Selected article for: "Chi square test and continuous variable"
Author: Zhiqi Yang; Daiying Lin; Xiaofeng Chen; Jinming Qiu; Shengkai Li; Ruibin Huang; Hongfu Sun; Yuting Liao; Jianning Xiao; Yanyan Tang; Guorui Liu; Renhua Wu; Xiangguang Chen; Zhuozhi Dai
Title: Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features
  • Document date: 2020_4_22
    • ID: ggwh91cc_16
    Snippet: The CT imaging and clinical features were compared between COVID-19 and influenza pneumonia group by using the chi-square test (for nominal variable), the Kruskal-Wallis H test (for ordinal variable), or the student's t test (for continuous variable). The features with a significant difference between the two groups were extracted. Spearman or Kendall correlation test between feature metrics and diagnosis outcomes (i.e., 1 for COVID-19 and 0 for .....
    Document: The CT imaging and clinical features were compared between COVID-19 and influenza pneumonia group by using the chi-square test (for nominal variable), the Kruskal-Wallis H test (for ordinal variable), or the student's t test (for continuous variable). The features with a significant difference between the two groups were extracted. Spearman or Kendall correlation test between feature metrics and diagnosis outcomes (i.e., 1 for COVID-19 and 0 for influenza pneumonia) were assessed for each extracted feature. The diagnostic performance of clinical and CT features in differentiating COVID-19 from influenza pneumonia was evaluated with univariate analysis. Additionally, corresponding area under the curve (AUC), accuracy, specificity, sensitivity and threshold were calculated. All statistical analyses for this study were performed with R (version 3.6.4, http: //www.r-project.org/). A two-tailed P-value < 0.05 indicated statistical significance.

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