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Overlap between adverse events (AEs) and serious adverse events (SAEs): a case study of a phase III cancer clinical trial

BACKGROUND: Safety data is required to be collected in all clinical trials and can be separated into two types of data, adverse events and serious adverse events. Often, these types of safety data are collected as two discrete data sets, where adverse events that also meet the criteria for seriousne...

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Autores principales: James, Elizabeth C., Dunn, David, Cook, Adrian D., Clamp, Andrew R., Sydes, Matthew R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495966/
https://www.ncbi.nlm.nih.gov/pubmed/32943106
http://dx.doi.org/10.1186/s13063-020-04718-z
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author James, Elizabeth C.
Dunn, David
Cook, Adrian D.
Clamp, Andrew R.
Sydes, Matthew R.
author_facet James, Elizabeth C.
Dunn, David
Cook, Adrian D.
Clamp, Andrew R.
Sydes, Matthew R.
author_sort James, Elizabeth C.
collection PubMed
description BACKGROUND: Safety data is required to be collected in all clinical trials and can be separated into two types of data, adverse events and serious adverse events. Often, these types of safety data are collected as two discrete data sets, where adverse events that also meet the criteria for seriousness should be reported in both datasets. Safety analyses are often conducted using only the adverse event dataset, which should feature all safety events reported. We investigated whether the reporting of safety in both datasets was systematically followed and explored the impact of this on safety analyses in ICON8, an ovarian cancer clinical trial. METHODS: Text searches of serious adverse event data identified events that could potentially match the data reported in the adverse event dataset (looking at pre-specified AE terms only). These serious adverse events were then mapped to adverse event data according to predefined criteria: (a) event term matches, (b) date of onset and date of assessment within 30 days of each other, (c) date of assessment lies between date of onset and date of resolution and (d) events confirmed to occur in the same chemotherapy cycle. A combined dataset of all unique safety events (whether originally reported in the adverse event or serious adverse event dataset) was created and safety analyses re-performed. RESULTS: 51,019 adverse events were reported in ICON8, of which 42,410 were included in the mapping exercise. One thousand five hundred six serious adverse event elements were reported, of which 668 were included in the mapping exercise. Sixty-one percent of serious adverse event elements was matched to an already-reported adverse event. Supplementing these additional safety events and re-performing safety analyses increased the proportion of patients with at least one grade 3 or worse safety events in all arms from 42 to 47% in the control arm and 61 to 65% and 52 to 59% in the research arms. The difference in proportions of grade 3 or worse event in the research arms compared to the control arm changed by 18% (95% confidence interval [CI] 12 to 24%) and 12% (95% CI 6 to 18%), respectively. CONCLUSIONS: There was low agreement in mapping serious adverse events to already reported adverse events, with nearly 40% of serious adverse events included in the mapping exercise not mapped to an already reported adverse event. Any analyses of safety data that use only adverse event datasets or do not clearly account for serious adverse event data will likely be missing important safety information. Reporting standards should make clear which datasets were used for analyses.
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spelling pubmed-74959662020-09-17 Overlap between adverse events (AEs) and serious adverse events (SAEs): a case study of a phase III cancer clinical trial James, Elizabeth C. Dunn, David Cook, Adrian D. Clamp, Andrew R. Sydes, Matthew R. Trials Methodology BACKGROUND: Safety data is required to be collected in all clinical trials and can be separated into two types of data, adverse events and serious adverse events. Often, these types of safety data are collected as two discrete data sets, where adverse events that also meet the criteria for seriousness should be reported in both datasets. Safety analyses are often conducted using only the adverse event dataset, which should feature all safety events reported. We investigated whether the reporting of safety in both datasets was systematically followed and explored the impact of this on safety analyses in ICON8, an ovarian cancer clinical trial. METHODS: Text searches of serious adverse event data identified events that could potentially match the data reported in the adverse event dataset (looking at pre-specified AE terms only). These serious adverse events were then mapped to adverse event data according to predefined criteria: (a) event term matches, (b) date of onset and date of assessment within 30 days of each other, (c) date of assessment lies between date of onset and date of resolution and (d) events confirmed to occur in the same chemotherapy cycle. A combined dataset of all unique safety events (whether originally reported in the adverse event or serious adverse event dataset) was created and safety analyses re-performed. RESULTS: 51,019 adverse events were reported in ICON8, of which 42,410 were included in the mapping exercise. One thousand five hundred six serious adverse event elements were reported, of which 668 were included in the mapping exercise. Sixty-one percent of serious adverse event elements was matched to an already-reported adverse event. Supplementing these additional safety events and re-performing safety analyses increased the proportion of patients with at least one grade 3 or worse safety events in all arms from 42 to 47% in the control arm and 61 to 65% and 52 to 59% in the research arms. The difference in proportions of grade 3 or worse event in the research arms compared to the control arm changed by 18% (95% confidence interval [CI] 12 to 24%) and 12% (95% CI 6 to 18%), respectively. CONCLUSIONS: There was low agreement in mapping serious adverse events to already reported adverse events, with nearly 40% of serious adverse events included in the mapping exercise not mapped to an already reported adverse event. Any analyses of safety data that use only adverse event datasets or do not clearly account for serious adverse event data will likely be missing important safety information. Reporting standards should make clear which datasets were used for analyses. BioMed Central 2020-09-17 /pmc/articles/PMC7495966/ /pubmed/32943106 http://dx.doi.org/10.1186/s13063-020-04718-z Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
James, Elizabeth C.
Dunn, David
Cook, Adrian D.
Clamp, Andrew R.
Sydes, Matthew R.
Overlap between adverse events (AEs) and serious adverse events (SAEs): a case study of a phase III cancer clinical trial
title Overlap between adverse events (AEs) and serious adverse events (SAEs): a case study of a phase III cancer clinical trial
title_full Overlap between adverse events (AEs) and serious adverse events (SAEs): a case study of a phase III cancer clinical trial
title_fullStr Overlap between adverse events (AEs) and serious adverse events (SAEs): a case study of a phase III cancer clinical trial
title_full_unstemmed Overlap between adverse events (AEs) and serious adverse events (SAEs): a case study of a phase III cancer clinical trial
title_short Overlap between adverse events (AEs) and serious adverse events (SAEs): a case study of a phase III cancer clinical trial
title_sort overlap between adverse events (aes) and serious adverse events (saes): a case study of a phase iii cancer clinical trial
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495966/
https://www.ncbi.nlm.nih.gov/pubmed/32943106
http://dx.doi.org/10.1186/s13063-020-04718-z
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