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The SMART Safety: An empirical dataset for evidence synthesis of adverse events

Evidence synthesis serves an important role to promote informed decision-making in healthcare practice. A key issue of evidence synthesis is the approach to deal with rare adverse events and the methods to address bias of harm effects. Empirical data is essential to help methodologists and statistic...

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Detalles Bibliográficos
Autores principales: Fan, Shiqi, Yu, Tianqi, Yang, Xi, Zhang, Rui, Furuya-Kanamori, Luis, Xu, Chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589771/
https://www.ncbi.nlm.nih.gov/pubmed/37869626
http://dx.doi.org/10.1016/j.dib.2023.109639
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author Fan, Shiqi
Yu, Tianqi
Yang, Xi
Zhang, Rui
Furuya-Kanamori, Luis
Xu, Chang
author_facet Fan, Shiqi
Yu, Tianqi
Yang, Xi
Zhang, Rui
Furuya-Kanamori, Luis
Xu, Chang
author_sort Fan, Shiqi
collection PubMed
description Evidence synthesis serves an important role to promote informed decision-making in healthcare practice. A key issue of evidence synthesis is the approach to deal with rare adverse events and the methods to address bias of harm effects. Empirical data is essential to help methodologists and statisticians to solve the issues in evidence synthesis of adverse events. For this reason, we have established SMART Safety dataset, the largest empirical dataset of meta-analyses of adverse events. The dataset contains 151 systematic reviews with 629 meta-analyses on safety outcomes, which covers more than 2,300 randomized controlled trials and 362 harm outcomes, with 10,069 rows and 45 columns of trial level information. All information was double- or even quadra-checked and further verified by referring the original source (e.g., the full-text of the included randomized trials) to ensure high validity of the data.
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spelling pubmed-105897712023-10-22 The SMART Safety: An empirical dataset for evidence synthesis of adverse events Fan, Shiqi Yu, Tianqi Yang, Xi Zhang, Rui Furuya-Kanamori, Luis Xu, Chang Data Brief Data Article Evidence synthesis serves an important role to promote informed decision-making in healthcare practice. A key issue of evidence synthesis is the approach to deal with rare adverse events and the methods to address bias of harm effects. Empirical data is essential to help methodologists and statisticians to solve the issues in evidence synthesis of adverse events. For this reason, we have established SMART Safety dataset, the largest empirical dataset of meta-analyses of adverse events. The dataset contains 151 systematic reviews with 629 meta-analyses on safety outcomes, which covers more than 2,300 randomized controlled trials and 362 harm outcomes, with 10,069 rows and 45 columns of trial level information. All information was double- or even quadra-checked and further verified by referring the original source (e.g., the full-text of the included randomized trials) to ensure high validity of the data. Elsevier 2023-10-04 /pmc/articles/PMC10589771/ /pubmed/37869626 http://dx.doi.org/10.1016/j.dib.2023.109639 Text en © 2023 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Fan, Shiqi
Yu, Tianqi
Yang, Xi
Zhang, Rui
Furuya-Kanamori, Luis
Xu, Chang
The SMART Safety: An empirical dataset for evidence synthesis of adverse events
title The SMART Safety: An empirical dataset for evidence synthesis of adverse events
title_full The SMART Safety: An empirical dataset for evidence synthesis of adverse events
title_fullStr The SMART Safety: An empirical dataset for evidence synthesis of adverse events
title_full_unstemmed The SMART Safety: An empirical dataset for evidence synthesis of adverse events
title_short The SMART Safety: An empirical dataset for evidence synthesis of adverse events
title_sort smart safety: an empirical dataset for evidence synthesis of adverse events
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589771/
https://www.ncbi.nlm.nih.gov/pubmed/37869626
http://dx.doi.org/10.1016/j.dib.2023.109639
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