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Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model
Background: Spontaneous Reporting Systems (SRSs) are passive systems composed of reports of suspected Adverse Drug Events (ADEs), and are used for Pharmacovigilance (PhV), namely, drug safety surveillance. Exploration of analytical methodologies to enhance SRS-based discovery will contribute to more...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4793323/ https://www.ncbi.nlm.nih.gov/pubmed/26989609 http://dx.doi.org/10.7717/peerj.1753 |
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author | Narushima, Daichi Kawasaki, Yohei Takamatsu, Shoji Yamada, Hiroshi |
author_facet | Narushima, Daichi Kawasaki, Yohei Takamatsu, Shoji Yamada, Hiroshi |
author_sort | Narushima, Daichi |
collection | PubMed |
description | Background: Spontaneous Reporting Systems (SRSs) are passive systems composed of reports of suspected Adverse Drug Events (ADEs), and are used for Pharmacovigilance (PhV), namely, drug safety surveillance. Exploration of analytical methodologies to enhance SRS-based discovery will contribute to more effective PhV. In this study, we proposed a statistical modeling approach for SRS data to address heterogeneity by a reporting time point. Furthermore, we applied this approach to analyze ADEs of incretin-based drugs such as DPP-4 inhibitors and GLP-1 receptor agonists, which are widely used to treat type 2 diabetes. Methods: SRS data were obtained from the Japanese Adverse Drug Event Report (JADER) database. Reported adverse events were classified according to the MedDRA High Level Terms (HLTs). A mixed effects logistic regression model was used to analyze the occurrence of each HLT. The model treated DPP-4 inhibitors, GLP-1 receptor agonists, hypoglycemic drugs, concomitant suspected drugs, age, and sex as fixed effects, while the quarterly period of reporting was treated as a random effect. Before application of the model, Fisher’s exact tests were performed for all drug-HLT combinations. Mixed effects logistic regressions were performed for the HLTs that were found to be associated with incretin-based drugs. Statistical significance was determined by a two-sided p-value <0.01 or a 99% two-sided confidence interval. Finally, the models with and without the random effect were compared based on Akaike’s Information Criteria (AIC), in which a model with a smaller AIC was considered satisfactory. Results: The analysis included 187,181 cases reported from January 2010 to March 2015. It showed that 33 HLTs, including pancreatic, gastrointestinal, and cholecystic events, were significantly associated with DPP-4 inhibitors or GLP-1 receptor agonists. In the AIC comparison, half of the HLTs reported with incretin-based drugs favored the random effect, whereas HLTs reported frequently tended to favor the mixed model. Conclusion: The model with the random effect was appropriate for analyzing frequently reported ADEs; however, further exploration is required to improve the model. The core concept of the model is to introduce a random effect of time. Modeling the random effect of time is widely applicable to various SRS data and will improve future SRS data analyses. |
format | Online Article Text |
id | pubmed-4793323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-47933232016-03-17 Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model Narushima, Daichi Kawasaki, Yohei Takamatsu, Shoji Yamada, Hiroshi PeerJ Drugs and Devices Background: Spontaneous Reporting Systems (SRSs) are passive systems composed of reports of suspected Adverse Drug Events (ADEs), and are used for Pharmacovigilance (PhV), namely, drug safety surveillance. Exploration of analytical methodologies to enhance SRS-based discovery will contribute to more effective PhV. In this study, we proposed a statistical modeling approach for SRS data to address heterogeneity by a reporting time point. Furthermore, we applied this approach to analyze ADEs of incretin-based drugs such as DPP-4 inhibitors and GLP-1 receptor agonists, which are widely used to treat type 2 diabetes. Methods: SRS data were obtained from the Japanese Adverse Drug Event Report (JADER) database. Reported adverse events were classified according to the MedDRA High Level Terms (HLTs). A mixed effects logistic regression model was used to analyze the occurrence of each HLT. The model treated DPP-4 inhibitors, GLP-1 receptor agonists, hypoglycemic drugs, concomitant suspected drugs, age, and sex as fixed effects, while the quarterly period of reporting was treated as a random effect. Before application of the model, Fisher’s exact tests were performed for all drug-HLT combinations. Mixed effects logistic regressions were performed for the HLTs that were found to be associated with incretin-based drugs. Statistical significance was determined by a two-sided p-value <0.01 or a 99% two-sided confidence interval. Finally, the models with and without the random effect were compared based on Akaike’s Information Criteria (AIC), in which a model with a smaller AIC was considered satisfactory. Results: The analysis included 187,181 cases reported from January 2010 to March 2015. It showed that 33 HLTs, including pancreatic, gastrointestinal, and cholecystic events, were significantly associated with DPP-4 inhibitors or GLP-1 receptor agonists. In the AIC comparison, half of the HLTs reported with incretin-based drugs favored the random effect, whereas HLTs reported frequently tended to favor the mixed model. Conclusion: The model with the random effect was appropriate for analyzing frequently reported ADEs; however, further exploration is required to improve the model. The core concept of the model is to introduce a random effect of time. Modeling the random effect of time is widely applicable to various SRS data and will improve future SRS data analyses. PeerJ Inc. 2016-03-08 /pmc/articles/PMC4793323/ /pubmed/26989609 http://dx.doi.org/10.7717/peerj.1753 Text en © 2016 Narushima et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Drugs and Devices Narushima, Daichi Kawasaki, Yohei Takamatsu, Shoji Yamada, Hiroshi Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model |
title | Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model |
title_full | Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model |
title_fullStr | Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model |
title_full_unstemmed | Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model |
title_short | Adverse events associated with incretin-based drugs in Japanese spontaneous reports: a mixed effects logistic regression model |
title_sort | adverse events associated with incretin-based drugs in japanese spontaneous reports: a mixed effects logistic regression model |
topic | Drugs and Devices |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4793323/ https://www.ncbi.nlm.nih.gov/pubmed/26989609 http://dx.doi.org/10.7717/peerj.1753 |
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