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Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses
BACKGROUND: Individual participant data meta-analyses (IPD-MAs), which involve harmonising and analysing participant-level data from related studies, provide several advantages over aggregate data meta-analyses, which pool study-level findings. IPD-MAs are especially important for building and evalu...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327330/ https://www.ncbi.nlm.nih.gov/pubmed/37415216 http://dx.doi.org/10.1186/s12913-023-09726-8 |
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author | Maxwell, Lauren Shreedhar, Priya Levis, Brooke Chavan, Sayali Arvind Akter, Shaila Carabali, Mabel |
author_facet | Maxwell, Lauren Shreedhar, Priya Levis, Brooke Chavan, Sayali Arvind Akter, Shaila Carabali, Mabel |
author_sort | Maxwell, Lauren |
collection | PubMed |
description | BACKGROUND: Individual participant data meta-analyses (IPD-MAs), which involve harmonising and analysing participant-level data from related studies, provide several advantages over aggregate data meta-analyses, which pool study-level findings. IPD-MAs are especially important for building and evaluating diagnostic and prognostic models, making them an important tool for informing the research and public health responses to COVID-19. METHODS: We conducted a rapid systematic review of protocols and publications from planned, ongoing, or completed COVID-19-related IPD-MAs to identify areas of overlap and maximise data request and harmonisation efforts. We searched four databases using a combination of text and MeSH terms. Two independent reviewers determined eligibility at the title-abstract and full-text stages. Data were extracted by one reviewer into a pretested data extraction form and subsequently reviewed by a second reviewer. Data were analysed using a narrative synthesis approach. A formal risk of bias assessment was not conducted. RESULTS: We identified 31 COVID-19-related IPD-MAs, including five living IPD-MAs and ten IPD-MAs that limited their inference to published data (e.g., case reports). We found overlap in study designs, populations, exposures, and outcomes of interest. For example, 26 IPD-MAs included RCTs; 17 IPD-MAs were limited to hospitalised patients. Sixteen IPD-MAs focused on evaluating medical treatments, including six IPD-MAs for antivirals, four on antibodies, and two that evaluated convalescent plasma. CONCLUSIONS: Collaboration across related IPD-MAs can leverage limited resources and expertise by expediting the creation of cross-study participant-level data datasets, which can, in turn, fast-track evidence synthesis for the improved diagnosis and treatment of COVID-19. TRIAL REGISTRATION: 10.17605/OSF.IO/93GF2. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09726-8. |
format | Online Article Text |
id | pubmed-10327330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103273302023-07-08 Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses Maxwell, Lauren Shreedhar, Priya Levis, Brooke Chavan, Sayali Arvind Akter, Shaila Carabali, Mabel BMC Health Serv Res Research Article BACKGROUND: Individual participant data meta-analyses (IPD-MAs), which involve harmonising and analysing participant-level data from related studies, provide several advantages over aggregate data meta-analyses, which pool study-level findings. IPD-MAs are especially important for building and evaluating diagnostic and prognostic models, making them an important tool for informing the research and public health responses to COVID-19. METHODS: We conducted a rapid systematic review of protocols and publications from planned, ongoing, or completed COVID-19-related IPD-MAs to identify areas of overlap and maximise data request and harmonisation efforts. We searched four databases using a combination of text and MeSH terms. Two independent reviewers determined eligibility at the title-abstract and full-text stages. Data were extracted by one reviewer into a pretested data extraction form and subsequently reviewed by a second reviewer. Data were analysed using a narrative synthesis approach. A formal risk of bias assessment was not conducted. RESULTS: We identified 31 COVID-19-related IPD-MAs, including five living IPD-MAs and ten IPD-MAs that limited their inference to published data (e.g., case reports). We found overlap in study designs, populations, exposures, and outcomes of interest. For example, 26 IPD-MAs included RCTs; 17 IPD-MAs were limited to hospitalised patients. Sixteen IPD-MAs focused on evaluating medical treatments, including six IPD-MAs for antivirals, four on antibodies, and two that evaluated convalescent plasma. CONCLUSIONS: Collaboration across related IPD-MAs can leverage limited resources and expertise by expediting the creation of cross-study participant-level data datasets, which can, in turn, fast-track evidence synthesis for the improved diagnosis and treatment of COVID-19. TRIAL REGISTRATION: 10.17605/OSF.IO/93GF2. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09726-8. BioMed Central 2023-07-06 /pmc/articles/PMC10327330/ /pubmed/37415216 http://dx.doi.org/10.1186/s12913-023-09726-8 Text en © The Author(s) 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 | Research Article Maxwell, Lauren Shreedhar, Priya Levis, Brooke Chavan, Sayali Arvind Akter, Shaila Carabali, Mabel Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses |
title | Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses |
title_full | Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses |
title_fullStr | Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses |
title_full_unstemmed | Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses |
title_short | Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses |
title_sort | overlapping research efforts in a global pandemic: a rapid systematic review of covid-19-related individual participant data meta-analyses |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327330/ https://www.ncbi.nlm.nih.gov/pubmed/37415216 http://dx.doi.org/10.1186/s12913-023-09726-8 |
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