Sentence examples for backward stepwise removal from inspiring English sources

For variable selection, we used backward stepwise removal of variables based on likelihood-ratio judgments.

We applied a backward stepwise removal procedure (Grafen & Hails, 2002) to avoid problems from including nonsignificant terms (Engqvist, 2005) and the removed variables were re-entered one by one to the final model to obtain relevant statistics.

We included all two-way interactions into initial models and applied a backward stepwise removal procedure [ 63] to avoid problems because of inclusion of non-significant terms (P < 0.05) [ 64].

We only included biologically meaningful two-way interactions into all initial models and applied a backward stepwise removal procedure (Grafen and Hails 2002) to avoid problems due to inclusion of nonsignificant terms (Engqvist 2005).

In order to find the best combination of predictors for antibiotic treatment failure, all parameters significant at a p value of ≤ 0.05 in univariate logistic regression analysis were entered into multiple logistic regression (MLR) analysis with backward stepwise removal of parameters in order of insignificance.

Backward stepwise selection with removal testing was used, which based on probability of the likelihood ratio statistic.

Variables were selected for the multivariable survival analyses by backward stepwise selection, with removal testing based on the probability of the likelihood-ratio statistic, at a P > 0.10.

From the full model non-significant variables were sequentially removed in a backward stepwise procedure after the removal of the least significant term (P < 0.005).

We tested the differences in dichotomous variables by χ2 test or Fisher's exact test, and multivariate analysis was conducted using logistic regression (backward conditional, P for stepwise removal 0.05).

Multifactorial regression (i.e., regression with multiple independent variables but a single dependent variable) was based on linear or logistic regression with backward stepwise selection (P for removal 0.10).

We created three initial models by using a backward stepwise approach (that is, removal of non-significant (P>0.05) variables in a stepwise manner) of comorbidity (for the random control model, six variables reduced to four), historical symptoms (11 variables reduced to five), and physical examination variables (seven variables reduced to three).