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In our case the submodels have a natural nesting by variable selection, because each model represents a special case of the most general microsatellite mutation model in our family.
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The RF is a classification and regression technique introduced by Breiman [37], and this method is extremely useful in the study highly determinant variable selection because it provides variable importance measure as a part of the analysis results [37, 38].
The final multivariate model was chosen by automatic variable selection.
By backward variable selection, only variables with p <0.1 were analyzed further.
Out of 48 variables analyzed, 8 were included into the final model by all variable selection methods.
The simplified models were obtained by stepwise variable selection using Bayesian Information Criterion.
Family history of ovarian cancer was dropped by the variable selection analysis.
Near collinearities can be eliminated by, for instance, variable selection.
Disease interactions were identified by a forward variable selection method.
The backward variable selection was used because it lowers the risk of making a type II error [ 29].
Multivariable logistic regression models were constructed aided by backward stepwise variable selection.
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