Spss for windows 19.0 program suite

For this study, descriptive statistical analysis was carried out using the SPSS for Windows 19.0 program suite (SPSS Inc. Chicago, IL, USA). Two-group analysis was assessed with Pearson χ² test for categorical variables. For continuous variables, Mann-Whitney U test was used. The level of significance was set at p < 0.05. Logistic regression models were used to identify the independent predictors of 3 month disability. The analysis was performed with the multivariate general linear model. In the models, disability at 3 months (mRS > 2 was the dependent variable, and the factors found to be associated with outcome by univariate analyses were entered as confounding variables. The variables were excluded from the analysis one by one, and the variable with p > 0.05 and closest to 1.0 was removed, until all features left in the model had p > 0.05.

Statistical analysis was carried out using the SPSS for Windows 19.0 program suite (SPSS Inc. Chicago, USA). Descriptive statistics were performed. Correlations between categorical variables were identified using Pearson's chi-squared test, and correlations between continuous variables were determined using the Mann–Whitney U-test. To compare each hemorrhagic transformation group, we used the Kruskal–Wallis test for non-parametric variables and the one-way ANOVA test for metric variables. The binary logistic regression analysis was used to assess outcomes at 3 months and at 1 year. Logistic regression models were used to identify the independent predictors of 3-month disability and 1-year case fatality. The analysis was performed with the multivariate general linear model (GLM). In the models, disability at 3 months (mRS >2) and case fatality at 1 year were the dependent variables, and those factors that were found to be associated with the outcome by univariate analyses were entered as confounding variables. The variables were excluded from the analysis one by one, and the variable with p > 0.05 and closest to 1.0 dropped out, until all features left in the model had p < 0.05. Survival analyses were done (Kaplan–Meier curve and logrank).
All tests were performed at a p-value of < 0.05 significance level.

Statistical analysis was carried out using the SPSS for Windows 19.0 program suite (SPSS Inc. Chicago, USA). Descriptive statistics was performed. Two-group analysis was assessed with Pearson χ² test for categorical variables. For continuous variables, Mann –Whitney U test was used. The level of significance was set at p < 0.05. Logistic regression models were used to identify the independent predictors of 3-month disability and 1-year case fatality. The analysis was performed with the multivariate general linear model (GLM). In the models, disability at 3 months (mRS >2), and case fatality at 1 year were the dependent variables, and the factors found to be associated with outcome by univariate analyses were entered as confounding variables. The variables were excluded from the analysis one by one, and the variable with p > 0.05 and closest to 1.0 was removed, until all features left in the model had p < 0.05.