Sample size calculation
REGRESSION, REGRESSION
The sample size of 230 patients with a 1:1 randomization of adjunct tele-yoga to the standard of care provides ~80% power to detect a 15% difference between treatment groups in time cumulative hospital discharge (i.e., with or without limiting abilities) rates of 80% in the adjunct tele-yoga group and 75% in the standard of care group, on day 14, using a two-sided 5% alpha. Analysis was performed with SPSS version 23 [IBM Corp., (N.Y., USA)].The trial was analyzed by comparing patients randomized to adjunct tele-yoga vs. those randomized to standard of care, with the placebo group serving as the referent. The primary outcome was analyzed with a multivariable proportional odds model adjusted with age, sex, and comorbidities. Further adjustments with baseline (pre-randomization) COVID Outcomes Scale category and duration of acute respiratory symptoms are reported as The heterogeneity of treatment effect by prespecified baseline characteristics was evaluated by adding an interaction term between randomized group assignment and the baseline characteristic of interest in the primary model. Baseline characteristics evaluated in the heterogeneity of treatment effect analyses included baseline COVID Outcomes Scale category, and duration of symptoms prior to randomization, age, sex, and race/ethnicity.All-cause mortality was estimated using the Kaplan–Meier product limit method. The adjunct tele-yoga group was compared with the standard of care group using the log-rank test, and the mean estimates and 95% CIs were provided.We also used the paramed command in SPSS to perform mediation analysis by fitting a linear regression model to the outcomes with yoga treatment, and the mediators included were the covariates. We applied the causal mediation method, to investigate whether biomarkers could be a causal pathway between intervention and the outcome. Regression models were fitted to the mediators with treatments included as covariates.