Sentence examples similar to multivariate analysis with stepwise selection from inspiring English sources

Variables achieving a significance level of 0.05 in univariate analyses were entered in the multivariate analysis with stepwise variable selection and p-values to enter and to stay in the model of 0.05.

The results of the multivariate analysis with stepwise criterion for variable selection for gait speed found that age was associated with a worse performance on this test.

Descriptive analysis, χ2 test, and multivariate analysis with stepwise logistic regression were performed.

Only two variables retained statistical significance following multivariate analysis with stepwise regression.

We developed models to predict in-ICU mortality using univariate and multivariate analysis with stepwise regression.

Multivariate Cox regression analysis with stepwise selection (α=0.05) was used to estimate the hazard ratio, 95% confidence interval (CI), and the effects of the clinical and pathological variables.

Prevalence ratios of frailty and pre-frailty were explored using multivariate and univariate Poisson regression analysis with Stepwise selection criteria.

Multivariate Cox proportional-hazards regression analysis with stepwise selection was used to evaluate independent prognostic factors associated with survival, and gene expression cluster defined by gene classifier (or subsets of gene classifier), tumor grade or stage, and age were used as covariates.

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.

A multivariate model with stepwise selection was used that included all the variables significant at the univariate level (P ≤ 0.05).

Using a cutoff of p≤.05, which corresponded to a minimum decrease of the objective function value (OFV) of 3.84 upon inclusion of each individual covariate based on the likelihood-ratio test, multivariate analysis with forward stepwise inclusion, backward stepwise deletion, and forward selection followed by backward elimination were applied to finalize the covariate model.