Model discrimination was measured using C Statistic.
Model discrimination was measured using C Statistic for survival analysis.
All three models did not improve model discrimination as measured by c-statistics.
Model discrimination was measured using the c statistic, which is equal to the area under the ROC curve.
Model fit was assessed with a calibration curve, and model discrimination was measured by the area under the receiver operating characteristic curve, approximated by the trapezoidal method and estimation of 95% confidence intervals [ 17, 18].
Model discrimination, as measured by the C-statistic (or the area under the receiver-operating-characteristic curve), was similar in the original Silber and validation studies (0.83 versus 0.78).
We evaluated the goodness of fit using Homer-Lemeshow chi-square test [ 20] and model discrimination by measuring the c-statistic, which is equivalent to the area-under-the-receiver operating characteristic (ROC) curve [ 21].
However, as Ware [ 29] has demonstrated, a new marker could be an important risk factor that significantly improves the fit of the model, but it may have almost no impact on model discrimination as measured by aROC.CVD risk scores are often compared using the aROC, with a significant increase in the aROC being taken as evidence that the discrimination of a risk score has improved.
Multiple studies have reported clinical variables associated with intensive care readmission after CVICU discharge; however, the clinical applicability of these models has been limited by a lack of model calibration or discrimination measures, validation, and/or patient information spanning the entire perioperative period [ 5- 9].
The new edition updates extensively many of these topics, especially quantitative model formulation, validation and model discrimination using information theory measures and Bayesian probability, and stability analysis and non-dimensionalization.
