2.2 Statistical analysis
±, respiratory failure
REGRESSION, RESPIRATORY FAILURE, SECONDARY, EVENT
A sample size of 132 patients (66 patients per arm) was estimated to provide 90% power, while a sample size of 100 patients (50 patients per arm) was estimated to provide 80% power, to detect a relative between-group difference of 20% reduction in composite ordinal scale (from baseline to day 14) with an assumption of a mean ± standard deviation composite ordinal scale value of 4.5 ± 1.6 at baseline and two-sided alpha value of 0.05.All baseline patient demographics, presenting symptomology, comorbidities, recent medication usage, and clinical outcome risk factors were summarized using range, mean and standard deviation (SD), and median with interquartile range (IQR) for continuous measures, or as frequencies with percentages for categorical measures. In order to compare these measures between the two treatment groups, Pearson Chi-Square and Fisher Exact tests were used for categorical measures, while independent student t-tests were used for normally distributed continuous measures, and Wilcoxon Rank Sum tests were used for ordinal and non-normally distributed continuous measures. Normality was assessed using Shapiro-Wilk and Kolmogorov–Smirnov testing along with visual inspection of quantile-quantile plots. Patient discharge measures, laboratory values, and primary and secondary outcomes were analyzed in the same way prior to any multivariable analyses.To further assess the primary and secondary outcomes, multivariable regression models were used. In order to examine the change in the composite COVID ordinal scale from baseline to day 14 and day 28, multivariable linear regression was used. For the patients’ raw COVID ordinal scale measure at day 14 and day 28, ordinal multivariable logistic regression was used. However, due to the small number of patients who belonged to most of the levels at both time points, the proportional odds assumption was violated. Therefore, these models were built using an unequal slopes assumption (for non-proportional odds), and results were summarized as odds ratios with 95% confidence by cumulative scale level. Additionally, due to low event rates, multivariable logistic regression with Firth’s penalized likelihood was used to model both all-cause mortality and respiratory failure at days 14 and 28, and results were summarized as odds ratios with 95% confidence intervals [In addition, Kaplan Meier curves were used to plot all-cause mortality at day 14, day 28, and overall. Log Rank tests were used to determine whether there were significant differences between the curves for the two treatment groups. All statistical analyses were completed using SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria), where a two-sided p-value < 0.05 was considered statistically significant.