The decision support model based on the univariate analysis was statistically superior to those based on multivariate analyses in terms of predictive power and discriminatory capacity, which was expressed by the area under the ROC curve (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1; Table 9 and Figure 4).
Our predictive algorithm was based on multivariate logistic regression modelling to derive predicted probabilities of PDAC based on the significant independent urinary biomarkers (uMMP-2 and uTIMP-1) with cutoff values for those biomarkers determined using the Youden index in ROC analysis to maximise sensitivity and specificity of prediction.
Prediction models based on multivariate analyses typically use logistic regression analysis, due to the advantage of its simpler interpretation of the relationship between predictive factors and outcomes [56].
Based on multivariate analysis, the four factors that were most predictive of survival after the first recurrence were disease-free interval, site of recurrence, progesterone receptor (PgR) status, and vascular involvement.
The candidates of the outliers were selected automatically by a jackknife outlier analysis based on multivariate statistics.
The CFR techniques used in both of the two reconstructions are based on multivariate linear regression approaches, which are based on calibrating proxy records against instrumental data12,13,14,15,16,17.
In this study an innovative statistical procedure based on multivariate techniques is applied.
Dang and Serfling [3] introduced nonparametric multivariate outlier identifiers based on multivariate depth functions.
This analysis is based on multivariate logistic regression analyses.
Based on the multivariate predictive studies, the most robust predictor of global satisfaction is the quality of interpersonal interactions with the ED provider.
