The basic information of these selected variables was detailed in Table 2.
The survey collected information on selected socio-demographic variables, such as age, gender, income, educational status, and self-reported racial group.
On the selected variables their scoring agreement percentage was 100%.
Through TWIST system, we established a consistent possibility to predict the status of being a MDS on the basis of 6 selected variables (Table 3) with 90% specificity, sensitivity and global accuracy (Table 4), this meaning that the selected variables contained specific information on the occurrence of a DS pregnancy.
Pairwise Pearson correlation coefficients for all predictor variables in our models were <0.4; therefore, all selected variables contributed unique information to the final models.
The block weight information can help to identify important regulatory factors from the selected variables.
Information on selected comorbid medical conditions was also obtained.
We selected variables for inclusion in the multivariate model based on the individual variable analysis.
The normalization of the cointegrating vector depends on the variable in question, which means that any selected variable can be normalized.
In multivariate logistic regression, we pre-selected variables for inclusion in the model based on prior information from studies from other settings on risk factors for default from new patients, with consideration for information available on routine records [5], [6], [7], [12], [13], [14], [15], [16], [17], [18].
