Selected article for: "logistic regression model and univariate analysis"

Author: Bernard-Stoecklin, Sibylle; Nikolay, Birgit; Assiri, Abdullah; Bin Saeed, Abdul Aziz; Ben Embarek, Peter Karim; El Bushra, Hassan; Ki, Moran; Malik, Mamunur Rahman; Fontanet, Arnaud; Cauchemez, Simon; Van Kerkhove, Maria D.
Title: Comparative Analysis of Eleven Healthcare-Associated Outbreaks of Middle East Respiratory Syndrome Coronavirus (Mers-Cov) from 2015 to 2017
  • Document date: 2019_5_14
  • ID: 1t3hg4wi_10
    Snippet: Individual-level analysis. We summarized case characteristics as frequencies and proportions for categorical variables, as median and interquartile ranges (IQR) for continuous variables. Chi-square tests were used to compare subgroups of cases when appropriate. A P value of less than 0.05 was used to indicate statistical significance. Univariate analysis identified variables significantly associated with fatal outcome, which were included in a mu.....
    Document: Individual-level analysis. We summarized case characteristics as frequencies and proportions for categorical variables, as median and interquartile ranges (IQR) for continuous variables. Chi-square tests were used to compare subgroups of cases when appropriate. A P value of less than 0.05 was used to indicate statistical significance. Univariate analysis identified variables significantly associated with fatal outcome, which were included in a multivariable model. Model selection was performed using a multilevel mixed-effects logistic regression with backwards selection taking into account clustering of individuals by outbreak. For the variable "age", the cut-off was fixed at 65, based on the results of the univariate analysis. Variables with p-values < 0.05 were retained in the final model.

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