Selected article for: "confidence interval and logistic regression"

Author: Kim, Chang Hyeun; Lee, Sang Weon; Kim, Young Ha; Sung, Soon Ki; Son, Dong Wuk; Song, Geun Sung
Title: Predictors of Hematoma Enlargement in Patients with Spontaneous Intracerebral Hemorrhage Treated with Rapid Administration of Antifibrinolytic Agents and Strict Conservative Management
  • Document date: 2019_9_11
  • ID: 5vklxexq_11
    Snippet: All statistical analyses were performed using SPSS statistics (version 22.0; IBM Corporation, Armonk, NY, USA). Continuous variables are presents as means±standard deviations. Categorical variables are presented as numbers and percentages. Student's t-test, χ 2 analysis, and the Fisher's exact test were used to assess between-group differences. Receiver operating characteristic (ROC) curves were created to determine the optimal hematoma volume .....
    Document: All statistical analyses were performed using SPSS statistics (version 22.0; IBM Corporation, Armonk, NY, USA). Continuous variables are presents as means±standard deviations. Categorical variables are presented as numbers and percentages. Student's t-test, χ 2 analysis, and the Fisher's exact test were used to assess between-group differences. Receiver operating characteristic (ROC) curves were created to determine the optimal hematoma volume and maximal diameter cut points, sensitivity, and specificity in hematoma expansion according to the second CT. Univariate and multivariate logistic regression analyses were used to assess risk factors for hematoma expansion. Multivariate logistic regressions were performed with independent variables selected from all factors with a value of p<0.1 in univariate analyses. The results are presented as odds ratios with a 95% confidence interval (CI). Statistical significance was set at p<0.05.

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