Selected article for: "acute infection and logistic regression"

Author: Knobbe, Rebecca B; Diallo, Abdallah; Fall, Amary; Gueye, Aida D; Dieng, Assane; van Immerzeel, Tabitha D; Ba, Abou; Diop, Amadou; Diop, Abdoulaye; Niang, Mbayame; Boye, Cheikh SB
Title: Pathogens Causing Respiratory Tract Infections in Children Less Than 5 Years of Age in Senegal
  • Document date: 2019_12_30
  • ID: q333qgps_11
    Snippet: Based on G*Power 3.1.9.2 version, a sample size of 182 was yielded, when using logistic regression, an alpha of .05, and a small-to-medium effect size with an odds ratio (OR) of 1.60. 28 Therefore, a sample size of 198 is sufficient to detect microorganisms as a predictor of acute respiratory infection. Baseline characteristics were compared between cases and controls. Continuous variables were expressed as medians and ranges. They were compared .....
    Document: Based on G*Power 3.1.9.2 version, a sample size of 182 was yielded, when using logistic regression, an alpha of .05, and a small-to-medium effect size with an odds ratio (OR) of 1.60. 28 Therefore, a sample size of 198 is sufficient to detect microorganisms as a predictor of acute respiratory infection. Baseline characteristics were compared between cases and controls. Continuous variables were expressed as medians and ranges. They were compared using the Mann-Whitney U test. Categorical variables were expressed as frequencies and proportions and were compared using the chi-square test. To identify potential predictive factors, a univariable logistic regression was used. Thereafter, established predictors of the outcomes (occurrence of respiratory infection and severity of disease) (P < .1) were included in multivariable logistic regression. Data were presented as ORs and 95% confidence intervals (CI). P < .05 was considered significant. Epi Info was used for analysis of the data.

    Search related documents:
    Co phrase search for related documents