Sentence examples for backward stepwise selection methods from inspiring English sources

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.

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.

Kaplan-Meier logrank and univariate and multivariate (backward stepwise selection method with probability for removal of 0.10) Cox proportional hazards regression models were used to identify the strongest predictors of overall time-tagged mortality using time to death as a continuous variable.

Because of the limited absolute number of events (35 deaths) and in order to avoid an overparameterized model, Cox regression analysis, with a backward stepwise selection method to eliminate unnecessary control variables, was carried out to evaluate the effect of the biochemical and clinical variables for all-cause mortality, and hazard ratios (HR) were calculated.

Simple linear regression with the backward-stepwise selection method was used to analyze which variables were best predictors of anxiety and depression.

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.