Sentence examples for models using stepwise from inspiring English sources

Using all 50 clinical and laboratory variables available on day 1 of critical care admission, Cox proportional hazards regression was fitted to select variables for inclusion in two prognostic models, using stepwise selection and nonparametric bootstrapping sampling techniques.

We built multiple linear regression models using stepwise backwards deletion and including main effects only (no interaction terms).

All variables that were found to have a statistically significant association were introduced into the multiple regression models using stepwise analysis, with a cut-off point of 0.10.

Nonsignificant terms were excluded from the models using stepwise elimination, and we give statistics for excluded variables before their exclusion from the model.

Two separate models using stepwise conditional forward entry were constructed - one for demography, comorbidities, clinical presentations, and laboratory tests on admission, and one for complications including organ failures and infections, if p < 0.1 in univariate analysis.

Variables with p<0.2 from the univariate model were placed in a multiple logistic regression model using stepwise selection.

Significant variables were chosen under this model using stepwise backward selection and a liberal p < 0.10 variable retention criterion.

All variables with P < 0.10 in univariate analyses were entered into the multivariate logistic model using stepwise selection.

In a model using stepwise selection to determine the most parsimonious model and including model 1, THP, progranulin, and clusterin were selected.

After producing our multivariable model using stepwise regression, we wanted to assess how the new model would perform in the clinical prediction of IR.

Variables found to be statistical significant at a P value of less than 0.1 in the univariate model were included into the multivariate model using stepwise backward elimination.