Statistical analyses
Propensity score matching was employed based on age, sex, COVID-19 type
upon admission, commodities (including diabetes mellitus, malignancy,
stroke, hypertension, cardiovascular diseases, pulmonary diseases),
vaccination status, and medications administered during hospitalization
such as corticosteroids (prednisone, dexamethasone, hydrocortisone,
methylprednisolone), antibiotics (β-lactams, moxifloxacin, levofloxacin,
metronidazole, vancomycin, azithromycin, linezolid, tigecycline,
caspofungin, voriconazole), antiviral drugs (entecavir, valacyclovir),
Chinese traditional medications (Lianhua Qingwen Granules, Qingkailing
Soft Capsules, Jinhua Qinggan Granules, Shufeng Jiedu Capsules),
immunomodulator (recombinant human interferon α2b injection, human
interleukin-11 for injection, human Granulocyte colony stimulating
factor injection, intravenous human immunoglobulin, thymalfasin), and
Chinese decoctions in a logistic regression model. The propensity-score
matching without replacement, utilizing a caliper width of 0.2. The
standardized mean differences (SMDs) for each covariate between the
groups prior to and following the propensity-score matching were
calculated. These differences were considered balanced if the SMD was
less than the threshold of 0.1. The evaluation of results was conducted
using Kaplan-Meier curves. Furthermore, the study also performed
subgroup analysis on the time to conversion from RT-PCR positive to
negative status for SARS-CoV-2. The specific subgroup analyses
considered factors such as patients’ age (≤60 years versus
>60 years), clinical classification, and vaccination
history (fully vaccinated versus partially vaccinated). Additionally,
the subgroup analysis was stratified based on the time interval between
the administration of nirmatrelvir-ritonavir and the onset of symptoms
or the first positive RT-PCR test. All the analyses were performed using
software SPSS 26.0 and R 4.2.1. Statistical tests were 2-side and the
statistical significance level set at P < 0.05.