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Jiao XF, Pu L, Lan S, Li H, Zeng L, Wang H, Zhang L. Adverse drug reaction signal detection methods in spontaneous reporting system: A systematic review. Pharmacoepidemiol Drug Saf 2024; 33:e5768. [PMID: 38419132 DOI: 10.1002/pds.5768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/09/2024] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND A series of signal detection methods have been developed to detect adverse drug reaction (ADR) signals in spontaneous reporting system. However, different signal detection methods yield quite different signal detection results, and we do not know which method has the best detection performance. How to choose the most suitable signal detection method is an urgent problem to be solved. In this study, we systematically reviewed the characteristics and application scopes of current signal detection methods, with the goal of providing references for the optimization selection of signal detection methods in spontaneous reporting system. METHODS We searched six databases from inception to January 2023. The search strategy targeted literatures regarding signal detection methods in spontaneous reporting system. We used thematic analysis approach to summarize the advantages, disadvantages, and application scope of each signal detection method. RESULTS A total of 93 literatures were included, including 27 reviews and 66 methodological studies. Moreover, 31 signal detection methods were identified in these literatures. Each signal detection method has its inherent advantages and disadvantages, resulting in different application scopes of these methods. CONCLUSION Our systematic review finds that there are variabilities in the advantages, disadvantages, and application scopes of different signal detection methods. This finding indicates that the most suitable signal detection method varies across different drug safety scenarios. Moreover, when selecting signal detection method in a particular drug safety scenario, the following factors need to be considered: purpose of research, database size, drug characteristics, adverse event characteristics, and characteristics of the relations between drugs and adverse events.
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Affiliation(s)
- Xue-Feng Jiao
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu, China
- Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration (NMPA) Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China
| | - Libin Pu
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Shan Lan
- Sichuan Center for Food and Drug Evaluation, Inspection & Monitoring, SCFDA Adverse Drug Reaction Monitoring Center Medical Device Technology Review and Evaluation Center, Chengdu, China
| | - Hailong Li
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu, China
- Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration (NMPA) Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China
| | - Linan Zeng
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu, China
- Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration (NMPA) Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China
| | - Huiqing Wang
- Medical Simulation Centre, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Lingli Zhang
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu, China
- Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University, Chengdu, China
- National Medical Products Administration (NMPA) Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China
- Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
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Zhu T, Zhang Y, Ye X, Hou Y, Liu J, Shi W, Xu J, Guo X, He J. Application of biclustering algorithm in adverse drug reaction monitoring system of China. Pharmacoepidemiol Drug Saf 2018; 27:1257-1264. [PMID: 30232823 DOI: 10.1002/pds.4661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 06/29/2018] [Accepted: 08/09/2018] [Indexed: 11/07/2022]
Abstract
PURPOSE Signal evaluation is considered to be a tedious process owing to the large number of disproportional signals detected. This study aimed to apply a biclustering algorithm in the spontaneous reporting system of China and to obtain the optimal parameters. The biclustering algorithm is expected to improve the efficiency of signal evaluation by identifying similar signal groups. METHODS Information component (IC) was the method used for disproportionality analysis. By using IC thresholds of various strengths (0.05-4.00), the original quantitative data matrix was transformed into 80 different binary data matrices, where each cell contained either a 1 or 0. The biclustering results were obtained using a total of 720 Bimax algorithm parameters (minimal number of columns and rows was 3, 4, or 5). Next, the optimal parameters were determined through the comprehensive evaluation of the rank sum ration. Finally, we examined the biclustering results under the optimal parameters and evaluated the effect of biclustering analysis on adverse drug reaction (ADR) data in China. RESULTS The optimal strength of the IC threshold was 0.80, and the minimum number of rows and columns was 3. After taxonomic evaluation, we also found that 1836 biclusters (42.8%) contained similar drugs or similar ADRs, which accounted for 72.3% of signals unevaluated. CONCLUSIONS Applying biclustering analysis in spontaneous reporting system could provide support in confirming unrecognized ADRs, identifying rare ADRs, and screening drug-ADR pairs, which need more attention. Biclustering algorithm could improve the efficiency of signal detection and evaluation in China.
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Affiliation(s)
- Tiantian Zhu
- Department of Health Statistics, Second Military Medical University, Shanghai, China.,General Hospital of Jinan Military Command, Jinan, Shandong, China
| | - Yuan Zhang
- Department of Health Statistics, Second Military Medical University, Shanghai, China.,Quality Control Section, Department of Medical Affairs, Hangzhou, China
| | - Xiaofei Ye
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | - Yongfang Hou
- National Center for ADR Monitoring, Beijing, China
| | - Jia Liu
- National Center for ADR Monitoring, Beijing, China
| | - Wentao Shi
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | - Jinfang Xu
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | - Xiaojing Guo
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | - Jia He
- Department of Health Statistics, Second Military Medical University, Shanghai, China.,Tongji University School of Medicine, Shanghai, China
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Adjuvanted (AS03) A/H1N1 2009 Pandemic Influenza Vaccines and Solid Organ Transplant Rejection: Systematic Signal Evaluation and Lessons Learnt. Drug Saf 2018; 40:693-702. [PMID: 28417321 PMCID: PMC5519647 DOI: 10.1007/s40264-017-0532-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Introduction We investigated a signal of solid organ transplant (SOT) rejection after immunisation with (AS03) A/H1N1 2009 pandemic influenza vaccines. Methods Potential immunological mechanisms were reviewed and quantitative analyses were conducted. The feasibility of pharmacoepidemiological studies was explored. Results Overall results, including data from a pharmacoepidemiological study, support the safety of adjuvanted (AS03) pandemic influenza vaccination in SOT recipients. The regulatory commitment to evaluate the signal through a stepwise investigation was closed in 2014. Conclusion Lessons learned highlight the importance of investigating plausible biological mechanisms between vaccines and potentially associated adverse outcomes, and the importance of selecting appropriate study settings and designs for safety signal investigations. Electronic supplementary material The online version of this article (doi:10.1007/s40264-017-0532-3) contains supplementary material, which is available to authorized users.
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Wisniewski AFZ, Bate A, Bousquet C, Brueckner A, Candore G, Juhlin K, Macia-Martinez MA, Manlik K, Quarcoo N, Seabroke S, Slattery J, Southworth H, Thakrar B, Tregunno P, Van Holle L, Kayser M, Norén GN. Good Signal Detection Practices: Evidence from IMI PROTECT. Drug Saf 2016; 39:469-90. [PMID: 26951233 PMCID: PMC4871909 DOI: 10.1007/s40264-016-0405-1] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting. PROTECT has also pointed to areas of potentially fruitful future research and some areas where further effort is likely to yield less.
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Affiliation(s)
| | | | - Cedric Bousquet
- INSERM, UMR_S1142, LIMICS, Paris, France
- Department of Public Health and Medical Informatics, CHU University Hospital of Saint Etienne, Saint-Étienne, France
| | | | | | | | | | | | | | - Suzie Seabroke
- Medicines and Healthcare Products Regulatory Agency, London, UK
| | | | | | | | - Phil Tregunno
- Medicines and Healthcare Products Regulatory Agency, London, UK
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Seabroke S, Candore G, Juhlin K, Quarcoo N, Wisniewski A, Arani R, Painter J, Tregunno P, Norén GN, Slattery J. Performance of Stratified and Subgrouped Disproportionality Analyses in Spontaneous Databases. Drug Saf 2016; 39:355-64. [DOI: 10.1007/s40264-015-0388-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Gould AL, Lystig TC, Lu Y, Fu H, Ma H. Methods and Issues to Consider for Detection of Safety Signals From Spontaneous Reporting Databases: A Report of the DIA Bayesian Safety Signal Detection Working Group. Ther Innov Regul Sci 2015; 49:65-75. [PMID: 30222465 DOI: 10.1177/2168479014533114] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | | | - Yun Lu
- 3 US Food and Drug Administration, Rockville, MD, USA
| | - Haoda Fu
- 4 Eli Lilly & Co, Indianapolis, IN, USA
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Van Holle L, Bauchau V. The upper bound to the Relative Reporting Ratio-a measure of the impact of the violation of hidden assumptions underlying some disproportionality methods used in signal detection. Pharmacoepidemiol Drug Saf 2014; 23:787-94. [PMID: 24395594 PMCID: PMC4265853 DOI: 10.1002/pds.3556] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Revised: 10/31/2013] [Accepted: 11/05/2013] [Indexed: 12/01/2022]
Abstract
Purpose For disproportionality measures based on the Relative Reporting Ratio (RRR) such as the Information Component (IC) and the Empirical Bayesian Geometrical Mean (EBGM), each product and event is assumed to represent a negligible fraction of the spontaneous report database (SRD). Here, we provide the tools for allowing signal detection experts to assess the consequence of the violation of this assumption on their specific SRD. Methods For each product–event pair (P–E), a worst-case scenario associated all the reported events-of-interest with the product of interest. The values of the RRR under this scenario were measured for different sets of stratification factors using the GlaxoSmithKline vaccines SRD. These values represent the RRR upper bound that RRR cannot exceed whatever the true strength of association. Results Depending on the choice of stratification factors, the RRR could not exceed an upper bound of 2 for up to 2.4% of the P–Es. For Engerix™, 23.4% of all reports in the SDR, the RRR could not exceed an upper bound of 2 for up to 13.8% of pairs. For the P–E Rotarix™-Intussusception, the choice of stratification factors impacted the upper bound to RRR: from 52.5 for an unstratified RRR to 2.0 for a fully stratified RRR. Conclusions The quantification of the upper bound can indicate whether measures such as EBGM, IC, or RRR can be used for SRD for which products or events represent a non-negligible fraction of the entire SRD. In addition, at the level of the product or P–E, it can also highlight detrimental impact of overstratification. © 2014 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Lionel Van Holle
- Vaccine Safety Research Group, Vaccine Clinical Safety and Pharmacovigilance, GlaxoSmithKline Vaccines, Wavre, Belgium
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van Holle L, Bauchau V. Signal detection on spontaneous reports of adverse events following immunisation: a comparison of the performance of a disproportionality-based algorithm and a time-to-onset-based algorithm. Pharmacoepidemiol Drug Saf 2013; 23:178-85. [PMID: 24038719 PMCID: PMC4265288 DOI: 10.1002/pds.3502] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Revised: 07/01/2013] [Accepted: 07/25/2013] [Indexed: 11/07/2022]
Abstract
PURPOSE Disproportionality methods measure how unexpected the observed number of adverse events is. Time-to-onset (TTO) methods measure how unexpected the TTO distribution of a vaccine-event pair is compared with what is expected from other vaccines and events. Our purpose is to compare the performance associated with each method. METHODS For the disproportionality algorithms, we defined 336 combinations of stratification factors (sex, age, region and year) and threshold values of the multi-item gamma Poisson shrinker (MGPS). For the TTO algorithms, we defined 18 combinations of significance level and time windows. We used spontaneous reports of adverse events recorded for eight vaccines. The vaccine product labels were used as proxies for true safety signals. Algorithms were ranked according to their positive predictive value (PPV) for each vaccine separately; amedian rank was attributed to each algorithm across vaccines. RESULTS The algorithm with the highest median rank was based on TTO with a significance level of 0.01 and a time window of 60 days after immunisation. It had an overall PPV 2.5 times higher than for the highest-ranked MGPS algorithm, 16(th) rank overall, which was fully stratified and had a threshold value of 0.8. A TTO algorithm with roughly the same sensitivity as the highest-ranked MGPS had better specificity but longer time-to-detection. CONCLUSIONS Within the scope of this study, the majority of the TTO algorithms presented a higher PPV than for any MGPS algorithm. Considering the complementarity of TTO and disproportionality methods, a signal detection strategy combining them merits further investigation. © 2013 GlaxoSmithKline. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Lionel van Holle
- Vaccine Safety Research Group (VSRG), Vaccines Clinical Safety & Pharmacovigilance (VCSP), GlaxoSmithKline Vaccines, Wavre, Belgium
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