Abstract
Purpose
A Bayesian confidence propagation neural network (BCPNN) is a signal detection method used by the World Health Organization Uppsala Monitoring Centre to analyze spontaneous reporting system databases. We modify the BCPNN to increase its sensitivity for detecting potential adverse drug reactions (ADRs).
Method
In a BCPNN, the information component (IC) is defined as an index of disproportionality between the observed and expected number of reported drugs and events. Our proposed method adjusts the IC value by borrowing information about events that have occurred in drugs defined as similar to the target drug. We compare the performance of our method with that of a traditional BCPNN through a simulation study.
Results
The false positive rate of the proposed method was lower than that of the traditional BCPNN method and close to the nominal value, 0.025, around the true difference in ICs between the target drug and similar drugs equal to 0. The sensitivity of the proposed method was much higher than that of the traditional BCPNN method in case in which the difference in ICs between the target drug and similar drugs ranges from 0 to 2. When applied to a database managed by Japanese regulatory authority, the proposed method could detect known ADRs earlier than the traditional method.
Conclusions
The proposed method is a novel criterion for early detection of signals if similar drugs have the same tendencies. The proposed BCPNN tends to have higher sensitivity when the true difference is greater than 0.
This is a preview of subscription content, access via your institution.
References
- 1.
Rothman KJ, Lanes S, Sacks ST (2004) The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiol Drug Saf 13(8):519–523
- 2.
Evans SJW, Waller PC, Davis S (2001) Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf 10(6):483–486
- 3.
Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A, De Freitas RM (1998) A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 54(4):315–321
- 4.
DuMouchel W (1999) Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Am Stat 53(3):177–190
- 5.
Ahmed I, Thiessard F, Miremont-Salamé G, Begaud B, Tubert-Bitter P (2010) Pharmacovigilance data mining with methods based on false discovery rates: a comparative simulation study. Clin Pharmacol Ther 88(4):492–498
- 6.
Chen M, Zhu L, Chiruvolu P, Jiang Q (2015) Evaluation of statistical methods for safety signal detection: a simulation study. Pharm Stat 14(1):11–19
- 7.
Matsushita Y, Kuroda Y, Niwa S, Sonehara S, Hamada C, Yoshimura I (2007) Criteria revision and performance comparison of three methods of signal detection applied to the spontaneous reporting database of a pharmaceutical manufacturer. Drug Saf 30(8):715–726
- 8.
van Puijenbroek EP, Bate A, Leufkens HG, Lindquist M, Orre R, Egberts AC (2002) A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf 11(1):3–10
- 9.
Kubota K, Koide D, Hirai T (2004) Comparison of data mining methodologies using Japanese spontaneous reports. Pharmacoepidemiol Drug Saf 13(6):387–394
- 10.
Bunchuailua W, Zuckerman IH, Kulsomboon V, Suwankesawong W, Singhasivanon P, Kaewkungwal J (2010) Detection of adverse drug reaction signals in the Thai FDA database: comparison between reporting odds ratio and Bayesian confidence propagation neural network methods. Drug Inf J 44(4):393–403
- 11.
Almenoff JS, LaCroix KK, Yuen NA, Fram D, DuMouchel W (2006) Comparative performance of two quantitative safety signalling methods. Drug Saf 29(10):875–887
- 12.
Hochberg AM, Hauben M, Pearson RK, O’Hara DJ, Reisinger SJ, Goldsmith DI, Gould AL, Madigan D (2009) An evaluation of three signal-detection algorithms using a highly inclusive reference event database. Drug Saf 32(6):509–525
- 13.
Lindquist M, Ståhl M, Bate A, Edwards IR, Meyboom RH (2000) A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Saf 23(6):533–542
- 14.
Norén GN, Bate A, Orre R, Edwards IR (2006) Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events. Stat Med 25(21):3740–3757
- 15.
Gould LA (2003) Practical pharmacovigilance analysis strategies. Pharmacoepidemiol Drug Saf 12(7):559–574
- 16.
WHODrug Standardised Drug Groupings 2017 https://www.who-umc.org/media/2950/whodrugsdg_web.pdf. Accessed 15 Oct 2019.
- 17.
Ibrahim JG, Ming-Hui C (2000) Power prior distributions for regression models. Stat Sci:46–60
- 18.
Scheen AJ (2018) The safety of gliptins: updated data in 2018. Expert Opin Drug Saf 17(4):387–405
- 19.
Scott MB, Donald AB (2004) Accounting for multiplicities in assessing drug safety: a three-level hierarchical mixture model. Biom 60(2):418–426
Author information
Affiliations
Contributions
The first draft of the manuscript was written by Keisuke Tada and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(PDF 110 kb)
Rights and permissions
About this article
Cite this article
Tada, K., Maruo, K., Isogawa, N. et al. Borrowing external information to improve Bayesian confidence propagation neural network. Eur J Clin Pharmacol 76, 1311–1319 (2020). https://doi.org/10.1007/s00228-020-02909-w
Received:
Accepted:
Published:
Issue Date:
Keywords
- Pharmacovigilance
- Signal detection
- Information component
- Dynamic borrowing