Parameter estimation and model comparison
REGRESSION
We used Bayesian parameter estimation fitting all models using R software version 4.2.1 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria) and Stan [The original analyses of the COVID STEROID 2 trial were adjusted for stratification variables [To facilitate comparison of different models, we estimated the expected mean number of DAWOLS for each patient/treatment group using predictions for patients in each treatment group calculated by combining predictions from all parts of the hurdle-negative binomial model, the zero–one-inflated beta model (with proportions multiplied by the maximum number of days possible), and the cumulative logistic regression model (with probabilities multiplied by each possible number of days and summed); predicted values were all truncated to the possible outcome space where necessary (including for -1 assigned to non-survivors in the cumulative logistic regression model, which was both replaced with 0 and used as is). We used these values to estimate the mean differences (the most common estimand for these outcomes [We compared models using Model fitness was in particular assessed using graphical posterior predictive checks [Finally, we produced several examples of how the raw data can be presented graphically to provide additional information and to supplement the statistical summary measures by illustrating potential differences across the full range of distributions, inspired by previous RCTs [