Open AccessRethinking remdesivir for COVID-19: A Bayesian reanalysis of trial findings
Author(s) -
Joyce M. Hoek,
Sarahanne Miranda Field,
Ymkje Anna de Vries,
Maximilian Linde,
Merle Marie Pittelkow,
Jasmine Muradchanian,
Don van Ravenzwaaij
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0255093
Subject(s) - frequentist inference , bayesian probability , covid-19 , clinical trial , medicine , statistical power , outcome (game theory) , medline , econometrics , bayesian inference , statistics , economics , mathematics , pathology , political science , law , disease , mathematical economics , outbreak , infectious disease (medical specialty)
Background Following testing in clinical trials, the use of remdesivir for treatment of COVID-19 has been authorized for use in parts of the world, including the USA and Europe. Early authorizations were largely based on results from two clinical trials. A third study published by Wang et al. was underpowered and deemed inconclusive. Although regulators have shown an interest in interpreting the Wang et al. study, under a frequentist framework it is difficult to determine if the non-significant finding was caused by a lack of power or by the absence of an effect. Bayesian hypothesis testing does allow for quantification of evidence in favor of the absence of an effect. Findings Results of our Bayesian reanalysis of the three trials show ambiguous evidence for the primary outcome of clinical improvement and moderate evidence against the secondary outcome of decreased mortality rate. Additional analyses of three studies published after initial marketing approval support these findings. Conclusions We recommend that regulatory bodies take all available evidence into account for endorsement decisions. A Bayesian approach can be beneficial, in particular in case of statistically non-significant results. This is especially pressing when limited clinical efficacy data is available.