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For this analysis, a backward stepwise selection method was used.
Multivariate (backward stepwise selection method with probability for the removal of 0.10) logistic regression analyses were used to determine the association of variables with 1-year mortality.
The factors included in the multivariable model were selected from the results of the single-variable analyses, and by application of a backward stepwise selection method using the Akaike information criterion (AIC) to exclude irrelevant factors.
Cox proportional hazards regression model was used to estimate the risk of death by multivariate analysis (backward stepwise selection method with probability for the removal of 0.10) for the whole population.
Univariate and multivariate (backward stepwise selection method with probability for the removal of 0.10) logistic regression analyses were used to determine the association of variables with 28-day mortality.
A multivariate Cox regression analysis was then performed using a backward stepwise selection method, with a P value less than 0.05 as the entry criterion and a P value 0.10 or higher as the removal criterion.
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Selection of predictors, both fixed and random effects, for inclusion in the models was informed using backward stepwise selection methods where covariates below the specified significance level (p < 0.05/t < 2) were identified and removed.
Simple linear regression with the backward-stepwise selection method was used to analyze which variables were best predictors of anxiety and depression.
# The variables were selected using the backward Wald stepwise selection method.
We then built linear regression models with the RARBIS as the dependent variable and the administrative data variables as the independent variables using SAS (Cary NC) automated procedures and the forward, backward and stepwise selection methods to select the best model.
Backward stepwise selection (BSS) is a method especially designed for coping with variable selection.
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