Sentence examples for stepwise order of from inspiring English sources

As DJ joints were not detected by Southern blotting, we deduced that the stepwise order of VDJ assembly for the TCRβDMF is mainly V-to-D rearrangement, followed by VD-to-J rearrangement.

Each risk factor was added in a forward stepwise manner in order of their contribution to improving the AUC.

Subsequently, we removed predictors with non-significant effects stepwise, in reversed order of significance, until only significant effects remained (alpha = 5%%).

Factors were then removed stepwise in descending order of P-values whenever they were found to be non-significant (P < 0.05), yielding the final model.

This model was created by adding these predictors stepwise in descending order of individual impact on decision satisfaction (determined by the change in −2log likelihood in their separate models representing the quantity of improvement of model fit).

Our data suggest that the order of stepwise exclusion, rather than the absolute M value, is a better index of the relative expression stability of individual candidate genes within a gene panel.

Regression analysis with a stepwise approach confirmed this order of relevance of the tested variables on the changes in cognitive function (data not shown).

Tests of significance were based on likelihood-ratio tests and in all models explanatory variables were stepwise removed in the order of statistical relevance.

We included all the variables in the univariable analysis in a multivariable logistic regression model using a forward stepwise method, in descending order of the magnitude of association with death.

Multivariate regression used stepwise comparison to determine the order of variables entering the model and used likelihood-ratio tests to determine the statistical significance of each variable.

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.