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In order to detect interaction between the candidate genes a multivariate stepwise binary logistic regression analysis was performed on ASY subjects and CCC patients (see Table 9).
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Multivariate modelling, using a stepwise binary building technique, was employed with predictive variables selected if their bivariate significance was p=0.1 to accommodate the possibility of variable achieving statistical significance once the confounding effect of another variable was controlled.
To determine factors associated with mother's satisfaction bivariate binary logistic regression and multivariate stepwise logistic regression were applied.
Multivariate analysis of the data included backward stepwise binary logistic regression with the test result as the outcome (negative = 0/positive = 1) and the herd characteristics as categorical covariates to estimate odds ratios (OR) including those variables significantly associated with the outcome in the univariate analysis.
A multivariate stepwise logistic regression was then performed to evaluate each of these as independent predictors.
We performed multivariate, stepwise (forward) logistic regression.
Multivariate stepwise logistic regression analysis was performed.
A forward, stepwise binary logistic regression analysis was also performed.
In our stepwise binary logistic regression analysis, genotypes were considered as binary covariates.
Effects of covariates were assessed by multivariate stepwise logistic analysis.
Afterwards, multivariate stepwise regression analyses were carried out.
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