Rethinking remdesivir for COVID-19: A Bayesian reanalysis of trial findings

Abstract

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.

Current Trend of Prescribing Anticoagulant and Antiviral Drugs in the Treatment of COVID- 19 Patients in a Corona Dedicated Hospital in Khulna

“…The use of remdesivir in the treatment of COVID-19 is very common in other countries. 11,12,14,18,[30][31][32][33][34] In this study, favipiravir was used in mild (40%) & moderate (18.75%) cases only. Pilkington et al showed patients treated with favipiravir had a beneficial effect.…”

A re‐analysis of 150 women's health trials to investigate how the Bayesian approach may offer a solution to the misinterpretation of statistical findings

“…In this application we used standard informative priors illustrating how the approach can be used without dependency on ‘priors’, which might induce concerns of lack of reproducibility 32 . The finding that the Bayesian and frequentist point estimates, confidence/credible intervals and P s/posterior probabilities showed strong concordance gives confidence that inferences are not dependent on the chosen prior 19,20,21,22,33 . Furthermore, the Bayesian approach is pitched here as an aid to interpretation and not as a technique that will radically change the numerical results; thus it might even have a place alongside a conventional frequentist analysis.…”

“…In a sensitivity analysis we set 97.5% as the cut-point for strong statistical evidence, 95% for moderate statistical evidence and anything <95% for weak statistical evidence. Conventionally posterior probabilities are reported without any such categorisation, [19][20][21][22] although others have also proposed categorising, for example using >80%, 90% or 95% posterior probabilities as convincing evidence. 23,24 Finally, we classified the overall statistical evidence as strong if there was strong statistical evidence of either at least a small effect (which includes moderate and larger effects), a trivial effect or an unanticipated harmful effect.…”

“…32 The finding that the Bayesian and frequentist point estimates, confidence/ credible intervals and Ps/posterior probabilities showed strong concordance gives confidence that inferences are not dependent on the chosen prior. 19,20,21,22,33 Furthermore, the Bayesian approach is pitched here as an aid to interpretation and not as a technique that will radically change the numerical results; thus it might even have a place alongside a conventional frequentist analysis. However, the approach might also be used in conjunction with an informative prior, fully embracing the Bayesian philosophy, and this might be particularly important in rare diseases or interventions in difficult to recruit populations.…”