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SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data
One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19’s emergence will certainly boost future...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825316/ https://www.ncbi.nlm.nih.gov/pubmed/35156066 http://dx.doi.org/10.1016/j.patter.2022.100453 |
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author | Khorchani, Takoua Gadiya, Yojana Witt, Gesa Lanzillotta, Delia Claussen, Carsten Zaliani, Andrea |
author_facet | Khorchani, Takoua Gadiya, Yojana Witt, Gesa Lanzillotta, Delia Claussen, Carsten Zaliani, Andrea |
author_sort | Khorchani, Takoua |
collection | PubMed |
description | One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19’s emergence will certainly boost future robust and innovative approaches of statistical sciences applied to clinical fields. Here, we report the development of SASC, a simple but efficient approach to generate COVID-19-related synthetic clinical data through a web application. SASC takes basic summary statistics for each group of patients and attempts to generate single variables according to internal correlations. To assess the “reliability” of the results, statistical comparisons with Synthea, a known synthetic patient generator tool, and, more importantly, with clinical data of real COVID-19 patients are provided. The source code and web application are available on GitHub, Zenodo, and Mendeley Data. |
format | Online Article Text |
id | pubmed-8825316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-88253162022-02-09 SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data Khorchani, Takoua Gadiya, Yojana Witt, Gesa Lanzillotta, Delia Claussen, Carsten Zaliani, Andrea Patterns (N Y) Descriptor One of the impacts of the coronavirus disease 2019 (COVID-19) pandemic has been a push for researchers to better exploit synthetic data and accelerate the design, analysis, and modeling of clinical trials. The unprecedented clinical efforts caused by COVID-19’s emergence will certainly boost future robust and innovative approaches of statistical sciences applied to clinical fields. Here, we report the development of SASC, a simple but efficient approach to generate COVID-19-related synthetic clinical data through a web application. SASC takes basic summary statistics for each group of patients and attempts to generate single variables according to internal correlations. To assess the “reliability” of the results, statistical comparisons with Synthea, a known synthetic patient generator tool, and, more importantly, with clinical data of real COVID-19 patients are provided. The source code and web application are available on GitHub, Zenodo, and Mendeley Data. Elsevier 2022-02-09 /pmc/articles/PMC8825316/ /pubmed/35156066 http://dx.doi.org/10.1016/j.patter.2022.100453 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Descriptor Khorchani, Takoua Gadiya, Yojana Witt, Gesa Lanzillotta, Delia Claussen, Carsten Zaliani, Andrea SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data |
title | SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data |
title_full | SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data |
title_fullStr | SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data |
title_full_unstemmed | SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data |
title_short | SASC: A simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from COVID-19 clinical data |
title_sort | sasc: a simple approach to synthetic cohorts for generating longitudinal observational patient cohorts from covid-19 clinical data |
topic | Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825316/ https://www.ncbi.nlm.nih.gov/pubmed/35156066 http://dx.doi.org/10.1016/j.patter.2022.100453 |
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