title: Effects of Green Tea Gargling on the Prevention of Influenza Infection: An Analysis Using Bayesian Approaches.
abstract: OBJECTIVE
  The aim of this study is to analyze the data obtained from a randomized trial on the prevention of influenza by gargling with green tea, which gave nonsignificant results based on frequentist approaches, by using Bayesian approaches.
  
  
  METHODS
  The posterior proportion, with 95% credible interval (CrI), of influenza in each group was calculated. The Bayesian index θ is the probability that a hypothesis is true. In this case, θ is the probability that the hypothesis that green tea gargling reduced influenza compared with water gargling is true. Univariate and multivariate logistic regression analyses were also performed by using the Markov chain Monte Carlo method.
  
  
  RESULTS
  The full analysis set included 747 participants. During the study period, influenza occurred in 44 participants (5.9%). The difference between the two independent binominal proportions was -0.019 (95% CrI, -0.054 to 0.015; θ = 0.87). The partial regression coefficients in the univariate analysis were -0.35 (95% CrI, -1.00 to 0.24) with use of a uniform prior and -0.34 (95% CrI, -0.96 to 0.27) with use of a Jeffreys prior. In the multivariate analysis, the values were -0.37 (95% CrI, -0.96 to 0.30) and -0.36 (95% CrI, -1.03 to 0.21), respectively.
  
  
  CONCLUSIONS
  The difference between the two independent binominal proportions was less than 0, and θ was greater than 0.85. Therefore, green tea gargling may slightly reduce influenza compared with water gargling. This analysis suggests that green tea gargling can be an additional preventive measure for use with other pharmaceutical and nonpharmaceutical measures and indicates the need for additional studies to confirm the effect of green tea gargling.
authors.affiliation: Department of Drug Evaluation & Informatics, Graduate School of Pharmaceutical Sciences, University of Shizuoka , Shizuoka, Japan .
authors.forename: Kazuki
authors.initials: K
authors.lastname: Ide
authors.affiliation: Department of Drug Evaluation & Informatics, Graduate School of Pharmaceutical Sciences, University of Shizuoka , Shizuoka, Japan .
authors.forename: Yohei
authors.initials: Y
authors.lastname: Kawasaki
authors.affiliation: Department of Drug Evaluation & Informatics, Graduate School of Pharmaceutical Sciences, University of Shizuoka , Shizuoka, Japan .
authors.forename: Maiko
authors.initials: M
authors.lastname: Akutagawa
authors.affiliation: Department of Drug Evaluation & Informatics, Graduate School of Pharmaceutical Sciences, University of Shizuoka , Shizuoka, Japan .
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Yamada
mesh_terms: D000293:Adolescent; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D007251:Influenza, Human / Q000188:drug therapy* / Q000517:prevention & control*; D008297:Male; D009067:Mouthwashes / Q000627:therapeutic use*; D028321:Plant Preparations / Q000627:therapeutic use*; D013662:Tea
chemical_list: D009067:Mouthwashes; D028321:Plant Preparations; D013662:Tea
keywords: Bayesian approaches; Monte Carlo method; gargling; green tea; influenza; prevention; probabilities