How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov

Author:

He Zhe1ORCID,Erdengasileng Arslan2,Luo Xiao3,Xing Aiwen2,Charness Neil4,Bian Jiang5

Affiliation:

1. School of Information, Florida State University, Tallahassee, Florida, USA

2. Department of Statistics, Florida State University, Tallahassee, Florida, USA

3. Department of Computer Information and Graphics Technology, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana, USA

4. Department of Psychology, Florida State University, Tallahassee, Florida, USA

5. Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA

Abstract

Abstract Objective In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability. Methods We analyzed 3765 COVID-19 studies registered in the largest public registry—ClinicalTrials.gov, leveraging natural language processing (NLP) and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population. Results Our analysis included 2295 interventional studies and 1470 observational studies. Most trials did not explicitly exclude older adults with common chronic conditions. However, known risk factors such as diabetes and hypertension were considered by less than 5% of trials based on their trial description. Pregnant women were excluded by 34.9% of the studies. Conclusions Most COVID-19 clinical studies included both genders and older adults. However, risk factors such as diabetes, hypertension, and pregnancy were under-represented, likely skewing the population that was sampled. A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.

Funder

National Institute on Aging (NIA) of the National Institutes of Health

Florida State University-University of Florida Clinical and Translational Science

National Center for Advancing Translational Sciences

National Library of Medicine

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

Reference39 articles.

1. The novel coronavirus outbreak: what we know and what we don’t;Koopmans;Cell,2020

2. Accelerating COVID-19 therapeutic interventions and vaccines (ACTIV): an unprecedented partnership for unprecedented times;Collins;JAMA,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3