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Thoughts on the design of COVID-19 diagnostic trials
Received:March 12, 2020  Revised:April 13, 2020  Click here to download the full text
Citation of this paper:Lü Min-zhi,LIU Tian-shu,ZHU Chou-wen.Thoughts on the design of COVID-19 diagnostic trials[J].Chinese Journal of Clinical Medicine,2020,27(3):345-348
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Author NameAffiliationE-mail
Lü Min-zhi Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China
Center of Evidence-Based Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China 
 
LIU Tian-shu Department of Oncology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
Center of Evidence-Based Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China 
liu.tianshu@zs-hospital.sh.cn 
ZHU Chou-wen Department of Gastroenterology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
Center of Evidence-Based Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China 
zhu.chouwen@zs-hospital.sh.cn 
Abstract:COVID-19 epidemic is threatening, accurate and rapid diagnosis and screening methods are particularly important. Numerous domestic scientists have been actively conducting relevant diagnostic trials and researches. This study reviewed the registration information of more than 10 diagnostic trials and discussed the common problems. This article focuses on how to use the PICOS principle to construct research questions for the COVID-19 diagnostic trial. The design points of the diagnostic test such as the setting of the gold standard, the representativeness of the subjects, the calculation of the sample size, the determination of synchronous, independent and blind method are explained in detail. The purpose of this paper is to provide suggestions for the design of COVID-19 diagnostic trials for researchers, help them to reduce and avoid bias in the top-level design stage, to complete high-quality clinical research, and provide evidence-based medicine for clinical diagnosis and treatment.
keywords:COVID-19  diagnostic trial  study design  evidence-based medicine
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