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A snapshot of the ongoing clinical research on COVID-19

The pandemic of coronavirus disease 2019 (COVID-19) presents an unprecedented challenge to rapidly develop new diagnostic, preventive and therapeutic strategies. Currently, thousands of new COVID-19 patients are quickly enrolled in clinical studies. We aimed to investigate the characteristics of the...

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Detalles Bibliográficos
Autores principales: Piovani, Daniele, Pansieri, Claudia, Peyrin-Biroulet, Laurent, Danese, Silvio, Bonovas, Stefanos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256468/
https://www.ncbi.nlm.nih.gov/pubmed/32518636
http://dx.doi.org/10.12688/f1000research.23843.1
Descripción
Sumario:The pandemic of coronavirus disease 2019 (COVID-19) presents an unprecedented challenge to rapidly develop new diagnostic, preventive and therapeutic strategies. Currently, thousands of new COVID-19 patients are quickly enrolled in clinical studies. We aimed to investigate the characteristics of the COVID-19 studies registered in ClinicalTrials.gov and report the extent to which they have incorporated features that are desirable for generating high-quality evidence. On April 28, 2020, a total of 945 studies on COVID-19 have been registered in ClinicalTrials.gov; 586 studies are interventional (62.0%), the most frequent allocation scheme is the parallel group assignment (437; 74.6%), they are open-label and the most common primary purpose is the research on treatment. Too many of the ongoing interventional studies have a small expected sample size and may not generate credible evidence at completion. This might lead to a delayed recognition of effective therapies that are urgently needed, and a waste of time and resources. In the COVID-19 pandemic era, it is crucial that the adoption of new diagnostic, preventive and therapeutic strategies is based upon evidence coming from well-designed, adequately powered and carefully conducted clinical trials.