Statistics
Patients were followed for 30 days after the onset of treatment (from DO to D0+30). Patients who did not became PCR-negative during the follow-up period, were censored on the date of their last available positive PCR test during the follow-up period. The survival function was estimated by non-parametric Kaplan-Meier survival analysis. We then used the multivariable Cox Proportional-Hazards model to identify factors associated with the probability of having a negative viral load during follow-up. Based on the available literature (see above), the model was adjusted for age, baseline PCR SARS-CoV-2 (CT) viral load, and the time from the onset of symptoms to the onset of treatment.
We also performed a sensitivity analysis using a competing risk approach. For patients who did not became PCR-negative during the follow-up period; when death occurred before the end of the follow-up period, it was considered a competing event. When patients were still alive at the end of the follow-up period, they were censored on the date of their last available positive PCR test. The time-cumulative incidence of patients with a negative viral load according to treatment group was estimated by non-parametric competing risk analysis. We then used the multivariable Fine-Gray sub-distribution hazard model (21) to identify factors associated with the probability of having a negative viral load during follow-up.
A two-sided α value of less than 0.05 was considered to be statistically significant. Competing risk analysis was carried out using the LIFETEST and PHREG procedures in the SAS 9.4 statistical software (SAS Institute, Cary, NC).