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These parameters were evaluated using multivariate logistic regression analysis (backward stepwise method) to determine independent predictors of mortality.
Then, multivariate linear regression analyses were performed (backward stepwise method) to look for independent associations between AD and the variables of interest.
Then, multivariate analysis was performed (backward stepwise method) to look for independent associations between HPV infection and the variables of interest.
Logistic regression modelling, that started with significant demographic and health characteristics (identified in the χ tests), was conducted using a backward stepwise method, to parsimoniously predict use of w3 FA supplements.
From the significant variables identified in the univariate analysis, we used the backward stepwise method to identify variables that were independently and significantly associated with the outcome using the likelihood ratio test.
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The following multivariate modeling strategy was applied: predictors associated with NCR at the p<0.2 level in univariate analysis were entered sequentially, starting from level 1 to level 2 and 3, using a backward stepwise method at each level.
A general linear model examined which demographic variables were significant predictors of the degree of change in the performance measure, with a backward stepwise method used to determine the best model.
The backward stepwise method was used to create the models.
A backward stepwise method was used to remove nonsignificant terms.
On multivariable analysis, a backward stepwise method was used to remove nonsignificant variables from the model.
A backward stepwise method was used to develop an optimal multivariable logistic model.
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