Characteristics of COVID-19 clinical trials registered with ClinicalTrials.gov: cross-sectional analysis

Abstract

ObjectivesTo characterise current COVID-19-related research activities.DesignCross-sectional analysis.SettingClinical trials registered with ClinicalTrials.gov testing interventions relevant to COVID-19.Data sourcesClinicalTrials.gov was searched for COVID-19 and related terms to identify trials registered between 1 December 2019 and 1 May 2020 that test interventions related to the COVID-19 pandemic.Main outcome measuresWe classified trials according to intervention type, and report key trial characteristics including recruitment status, location, funder type, target enrolment number, intervention model (single group, randomised or sequential assignment) and projected completion date.ResultsOf the 630 identified clinical trials related to COVID-19, 509 (81%) involved the study of drugs or biological agents. Of these trials of drugs and biologics, 305 (60%) use an open-label design, 43 (8%) are single blinded (participant only) and 161 (32%) are double blinded (participant and investigator). 94 (18%) of the drug/biological trials are non-randomised. Either hydroxychloroquine or chloroquine is administered as part of the study protocol in 152 (30%) of the drug/biological trials. The total planned enrolment for these hydroxychloroquine/chloroquine trials is over 200 000 participants, which represents 65% of the total planned enrolment for all registered trials of drugs or biologics. There are also at least 25 registered trials of azithromycin (n=53), convalescent plasma (n=38), lopinavir/ritonavir (n=30), stem cell treatments (n=29) and tocilizumab (n=25). 142 trials were registered in the first 3 months of 2020, and 488 trials were registered between 1 April and 1 May 2020.ConclusionsThese findings demonstrate a robust research response to the COVID-19 pandemic, though many of the currently planned and ongoing trials focus on a small number of potential therapies, and many also lack essential design features and power necessary to provide accurate treatment effect estimates.