Assessing efficacy of antiviral therapy for COVID-19 patients: A case study on remdesivir with bayesian synthesis design and multistate analysis

Background/purpose: A synthesis design and multistate analysis is required for assessing the clinical efficacy of antiviral therapy on dynamics of multistate disease progression and in reducing the mortality and enhancing the recovery of patients with COVID-19. A case study on remdesivir was illustrated for the clinical application of such a novel design and analysis. Methods: A Bayesian synthesis design was applied to integrating the empirical evidence on the one-arm compassion study and the two-arm ACTT-1 trial for COVID-19 patients treated with remdesivir. A multistate model was developed to model the dynamics of hospitalized COVID-19 patients from three transient states of low, medium-, and high-risk until the two outcomes of recovery and death. The outcome measures for clinical efficacy comprised high-risk state, death, and discharge. Results: The efficacy of remdesivir in reducing the risk of death and enhancing the odds of recovery were estimated as 31% (95% CI, 18–44%) and 10% (95% CI, 1–18%), respectively. Remdesivir therapy for patients with low-risk state showed the efficacy in reducing subsequent progression to high-risk state and death by 26% (relative rate (RR), 0.74; 95% CI, 0.55–0.93) and 62% (RR, 0.38; 95% CI, 0.29–0.48), respectively. Less but still statistically significant efficacy in mortality reduction was noted for the medium- and high-risk patients. Remdesivir treated patients had a significantly shorter period of hospitalization (9.9 days) compared with standard care group (12.9 days). Conclusion: The clinical efficacy of remdesvir therapy in reducing mortality and accelerating discharge has been proved by the Bayesian synthesis design and multistate analysis.