Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source
Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessme...
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| Published in | Briefings in bioinformatics Vol. 22; no. 6 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
05.11.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug–drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations. |
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| AbstractList | Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug-drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug-drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations. Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug–drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations. |
| Author | Tachi, Tomoya Teramachi, Hitomi Noguchi, Yoshihiro |
| Author_xml | – sequence: 1 givenname: Yoshihiro orcidid: 0000-0002-9110-9604 surname: Noguchi fullname: Noguchi, Yoshihiro organization: Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu 501-1196, Japan – sequence: 2 givenname: Tomoya surname: Tachi fullname: Tachi, Tomoya organization: Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu 501-1196, Japan – sequence: 3 givenname: Hitomi surname: Teramachi fullname: Teramachi, Hitomi organization: Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigakunishi, Gifu 501-1196, Japan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34453158$$D View this record in MEDLINE/PubMed |
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| Copyright | The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. |
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| Keywords | time to onset algorithm signal detection disproportionality analysis spontaneous reporting systems |
| Language | English |
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| PublicationYear | 2021 |
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| Title | Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source |
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