Sensitivity analyses
We used the following statistical methods to conduct sensitivity analyses to check the robustness of our findings. First, we performed traditional multivariable regression analysis using a generalized linear model. In this analysis, we created a generalized linear model using outcomes as dependent variable and glucocorticoids on the day of admission and all covariates as independent variables. Second, we performed a propensity score adjustment analysis. In this analysis, we created a generalized linear model using outcomes as dependent variables and estimated propensity score in the main analysis as independent variables. Third, we conducted an inverse probability of treatment weighting analysis.12 For this, we used a weighted generalized linear model with stabilized average treatment effect weight calculated from the estimated propensity scores in the main analysis.
Continuous variables are presented as mean and standard deviation (SD), and categorical variables as number and percentage. p<0.05 was defined as denoting statistical significance. We used Stata version 16.0 (StataCorp, College Station, TX, USA) to perform all statistical analyses.