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.