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Statistical review of Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial.

Authors: Dahly, Darren; Gates, Simon; Morris, Tim;

Statistical review of Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial.

Abstract

The following review has been prepared by members of the MRC-NIHR Trials Methodology Research Partnership. The reviewers are all qualified statisticians with experience in clinical trials. Our objective is to provide a rapid review of publications, preprints and protocols from clinical trials of COVID-19 treatments, independent of journal specific review processes. We aim to provide timely, constructive, focused, clear advice aimed at improving both the research outputs under review, as well as future studies. Given our collective expertise (clinical trial statistics) our reviews focus on the designs of the trials and other statistical content (methods, presentation and accuracy of results, inferences). Here we review Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial, by Gautret et al . When we started our review, the paper was being circulated via Google Drive. Shortly after, it was accepted for publication in the International Journal of Antimicrobial Agents, and posted on MedRxiv.org. However, these versions of the study report were the same as the one we reviewed, indicating no or limited external peer review for the final published version.

Keywords

Randomized Clinical Trials, COVID-19, Azithromycin, Hydroxychloroquine

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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