistical model for drug-drug interaction using spontaneous reporting systems. Pharm Res. 2020;37:86. https://doi.org/10.1007/s11095-020-02801-3.

    Article  CAS  PubMed  Google Scholar 

  37. Noguchi Y, Aoyama K, Kubo S, Tachi T, Teramachi H. Improved detection criteria for detecting drug–drug interaction signals using the proportional reporting ratio. Pharmaceuticals. 2020;14:4. https://doi.org/10.3390/ph14010004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kontsioti E, Maskell S, Dutta B, Pirmohamed M. A reference set of clinically relevant adverse drug–drug interactions. Sci Data. 2022;9:72. https://doi.org/10.1038/s41597-022-01159-y.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Zhao Z, Liu R, Wang L, Li L, Song C, Zhang P. A computational framework for identifying age risks in drug-adverse event pairs. AMIA Jt Summits Transl Sci Proc. 2022;2022:524–33.

    PubMed  PubMed Central  Google Scholar 

  40. Lu Z, Suzuki A, Wang D. Statistical methods for exploring spontaneous adverse event reporting databases for drug–host factor interactions. BMC Med Res Methodol. 2023;23:71. https://doi.org/10.1186/s12874-023-01885-w.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. Noguchi Y, Yoshimura T. Is it possible to identify risk factors for adverse drug reactions using a pharmacovigilance database based on spontaneous reporting? Pharmacoepidemiol Drug Saf. 2023. https://doi.org/10.1002/pds.5737.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu, 501-1196, Japan

    Yoshihiro Noguchi & Tomoaki Yoshimura

Authors
  1. Yoshihiro Noguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomoaki Yoshimura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoshihiro Noguchi.

Ethics declarations

Funding

This reanalysis was supported by JSPS KAKENHI grant number 22K12890.

Conflicts of Interest

All authors declare that there is no competing interest.

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Availability of Data and Material

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Code Availability

Not applicable.

Authors’ Contribution

All authors participated in the interpretation of the study and in the preparation of this manuscript. All authors read and approved the final version.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noguchi, Y., Yoshimura, T. Detection Algorithms for Simple Two-Group Comparisons Using Spontaneous Reporting Systems. Drug Saf (2024). https://doi.org/10.1007/s40264-024-01404-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40264-024-01404-w

Search

Navigation