Author: YAMANE, Itsuro; YAMAZAKI, Hisanori; ISHIZEKI, Sayoko; WATANABE, Yugo; OKUMURA, Hanako; OKUBO, Mitsuharu; KURE, Katsumasa; HAYAKAWA, Yuiko; FURUKAWA, Makoto; OOI, Munetaka; MIZUKAMI, Yoshihiro; ITO, Mitsugu
Title: Impact of a porcine epidemic diarrhea outbreak on swine productivity in Japan: a retrospective cohort study Document date: 2016_5_12
ID: k0mdkews_7
Snippet: Data analysis: AFI and all the productivity parameters were initially tested for the normality of their distributions by visually inspecting histograms of the data and with the Shapiro-Wilk test. Log-transformations were performed for variables that were not normally distributed, and these were then retested for normality. For variables, such as AFI, PRWM and POWM, the log-transformed data were normally distributed and were used for subsequent an.....
Document: Data analysis: AFI and all the productivity parameters were initially tested for the normality of their distributions by visually inspecting histograms of the data and with the Shapiro-Wilk test. Log-transformations were performed for variables that were not normally distributed, and these were then retested for normality. For variables, such as AFI, PRWM and POWM, the log-transformed data were normally distributed and were used for subsequent analyses. Group mean comparisons of the log-transformed AFI were made with Scheffe's test. Analysis of variance, with repeated measures, was used to determine the differences in the productivity parameters among the three groups, the study periods and their interactions. If the interactions were significant for any of the parameters, analysis of covariance, with log-transformed AFI as the covariate, was performed among the three groups at different time points. Multiple comparisons were made to detect differences between the means of the groups at different time points using Bonferroni's test. Statistical differences were defined as P<0.05. All statistical analyses were performed with SPSS version 21.0.0.0 for Windows and R version 2.13.0 (R Foundation for Statistical Computing, 2011).
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