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A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance
After new drugs enter the market, adverse events (AE) induced by their use must be tracked; rare AEs may not be detected during clinical trials. Some organizations have been collecting information on suspected drugs and AEs via a spontaneous reporting system to conduct post-market drug safety survei...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9522808/ https://www.ncbi.nlm.nih.gov/pubmed/36175526 http://dx.doi.org/10.1038/s41598-022-19998-5 |
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author | Park, Goeun Jung, Inkyung |
author_facet | Park, Goeun Jung, Inkyung |
author_sort | Park, Goeun |
collection | PubMed |
description | After new drugs enter the market, adverse events (AE) induced by their use must be tracked; rare AEs may not be detected during clinical trials. Some organizations have been collecting information on suspected drugs and AEs via a spontaneous reporting system to conduct post-market drug safety surveillance. These organizations use the information to detect a signal representing potential causality between drugs and AEs. The drug and AE data are often hierarchically structured. Accordingly, the tree-based scan statistic can be used as a statistical data mining method for signal detection. Most of the AE databases contain a large number of zero-count cells. Notably, not only an observational zero from the Poisson distribution, but also a true zero exists in zero-count cells. True zeros represent theoretically impossible observations or possible but unreported observations. The existing tree-based scan statistic assumes that all zeros are zero-valued observations from the Poisson distribution. Therefore, true zeros are not considered in the modeling, which can lead to bias in the inferences. In this study, we propose a tree-based scan statistic for zero-inflated count data in a hierarchical structure. According to our simulation study, in the presence of excess zeros, our proposed tree-based scan statistic provides better performance than the existing tree-based scan statistic. The two methods were illustrated using Korea Adverse Event Reporting System data from the Korea Institute of Drug Safety and Risk Management. |
format | Online Article Text |
id | pubmed-9522808 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95228082022-10-01 A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance Park, Goeun Jung, Inkyung Sci Rep Article After new drugs enter the market, adverse events (AE) induced by their use must be tracked; rare AEs may not be detected during clinical trials. Some organizations have been collecting information on suspected drugs and AEs via a spontaneous reporting system to conduct post-market drug safety surveillance. These organizations use the information to detect a signal representing potential causality between drugs and AEs. The drug and AE data are often hierarchically structured. Accordingly, the tree-based scan statistic can be used as a statistical data mining method for signal detection. Most of the AE databases contain a large number of zero-count cells. Notably, not only an observational zero from the Poisson distribution, but also a true zero exists in zero-count cells. True zeros represent theoretically impossible observations or possible but unreported observations. The existing tree-based scan statistic assumes that all zeros are zero-valued observations from the Poisson distribution. Therefore, true zeros are not considered in the modeling, which can lead to bias in the inferences. In this study, we propose a tree-based scan statistic for zero-inflated count data in a hierarchical structure. According to our simulation study, in the presence of excess zeros, our proposed tree-based scan statistic provides better performance than the existing tree-based scan statistic. The two methods were illustrated using Korea Adverse Event Reporting System data from the Korea Institute of Drug Safety and Risk Management. Nature Publishing Group UK 2022-09-29 /pmc/articles/PMC9522808/ /pubmed/36175526 http://dx.doi.org/10.1038/s41598-022-19998-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Park, Goeun Jung, Inkyung A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance |
title | A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance |
title_full | A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance |
title_fullStr | A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance |
title_full_unstemmed | A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance |
title_short | A tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance |
title_sort | tree-based scan statistic for zero-inflated count data in post-market drug safety surveillance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9522808/ https://www.ncbi.nlm.nih.gov/pubmed/36175526 http://dx.doi.org/10.1038/s41598-022-19998-5 |
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