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A main effect multivariable model was applied.
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P values for heterogeneity were derived from the cross product interaction term coefficient (exposure variable × binary effect modifier variable) added to the main effects multivariable model.
Any variables with a liberal significance level alpha = 0.20 were included for multi-variable modelling and a backward step-wise approach was used to create the final main effects multivariable model.
The independent variables considered for the full main effects multivariable GLM logistic regression model and their univariable associations with the dependent variable "weekly probability of PRRSV PCR positivity," are shown in Table 2.
The independent variables considered for the full main effects multivariable GLM logistic regression model and their univariable associations with the dependent variable "weekly probability of PRRSV ELISA positivity," are shown in Table 1.
This allowed the subsequent fit of a main-effect multivariable Cox regression model.
Main-effect multivariable Cox proportional hazards (CPH) [ 25] models were fit in the separate BRCA data sets and then in the menopausal stages.
When desired, interaction terms can be included by starting from the final main effect of the multivariable model.
Main effect was also tested in the multivariable model to assess the treatment difference.
For instance, in the multivariable model, the main effect of "Infective Stage" in the model that also includes the interaction " Giardia Dose by Infective Stage" corresponded to a difference between cysts and trophozoites at a mean dose.
The independent variables included in multivariable analysis were stage of production (main effect), type of operation, total number of barns, herd size (small or <1000, medium or ≥ 1000 –3000, and large or >3000), and the presence of other species.
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