Trial reportPloS one2026
Bayesian reanalysis of early remdesivir for the treatment of COVID-19 in outpatients with high risk of progression to severe disease.
Trial report in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThough 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.
methodsIn 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).
resultsUnder 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.
conclusionsIn 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.