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Cox regression analysis was applied by the backward stepwise method with CKD as the dependent variable.
Covariates for inclusion were selected by the backward stepwise method with a significance criterion for removal from the model of P > 0.25.
Finally, we will develop and validate an own predictive model using a binary logistic regression analysis with the backward stepwise method, with the dependent variable being the presence or not of undiagnosed DM (Yes/No).
The global model included resveratrol concentration, length of exposure, and the intercept between these two factors, and a backward stepwise method with 5% alpha level to exclude insignificant parameters.
Those risk factors with a P value less than 0.2 in bivariate analysis were selected to enter into the multivariate model and the final model was constructed by backward stepwise method with P values of 0.1 set as the significance level for removal from the model.
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The independent predictors of SVTE were identified by the forward and backward stepwise methods with inclusion and exclusion P values <0.05.
A school-level minimal adequate logistic regression model was developed by the backward stepwise method, whereby variables with P > 0.05 were removed in the order of least significance; all excluded variables were subsequently retested in the final model.
The relations between the measured variables and mortality or massive bleeding were analyzed by stepwise logistic regression analysis (the backward stepwise method based on likelihood) with the use of death or massive bleeding as dependent variables.
All covariates were entered in one block with the backward stepwise method as this was an explorative analysis.
We calculated the odds ratios (OR) and 95% confidence intervals (CI) by using logistic regression analysis with the backward stepwise method.
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.
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