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Similar to the logistic regression model, we established the probit model using a stepwise procedure, and we selected the best model based on the AIC.
Models were built using a stepwise procedure in which non-significant predictor variables (p > 0.05) were sequentially removed [39].
All analyses were performed separately for women and men, as well as for the total group, and multivariate logistic regression analysis using a stepwise procedure, adjusting for significant interactions (considering a threshold level of 0.15), was used to assess the relationship between independent variables (mtDNA, APOE4 status, age and gender) and the outcome.
Model selection was determined using a stepwise procedure.
A logistic model can be created from the significant candidate variables, using a stepwise procedure.
Multivariate model selection using a stepwise procedure based on the Akaike information criterion (AIC) was performed.
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We used a stepwise procedure to identify significant predictors.
For the covariable selection, we used a stepwise procedure excluding covariables with a P value greater than 0.10.
To overcome this problem, we used a stepwise procedure, similar to that employed with the Bayes model, to select variables to enter in the logistic regression model.
We will use a stepwise procedure to analyze the data, going from the mere descriptive analysis to the univariate- and multivariate analysis, using the appropriate tools in each step.
We used a stepwise procedure of selection of variables, using Akaike's information criterion (AIC) to compare models and as the basis for our selection of the best model.
More suggestions(15)
using a permutation procedure
using a chance procedure
using a segmentation procedure
using a multistep procedure
using a bootstrap procedure
using a urea procedure
using a stepwise selection
using a stepwise optimization
using a calibrating procedure
using a stepwise approach
using a stepwise differentiation
using a stepwise strategy
using a stepwise operation
using a test procedure
using a stepwise regression
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