Identification of independent risk factors
high disability
REGRESSION
Binary logistic regression was performed to evaluate independent risk factors for high disability by BI 180 days after study inclusion.Candidate risk factors for the multivariate model were selected based on clinical reasoning and the statistically significant results of the bivariate analyses. Multicollinearity was checked using matrix correlation and variable inflation factors (VIF). Backward selection was used to remove the variables from the model sequentially. In addition, model comparisons were made using the log likelihood test (nested models), calibration was assessed with the Hosmer–Lemeshow goodness-of-fit test, and the area under the receiver operating characteristic curve (AUC) was used to examine the discrimination ability. The best fit model was used as final model. The results of the univariate and multivariate analyses are presented as odds ratios (OR), 95% confidence intervals (CI), and 