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A univariate animal model was used to estimate variance components, while a bivariate model was used to estimate genetic correlations.
Then, a bivariate model was used to carry out a subsequent analysis of litter size data.
A bivariate model was used to calculate estimates of overall sensitivity and overall specificity.
Threshold effect existed (Spearman correlation coefficient: 0.657, p = 0.156 .Thus bivariate model was used to pool estimates.
Pedigree-based EBV were obtained as described above, except that a bivariate model was used in this case.
A bivariate model was used to calculate the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR).
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Variables deemed significant at p < 0.20 in a bivariate model were used in a multivariate model.
Where possible, bivariate modeling was used to generate summary estimates of sensitivity and specificity and predictive values were assessed.
Bivariate models were used to assess the association between Sept9 and all other available variables, including if any of the available variables modified the effect of the Sept9 test.
The reduced model can be written as: Finally, three specific bivariate models were used for the different association analyses, depending on indicator variable values included in X matrices: i. Mendelian inheritance: used in the analysis of the effect of RBP4, ESR1 and IGF2 polymorphisms.
A bivariate heritability model was used to assess the genetic and environmental influences underlying both specific trait variance and the covariance between the vessel traits.
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