Statistical Analysis
critically ill
REGRESSION, SECONDARY, CRITICALLY ILL
Trial results were analyzed in a bayesian framework, as described in the statistical analysis plan and study protocol (The primary analysis used a bayesian cumulative logistic regression model (presented in the statistical analysis plan in A post hoc sensitivity analysis was performed without the use of dynamic borrowing and after excluding patients who received therapeutic-dose heparin because this was deemed to be an inferior concurrent treatment in critically ill patients with COVID-19.Consistent with our prior approach,Logistic and cumulative logistic models were used for binary and ordinal outcomes in the secondary and subgroup analyses. The ORs and 95% CIs were estimated through a random-effects logistic regression model that accounted for the same covariates in the primary analysis model. For time remaining alive through 90 days, hazard ratios and 95% CIs were estimated through a frailty proportional hazards model that accounted for the same covariates in the primary analysis model.The proportional hazards assumptions were tested using 2 approaches: (1) comparison of survival curves and log[-log] curves by treatment and (2) including parameters for covariate × log(time) interaction. Both approaches suggested proportional hazards. For the subgroup analyses, we included a treatment × group interaction in the model and set up contrasts to compare use of a P2Y12 inhibitor with usual care within each subgroup.All frequentist analyses were run in SAS software, version 9.4 (SAS Institute Inc) with 2-sided tests at the significance level of 