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).