Statistical methodology
hospitalization/death
REGRESSION
To understand how treatment with CAS + IMD affects improvement of clinical symptoms over time, a two-step approach was applied to the longitudinal symptom dataHierarchical clustering was performed to group symptoms with similar treatment trajectories over time. An optimal number of clusters was determined using gap statisticsCox regression modeling was used to identify symptoms associated with a poor clinical outcome (i.e., hospitalization/death). For each symptom, the Cox regression model was fit using time to hospitalization/death as the outcome with the following covariates: baseline symptom score, daily symptom score change from baseline (time-varying), treatment indicator, age, sex, baseline BMI, and baseline viral load (in logSubgroup analyses were performed using the same two-step approach as described above for the following variables: age (< 50 years vs ≥ 50 years); sex; BMI (< 30 kg/mAll statistical analyses were done in R version 4.1.2 with the following packages:Emmeans (version 1.7.2) was used to generate the least-squares estimates from the logistic modelSurvival (version 3.2.13) was used to generate the HRs via the Cox model