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Disease interactions were identified by a forward variable selection method.
A forward variable selection yielded the same independent predictors.
Multivariate analysis was performed using a Cox model using a forward variable selection procedure.
The successive projections algorithm (SPA) is a forward variable selection method aimed at minimizing variable collinearity in modeling.
We integrated a forward variable selection procedure into flexible discriminant analysis to identify key SNP and/or GE markers with the highest cross-validation prediction accuracy.
The figure also provides the selection times of the identified markers across all cross-validations and which step a marker is selected in a forward variable selection procedure.
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19 20 A forward variable-selection approach was used and variables retained if they improved goodness-of-fit, based on the Akaike information criterion.
To form a parsimonious SEM model, a forward variable-selection procedure was performed using the normalized intensity values for each probe and the eight expected haplotype contributions for the most significant marker interval of the probe.
A new forward variable selection within a multivariate Cox regression was implemented.
To build the final prediction model, we used a new forward variable selection within a multivariate Cox regression.
Using a stepwise forward variable selection on a 2 1 split sample approach, 14 variables were found to be predictive of the total amount of fluid given to the patient on day two in the ICU.
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