R core team 2020 software

Statistical analyses for the weighted model scenario were conducted in R Core Team (2020) software (R Foundation for Statistical Computing, Vienna, Austria). Cost-effectiveness analyses were conducted using a bespoke model developed in Microsoft® Excel® 365 MSO supporting Visual Basic for Applications (VBA) (Microsoft Corporation, Redmond, WA).

Using R Core Team (2020) software (R Foundation for Statistical Computing, Version 4.0.1, Vienna, Austria), continuous data were expressed as mean with standard deviation (±SD) or median with interquartile range (IQR). If the normality assumption was met using a normal Q—Q plot and the Shapiro–Wilk test, we used the Student’s t-test for group comparisons; if not met, we used the Mann-Whitney. Categorical data were reported as frequencies and percentages and analyzed either using the Chi-square test for nxm tables or Fisher's exact test for 2 × 2 tables group comparisons. To obtain odds ratios, we performed a multivariable logistic regression model adjusting for the confounders (either P < 0.3 in a univariable logistic regression model or clinically important confounders) of age, sex, BMI, diabetes, hypertension, renal disease, number of comorbidities (diabetes, hypertension, cardiovascular disease [heart failure/coronary artery disease], stroke, renal disease, asthma, and obesity) and COVID-19 treatment regimen. All statistical inferences were drawn with 95% confidence intervals with P < 0.05. Bonferroni adjustments were applied to control for multiplicity [29 (link)].