Sentence examples for predictors were categorized from inspiring English sources

The predictors were categorized into individual characteristics, information/education/training, design to support worker needs, safety climate, competing goals, and problems with rules.

Predictors were categorized as potentially modifiable preventive factors (HR<1.0), potentially modifiable risk factors (HR>1.0), and patient characteristics.

The predictors were categorized in the same way that Sartorius et al. [ 5] described when they developed the MGAP.

These predictors were categorized into groups representing the size of the pathway (i.e., amount of genes/SNPs/SNP-gene ratio on each pathway).

In order to account for potential non-linearity of associations, predictors were categorized according to previously used cut-off points in this population.

After the LR model has been built using a forward stepwise selection procedure for the choice of a subset of predictor variables x i (i = 1, 2,..., d), each continuous predictor is categorized using a locally weighted scatterplot procedure to subjectively identify cut-off points on the basis of training data.

All but one FD predictor was categorized in binary form for analysis purposes; namely, primary language (English not first language =1), requiring care (1), receiving community support (1), education (primary only =1, primary and secondary only =2, any tertiary =3), and using a gait aid (1).

Other non-categorical predictor variables were categorized at a clinically accepted point (eg MMSE <10).

The predictors of interest were categorized as: (1) unmodifiable - age and gender; and (2) modifiable - BMI, comorbidity, depression and anxiety.

As predictors of congener levels in hand wipes, dust concentrations were categorized into tertiles, and as predictors of urinary PFR metabolites, both dust and hand wipe concentrations were categorized to minimize the effect of skewed data and outliers in regression analyses.

As predictors of TBB and TBPH in handwipes, dust concentrations were categorized (dichotomized at the median and categorized into tertiles), and as predictors of urinary TBBA, both dust and handwipe concentrations were categorized to minimize the effect of skewed data and outliers in regression analyses.