Bayesian reanalysis of early remdesivir for the treatment of COVID-19 in outpatients with high risk of progression to severe disease - Mazin Abdelghany; Fang Yu; Stephen Rennard; Yeongjin Gwon (PLoS ONE, 2026)
Background Though Bayesian methods are flexible, intuitive, and readily incorporated into clinical decision-making, with particular utility when prior information is available, they remain underutilized in the analysis of clinical trials. Methods In PINETREE, a Phase 3 randomized controlled trial (RCT) of remdesivir (RDV) for the treatment of outpatients with COVID-19 at high risk of severe disease, the primary outcome of COVID-19–related hospitalization or all-cause death was reanalyzed using a range of reference and data-driven priors. Posterior probability distributions were used to calculate the probability that the estimated hazard ratio (HR) was below a range of clinically meaningful specified thresholds and to estimate the treatment effect and its 95% credible interval (CrI). Results Under a minimally informative prior, the posterior probability of an estimated HR less than 1 for COVID-19–related hospitalization or all-cause death was 1 with a posterior median HR 0.13 and 95% CrI 0.02–0.47, recovering the frequentist estimates. Moreover, estimated posterior probability distributions, posterior median HRs, and 95% CrIs were robust across a range of both reference and data-driven prior choices, indicating the strength of the trial data. Lastly, using priors that incorporate historical RCT data, precision of the estimated posterior median HR and 95% CrI was improved over naïve, frequentist estimates. Conclusions In a Bayesian reanalysis of the PINETREE trial, there was a 98.9% or greater probability that treatment with RDV reduced the risk of COVID-19–related hospitalization or all-cause death across all prior probability distributions.